Learning for Personalized Medicine: A Comprehensive Review From a Deep Learning Perspective

ImageNet classification with deep convolutional neural networks

Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155 · see citing works

Long Short-Term Memory

Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works

Deep learning

LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126 · see citing works

Very Deep Convolutional Networks for Large-Scale Image Recognition

Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works

Gene Ontology: tool for the unification of biology

Ashburner, M., Ball, C. A., Blake, J. A., Botstein, D., Butler, H., Cherry, J. M., Davis, A. P., Dolinski, K., Dwight, S. S., Eppig, J. T., et al. (2000) · Nature Genetics · doi:10.1038/75556 · Cited by 43,206 · see citing works

Reducing the dimensionality of data with neural networks

Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928 · see citing works

Neural networks and physical systems with emergent collective computational abilities.

Hopfield, J. J. (1982) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.79.8.2554 · Cited by 19,270 · see citing works

A survey on deep learning in medical image analysis

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017) · Medical Image Analysis · doi:10.1016/j.media.2017.07.005 · Cited by 15,332 · see citing works

Untitled work

(2010) · Cited by 14,134 · see citing works

Representation Learning: A Review and New Perspectives

Bengio, Y., Courville, A., & Vincent, P. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.50 · Cited by 13,001 · see citing works

Backpropagation Applied to Handwritten Zip Code Recognition

LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., & Jackel, L. D. (1989) · Neural Computation · doi:10.1162/neco.1989.1.4.541 · Cited by 11,891 · see citing works

Finding structure in time

Elman, J. L. (1990) · Cognitive Science · doi:10.1207/s15516709cog1402_1 · Cited by 11,212 · see citing works

Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling

Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008 · see citing works

Gene expression profiling predicts clinical outcome of breast cancer

van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., Peterse, H. L., van der Kooy, K., Marton, M. J., Witteveen, A. T., et al. (2002) · Nature · doi:10.1038/415530a · Cited by 9,536 · see citing works

Speech recognition with deep recurrent neural networks

Graves, A., Mohamed, A.-R., & Hinton, G. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6638947 · Cited by 8,893 · see citing works

k-ANONYMITY: A MODEL FOR PROTECTING PRIVACY

SWEENEY, L. (2002) · International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems · doi:10.1142/s0218488502001648 · Cited by 8,571 · see citing works

Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs

Gulshan, V., Peng, L., Coram, M., Stumpe, M. C., Wu, D., Narayanaswamy, A., Venugopalan, S., Widner, K., Madams, T., Cuadros, J., et al. (2016) · JAMA · doi:10.1001/jama.2016.17216 · Cited by 7,697 · see citing works

Receptive fields and functional architecture of monkey striate cortex

Hubel, D. H., & Wiesel, T. N. (1968) · The Journal of Physiology · doi:10.1113/jphysiol.1968.sp008455 · Cited by 6,520 · see citing works

Learning to Forget: Continual Prediction with LSTM

Gers, F. A., Schmidhuber, J., & Cummins, F. (2000) · Neural Computation · doi:10.1162/089976600300015015 · Cited by 5,869 · see citing works

The Connectivity Map: Using Gene-Expression Signatures to Connect Small Molecules, Genes, and Disease

Lamb, J., Crawford, E. D., Peck, D., Modell, J. W., Blat, I. C., Wrobel, M. J., Lerner, J., Brunet, J.-P., Subramanian, A., Ross, K. N., et al. (2006) · Science · doi:10.1126/science.1132939 · Cited by 5,257 · see citing works

A unified architecture for natural language processing: deep neural networks with multitask learning

Collobert, R., & Weston, J. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390177 · Cited by 5,058 · see citing works

Bayesian Learning for Neural Networks

Neal, R. M. (1996) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-0745-0 · Cited by 4,276 · see citing works

Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion

(2010) · Journal of Machine Learning Research · doi:10.5555/1756006.1953039 · Cited by 4,051 · see citing works

Multi-column deep neural networks for image classification

Ciresan, D. C., Meier, U., & Schmidhuber, J. (2012) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2012.6248110 · Cited by 3,760 · see citing works

Deep Learning: Methods and Applications

Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480 · see citing works

Untitled work

(1998) · Cited by 3,476 · see citing works

The druggable genome

Hopkins, A. L., & Groom, C. R. (2002) · Nature Reviews Drug Discovery · doi:10.1038/nrd892 · Cited by 3,305 · see citing works

Deep learning for healthcare: review, opportunities and challenges

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics · doi:10.1093/bib/bbx044 · Cited by 3,076 · see citing works

Adverse drug reactions: definitions, diagnosis, and management

Edwards, I. R., & Aronson, J. K. (2000) · The Lancet · doi:10.1016/s0140-6736(00)02799-9 · Cited by 2,988 · see citing works

An analysis of single-layer networks in unsupervised feature learning

Coates, A., Ng, A. Y., & Lee, H. (2011) · Cited by 2,760 · see citing works

Learning Hierarchical Features for Scene Labeling

Farabet, C., Couprie, C., Najman, L., & LeCun, Y. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.231 · Cited by 2,620 · see citing works

Environmental epigenomics and disease susceptibility

Jirtle, R. L., & Skinner, M. K. (2007) · Nature Reviews Genetics · doi:10.1038/nrg2045 · Cited by 2,492 · see citing works

Pharmacogenomics: Translating Functional Genomics into Rational Therapeutics

Evans, W. E., & Relling, M. V. (1999) · Science · doi:10.1126/science.286.5439.487 · Cited by 2,491 · see citing works

Learning to forget: continual prediction with LSTM

Gers, F. A. (1999) · doi:10.1049/cp:19991218 · Cited by 2,436 · see citing works

Artificial intelligence in medicine

Hamet, P., & Tremblay, J. (2017) · Metabolism · doi:10.1016/j.metabol.2017.01.011 · Cited by 2,334 · see citing works

Convolutional Neural Networks for Speech Recognition

Abdel‐Hamid, O., Mohamed, A., Jiang, H., Deng, L., Penn, G., & Yu, D. (2014) · IEEE/ACM Transactions on Audio Speech and Language Processing · doi:10.1109/taslp.2014.2339736 · Cited by 2,268 · see citing works

COSMIC: exploring the world's knowledge of somatic mutations in human cancer

Forbes, S. A., Beare, D. M., Gunasekaran, P., Leung, K., Bindal, N., Boutselakis, H., Ding, M., Bamford, S., Cole, C. G., Ward, S. A., et al. (2014) · Nucleic Acids Research · doi:10.1093/nar/gku1075 · Cited by 2,261 · see citing works

Semantic Similarity in a Taxonomy: An Information-Based Measure and its Application to Problems of Ambiguity in Natural Language

Resnik, P. (1999) · Journal of Artificial Intelligence Research · doi:10.1613/jair.514 · Cited by 2,098 · see citing works

A small molecule–kinase interaction map for clinical kinase inhibitors

Fabian, M. A., Biggs, W., Treiber, D. K., Atteridge, C. E., Azimioara, M. D., Benedetti, M. G., Carter, T. A., Ciceri, P., Edeen, P. T., Floyd, M. B., et al. (2005) · Nature Biotechnology · doi:10.1038/nbt1068 · Cited by 1,901 · see citing works

Restricted Boltzmann machines for collaborative filtering

Salakhutdinov, R., Mnih, A., & Hinton, G. (2007) · Proceedings of the 24th international conference on Machine learning · doi:10.1145/1273496.1273596 · Cited by 1,885 · see citing works

Deep Learning: Methods and Applications

Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,785 · see citing works

The rise of deep learning in drug discovery

Chen, H., Engkvist, O., Wang, Y., Olivecrona, M., & Blaschke, T. (2018) · Drug Discovery Today · doi:10.1016/j.drudis.2018.01.039 · Cited by 1,770 · see citing works

Deep patient: An unsupervised representation to predict the future of patients from the electronic health records

Miotto, R., Li, L., Kidd, B. A., & Dudley, J. T. (2016) · Scientific Reports · doi:10.1038/srep26094 · Cited by 1,710 · see citing works

Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition

Scherer, D., Müller, A., & Behnke, S. (2010) · Lecture notes in computer science · doi:10.1007/978-3-642-15825-4_10 · Cited by 1,556 · see citing works

A Bayesian neural network method for adverse drug reaction signal generation

Bate, A., Lindquist, M., Edwards, I. R., Olsson, S., Orre, R., Lansner, A., & Freitas, R. M. D. (1998) · European Journal of Clinical Pharmacology · doi:10.1007/s002280050466 · Cited by 1,492 · see citing works

Deep learning for computational biology

Angermueller, C., Pärnamaa, T., Parts, L., & Stegle, O. (2016) · Molecular Systems Biology · doi:10.15252/msb.20156651 · Cited by 1,473 · see citing works

Tutorial on Variational Autoencoders

Doersch, C. (2016) · arXiv · doi:10.48550/arxiv.1606.05908 · Cited by 1,470 · see citing works

Predicting breast cancer survivability: a comparison of three data mining methods

Delen, D., Walker, G., & Kadam, A. (2004) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2004.07.002 · Cited by 1,215 · see citing works

Untitled work

(2011) · Cited by 1,192 · see citing works

Applications of Machine Learning in Cancer Prediction and Prognosis

Cruz, J. A., & Wishart, D. S. (2006) · Cancer Informatics · doi:10.1177/117693510600200030 · Cited by 1,113 · see citing works

Deep convolutional neural networks for LVCSR

Sainath, T. N., Mohamed, A., Kingsbury, B. E. D., & Ramabhadran, B. (2013) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2013.6639347 · Cited by 1,066 · see citing works

An empirical evaluation of deep architectures on problems with many factors of variation

Larochelle, H., Erhan, D., Courville, A. C., Bergstra, J., & Bengio, Y. (2007) · International Conference on Machine Learning · doi:10.1145/1273496.1273556 · Cited by 1,024 · see citing works

Untitled work

(2009) · neural information processing systems · Cited by 962 · see citing works

Data-driven prediction of drug effects and interactions

Tatonetti, N. P., Ye, P. P., Daneshjou, R., & Altman, R. B. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3003377 · Cited by 936 · see citing works

Deep belief networks

Hinton, G. E. (2009) · Scholarpedia · doi:10.4249/scholarpedia.5947 · Cited by 907 · see citing works

A side effect resource to capture phenotypic effects of drugs

Kuhn, M., Campillos, M., Letunic, I., Jensen, L. J., & Bork, P. (2010) · Molecular Systems Biology · doi:10.1038/msb.2009.98 · Cited by 899 · see citing works

PREDICT: a method for inferring novel drug indications with application to personalized medicine

Gottlieb, A., Stein, G. Y., Ruppin, E., & Sharan, R. (2011) · Molecular Systems Biology · doi:10.1038/msb.2011.26 · Cited by 892 · see citing works

Applications of machine learning in cancer prediction and prognosis.

Cruz, J. A., & Wishart, D. S. (2007) · PubMed Central · Cited by 879 · see citing works

Machine Learning for Precision Psychiatry: Opportunities and Challenges

Bzdok, D., & Meyer‐Lindenberg, A. (2017) · Biological Psychiatry Cognitive Neuroscience and Neuroimaging · doi:10.1016/j.bpsc.2017.11.007 · Cited by 833 · see citing works

A community effort to assess and improve drug sensitivity prediction algorithms

Community, N. D., Costello, J. C., Heiser, L. M., Georgii, E., Gönen, M., Menden, M. P., Wang, N. J., Bansal, M., Ammad-ud-din, M., Hintsanen, P., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.2877 · Cited by 761 · see citing works

Deep Learning in Drug Discovery

Gawehn, E., Hiss, J. A., & Schneider, G. (2016) · Molecular Informatics · doi:10.1002/minf.201501008 · Cited by 723 · see citing works

Applications of Deep Learning in Biomedicine

Mamoshina, P., Vieira, A., Lane, E., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.5b00982 · Cited by 701 · see citing works

On Contrastive Divergence Learning.

Carreira-Perpiñán, M. Á., & Hinton, G. E. (2005) · Cited by 630 · see citing works

Deep Learning Applications for Predicting Pharmacological Properties of Drugs and Drug Repurposing Using Transcriptomic Data

Aliper, A., Plis, S., Artemov, A., Ulloa, A., Mamoshina, P., & Zhavoronkov, A. (2016) · Molecular Pharmaceutics · doi:10.1021/acs.molpharmaceut.6b00248 · Cited by 610 · see citing works

Big data and new knowledge in medicine: the thinking, training, and tools needed for a learning health system.

Krumholz, H. M. (2014) · Health Affairs · doi:10.1377/hlthaff.2014.0053 · Cited by 589 · see citing works

Fast and accurate view classification of echocardiograms using deep learning

Madani, A., Arnaout, R., Mofrad, M., & Arnaout, R. (2018) · npj Digital Medicine · doi:10.1038/s41746-017-0013-1 · Cited by 585 · see citing works

Deep Feature Learning for Knee Cartilage Segmentation Using a Triplanar Convolutional Neural Network

Prasoon, A., Petersen, K., Igel, C., Lauze, F., Dam, E., & Nielsen, M. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_31 · Cited by 582 · see citing works

Biomarker development in the precision medicine era: lung cancer as a case study

Vargas, A. J., & Harris, C. C. (2016) · Nature reviews. Cancer · doi:10.1038/nrc.2016.56 · Cited by 553 · see citing works

Neural‐Symbolic Machine Learning for Retrosynthesis and Reaction Prediction

Segler, M. H. S., & Waller, M. P. (2017) · Chemistry – A European Journal · doi:10.1002/chem.201605499 · Cited by 548 · see citing works

Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

Menden, M. P., Iorio, F., Garnett, M. J., McDermott, U., Benes, C. H., Ballester, P. J., & Sáez-Rodríguez, J. (2013) · PLoS ONE · doi:10.1371/journal.pone.0061318 · Cited by 543 · see citing works

MYCOBACTERIA: LABORATORY METHODS FOR TESTING DRUG SENSITIVITY AND RESISTANCE.

(1963) · Europe PMC (PubMed Central) · Cited by 532 · see citing works

Adverse drug reactions. A critical review

Karch, F. E. (1975) · JAMA · doi:10.1001/jama.234.12.1236 · Cited by 482 · see citing works

Deep Learning Based Imaging Data Completion for Improved Brain Disease Diagnosis

Li, R., Zhang, W., Suk, H., Wang, L., Jiang, L., Shen, D. G., & Ji, S. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-10443-0_39 · Cited by 480 · see citing works

Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP

Yu, D., & Deng, L. (2010) · IEEE Signal Processing Magazine · doi:10.1109/msp.2010.939038 · Cited by 459 · see citing works

Predisposing Factors in Adverse Reactions to Drugs

Hurwitz, N. (1969) · BMJ · doi:10.1136/bmj.1.5643.536 · Cited by 434 · see citing works

Muscarinic acetylcholine receptors as CNS drug targets

Langmead, C. J., Watson, J. M., & Reavill, C. (2007) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2007.09.009 · Cited by 413 · see citing works

Deep Learning in Natural Language Processing

(2018) · Springer eBooks · doi:10.1007/978-981-10-5209-5 · Cited by 408 · see citing works

Ensemble machine learning on gene expression data for cancer classification.

Tan, A. C., & Gilbert, D. (2003) · PubMed · Cited by 400 · see citing works

Sparse Autoencoder-Based Feature Transfer Learning for Speech Emotion Recognition

Deng, J., Zhang, Z., Marchi, E., & Schuller, B. (2013) · 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction · doi:10.1109/acii.2013.90 · Cited by 366 · see citing works

Pitfalls and limitations in translation from biomarker discovery to clinical utility in predictive and personalised medicine

Drucker, E., & Krapfenbauer, K. (2013) · The EPMA Journal · doi:10.1186/1878-5085-4-7 · Cited by 352 · see citing works

Predicting the prognosis of breast cancer by integrating clinical and microarray data with Bayesian networks

Gevaert, O., Smet, F. D., Timmerman, D., Moreau, Y., & Moor, B. D. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl230 · Cited by 350 · see citing works

Machine Learning, a Probabilistic Perspective

Robert, C. P. (2014) · CHANCE · doi:10.1080/09332480.2014.914768 · Cited by 342 · see citing works

Adverse Drug Reactions

Karch, F. E. (1975) · JAMA · doi:10.1001/jama.1975.03260250028021 · Cited by 336 · see citing works

Computational Phenotype Discovery Using Unsupervised Feature Learning over Noisy, Sparse, and Irregular Clinical Data

Lasko, T. A., Denny, J. C., & Levy, M. A. (2013) · PLoS ONE · doi:10.1371/journal.pone.0066341 · Cited by 313 · see citing works

Electronic Health Records

Ammenwerth, E., & Hoerbst, A. (2010) · Methods of Information in Medicine · doi:10.3414/me10-01-0038 · Cited by 305 · see citing works

Human Disease-Drug Network Based on Genomic Expression Profiles

Hu, G., & Agarwal, P. (2009) · PLoS ONE · doi:10.1371/journal.pone.0006536 · Cited by 304 · see citing works

Muscarinic acetylcholine receptor subtypes in cerebral cortex and hippocampus

Volpicelli‐Daley, L. A., & Levey, A. I. (2004) · Progress in brain research · doi:10.1016/s0079-6123(03)45003-6 · Cited by 299 · see citing works

Similarity-based modeling in large-scale prediction of drug-drug interactions

Vilar, S., Uriarte, E., Santana, L., Lorberbaum, T., Hripcsak, G. M., Friedman, C., & Tatonetti, N. P. (2014) · Nature Protocols · doi:10.1038/nprot.2014.151 · Cited by 281 · see citing works

Empirical evaluation and combination of advanced language modeling techniques

Mikolov, T., Deoras, A., Kombrink, S., Burget, L., & Černocký, J. (2011) · Interspeech (USB) · doi:10.21437/interspeech.2011-242 · Cited by 278 · see citing works

Using Three Machine Learning Techniques for Predicting Breast Cancer Recurrence

Ahmad, L. G., & Eshlaghy, A. T. (2013) · Journal of Health & Medical Informatics · doi:10.4172/2157-7420.1000124 · Cited by 272 · see citing works

Precision medicine in cardiology

Antman, E. M., & Loscalzo, J. (2016) · Nature Reviews Cardiology · doi:10.1038/nrcardio.2016.101 · Cited by 251 · see citing works

The Next Era: Deep Learning in Pharmaceutical Research

Ekins, S. (2016) · Pharmaceutical Research · doi:10.1007/s11095-016-2029-7 · Cited by 237 · see citing works

Manifold Learning of Brain MRIs by Deep Learning

Initiative, A. D. N., Brosch, T., & Tam, R. C. (2013) · Lecture notes in computer science · doi:10.1007/978-3-642-40763-5_78 · Cited by 236 · see citing works

Resisting Resistance: Targeted Therapies in Lung Cancer

Lin, J. J., & Shaw, A. T. (2016) · Trends in cancer · doi:10.1016/j.trecan.2016.05.010 · Cited by 232 · see citing works

Integrative data analysis of multi-platform cancer data with a multimodal deep learning approach

Liang, M., Li, Z., Chen, T., & Zeng, J. (2015) · IEEE/ACM Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2377729 · Cited by 231 · see citing works

Combining Drug and Gene Similarity Measures for Drug-Target Elucidation

Perlman, L., Gottlieb, A., Atias, N., Ruppin, E., & Sharan, R. (2011) · Journal of Computational Biology · doi:10.1089/cmb.2010.0213 · Cited by 218 · see citing works

From Disease Association to Risk Assessment: An Optimistic View from Genome-Wide Association Studies on Type 1 Diabetes

Wei, Z., Wang, K., Qu, H., Zhang, H., Bradfield, J. P., Kim, C., Frackleton, E., Hou, C., Glessner, J., Chiavacci, R. M., et al. (2009) · PLoS Genetics · doi:10.1371/journal.pgen.1000678 · Cited by 207 · see citing works

The statistical distribution of nucleic acid similarities

Smith, T. F., Waterman, M. S., & Burks, C. (1985) · Nucleic Acids Research · doi:10.1093/nar/13.2.645 · Cited by 203 · see citing works

Predictive Models for Breast Cancer Susceptibility from Multiple Single Nucleotide Polymorphisms

Listgarten, J., Damaraju, S., Poulin, B., Cook, L. M., Dufour, J. H., Driga, A., Mackey, J. R., Wishart, D. S., Greiner, R., & Zanke, B. W. (2004) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-1115-03 · Cited by 200 · see citing works

Development of Novel Breast Cancer Recurrence Prediction Model Using Support Vector Machine

Kim, W., Kim, K. S., Lee, J. E., Noh, D., Kim, S.-W., Jung, Y. S., Park, M. Y., & Park, R. W. (2012) · Journal of Breast Cancer · doi:10.4048/jbc.2012.15.2.230 · Cited by 184 · see citing works

Combing signals from spontaneous reports and electronic health records for detection of adverse drug reactions

Harpaz, R., Vilar, S., DuMouchel, W., Salmasian, H., Haerian, K., Shah, N. H., Chase, H. S., & Friedman, C. (2012) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-000930 · Cited by 172 · see citing works

Systematic assessment of analytical methods for drug sensitivity prediction from cancer cell line data.

Jang, I. S., Neto, E. C., Guinney, J., Friend, S. H., & Margolin, A. A. (2014) · PubMed · Cited by 170 · see citing works

From public health genomics to precision public health: a 20-year journey

Khoury, M. J., Bowen, M., Clyne, M., Dotson, W. D., GWINN, M. L., Green, R. F., Kolor, K., Rodriguez, J. L., Wulf, A., & Yu, W. (2017) · Genetics in Medicine · doi:10.1038/gim.2017.211 · Cited by 168 · see citing works

Improving the Prediction of Complex Diseases by Testing for Multiple Disease-Susceptibility Genes

Yang, Q., Khoury, M. J., Botto, L. D., Friedman, J. M., & Flanders, W. D. (2003) · The American Journal of Human Genetics · doi:10.1086/367923 · Cited by 156 · see citing works

Evolving spiking neural networks for personalised modelling, classification and prediction of spatio-temporal patterns with a case study on stroke

Kasabov, N. K., Feigin, V. L., Hou, Z., Chen, Y., Liang, L., Krishnamurthi, R., Othman, M., & Parmar, P. G. K. (2014) · Neurocomputing · doi:10.1016/j.neucom.2013.09.049 · Cited by 149 · see citing works

Predicting potential side effects of drugs by recommender methods and ensemble learning

Zhang, W., Zou, H., Luo, L., Liu, Q., Wu, W., & Xiao, W. (2015) · Neurocomputing · doi:10.1016/j.neucom.2015.08.054 · Cited by 146 · see citing works

Deep Belief Networks for Electroencephalography: A Review of Recent Contributions and Future Outlooks

Movahedi, F., Coyle, J. L., & Sejdić, E. (2017) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2017.2727218 · Cited by 132 · see citing works

An Algorithmic Framework for Predicting Side Effects of Drugs

Atias, N., & Sharan, R. (2011) · Journal of Computational Biology · doi:10.1089/cmb.2010.0255 · Cited by 130 · see citing works

Application of machine learning to predict the recurrence-proneness for cervical cancer

Tseng, C.-J., Lu, C.-J., Chang, C.-C., & Chen, G.-D. (2014) · Neural Computing and Applications · doi:10.1007/s00521-013-1359-1 · Cited by 128 · see citing works

Improving Protein Fold Recognition by Deep Learning Networks

Jo, T., Hou, J., Eickholt, J., & Cheng, J. J. (2015) · Scientific Reports · doi:10.1038/srep17573 · Cited by 117 · see citing works

Classification by ensembles from random partitions of high-dimensional data

Ahn, H., Moon, H., Fazzari, M. J., Lim, N., Chen, J. J., & Kodell, R. L. (2007) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2006.12.043 · Cited by 100 · see citing works

Ensemble methods for classification of patients for personalized medicine with high-dimensional data

Moon, H., Ahn, H., Kodell, R. L., Baek, S., Lin, C.-J., & Chen, J. J. (2007) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2007.07.003 · Cited by 94 · see citing works

Multiparametric Decision Support System for the Prediction of Oral Cancer Reoccurrence

Exarchos, K., Goletsis, Y., & Fotiadis, D. I. (2011) · IEEE Transactions on Information Technology in Biomedicine · doi:10.1109/titb.2011.2165076 · Cited by 94 · see citing works

Breast cancer survivability prediction using labeled, unlabeled, and pseudo-labeled patient data

Kim, J., & Shin, H. (2013) · Journal of the American Medical Informatics Association · doi:10.1136/amiajnl-2012-001570 · Cited by 94 · see citing works

A gene signature for breast cancer prognosis using support vector machine

Xu, X., Zhang, Y., Zou, L., Wang, M., & Li, A. (2012) · International Conference on Biomedical Engineering and Informatics (BMEI) · doi:10.1109/bmei.2012.6513032 · Cited by 88 · see citing works

IBM’s Health Analytics and Clinical Decision Support

Sun, J., Knoop, S., Shabo, A., Carmeli, B., Sow, D., Syeda-Mahmood, T. F., Rapp, W., & Kohn, M. S. (2014) · Yearbook of Medical Informatics · doi:10.15265/iy-2014-0002 · Cited by 77 · see citing works

A strategy for combining minor genetic susceptibility genes to improve prediction of disease in type 1 diabetes

Winkler, C., Krumsiek, J., Lempainen, J., Achenbach, P., Grallert, H., Giannopoulou, E., Bunk, M., Theis, F. J., Bonifacio, E., & Ziegler, A.-G. (2012) · Genes & Immunity · doi:10.1038/gene.2012.36 · Cited by 68 · see citing works

Predicting cancer susceptibility from single-nucleotide polymorphism data

Waddell, M., Page, D., & Shaughnessy, J. D. (2005) · doi:10.1145/1134030.1134035 · Cited by 67 · see citing works

SAFS: A deep feature selection approach for precision medicine

Nezhad, M. Z., Zhu, D., Li, X., Yang, K., & Levy, P. D. (2016) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2016.7822569 · Cited by 64 · see citing works

Clinical Decision Support and Individualized Prediction of Survival in Colon Cancer: Bayesian Belief Network Model

Stojadinovic, A., Bilchik, A. J., Smith, D. D., Eberhardt, J. S., Ward, E. B., Nissan, A., Johnson, E. K., Protić, M., Peoples, G. E., Avital, I., et al. (2012) · Annals of Surgical Oncology · doi:10.1245/s10434-012-2555-4 · Cited by 61 · see citing works

SYSTEMATIC ASSESSMENT OF ANALYTICAL METHODS FOR DRUG SENSITIVITY PREDICTION FROM CANCER CELL LINE DATA

Jang, I. S., Neto, E. C., Guinney, J., Friend, S. H., & Margolin, A. A. (2013) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789814583220_0007 · Cited by 57 · see citing works

Integrative Gene Network Construction to Analyze Cancer Recurrence Using Semi-Supervised Learning

Park, C., Ahn, J., Kim, H., & Park, S. (2014) · PLoS ONE · doi:10.1371/journal.pone.0086309 · Cited by 55 · see citing works

Medical Data Mining on the Internet: Research on a Cancer Information System

Houston, A. L., Chen, H., HUBBARD, S. M., Schatz, B. R., Ng, T. D., Sewell, R. R., & Tolle, K. M. (1999) · Artificial Intelligence Review · doi:10.1023/a:1006548623067 · Cited by 47 · see citing works

Random forests of interaction trees for estimating individualized treatment effects in randomized trials

Su, X., Peña, A. T., Liu, L., & Levine, R. A. (2018) · Statistics in Medicine · doi:10.1002/sim.7660 · Cited by 42 · see citing works

Exploiting Convolutional Neural Network for Risk Prediction with Medical Feature EmbeddingPreprint

Che, Z., Cheng, Y., Sun, Z., & Liu, Y. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1701.07474 · Cited by 41 · see citing works

SMC: A Practical Schema for Privacy-Preserved Data Sharing over Distributed Data Streams

Liu, S., Qu, Q., Chen, L., & Ni, L. M.-S. (2015) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2015.2498156 · Cited by 40 · see citing works

Survival model in oral squamous cell carcinoma based on clinicopathological parameters, molecular markers and support vector machines

Rosado, P., Lequerica–Fernández, P., de Villalaín, L., Peña, I., Lasheras, F. S., & de Vicente, J. C. (2013) · Expert Systems with Applications · doi:10.1016/j.eswa.2013.02.032 · Cited by 39 · see citing works

Efficient Online Summarization of Large-Scale Dynamic Networks

Qu, Q., Liu, S., Zhu, F., & Jensen, C. S. (2016) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2016.2601611 · Cited by 37 · see citing works

The role of different sampling methods in improving biological activity prediction using deep belief network

Ghasemi, F., Fassihi, A., Pérez‐Sánchez, H., & Mehridehnavi, A. (2016) · Journal of Computational Chemistry · doi:10.1002/jcc.24671 · Cited by 35 · see citing works

Dr.VAE: Drug Response Variational AutoencoderPreprint

Rampášek, L., Hidru, D., Smirnov, P., Haibe‐Kains, B., & Goldenberg, A. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1706.08203 · Cited by 35 · see citing works

Dynamic collective routing using crowdsourcing data

Liu, S., & Qu, Q. (2016) · Transportation Research Part B Methodological · doi:10.1016/j.trb.2016.08.005 · Cited by 33 · see citing works

Automatic integration of drug indications from multiple health resources

Névéol, A., & Lu, Z. (2010) · doi:10.1145/1882992.1883096 · Cited by 31 · see citing works

Integrating non-spatial preferences into spatial location queries

Qu, Q., Liu, S., Yang, B., & Jensen, C. S. (2014) · International Conference on Scientific and Statistical Database Management · doi:10.1145/2618243.2618247 · Cited by 28 · see citing works

Efficient Top-k Spatial Locality Search for Co-located Spatial Web Objects

Qu, Q., Liu, S., Yang, B., & Jensen, C. S. (2014) · IEEE International Conference on Mobile Data Management (MDM) · doi:10.1109/mdm.2014.39 · Cited by 26 · see citing works

Protecting Anonymity in Data-Driven Biomedical Science

Kieseberg, P., Hobel, H., Schrittwieser, S., Weippl, E. R., & Holzinger, A. (2014) · Lecture notes in computer science · doi:10.1007/978-3-662-43968-5_17 · Cited by 22 · see citing works

An Effective Privacy Architecture to Preserve User Trajectories in Reward-Based LBS Applications

Hasan, A. S. M. T., Qu, Q., Li, C., Chen, L., & Jiang, Q. (2018) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi7020053 · Cited by 22 · see citing works

Integrating domain knowledge in supervised machine learning to assess the risk of breast cancer

Bochare, A., Gangopadhyay, A., Yesha, Y., Joshi, A., Yesha, Y., Brady, M. C., Grasso, M., & Rishe, N. D. (2014) · International Journal of Medical Engineering and Informatics · doi:10.1504/ijmei.2014.060245 · Cited by 16 · see citing works

SUBIC: A Supervised Bi-Clustering Approach for Precision Medicine

Nezhad, M. Z., Zhu, D., Sadati, N., Yang, K., & Levi, P. (2017) · International Conference on Machine Learning and Applications · doi:10.1109/icmla.2017.00-68 · Cited by 14 · see citing works

Medical image super-resolution with non-local embedding sparse representation and improved IBP

Zhang, M., Desrosiers, C., Qu, Q., Guo, F., & Zhang, C. (2016) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2016.7471803 · Cited by 13 · see citing works

Augmented Outcome-weighted Learning for Optimal Treatment RegimesPreprint

Zhou, X., & Kosorok, M. R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.10654 · Cited by 9 · see citing works

Causal nearest neighbor rules for optimal treatment regimesPreprint

Zhou, X., & Kosorok, M. R. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1711.08451 · Cited by 9 · see citing works

A Predictive Approach Using Deep Feature Learning for Electronic Medical Records: A Comparative Study.Preprint

Nezhad, M. Z., Zhu, D., Sadati, N., & Yang, K. (2018) · arXiv (Cornell University) · Cited by 7 · see citing works

A Noise-Filtering Approach for Cancer Drug Sensitivity PredictionPreprint

Turki, T., & Wei, Z. (2016) · arXiv (Cornell University) · doi:10.48550/arxiv.1612.00525 · Cited by 3 · see citing works

AI in drug discovery and its clinical relevance

Qureshi, R., Irfan, M., Gondal, T. M., Khan, S., Wu, J., Hadi, M. U., Heymach, J., Le, X., Yan, H., & Alam, T. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e17575 · Cited by 319 · see citing works

Quality, Usability, and Effectiveness of mHealth Apps and the Role of Artificial Intelligence: Current Scenario and Challenges

Déniz-García, A., Fabelo, H. A., Rodríguez-Almeida, A. J., Zamora-Zamorano, G., Castro-Fernández, M., Alberiche-Ruano, M. P., Solvoll, T. G., Granja, C., Schopf, T. R., Callicó, G. M., et al. (2023) · Journal of Medical Internet Research · doi:10.2196/44030 · Cited by 293 · see citing works

Medical deep learning—A systematic meta-review

Egger, J., Gsaxner, C., Pepe, A., Pomykala, K. L., Jonske, F., Kurz, M., Li, J., & Kleesiek, J. (2022) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2022.106874 · Cited by 270 · see citing works

Precision and Personalized Medicine: How Genomic Approach Improves the Management of Cardiovascular and Neurodegenerative Disease

Strianese, O., Rizzo, F. M., Ciccarelli, M., Galasso, G., D’Agostino, Y., Salvati, A., Giudice, C. D., Tesorio, P., & Rusciano, M. R. (2020) · Genes · doi:10.3390/genes11070747 · Cited by 258 · see citing works

Multi-Disease Prediction Based on Deep Learning: A Survey

Xie, S., Yu, Z., & Lv, Z. (2021) · Computer Modeling in Engineering & Sciences · doi:10.32604/cmes.2021.016728 · Cited by 240 · see citing works

Review of multimodal machine learning approaches in healthcare

Krones, F., Marikkar, U., Parsons, G., Szmul, A., & Mahdi, A. (2025) · Information Fusion · doi:10.1016/j.inffus.2024.102690 · Cited by 235 · see citing works

Time to reality check the promises of machine learning-powered precision medicine.

Wilkinson, J., Arnold, K. F., Murray, E. J., van Smeden, M., Carr, K., Sippy, R., de Kamps, M., Beam, A., Konigorski, S., Lippert, C., et al. (2020) · The Lancet Digital Health · doi:10.1016/s2589-7500(20)30200-4 · Cited by 233 · see citing works

Causal machine learning for healthcare and precision medicine

Sanchez, P., Voisey, J. P., Xia, T., Watson, H. I., O’Neil, A. Q., & Tsaftaris, S. A. (2022) · Royal Society Open Science · doi:10.1098/rsos.220638 · Cited by 225 · see citing works

Artificial Intelligence and Biosensors in Healthcare and Its Clinical Relevance: A Review

(2023) · doi:10.1109/access.2023.3285596 · Cited by 205 · see citing works

AlphaFold, Artificial Intelligence (AI), and Allostery

Nussinov, R., Zhang, M., Liu, Y., & Jang, H. (2022) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.2c04346 · Cited by 190 · see citing works

Artificial intelligence for cardiovascular disease risk assessment in personalised framework: a scoping review

Singh, M., Kumar, A., Khanna, N. N., Laird, J. R., Nicolaides, A. N., Faa, G., Johri, A. M., Mantella, L. E., Fernandes, J. F. E., Teji, J. S., et al. (2024) · EClinicalMedicine · doi:10.1016/j.eclinm.2024.102660 · Cited by 186 · see citing works

Reliable and Resilient AI and IoT-Based Personalised Healthcare Services: A Survey

Taimoor, N., & Rehman, S. (2021) · IEEE Access · doi:10.1109/access.2021.3137364 · Cited by 183 · see citing works

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches

Lin, E., Lin, C., & Lane, H. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21030969 · Cited by 170 · see citing works

Popular deep learning algorithms for disease prediction: a review

Yu, Z., Wang, K., Wan, Z., Xie, S., & Lv, Z. (2022) · Cluster Computing · doi:10.1007/s10586-022-03707-y · Cited by 121 · see citing works

Deep Learning-based Smart Predictive Evaluation for Interactive Multimedia-enabled Smart Healthcare

Lv, Z., Yu, Z., Xie, S., & Alamri, A. M. (2022) · ACM Transactions on Multimedia Computing Communications and Applications · doi:10.1145/3468506 · Cited by 101 · see citing works

An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives

Ahmad, S., & Raza, K. (2024) · Journal of drug targeting · doi:10.1080/1061186x.2024.2347358 · Cited by 65 · see citing works

Deep Learning for Medication Recommendation: A Systematic Survey

Ali, Z., Huang, Y., Ullah, I., Feng, J., Deng, C., Thierry, N., Khan, A., Jan, A. U., Shen, X., Rui, W., et al. (2022) · Data Intelligence · doi:10.1162/dint_a_00197 · Cited by 64 · see citing works

Classification of Histopathology Images of Lung Cancer Using Convolutional Neural Network (CNN)

Baranwal, N., Doravari, P., & Kachhoria, R. (2022) · CRC Press eBooks · doi:10.1201/9781003272694-7 · Cited by 63 · see citing works

Deep learning in systems medicine

Wang, H., Pujos‐Guillot, E., Comte, B., de Miranda, J. L., Spiwok, V., Chorbev, I., Castiglione, F., Tieri, P., Watterson, S., McAllister, R., et al. (2020) · Briefings in Bioinformatics · doi:10.1093/bib/bbaa237 · Cited by 53 · see citing works

Artificial intelligence−enhanced white-light colonoscopy with attention guidance predicts colorectal cancer invasion depth

Luo, X., Wang, J., Han, Z., Yu, Y., Chen, Z., Huang, F., Xu, Y., Cai, J., Zhang, Q., Qiao, W., et al. (2021) · Gastrointestinal Endoscopy · doi:10.1016/j.gie.2021.03.936 · Cited by 53 · see citing works

A Machine Learning-Based Framework for the Prediction of Cervical Cancer Risk in Women

Kaushik, K., Bhardwaj, A., Bharany, S., Alsharabi, N., Rehman, A. U., Eldin, S. M., & Ghamry, N. A. (2022) · Sustainability · doi:10.3390/su141911947 · Cited by 44 · see citing works

Ensemble Machine Learning Approach for Quantitative Structure Activity Relationship Based Drug Discovery: A Review

Noviandy, T. R., Maulana, A., Idroes, G. M., Emran, T. B., Tallei, T. E., Helwani, Z., & Idroes, R. (2023) · Infolitika Journal of Data Science · doi:10.60084/ijds.v1i1.91 · Cited by 43 · see citing works

Demystifying machine learning: a primer for physicians

Scott, I. A. (2021) · Internal Medicine Journal · doi:10.1111/imj.15200 · Cited by 43 · see citing works

Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy

Tanphiriyakun, T., Rojanasthien, S., & Khumrin, P. (2021) · Scientific Reports · doi:10.1038/s41598-021-93152-5 · Cited by 42 · see citing works

Survey on Deep Fuzzy Systems in Regression Applications: A View on Interpretability

Júnior, J. S. S., Mendes, J., Souza, F. A. A., & Premebida, C. (2023) · International Journal of Fuzzy Systems · doi:10.1007/s40815-023-01544-8 · Cited by 39 · see citing works

Machine learning does not outperform traditional statistical modelling for kidney allograft failure prediction

Truchot, A., Raynaud, M., Kamar, N., Naesens, M., Legendre, C. M., Delahousse, M., Thaunat, O., Büchler, M., Crespo, M., Linhares, K., et al. (2022) · Kidney International · doi:10.1016/j.kint.2022.12.011 · Cited by 37 · see citing works

Deep learning for personalized health monitoring and prediction: A review

Damaševičius, R., Jagatheesaperumal, S. K., Rajesh, K. N. V. P. S., Hussain, S., Alizadehsani, R., & Górriz, J. M. (2024) · Computational Intelligence · doi:10.1111/coin.12682 · Cited by 37 · see citing works

Machine Learning Assists in the Design and Application of Microneedles

He, W., Kong, S., Lin, R., Xie, Y., Zheng, S., Yin, Z., Huang, X. S., Su, L., & Zhang, X. (2024) · Biomimetics · doi:10.3390/biomimetics9080469 · Cited by 35 · see citing works

On Algorithmic Fairness in Medical Practice

Grote, T., & Keeling, G. (2022) · Cambridge Quarterly of Healthcare Ethics · doi:10.1017/s0963180121000839 · Cited by 32 · see citing works

Machine Learning and Deep Learning for the Pharmacogenomics of Antidepressant Treatments

Lin, E., Lin, C., & Lane, H. (2021) · Clinical Psychopharmacology and Neuroscience · doi:10.9758/cpn.2021.19.4.577 · Cited by 29 · see citing works

Comprehensive clinical evaluation of deep learning-based auto-segmentation for radiotherapy in patients with cervical cancer

Chung, S. Y., Chang, J. S., & Kim, Y. B. (2023) · Frontiers in Oncology · doi:10.3389/fonc.2023.1119008 · Cited by 26 · see citing works

AI-Based Computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey

Rajaei, F., Minoccheri, C., Wittrup, E. M., Wilson, R. C., Athey, B. D., Omenn, G. S., & Najarian, K. (2024) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2024.3492708 · Cited by 23 · see citing works

Machine learning approaches to natural fiber composites: A review of methodologies and applications

Palanisamy, S., Ayrılmış, N., Sureshkumar, K., Santulli, C., Khan, T., Junaedi, H., & Sebaey, T. A. (2025) · BioResources · doi:10.15376/biores.20.1.palanisamy · Cited by 23 · see citing works

Pathway to a fully data-driven geotechnics: Lessons from materials informatics

Wu, S., Otake, Y., Higo, Y., & Yoshida, I. (2024) · SOILS AND FOUNDATIONS · doi:10.1016/j.sandf.2024.101471 · Cited by 22 · see citing works

Introduction to deep learning and diagnosis in medicine

Oğuz, A., & Ertuğrul, Ö. F. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-96129-5.00003-2 · Cited by 21 · see citing works

Machine Learning in Neural Networks

Lin, E., & Tsai, S. (2019) · Advances in experimental medicine and biology · doi:10.1007/978-981-32-9721-0_7 · Cited by 20 · see citing works

Recent Advancement and Challenges in Deep Learning, Big Data in Bioinformatics

Sharma, A. K., & Kumar, R. (2022) · Studies in big data · doi:10.1007/978-3-030-95419-2_12 · Cited by 20 · see citing works

Machine Learning Models Prognosticate Functional Outcomes Better than Clinical Scores in Spontaneous Intracerebral Haemorrhage

Lim, M. J. R., Quek, R. H. C., Ng, K. J., Loh, N. H. W., Lwin, S., Teo, K., Nga, V. D. W., Yeo, T. T., & Motani, M. (2021) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2021.106234 · Cited by 20 · see citing works

Review of Non-Invasive Fetal Electrocardiography Monitoring Techniques

Li, X., Wan, J., & Peng, X. (2025) · Sensors · doi:10.3390/s25051412 · Cited by 19 · see citing works

A Survey of Machine Learning-Based Approaches for Missing Value Imputation

Gond, V. K., Dubey, A., & Rasool, A. (2021) · International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca51532.2021.9544957 · Cited by 19 · see citing works

Advancing ophthalmology with large language models: Applications, challenges, and future directions

Zhang, Q., Wang, S., Wang, X., Xu, C., Liang, J., & Liu, Z. (2025) · Survey of Ophthalmology · doi:10.1016/j.survophthal.2025.02.009 · Cited by 18 · see citing works

Machine Learning for Predictive Modeling in Nanomedicine‐Based Cancer Drug Delivery

Sahu, R. C., Arora, S., Kumar, D. K., & Agrawal, A. K. (2025) · Med Research · doi:10.1002/mdr2.70043 · Cited by 18 · see citing works

Integration of Federated Learning and Blockchain in Healthcare: A TutorialPreprint

Shahsavari, Y., Dambri, O. A., Baseri, Y., Hafid, A. S., & Makrakis, D. (2024) · doi:10.36227/techrxiv.171340632.25905526/v1 · Cited by 18 · see citing works

CalibrationPhys: Self-Supervised Video-Based Heart and Respiratory Rate Measurements by Calibrating Between Multiple Cameras

Akamatsu, Y., Umematsu, T., & Imaoka, H. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2023.3345486 · Cited by 17 · see citing works

Artificial intelligence in healthcare—the road to precision medicine

Tran, T., du Toit, C., & Padmanabhan, S. (2021) · Journal of Hospital Management and Health Policy · doi:10.21037/jhmhp-20-132 · Cited by 17 · see citing works

Applications of Innovation Technologies for Personalized Cancer Medicine: Stem Cells and Gene-Editing Tools

Hasanzadeh, A., Ebadati, A., Dastanpour, L., Aref, A. R., Zangabad, P. S., Kalbasi, A., Dai, X., Mehta, G., Ghasemi, A., Fatahi, Y., et al. (2023) · ACS Pharmacology & Translational Science · doi:10.1021/acsptsci.3c00102 · Cited by 16 · see citing works

Cognitive Biases Cloud Our Clinical Decisions and Patient Expectations: A Narrative Review to Help Bridge the Gap between Evidence-Based and Personalized Medicine

Gojanovic, B., Fourchet, F., & Gremeaux, V. (2022) · Annals of Physical and Rehabilitation Medicine · doi:10.1016/j.rehab.2021.101551 · Cited by 15 · see citing works

Resting state network mapping in individuals using deep learning

Luckett, P. H., Lee, J. J., Park, K. Y., Raut, R. V., Meeker, K. L., Gordon, E. M., Snyder, A. Z., Ances, B. M., Leuthardt, E. C., & Shimony, J. S. (2023) · Frontiers in Neurology · doi:10.3389/fneur.2022.1055437 · Cited by 14 · see citing works

Augmented surgical decision-making for glioblastoma: integrating AI tools into education and practice

Mut, M., Zhang, M., Gupta, I., Fletcher, P. T., Farzad, F., & Nwafor, D. C. (2024) · Frontiers in Neurology · doi:10.3389/fneur.2024.1387958 · Cited by 14 · see citing works

Harnessing Small‐Data Machine Learning for Transformative Mental Health Forecasting: Towards Precision Psychiatry With Personalised Digital Phenotyping

Wang, P., Liu, H., Shi, Y., Liu, A., Zhu, Q., Albu, I., Pacholec, M., Cheng, L. L., Sun, X. S., & Chi, X. (2025) · Med Research · doi:10.1002/mdr2.70017 · Cited by 13 · see citing works

Role of machine learning for predictive modeling for Industry 5.0 and Society 5.0

Bamakan, S. M. H., Dehghan, F., & Akbarpour, S. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-33498-6.00008-x · Cited by 11 · see citing works

From Information Overload to Lucidity: A Survey on Leveraging GPTs for Systematic Summarization of Medical and Biomedical Artifacts

Palanisamy, B., Chakrabarti, A., Singh, A., Hassija, V., Chalapathi, G., & Singh, A. R. (2024) · IEEE Access · doi:10.1109/access.2024.3521596 · Cited by 10 · see citing works

Artificial Intelligence (AI) in Medicine and Modern Healthcare Systems

Kejriwal, R., & Mohana (2022) · International Conference on Augmented Intelligence and Sustainable Systems (ICAISS) · doi:10.1109/icaiss55157.2022.10010939 · Cited by 10 · see citing works

Personalizing Cochlear Implant Care in Single-Sided Deafness: A Distinct Paradigm from Bilateral Hearing Loss

Lin, E. Y., Younan, S. M., Barrett, K. C., & Jiam, N. T. (2025) · Journal of Personalized Medicine · doi:10.3390/jpm15090439 · Cited by 9 · see citing works

Data harmonization for the analysis of personalized treatment of psychosis with metacognitive training

König, C., Copado, P., Lamarca, M., Guendouz, W., Fischer, R., Schlechte, M., Acuña, V., Berna, F., Gawęda, L., Vellido, A., et al. (2025) · Scientific Reports · doi:10.1038/s41598-025-94815-3 · Cited by 6 · see citing works

Revealing sleep and pain reciprocity with wearables and machine learning

Kim, S., Zeitzer, J. M., Mackey, S., & Darnall, B. D. (2025) · Communications Medicine · doi:10.1038/s43856-025-00886-8 · Cited by 8 · see citing works

Algorithms on Electrical Impedance Tomography, Focusing on Deep Learning Architectures and Their Implementations: A Review

Carcavilla, A. C., & Meribout, M. (2025) · IEEE Sensors Journal · doi:10.1109/jsen.2025.3589157 · Cited by 8 · see citing works

An artificial intelligence-based platform for personalized predictions of Metacognitive Training effectiveness

König, C., Copado, P., Vellido, A., Àngela Nebot, B., Ángulo, C., Lamarca, M., Vanessa Acuña, P., Berna, F. S., Moritz, S., Gawęda, Ł., et al. (2025) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2025.07.051 · Cited by 8 · see citing works

A Review of Biosensors and Artificial Intelligence in Healthcare and their Clinical Significance

Tariq, M. (2024) · Psychology & Psychological Research International Journal · doi:10.23880/pprij-16000392 · Cited by 8 · see citing works

Securing Predictive Psychological Assessments: The Synergy of Blockchain Technology and？Artificial Intelligence

de Filippis, R., & Foysal, A. A. (2024) · OALib · doi:10.4236/oalib.1112378 · Cited by 8 · see citing works

Enhancing Cocoa Pod Disease Classification via Transfer Learning and Ensemble Methods: Toward Robust Predictive Modeling

Anduyan, D., Gultiano, N., Cabillo, N., & Pacot, M. P. B. (2025) · International Symposium on Multimedia and Communication Technology (ISMAC) · doi:10.1109/ismac65024.2025.11176000 · Cited by 8 · see citing works

Network Medicine: From Conceptual Frameworks to Applications and Future Trends

Ayar, E. S., Dadmand, S., & Tunçbağ, N. (2023) · IEEE Transactions on Molecular Biological and Multi-Scale Communications · doi:10.1109/tmbmc.2023.3308689 · Cited by 8 · see citing works

Early Heart Disease Detection Using Mel-Spectrograms and Deep Learning

Donkada, S., Pouriyeh, S., Parizi, R. M., Xie, C. Y., & Shahriar, H. (2023) · IEEE Symposium on Computers and Communications (ISCC) · doi:10.1109/iscc58397.2023.10217915 · Cited by 8 · see citing works

Sensitive detection of quinoline-derivatized sitagliptin in small volumes of human plasma by MALDI-TOF mass spectrometry

Li, Y., Tseng, W., & Lu, C. (2020) · Talanta · doi:10.1016/j.talanta.2020.121143 · Cited by 8 · see citing works

Artificial Intelligence in Genetics

Sanders, M. A. (2024) · Elsevier eBooks · doi:10.1016/b978-0-443-12128-9.00006-1 · Cited by 7 · see citing works

Heterogeneous graph neural network-based prediction of immune-related adverse events

He, X., Ni, Q., Cui, C., Li, H., Ni, L., Zhou, J., Tang, L., & Peng, B. (2025) · Scientific Reports · doi:10.1038/s41598-025-30266-0 · Cited by 7 · see citing works

A Novel Microfluidic Platform for Personalized Anticancer Drug Screening Through Image Analysis

Lipreri, M. V., Totaro, M. T., Boos, J. A., Basile, M. S., Baldini, N., & Avnet, S. (2024) · Micromachines · doi:10.3390/mi15121521 · Cited by 7 · see citing works

Reclaiming magical incantation in graduate medical education

Katz, J. D., & George, D. T. (2019) · Clinical Rheumatology · doi:10.1007/s10067-019-04812-x · Cited by 7 · see citing works

Towards precision medicine for otology and neurotology: Machine learning applications and challenges

Adcock, K. S., Arulchelvan, E., Shields, N., & Vanneste, S. (2025) · Hearing Research · doi:10.1016/j.heares.2025.109473 · Cited by 6 · see citing works

Utilizing Gamification, Artificial Intelligence, and mHealth for the Professional Development of Maternal Care Providers: Exploratory Pilot Cross-Sectional Study Assessing Providers' Satisfaction in Primary Health Care Centers in Lebanon

Alameddine, M. S., Sabra, N., Arnaout, N. E., Dakdouki, A. E., Jaouni, M. E., Hamadeh, R. S., Shanaa, A., & Saleh, S. S. (2025) · JMIR Serious Games · doi:10.2196/53735 · Cited by 6 · see citing works

Artificial neural network (ANN) in drug delivery

Piroozmand, F., Mohammadipanah, F., & Sajedi, H. (2023) · Elsevier eBooks · doi:10.1016/b978-0-323-89925-3.00005-8 · Cited by 6 · see citing works

Açıklanabilir Evrişimsel Sinir Ağları ile Beyin Tümörü Tespiti

Orman, A., Köse, U., & Yi̇ği̇t, T. (2021) · El-Cezeri Fen ve Mühendislik Dergisi · doi:10.31202/ecjse.924446 · Cited by 6 · see citing works

Predicting Synergistic Drug Interaction with DNN and GAT

Numcharoenpinij, N., Termsaithong, T., Phunchongharn, P., & Piyayotai, S. (2022) · IEEE International Conference on Knowledge Innovation and Invention (ICKII) · doi:10.1109/ickii55100.2022.9983579 · Cited by 5 · see citing works

Optimizing the Performance of AI Model for Non-Invasive Continuous Glucose Monitoring: Hyperparameter Tuning and Random Oversampling Approach

Putra, K. T., Kusumo, M. P., Prayitno, P., Wicaksana, D., Arrayyan, A. Z., Pratama, S. G., Al-Kamel, M. A., & Chen, H. (2024) · JOIV International Journal on Informatics Visualization · doi:10.62527/joiv.8.2.2047 · Cited by 4 · see citing works

Application of Deep-Q learning in personalized health care Internet of Things ecosystem

Mundru, Y., Yogi, M. K., & Chatterjee, J. M. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-19413-9.00024-2 · Cited by 4 · see citing works

Personalized Therapy Using Deep Learning Advances

Gaur, N., Dharwadkar, R., & Thomas, J. (2022) · Wiley eBooks · doi:10.1002/9781119857983.ch6 · Cited by 4 · see citing works

AI-Driven Insights: Paving the Path to Next-Generation Therapeutics

Kalyankar, P. D. S., Bhagat, M. C. G., Kadu, M. A. D., Tambade, M. A. P., & Dhoran, M. K. S. (2024) · International Journal of Advanced Research in Science Communication and Technology · doi:10.48175/ijarsct-22854 · Cited by 3 · see citing works

Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study

Beuken, M. J., Kleynen, M., Braun, S. M., van Berkel, K., Kallen, C., Koster, A., Bosma, H., Berendschot, T. T. J. M., Houben, A. J. H. M., Dukers–Muijrers, N. H. T. M., et al. (2025) · JMIR Medical Informatics · doi:10.2196/64479 · Cited by 3 · see citing works

Development and use of machine learning models for prediction of male sling success

Kim, J. K., McCammon, K., Kim, K. J., Rickard, M., Lorenzo, A. J., & Chua, M. E. (2023) · Canadian Urological Association Journal · doi:10.5489/cuaj.8265 · Cited by 3 · see citing works

A Deep CNN-BiGRU model for Lymphoma and Residual Masses Recognition

Ferjaoui, R., Boujnah, S., & Khalifa, A. B. (2024) · International Conference on Advanced Technologies for Signal and Image Processing · doi:10.1109/atsip62566.2024.10639019 · Cited by 3 · see citing works

A Holistic Architecture for a Sales Enablement Sensing-as-a-Service Model in the IoT Environment

Olanrewaju, R. F., Khan, B. U. I., Goh, K. W., Hashim, A. H. A., Sidek, K. A., Khan, Z. I., & Daniyal, H. (2022) · Information · doi:10.3390/info13110514 · Cited by 3 · see citing works

Pathway to Detect Cancer Tumor by Genetic Mutation

Mohanty, A., Prusty, A. R., & Dasig, D. D. (2024) · Studies in computational intelligence · doi:10.1007/978-981-99-8853-2_11 · Cited by 3 · see citing works

Efficient distributed continual learning for steering experiments in real-time

Bouvier, T., Nicolae, B., Costan, A., Biçer, T., Foster, I. T., & Antoniu, G. (2024) · Future Generation Computer Systems · doi:10.1016/j.future.2024.07.016 · Cited by 3 · see citing works

Machine Learning based Prediction Model for Health Care Sector - A Survey

Sathyaseelan, K., & Sarathambekai, S. (2021) · Innovations in Power and Advanced Computing Technologies (i-PACT) · doi:10.1109/i-pact52855.2021.9696646 · Cited by 3 · see citing works

Multiclass classification models for Personalized Medicine prediction based on patients Genetic Variants

Biswas, S., Kumar, V., & Das, S. (2021) · IEEE International Conference on Technology, Research, and Innovation for Betterment of Society (TRIBES) · doi:10.1109/tribes52498.2021.9751631 · Cited by 3 · see citing works

Improving Health Care Services via Personalized Medicine

Rhatassi, F. E. E., Ghali, B. E., & Daoudi, N. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-28387-1_37 · Cited by 3 · see citing works

Exploring the impact of pathogenic microbiome in orthopedic diseases: machine learning and deep learning approaches

Shao, Z., Gao, H., Wang, B., & Zhang, S. (2024) · Frontiers in Cellular and Infection Microbiology · doi:10.3389/fcimb.2024.1380136 · Cited by 3 · see citing works

Nonprecsion (Standard) Psychosocial Interventions for the Treatment of Mental Disorders

Kazdin, A. E. (2022) · International Journal of Mental Health Promotion · doi:10.32604/ijmhp.2022.022522 · Cited by 3 · see citing works

Low-Cost Joystick for Pediatric Respiratory Exercises

Sobrinho, A. S. F., Scalassara, P. R., & Dájer, M. E. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01655-x · Cited by 3 · see citing works

Nyctale: Neuro-Evidence Transformer for Adaptive and Personalized Lung Nodule Invasiveness Prediction

Khademi, S., Oikonomou, A., Plataniotis, K. N., & Mohammadi, A. (2024) · Proceedings - International Conference on Image Processing · doi:10.1109/icip51287.2024.10648264 · Cited by 2 · see citing works

An Optimized Machine Learning-Based Stroke Prediction: Enhancing Precision Medicine and Public Health

Khatri, P., Sharma, A., & Payal (2024) · International Conference on Data Science and Network Security (ICDSNS) · doi:10.1109/icdsns62112.2024.10690944 · Cited by 2 · see citing works

Into the Void: Mapping the Unseen Gaps in High Dimensional Data

Zhang, X., Estro, T., Kuenning, G., Zadok, E., & Klaus, M. (2025) · IEEE Transactions on Visualization and Computer Graphics · doi:10.1109/tvcg.2025.3572850 · Cited by 2 · see citing works

Multi Level interdependencies Management for resilience in Critical Infrastructures

Kasmi, O., Baïna, A., & Bellafkih, M. (2020) · International Conference on Intelligent Systems: Theories and Applications · doi:10.1145/3419604.3419791 · Cited by 2 · see citing works

Use of Deep Learning in Personalized Medicine: Current Trends and the Future Perspective

Hussain, A., & Naaz, S. (2021) · International Conference ICT for Digital, Smart and Sustainable Development · doi:10.4108/eai.27-2-2020.2303118 · Cited by 2 · see citing works

SAGAD: Synthetic Data Generator for Tabular Datasets

da Silva, H. M. F., Silva, R. S. P., & Porto, F. A. M. (2021) · Simpósio Brasileiro de Bancos de Dados (SBBD) · doi:10.5753/sbbd.2021.17861 · Cited by 2 · see citing works

Machine Learning-Based Diagnosis of Diseases Associated with Abnormal and Heavy Menstrual Bleeding: A Literature Review

Raji, P., & Jose, P. S. H. (2022) · Lecture notes in electrical engineering · doi:10.1007/978-981-19-2177-3_47 · Cited by 2 · see citing works

GlioMod: Spatiotemporal-Aware Glioblastoma Multiforme Tumor Growth Modeling with Deep Encoder-Decoder NetworksPreprint

Jain, R. K., Gupta, A., Ali, W. H., & Lermusiaux, P. F. J. (2022) · medRxiv · doi:10.1101/2022.11.06.22282010 · Cited by 2 · see citing works

A Systematic Review on Artificial Intelligence (AI) Assisted Drug and Vaccine Development against SARS-CoV-2

Singha, V., Ghosh, S., Singha, P. S., Datta, S., & Ghosh, D. (2024) · Coronaviruses · doi:10.2174/0126667975309811240530114325 · Cited by 1 · see citing works

Pharmacotherapeutic Advancements in Personalized Medicine: A Comprehensive Review of Emerging Trends in Clinical Pharmacy Practice

Bansal, D. R., William, H., Kher, M., Tank, Y. P., Tank, B. Y., & Shukla, A. (2026) · Cureus · doi:10.7759/cureus.108329 · Cited by 1 · see citing works

Evaluating Privacy Preservation in Regression Models for a Small Medical Dataset

König, C., Copado, P. J., Cecilio Angulo, A., Àngela Nebot, B., & Vellido, A. (2026) · Lecture notes in computer science · doi:10.1007/978-3-032-27317-8_36 · Cited by 1 · see citing works

Applicability of Machine Learning for Personalized Medicine

Fadnavis, R. A., & Kshirsagar, M. (2023) · Smart innovation, systems and technologies · doi:10.1007/978-981-99-3982-4_27

Deep learning opens new horizons in personalized medicine (Review)

Papadakis, G. Z., Karantanas, A. H., Tsiknakis, M., Tsatsakis, A. M., Spandidos, D. A., & Kostas Marias, A. (2019) · Biomedical Reports · doi:10.3892/br.2019.1199 · Cited by 41 · see citing works

Deep Learning for Personalized Medicine: A Comprehensive Review

Mirzapure, I. A., Telrandhe, U. B., Dandekar, M. R., & Salve, Y. M. (2025) · International Conference on Sentiment Analysis and Deep Learning (ICSADL) · doi:10.1109/icsadl65848.2025.10933299 · Cited by 1 · see citing works

Machine learning for personalized medicine: Tailoring treatment strategies through data analysis

Verma, P., Mishra, N., & Srivastava, V. (2019) · The Pharma Innovation · doi:10.22271/tpi.2019.v8.i3sa.25249 · Cited by 9 · see citing works

Deep Learning for Personalized Medicine - Enhancing Precision Health With AI

Raparthi, M., Kasaraneni, B. P., Gayam, S. R., Kondapaka, K. K., Pattyam, S. P., Putha, S., Sahu, M. K., Kuna, S. S., Nimmagadda, V. S. P., & Thuniki, P. (2020) · Journal of Science & Technology