Deep Learning in Biomedical Data Science

Long Short-Term Memory

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

You Only Look Once: Unified, Real-Time Object Detection

Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.91 · Cited by 53,803

Deep learning in neural networks: an overview

Schmidhuber, J. (2015) · Neural Networks · doi:10.1016/j.neunet.2014.09.003 · Cited by 18,570

A logical calculus of the ideas immanent in nervous activity

McCulloch, W. S., & Pitts, W. (1943) · The Bulletin of Mathematical Biophysics · doi:10.1007/bf02478259 · Cited by 18,566

"Why Should I Trust You?": Explaining the Predictions of Any Classifier

Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555

Dermatologist-level classification of skin cancer with deep neural networks

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S. M., Blau, H. M., & Thrun, S. (2017) · Nature · doi:10.1038/nature21056 · Cited by 14,362

Approximation by superpositions of a sigmoidal function

Cybenko, G. (1989) · Mathematics of Control, Signals, and Systems · doi:10.1007/bf02551274 · Cited by 13,883

The perceptron: A probabilistic model for information storage and organization in the brain.

Rosenblatt, F. (1958) · Psychological Review · doi:10.1037/h0042519 · Cited by 12,280

Untitled work

(2012) · Cited by 9,062

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

Base-Calling of Automated Sequencer Traces Using Phred. I. Accuracy AssessmentPreprint

Ewing, B., Hillier, L., Wendl, M. C., & Green, P. (1998) · Genome Research · doi:10.1101/gr.8.3.175 · Cited by 6,993

LSTM: A Search Space Odyssey

Greff, K., Srivastava, R. K., Koutnik, J., Steunebrink, B. R., & Schmidhuber, J. (2017) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2016.2582924 · Cited by 6,955

Untitled work

(2009) · Cited by 6,153

Identification of prokaryotic and eukaryotic signal peptides and prediction of their cleavage sites

Nielsen, H., Engelbrecht, J., Brunak, S., & von HEIJNE, G. (1997) · Protein Engineering Design and Selection · doi:10.1093/protein/10.1.1 · Cited by 5,343

On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation

Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524

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 3,880

Predicting the future - big data, machine learning, and clinical medicine

Obermeyer, Z., & Emanuel, E. J. (2016) · New England Journal of Medicine · doi:10.1056/nejmp1606181 · Cited by 3,652

Prediction of protein conformation

Chou, P. Y., & Fasman, G. D. (1974) · Biochemistry · doi:10.1021/bi00699a002 · Cited by 3,309

Prediction of Protein Secondary Structure at Better than 70% Accuracy

Rost, B., & Sander, C. (1993) · Journal of Molecular Biology · doi:10.1006/jmbi.1993.1413 · Cited by 2,927

Predicting the sequence specificities of DNA- and RNA-binding proteins by deep learning

Alipanahi, B., Delong, A., Weirauch, M. T., & Frey, B. J. (2015) · Nature Biotechnology · doi:10.1038/nbt.3300 · Cited by 2,873

Predicting effects of noncoding variants with deep learning–based sequence model

Zhou, J., & Troyanskaya, O. G. (2015) · Nature Methods · doi:10.1038/nmeth.3547 · Cited by 2,338

Opportunities and obstacles for deep learning in biology and medicine

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface · doi:10.1098/rsif.2017.0387 · Cited by 2,310

Conformational parameters for amino acids in helical, β-sheet, and random coil regions calculated from proteins

Chou, P. Y., & Fasman, G. D. (1974) · Biochemistry · doi:10.1021/bi00699a001 · Cited by 2,172

Universal approximation of an unknown mapping and its derivatives using multilayer feedforward networks

Hornik, K., Stinchcombe, M. B., & White, H. (1990) · Neural Networks · doi:10.1016/0893-6080(90)90005-6 · Cited by 2,136

Prediction of post‐translational glycosylation and phosphorylation of proteins from the amino acid sequence

Blom, N., Sicheritz‐Pontén, T., Gupta, R., Gammeltoft, S., & Brunak, S. (2004) · PROTEOMICS · doi:10.1002/pmic.200300771 · Cited by 2,076

Model compression

Buciluǎ, C., Caruana, R., & Niculescu-Mizil, A. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150464 · Cited by 2,068

Hidden Markov Models in Computational Biology

Krogh, A., Brown, M., Mian, S. I., Sjölander, K. V., & Haussler, D. H. (1994) · Journal of Molecular Biology · doi:10.1006/jmbi.1994.1104 · Cited by 1,948

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

Using goal-driven deep learning models to understand sensory cortex

Yamins, D. L. K., & DiCarlo, J. J. (2016) · Nature Neuroscience · doi:10.1038/nn.4244 · Cited by 1,621

Protein Structure Prediction and Structural Genomics

Baker, D., & Sali, A. (2001) · Science · doi:10.1126/science.1065659 · Cited by 1,587

Mitosis Detection in Breast Cancer Histology Images with Deep Neural Networks

Cireşan, D. C., Giusti, A., Gambardella, L. M., & Schmidhuber, J. (2013) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-40763-5_51 · Cited by 1,536

Quantum-chemical insights from deep tensor neural networks

Schütt, K. T., Arbabzadah, F., Chmiela, S., Müller, K. R., & Tkatchenko, A. (2017) · Nature Communications · doi:10.1038/ncomms13890 · Cited by 1,445

Combining evolutionary information and neural networks to predict protein secondary structure

Rost, B., & Sander, C. (1994) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.340190108 · Cited by 1,443

The human splicing code reveals new insights into the genetic determinants of disease

Xiong, H. Y., Alipanahi, B., Lee, L. J., Bretschneider, H., Merico, D., Yuen, R. K. C., Hua, Y., Gueroussov, S., Najafabadi, H. S., Hughes, T., et al. (2014) · Science · doi:10.1126/science.1254806 · Cited by 1,243

Predicting the secondary structure of globular proteins using neural network models

Ning Qian, A., & B, T. J. S. A. (1988) · Journal of Molecular Biology · doi:10.1016/0022-2836(88)90564-5 · Cited by 1,231

A back-propagation programmed network that simulates response properties of a subset of posterior parietal neurons

Zipser, D., & Andersen, R. A. (1988) · Nature · doi:10.1038/331679a0 · Cited by 1,172

Prediction of protein stability changes for single‐site mutations using support vector machines

Cheng, J., Randall, A. Z., & Baldi, P. (2005) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20810 · Cited by 1,170

Reliable prediction of T‐cell epitopes using neural networks with novel sequence representations

Nielsen, M., Lundegaard, C., Worning, P., Lauemøller, S. L., Lamberth, K., Buus, S., Brunak, S., & Lund, O. (2003) · Protein Science · doi:10.1110/ps.0239403 · Cited by 1,075

DANN: a deep learning approach for annotating the pathogenicity of genetic variants

Quang, D. X., Chen, Y., & Xie, X. S. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu703 · Cited by 1,073

Unintended consequences of machine learning in medicine

Cabitza, F., Rasoini, R., & Gensini, G. F. (2017) · JAMA · doi:10.1001/jama.2017.7797 · Cited by 1,066

SCRATCH: a protein structure and structural feature prediction server

Cheng, J., Randall, A. Z., Sweredoski, M. J., & Baldi, P. (2005) · Nucleic Acids Research · doi:10.1093/nar/gki396 · Cited by 1,048

Basset: learning the regulatory code of the accessible genome with deep convolutional neural networks

Kelley, D. R., Snoek, J., & Rinn, J. L. (2016) · Genome Research · doi:10.1101/gr.200535.115 · Cited by 1,048

Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

Wang, S., Sun, S., Li, Z., Zhang, R., & Xu, J. (2017) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1005324 · Cited by 931

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences

Quang, D. X., & Xie, X. S. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkw226 · Cited by 858

Continuous Distributed Representation of Biological Sequences for Deep Proteomics and Genomics

Asgari, E., & Mofrad, M. R. K. (2015) · PLoS ONE · doi:10.1371/journal.pone.0141287 · Cited by 833

Splice site prediction in Arabidopsis thaliana pre-mRNA by combining local and global sequence information

Hebsgaard, S. (1996) · Nucleic Acids Research · doi:10.1093/nar/24.17.3439 · Cited by 815

Prediction of human mRNA donor and acceptor sites from the DNA sequence

Brunak, S., Engelbrecht, J., & Knudsen, S. (1991) · Journal of Molecular Biology · doi:10.1016/0022-2836(91)90380-o · Cited by 794

PSICOV: precise structural contact prediction using sparse inverse covariance estimation on large multiple sequence alignments

Jones, D. T., Buchan, D. W. A., Cozzetto, D., & Pontil, M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr638 · Cited by 787

Determination of three‐dimensional structures of proteins from interproton distance data by hybrid distance geometry‐dynamical simulated annealing calculations

Nilges, M., Clore, G. M., & Gronenborn, A. M. (1988) · FEBS Letters · doi:10.1016/0014-5793(88)81148-7 · Cited by 767

Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy

Urban, G., Tripathi, P. V., Alkayali, T., Mittal, M., Jalali, F., Karnes, W. E., & Baldi, P. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.06.037 · Cited by 763

Improving the prediction of protein secondary structure in three and eight classes using recurrent neural networks and profiles

Pollastri, G., Przybylski, D., Rost, B., & Baldi, P. (2002) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10082 · Cited by 741

SOLpro: accurate sequence-based prediction of protein solubility

Magnan, C. N., Randall, A. Z., & Baldi, P. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp386 · Cited by 694

Locating protein-coding regions in human DNA sequences by a multiple sensor-neural network approach.

Uberbacher, E. C., & Mural, R. (1991) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.88.24.11261 · Cited by 652

Use of the ‘Perceptron’ algorithm to distinguish translational initiation sites in E. coli

Stormo, G. D., Schneider, T. D., Gold, L., & Ehrenfeucht, A. (1982) · Nucleic Acids Research · doi:10.1093/nar/10.9.2997 · Cited by 651

Multi-scale Convolutional Neural Networks for Lung Nodule Classification

Shen, W., Zhou, M., Yang, F., Yang, C., & Tian, J. (2015) · Lecture notes in computer science · doi:10.1007/978-3-319-19992-4_46 · Cited by 649

Towards the Improved Discovery and Design of Functional Peptides: Common Features of Diverse Classes Permit Generalized Prediction of Bioactivity

Mooney, C., Haslam, N. J., Pollastri, G., & Shields, D. C. (2012) · PLoS ONE · doi:10.1371/journal.pone.0045012 · Cited by 629

Protein structure prediction from sequence variation

Marks, D. S., Hopf, T. A., & Sander, C. (2012) · Nature Biotechnology · doi:10.1038/nbt.2419 · Cited by 622

A hierarchical unsupervised growing neural network for clustering gene expression patterns

Herrero, J., Valencia, A., & Dopazo, J. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.2.126 · Cited by 622

Deep Architectures and Deep Learning in Chemoinformatics: The Prediction of Aqueous Solubility for Drug-Like Molecules

Lusci, A., Pollastri, G., & Baldi, P. (2013) · Journal of Chemical Information and Modeling · doi:10.1021/ci400187y · Cited by 575

Determination of three‐dimensional structures of proteins from interproton distance data by dynamical simulated annealing from a random array of atoms Circumventing problems associated with folding

Nilges, M., Clore, G. M., & Gronenborn, A. M. (1988) · FEBS Letters · doi:10.1016/0014-5793(88)80559-3 · Cited by 538

High-throughput prediction of protein antigenicity using protein microarray data

Magnan, C. N., Zeller, M. D., Kayala, M. A., Vigil, A., Randall, A. Z., Felgner, P. L., & Baldi, P. (2010) · Bioinformatics · doi:10.1093/bioinformatics/btq551 · Cited by 537

DeepCpG: accurate prediction of single-cell DNA methylation states using deep learning

Angermueller, C., Lee, H. J., Reik, W., & Stegle, O. (2017) · Genome Biology · doi:10.1186/s13059-017-1189-z · Cited by 536

Protein secondary structure prediction with a neural network.

Holley, L. H., & Karplus, M. (1989) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.86.1.152 · Cited by 502

Exploiting the past and the future in protein secondary structure prediction

Baldi, P., Brunak, S., Frasconi, P., Soda, G., & Pollastri, G. (1999) · Bioinformatics · doi:10.1093/bioinformatics/15.11.937 · Cited by 501

Bioinformatics: The Machine Learning Approach

Leunissen, J. A. M. (2002) · Briefings in Bioinformatics · doi:10.1093/bib/3.3.321 · Cited by 477

Deep learning of the tissue-regulated splicing code

Leung, M. K. K., Xiong, H. Y., Lee, L. J., & Frey, B. J. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu277 · Cited by 459

Hidden Markov models of biological primary sequence information.

Baldi, P., Chauvin, Y., Hunkapiller, T., & McClure, M. A. (1994) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.91.3.1059 · Cited by 451

Predicting healthcare trajectories from medical records: a deep learning approach

Pham, T., Tran, T., Phung, D., & Venkatesh, S. (2017) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2017.04.001 · Cited by 447

Gene expression inference with deep learning

Chen, Y., Li, Y., Narayan, R., Subramanian, A., & Xie, X. S. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw074 · Cited by 435

Temporal disease trajectories condensed from population-wide registry data covering 6.2 million patients

Jensen, A. B., Moseley, P. L., Oprea, T. I., Ellesøe, S. G., Eriksson, R., Schmock, H., Jensen, P. B., Jensen, L. J., & Brunak, S. (2014) · Nature Communications · doi:10.1038/ncomms5022 · Cited by 407

SSpro/ACCpro 5: almost perfect prediction of protein secondary structure and relative solvent accessibility using profiles, machine learning and structural similarity

Magnan, C. N., & Baldi, P. (2014) · Bioinformatics · doi:10.1093/bioinformatics/btu352 · Cited by 365

Practical limits of function prediction

Devos, D. P., & Valencia, A. (2000) · Proteins Structure Function and Bioinformatics · doi:10.1002/1097-0134(20001001)41:1 · Cited by 358

The dropout learning algorithm

Baldi, P., & Sadowski, P. (2014) · Artificial Intelligence · doi:10.1016/j.artint.2014.02.004 · Cited by 340

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 317

Prediction of Human Protein Function from Post-translational Modifications and Localization Features

Jensen, L. J., Gupta, R., Blom, N., Devos, D. P., Tamames, J., Keşmir, C., Nielsen, H., Stærfeldt, H., Rapacki, K., Workman, C. T., et al. (2002) · Journal of Molecular Biology · doi:10.1016/s0022-2836(02)00379-0 · Cited by 317

Opportunities and obstacles for deep learning in biology and medicinePreprint

Ching, T. H., Himmelstein, D. S., Beaulieu‐Jones, B. K., et al. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/142760 · Cited by 308

A Deep Learning Network Approach to ab initio Protein Secondary Structure Prediction

Spencer, M., Eickholt, J., & Cheng, J. (2014) · IEEE Transactions on Computational Biology and Bioinformatics · doi:10.1109/tcbb.2014.2343960 · Cited by 298

Can three-dimensional contacts in protein structures be predicted by analysis of correlated mutations?

Shindyalov, I. N., Kolchanov, N. A., & Sander, C. (1994) · "Protein Engineering, Design and Selection" · doi:10.1093/protein/7.3.349 · Cited by 297

Recovery of protein structure from contact maps

Vendruscolo, M., Kussell, E. L., & Domany, E. (1997) · Folding and Design · doi:10.1016/s1359-0278(97)00041-2 · Cited by 270

Deep architectures for protein contact map prediction

Lena, P. D., Nagata, K., & Baldi, P. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts475 · Cited by 269

PEPITO: improved discontinuous B-cell epitope prediction using multiple distance thresholds and half sphere exposure

Sweredoski, M. J., & Baldi, P. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn199 · Cited by 263

DeepNano: Deep recurrent neural networks for base calling in MinION nanopore reads

Boža, V., Brejová, B., & Vinař, T. (2017) · PLoS ONE · doi:10.1371/journal.pone.0178751 · Cited by 256

A deep learning framework for modeling structural features of RNA-binding protein targets

Zhang, S., Zhou, J., Hu, H., Gong, H., Chen, L., Cheng, C., & Zeng, J. (2015) · Nucleic Acids Research · doi:10.1093/nar/gkv1025 · Cited by 253

Prediction of coordination number and relative solvent accessibility in proteins

Pollastri, G., Baldi, P., Fariselli, P., & Casadio, R. (2002) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.10069 · Cited by 250

Improved residue contact prediction using support vector machines and a large feature set

Cheng, J. J., & Baldi, P. (2007) · BMC Bioinformatics · doi:10.1186/1471-2105-8-113 · Cited by 241

Detecting Cardiovascular Disease from Mammograms With Deep Learning

Wang, J., Ding, H. J., Bidgoli, F. A., Zhou, B., Iribarren, C., Molloi, S. Y., & Baldi, P. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2655486 · Cited by 234

A machine learning information retrieval approach to protein fold recognition

Cheng, J., & Baldi, P. (2006) · Bioinformatics · doi:10.1093/bioinformatics/btl102 · Cited by 230

Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data

Cheng, J., Sweredoski, M. J., & Baldi, P. (2005) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-005-0001-y · Cited by 222

Prediction of contact maps with neural networks and correlated mutations

Fariselli, P., Olmea, O., Valencia, A., & Casadio, R. (2001) · Protein Engineering Design and Selection · doi:10.1093/protein/14.11.835 · Cited by 206

FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data

Quang, D., & Xie, X. (2019) · Methods · doi:10.1016/j.ymeth.2019.03.020 · Cited by 204

Improving contact predictions by the combination of correlated mutations and other sources of sequence information

Olmea, O., & Valencia, A. (1997) · Folding and Design · doi:10.1016/s1359-0278(97)00060-6 · Cited by 203

Learning to Predict Chemical Reactions

Kayala, M. A., Azencott, C., Chen, J. H., & Baldi, P. (2011) · Journal of Chemical Information and Modeling · doi:10.1021/ci200207y · Cited by 194

COBEpro: a novel system for predicting continuous B-cell epitopes

Sweredoski, M. J., & Baldi, P. (2008) · Protein Engineering Design and Selection · doi:10.1093/protein/gzn075 · Cited by 193

ReactionPredictor: Prediction of Complex Chemical Reactions at the Mechanistic Level Using Machine Learning

Kayala, M. A., & Baldi, P. (2012) · Journal of Chemical Information and Modeling · doi:10.1021/ci3003039 · Cited by 186

Deep autoencoder neural networks for gene ontology annotation predictions

Chicco, D., Sadowski, P., & Baldi, P. (2014) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/2649387.2649442 · Cited by 185

Development and Validation of a Deep Neural Network Model for Prediction of Postoperative In-hospital Mortality

Lee, C. K., Hofer, I., Gabel, E., Baldi, P., & Cannesson, M. (2018) · Anesthesiology · doi:10.1097/aln.0000000000002186 · Cited by 185

Protein secondary structure and homology by neural networks The α‐helices in rhodopsin

Bohr, H. G., Bohr, J., Brunak, S., COTTERILL, R. M. J., Lautrup, B. E., Nørskov, L., Olsen, O. H., & Petersen, S. B. (1988) · FEBS Letters · doi:10.1016/0014-5793(88)81066-4 · Cited by 177

Semi-supervised learning of the electronic health record for phenotype stratification

Beaulieu‐Jones, B. K., & Greene, C. S. (2016) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2016.10.007 · Cited by 175

Neural Networks for Fingerprint Recognition

Baldi, P., & Chauvin, Y. (1993) · Neural Computation · doi:10.1162/neco.1993.5.3.402 · Cited by 171

CPPpred: prediction of cell penetrating peptides

Holton, T. A., Pollastri, G., Shields, D. C., & Mooney, C. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt518 · Cited by 164

A neural network based predictor of residue contacts in proteins

Fariselli, P., & Casadio, R. (1999) · Protein Engineering Design and Selection · doi:10.1093/protein/12.1.15 · Cited by 164

DeepQA: improving the estimation of single protein model quality with deep belief networks

Cao, R., Bhattacharya, D., Hou, J., & Cheng, J. (2016) · BMC Bioinformatics · doi:10.1186/s12859-016-1405-y · Cited by 164

Predicting protein residue–residue contacts using deep networks and boosting

Eickholt, J., & Cheng, J. J. (2012) · Bioinformatics · doi:10.1093/bioinformatics/bts598 · Cited by 161

Improving Prediction of Protein Secondary Structure Using Structured Neural Networks and Multiple Sequence Alignments

Riis, S. K., & Krogh, A. (1996) · Journal of Computational Biology · doi:10.1089/cmb.1996.3.163 · Cited by 157

Large‐scale prediction of disulphide bridges using kernel methods, two‐dimensional recursive neural networks, and weighted graph matching

Cheng, J. J., Saigo, H., & Baldi, P. (2005) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.20787 · Cited by 155

Protein distance constraints predicted by neural networks and probability density functions

Lund, O., Frimand, K., Gorodkin, J., Bohr, H. G., Bohr, J., Hansen, J. E., & Brunak, S. (1997) · Protein Engineering Design and Selection · doi:10.1093/protein/10.11.1241 · Cited by 154

Prediction of contact maps by GIOHMMs and recurrent neural networks using lateral propagation from all four cardinal corners

Pollastri, G., & Baldi, P. (2002) · Bioinformatics · doi:10.1093/bioinformatics/18.suppl_1.s62 · Cited by 151

Predicting backbone Cα angles and dihedrals from protein sequences by stacked sparse auto‐encoder deep neural network

Lyons, J., Dehzangi, A., Heffernan, R., Sharma, A. K., Paliwal, K. K., Sattar, A., Zhou, Y., & Yang, Y. (2014) · Journal of Computational Chemistry · doi:10.1002/jcc.23718 · Cited by 145

Machine Learning Methods for Protein Structure Prediction

Cheng, J., Tegge, A. N., & Baldi, P. (2008) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2008.2008239 · Cited by 139

A multi-resolution approach for spinal metastasis detection using deep Siamese neural networks

Wang, J., Fang, Z., Lang, N., Yuan, H., Su, M., & Baldi, P. (2017) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2017.03.024 · Cited by 137

Global Fold Determination from a Small Number of Distance Restraints

Aszódi, A., Gradwell, M. J., & Taylor, W. R. (1995) · Journal of Molecular Biology · doi:10.1006/jmbi.1995.0436 · Cited by 133

Porter, PaleAle 4.0: high-accuracy prediction of protein secondary structure and relative solvent accessibility

Mirabello, C., & Pollastri, G. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt344 · Cited by 122

Improving Protein Fold Recognition by Deep Learning Networks

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

Enhancing Evolutionary Couplings with Deep Convolutional Neural Networks

Liu, Y., Palmedo, P., Ye, Q., Berger, B., & Peng, J. (2018) · Cell Systems · doi:10.1016/j.cels.2017.11.014 · Cited by 114

What time is it? Deep learning approaches for circadian rhythms

Agostinelli, F., Ceglia, N., Shahbaba, B., Sassone‐Corsi, P., & Baldi, P. (2016) · Bioinformatics · doi:10.1093/bioinformatics/btw243 · Cited by 106

DOMpro: Protein Domain Prediction Using Profiles, Secondary Structure, Relative Solvent Accessibility, and Recursive Neural Networks

Cheng, J. J., Sweredoski, M. J., & Baldi, P. (2006) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-005-0023-5 · Cited by 104

HLA class I binding prediction via convolutional neural networks

Vang, Y. S., & Xie, X. S. (2017) · Bioinformatics · doi:10.1093/bioinformatics/btx264 · Cited by 101

Imputation for transcription factor binding predictions based on deep learning

Qin, Q., & Feng, J. (2017) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1005403 · Cited by 96

Detection of Sclerotic Spine Metastases via Random Aggregation of Deep Convolutional Neural Network Classifications

Roth, H. R., Yao, J.-H., Lu, L., Stieger, J., Burns, J. E., & Summers, R. (2014) · arXiv.org · doi:10.1007/978-3-319-14148-0_1 · Cited by 89

DNdisorder: predicting protein disorder using boosting and deep networks

Eickholt, J., & Cheng, J. J. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-88 · Cited by 89

Prediction of Short Linear Protein Binding Regions

Mooney, C., Pollastri, G., Shields, D. C., & Haslam, N. J. (2011) · Journal of Molecular Biology · doi:10.1016/j.jmb.2011.10.025 · Cited by 87

PeptideLocator: prediction of bioactive peptides in protein sequences

Mooney, C., Haslam, N. J., Holton, T. A., Pollastri, G., & Shields, D. C. (2013) · Bioinformatics · doi:10.1093/bioinformatics/btt103 · Cited by 86

A theory of local learning, the learning channel, and the optimality of backpropagation

Baldi, P., & Sadowski, P. (2016) · Neural Networks · doi:10.1016/j.neunet.2016.07.006 · Cited by 84

TMBpro: secondary structure, β-contact and tertiary structure prediction of transmembrane β-barrel proteins

Randall, A. Z., Cheng, J. J., Sweredoski, M. J., & Baldi, P. (2007) · Bioinformatics · doi:10.1093/bioinformatics/btm548 · Cited by 80

A fast learning algorithm for image segmentation with max-pooling convolutional networks

Masci, J., Giusti, A., Ciresan, D. C., Fricout, G., & Schmidhuber, J. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738559 · Cited by 76

Immunological Bioinformatics

Lund, O., Nielsen, M., Lundegaard, C., Keşmir, C., & Brunak, S. (2005) · The MIT Press eBooks · doi:10.7551/mitpress/3679.001.0001 · Cited by 74

SCLpred: protein subcellular localization prediction by N-to-1 neural networks

Mooney, C., Wang, Y., & Pollastri, G. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr494 · Cited by 70

SIDEpro: A novel machine learning approach for the fast and accurate prediction of side‐chain conformations

Nagata, K., Randall, A. Z., & Baldi, P. (2011) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.23170 · Cited by 66

Neural network detects errors in the assignment of mRNA splice sites

Brunak, S., Engelbrecht, J., & Knudsen, S. (1990) · Nucleic Acids Research · doi:10.1093/nar/18.16.4797 · Cited by 64

FT-COMAR: fault tolerant three-dimensional structure reconstruction from protein contact maps

Vassura, M., Margara, L., Lena, P. D., Medri, F., Fariselli, P., & Casadio, R. (2008) · Bioinformatics · doi:10.1093/bioinformatics/btn115 · Cited by 59

Beyond the Twilight Zone: Automated prediction of structural properties of proteins by recursive neural networks and remote homology information

Mooney, C., & Pollastri, G. (2009) · Proteins Structure Function and Bioinformatics · doi:10.1002/prot.22429 · Cited by 57

Accurate prediction of protein enzymatic class by N-to-1 Neural Networks

Volpato, V., Adelfio, A., & Pollastri, G. (2013) · BMC Bioinformatics · doi:10.1186/1471-2105-14-s1-s11 · Cited by 56

Improved prediction of the number of residue contacts in proteins by recurrent neural networks

Pollastri, G., Baldi, P., Fariselli, P., & Casadio, R. (2001) · Bioinformatics · doi:10.1093/bioinformatics/17.suppl_1.s234 · Cited by 51

Toward an accurate prediction of inter-residue distances in proteins using 2D recursive neural networks

Kukić, P., Mirabello, C., Tradigo, G., Walsh, I., Veltri, P., & Pollastri, G. (2014) · BMC Bioinformatics · doi:10.1186/1471-2105-15-6 · Cited by 49

De novo identification of replication-timing domains in the human genome by deep learning

Liu, F., Ren, C., Li, H., Zhou, P., Bo, X., & Shu, W. (2015) · Bioinformatics · doi:10.1093/bioinformatics/btv643 · Cited by 46

Hybrid Modeling, HMM/NN Architectures, and Protein Applications

Baldi, P., & Chauvin, Y. (1996) · Neural Computation · doi:10.1162/neco.1996.8.7.1541 · Cited by 44

Predicting Binding within Disordered Protein Regions to Structurally Characterised Peptide-Binding Domains

Khan, W., Duffy, F. J., Pollastri, G., Shields, D. C., & Mooney, C. (2013) · PLoS ONE · doi:10.1371/journal.pone.0072838 · Cited by 42

FactorNet: a deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential dataPreprint

Quang, D. X., & Xie, X. S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/151274 · Cited by 33

Learning in the machine: The symmetries of the deep learning channel

Baldi, P., Sadowski, P., & Lu, Z. (2017) · Neural Networks · doi:10.1016/j.neunet.2017.08.008 · Cited by 28

DeeperBind: Enhancing Prediction of Sequence Specificities of DNA Binding ProteinsPreprint

Hassanzadeh, H. R., & Wang, M. D. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/099754 · Cited by 26

Modular DAG–RNN Architectures for Assembling Coarse Protein Structures

Pollastri, G., Vullo, A., Frasconi, P., & Baldi, P. (2006) · Journal of Computational Biology · doi:10.1089/cmb.2006.13.631 · Cited by 25

DeepSF

Hou, J., Adhikari, B., & Cheng, J. J. (2018) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3233547.3233716 · Cited by 10

Understanding Sequence Conservation With Deep Learning

Li, Y., Quang, D. X., & Xie, X. S. (2017) · ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB) · doi:10.1145/3107411.3107425 · Cited by 10

The inner and outer approaches to the design of recursive neural architectures

Baldi, P. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-017-0531-0 · Cited by 8

Understanding sequence conservation with deep learningPreprint

Li, Y., Quang, D. X., & Xie, X. S. (2017) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/103929 · Cited by 5

Accurate Ab Initio and Template-Based Prediction of Short Intrinsically-Disordered Regions by Bidirectional Recurrent Neural Networks Trained on Large-Scale Datasets

Volpato, V., Al-Shomrani, B. M., & Pollastri, G. (2015) · International Journal of Molecular Sciences · doi:10.3390/ijms160819868 · Cited by 2

Highly Accurate Machine Fault Diagnosis Using Deep Transfer Learning

Shao, S., McAleer, S., Yan, R., & Baldi, P. (2018) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2864759 · Cited by 1,690

Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy

Urban, G., Tripathi, P. V., Alkayali, T., Mittal, M., Jalali, F., Karnes, W. E., & Baldi, P. (2018) · Gastroenterology · doi:10.1053/j.gastro.2018.06.037 · Cited by 763

Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review

Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021) · Applied Sciences · doi:10.3390/app11115088 · Cited by 629

Deep learning in biomedicine

Wainberg, M., Merico, D., Delong, A., & Frey, B. J. (2018) · Nature Biotechnology · doi:10.1038/nbt.4233 · Cited by 596

Machine learning-based lung and colon cancer detection using deep feature extraction and ensemble learning

Talukder, M. A., Islam, M. M., Uddin, M. A., Akhter, A., Hasan, K. F., & Moni, M. A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.117695 · Cited by 330

The Application of Deep Learning in Biomedical Informatics

Wang, S., Fu, L., Yao, J., & Li, Y. (2018) · International Conference on Robots & Intelligent System · doi:10.1109/icris.2018.00104 · Cited by 6

Review of the Applications of Deep Learning in Bioinformatics

Zhang, Y., Yan, J., Chen, S., Gong, M., Gao, D., Zhu, M., & Gan, W. (2020) · Current Bioinformatics · doi:10.2174/1574893615999200711165743 · Cited by 59

RECENT ADVANCES, METHODS AND OPPORTUNITIES OF DEEP LEARNING IN COMPUTATIONAL AND SYSTEMS BIOLOGY

Menon, S., & Thomas, B. (2021) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.5553236

[Progress in biomedical data analysis based on deep learning].

Li, S., Tang, S., Li, F., Qi, J., & Xiong, W. (2020) · PubMed Central · doi:10.7507/1001-5515.201907016 · Cited by 3

Bioinformatics and Medicine in the Era of Deep Learning

Bacciu, D., Lisboa, P. J. G., Martín‐Guerrero, J. D., Stoean, R., & Vellido, A. (2018) · RECERCAT (Consorci de Serveis Universitaris de Catalunya) · doi:10.48550/arxiv.1802.09791 · Cited by 19