Data-driven advice for applying machine learning to bioinformatics problems

"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

An empirical comparison of supervised learning algorithms

Caruana, R., & Niculescu-Mizil, A. (2006) · Proceedings of the 23rd international conference on Machine learning - ICML '06 · doi:10.1145/1143844.1143865 · Cited by 2,780

Data mining in bioinformatics using Weka

Frank, E., Hall, M., Trigg, L., Holmes, G., & Witten, I. H. (2004) · Bioinformatics · doi:10.1093/bioinformatics/bth261 · Cited by 885

Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science

Olson, R. S., Bartley, N., Urbanowicz, R. J., & Moore, J. H. (2016) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/2908812.2908918 · Cited by 679

Detecting Cancer Metastases on Gigapixel Pathology Images

Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G. E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P. Q., Corrado, G. S., et al. (2017) · arXiv · doi:10.48550/arxiv.1703.02442 · Cited by 506

A balanced accuracy function for epistasis modeling in imbalanced datasets using multifactor dimensionality reduction

Velez, D. R., White, B. C., Motsinger‐Reif, A. A., Bush, W. S., Ritchie, M. D., Williams, S. M., & Moore, J. H. (2007) · Genetic Epidemiology · doi:10.1002/gepi.20211 · Cited by 386

PMLB: a large benchmark suite for machine learning evaluation and comparison

Olson, R. S., Cava, W. L., Orzechowski, P., Urbanowicz, R. J., & Moore, J. H. (2017) · BioData Mining · doi:10.1186/s13040-017-0154-4 · Cited by 373

Machine Learning for Detecting Gene-Gene Interactions

McKinney, B. A., Reif, D. M., Ritchie, M. D., & Moore, J. H. (2006) · Applied Bioinformatics · doi:10.2165/00822942-200605020-00002 · Cited by 257

Machine learning in bioinformatics: A brief survey and recommendations for practitioners

Bhaskar, H., Hoyle, D. C., & Singh, S. (2006) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2005.09.002 · Cited by 109

An empirical comparison of supervised machine learning techniques in bioinformatics

Tan, A. C., & Gilbert, D. (2003) · Brunel University Research Archive (BURA) (Brunel University London) · Cited by 62

A guide to machine learning for biologists

Greener, J. G., Kandathil, S. M., Moffat, L., & Jones, D. T. (2022) · Nature Reviews Molecular Cell Biology · doi:10.1038/s41580-021-00407-0 · Cited by 2,127

Automated Machine Learning

Hutter, F., Kotthoff, L., & Vanschoren, J. (2019) · The Springer series on challenges in machine learning · doi:10.1007/978-3-030-05318-5 · Cited by 1,691

Leakage and the reproducibility crisis in machine-learning-based science

Kapoor, S., & Narayanan, A. (2023) · Patterns · doi:10.1016/j.patter.2023.100804 · Cited by 1,387

Hyperparameter Optimization

Feurer, M., & Hutter, F. (2019) · The Springer Series on Challenges in Machine Learning · doi:10.1007/978-3-030-05318-5_1 · Cited by 1,414

Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables

Tomislav Hengl, A., Nussbaum, M., Wright, M. N., Heuvelink, G. B. M., & Gräler, B. (2018) · PeerJ · doi:10.7717/peerj.5518 · Cited by 1,177

Scaling tree-based automated machine learning to biomedical big data with a feature set selector

Le, T. T., Fu, W., & Moore, J. H. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz470 · Cited by 479

Deep learning for cardiovascular medicine: a practical primer

Krittanawong, C., Johnson, K. W., Rosenson, R. S., Wang, Z., Aydar, M., Baber, U., Min, J. K., Tang, W. H. W., Halperin, J. L., & Narayan, S. M. (2019) · European Heart Journal · doi:10.1093/eurheartj/ehz056 · Cited by 452

Comprehensive prediction of secondary metabolite structure and biological activity from microbial genome sequences

Skinnider, M. A., Johnston, C. W., Gunabalasingam, M., Merwin, N. J., Kieliszek, A., MacLellan, R. J., Li, H., Ranieri, M. R. M., Webster, A., Cao, M. P. T., et al. (2020) · Nature Communications · doi:10.1038/s41467-020-19986-1 · Cited by 380

Outcome-Oriented Predictive Process Monitoring

Teinemaa, I., Dumas, M., Rosa, M. L., & Maggi, F. M. (2019) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3301300 · Cited by 352

Machine learning algorithms for outcome prediction in (chemo)radiotherapy: An empirical comparison of classifiers

Deist, T. M., Dankers, F. J. W. M., Valdés, G., Wijsman, R., Hsu, I.-C. J., Oberije, C., Lustberg, T., van Soest, J., Hoebers, F. J. P., Jochems, A., et al. (2018) · Medical Physics · doi:10.1002/mp.12967 · Cited by 304

Machine Learning for Sociology

Molina, M., & Garip, F. (2019) · Annual Review of Sociology · doi:10.1146/annurev-soc-073117-041106 · Cited by 295

A Review of Machine Learning Applications to Coastal Sediment Transport and MorphodynamicsPreprint

Goldstein, E. B., Coco, G., & Plant, N. G. (2018) · doi:10.1016/j.earscirev.2019.04.022 · Cited by 238

Survey and Cross-benchmark Comparison of Remaining Time Prediction Methods in Business Process Monitoring

Verenich, I., Dumas, M., Rosa, M. L., Maggi, F., & Teinemaa, I. (2018) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3331449 · Cited by 185

Precision medicine in stroke: towards personalized outcome predictions using artificial intelligence

Bonkhoff, A. K., & Grefkes, C. (2021) · Brain · doi:10.1093/brain/awab439 · Cited by 216

Spatial prediction of soil organic carbon using machine learning techniques in western Iran

Mahmoudzadeh, H., Matinfar, H. R., Taghizadeh‐Mehrjardi, R., & Kerry, R. (2020) · Geoderma Regional · doi:10.1016/j.geodrs.2020.e00260 · Cited by 184

Design and Selection of Machine Learning Methods Using Radiomics and Dosiomics for Normal Tissue Complication Probability Modeling of Xerostomia

Gabryś, H. S., Buettner, F., Sterzing, F., Hauswald, H., & Bangert, M. (2018) · Frontiers in Oncology · doi:10.3389/fonc.2018.00035 · Cited by 180

Detection of spam-posting accounts on Twitter

Inuwa-Dutse, I., Liptrott, M., & Korkontzelos, I. (2018) · Neurocomputing · doi:10.1016/j.neucom.2018.07.044 · Cited by 159

Digital Biomarkers for Depression Screening With Wearable Devices: Cross-sectional Study With Machine Learning Modeling

Rykov, Y., Thach, T.-Q., Bojic, I., Christopoulos, G., & Car, J. (2021) · JMIR mHealth and uHealth · doi:10.2196/24872 · Cited by 151

Where are we now?

Orzechowski, P., Cava, W. L., & Moore, J. H. (2018) · Genetic and Evolutionary Computation Conference (GECCO) · doi:10.1145/3205455.3205539 · Cited by 142

Enhanced Sampling with Machine Learning

Mehdi, S., Smith, Z., Herron, L., Zou, Z., & Tiwary, P. (2024) · Annual Review of Physical Chemistry · doi:10.1146/annurev-physchem-083122-125941 · Cited by 159

Random forest-based prediction of stroke outcome

Fernandez-Lozano, C., Hervella, P., Mato‐Abad, V., Rodríguez‐Yáñez, M., Suárez, S., López‐Dequidt, I., Estany‐Gestal, A., Sobrino, T., Campos, F., Castillo, J., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-89434-7 · Cited by 133

Encoding High-Cardinality String Categorical Variables

Cerda, P., & Varoquaux, G. P. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.2992529 · Cited by 138

Automating the design-build-test-learn cycle towards next-generation bacterial cell factories

Gurdo, N., Volke, D. C., McCloskey, D., & Nikel, P. I. (2023) · New Biotechnology · doi:10.1016/j.nbt.2023.01.002 · Cited by 121

Predicting risk of stillbirth and preterm pregnancies with machine learning

Koivu, A., & Sairanen, M. (2020) · Health Information Science and Systems · doi:10.1007/s13755-020-00105-9 · Cited by 111

Suicide Risk Assessment Using Machine Learning and Social Networks: a Scoping Review

Castillo-Sánchez, G., Marques, G., Dorronzoro, E., Rivera-Romero, O., Franco, M., & Díez, I. D. L. T. (2020) · Journal of Medical Systems · doi:10.1007/s10916-020-01669-5 · Cited by 121

Thermal Imaging and Vibration-Based Multisensor Fault Detection for Rotating Machinery

Janssens, O., Loccufier, M., & Hoecke, S. V. (2019) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2018.2873175 · Cited by 108

Comparative analysis of molecular fingerprints in prediction of drug combination effects

Zagidullin, B., Wang, Z., Guan, Y., Pitkänen, E., & Tang, J. P. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab291 · Cited by 110

Mapping soil hydraulic properties using random-forest-based pedotransfer functions and geostatistics

Szabó, B., Szatmári, G., Takács, K., Laborczi, A., Makó, A., Rajkai, K., & Pásztor, L. (2019) · Hydrology and earth system sciences · doi:10.5194/hess-23-2615-2019 · Cited by 101

ImaGene: a convolutional neural network to quantify natural selection from genomic data

Torada, L., Lorenzon, L., Beddis, A., Işıldak, U., Pattini, L., Mathieson, S., & Fumagalli, M. (2019) · BMC Bioinformatics · doi:10.1186/s12859-019-2927-x · Cited by 107

Reviewing methods of deep learning for intelligent healthcare systems in genomics and biomedicine

Zafar, I., Anwar, S., kanwal, F., Yousaf, W., Nisa, F. U., Kausar, T., Ain, Q. U., Unar, A., Kamal, M. A., Rashid, S., et al. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105263 · Cited by 104

Greed Is Good: Rapid Hyperparameter Optimization and Model Selection Using Greedy k-Fold Cross Validation

Soper, D. S. (2021) · Electronics · doi:10.3390/electronics10161973 · Cited by 100

High-resolution agriculture soil property maps from digital soil mapping methods, Czech Republic

Žížala, D., Minařík, R., Skála, J., Beitlerová, H., Juřicová, A., Rojas, J. R., Penížek, V., & Zádorová, T. (2022) · CATENA · doi:10.1016/j.catena.2022.106024 · Cited by 88

Integrating satellite imagery and environmental data to predict field-level cane and sugar yields in Australia using machine learning

Shendryk, Y., Davy, R. J., & Thorburn, P. J. (2020) · Field Crops Research · doi:10.1016/j.fcr.2020.107984 · Cited by 82

Updated European hydraulic pedotransfer functions with communicated uncertainties in the predicted variables (euptfv2)

Szabó, B., Weynants, M., & Weber, T. K. D. (2021) · Geoscientific model development · doi:10.5194/gmd-14-151-2021 · Cited by 81

Machine learning-based multiparametric MRI radiomics for predicting the aggressiveness of papillary thyroid carcinoma

Wang, H., Song, B., Ye, N., Ren, J., Sun, X., Dai, Z., Zhang, Y., & Chen, B. T. (2019) · European Journal of Radiology · doi:10.1016/j.ejrad.2019.108755 · Cited by 77

MicroPheno: predicting environments and host phenotypes from 16S rRNA gene sequencing using a k-mer based representation of shallow sub-samples

Asgari, E., Garakani, K., McHardy, A. C., & Mofrad, M. R. K. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty296 · Cited by 85

Exploring forest fire susceptibility and management strategies in Western Himalaya: Integrating ensemble machine learning and explainable AI for accurate prediction and comprehensive analysis

Hằng, H. T., Mallick, J., Alqadhi, S. D., Bindajam, A. A., & Abdo, H. G. (2024) · Environmental Technology & Innovation · doi:10.1016/j.eti.2024.103655 · Cited by 74

Automatic Machine Learning to Differentiate Pediatric Posterior Fossa Tumors on Routine MR Imaging

Zhou, H., Hu, R., Tang, O. Y., Hu, C., Tang, L., Chang, K., Shen, Q., Wu, J., Zou, B., Xiao, B., et al. (2020) · American Journal of Neuroradiology · doi:10.3174/ajnr.a6621 · Cited by 71

Predictors of Patient Satisfaction Following Primary Total Knee Arthroplasty: Results from a Traditional Statistical Model and a Machine Learning Algorithm

Farooq, H., Deckard, E. R., Ziemba-Davis, M. M., Madsen, A. A., & Meneghini, R. M. (2020) · The Journal of Arthroplasty · doi:10.1016/j.arth.2020.05.077 · Cited by 70

Computational Machine Learning Approach for Flood Susceptibility Assessment Integrated with Remote Sensing and GIS Techniques from Jeddah, Saudi Arabia

Al‐Areeq, A. M., Abba, S. I., Yassin, M. A., Benaafi, M., Ghaleb, M., & Aljundi, I. H. (2022) · Remote Sensing · doi:10.3390/rs14215515 · Cited by 69

Combining machine learning and conventional statistical approaches for risk factor discovery in a large cohort study

Madakkatel, I., Zhou, A., McDonnell, M. D., & Hyppönen, E. (2021) · Scientific Reports · doi:10.1038/s41598-021-02476-9 · Cited by 65

Identifying cardiovascular disease risk in the U.S. population using environmental volatile organic compounds exposure: A machine learning predictive model based on the SHAP methodology

Fu, Q., Wu, Y., Zhu, M. S., Xia, Y., Yu, Q., Liu, Z., Ma, X., & Yang, R. (2024) · Ecotoxicology and Environmental Safety · doi:10.1016/j.ecoenv.2024.117210 · Cited by 66

METTLE: A METamorphic Testing Approach to Assessing and Validating Unsupervised Machine Learning Systems

Xie, X., Zhang, Z., Chen, T. Y., Liu, Y., Poon, P.-L., & Xu, B. (2020) · IEEE Transactions on Reliability · doi:10.1109/tr.2020.2972266 · Cited by 80

Benchmarking missing-values approaches for predictive models on health databases

Perez-Lebel, A., Varoquaux, G. P., Morvan, M. L., Josse, J., & Poline, J. (2022) · GigaScience · doi:10.1093/gigascience/giac013 · Cited by 82

It is Not “Accuracy vs. Explainability”—We Need Both for Trustworthy AI Systems

Petkovic, D. (2023) · IEEE Transactions on Technology and Society · doi:10.1109/tts.2023.3239921 · Cited by 63

Model selection for metabolomics: predicting diagnosis of coronary artery disease using automated machine learning

Orlenko, A., Kofink, D., Lyytikäinen, L., Nikus, K. C., Mishra, P. P., Kuukasjärvi, P., Karhunen, P. J., Kähönen, M. A. P., Laurikka, J. O., Lehtimäki, T. J., et al. (2019) · Bioinformatics · doi:10.1093/bioinformatics/btz796 · Cited by 59

Elite‐public interaction on twitter: EU issue expansion in the campaign

Fazekas, Z., Popa, S. A., Schmitt, H., Barberá, P., & Theocharis, Y. (2020) · European Journal of Political Research · doi:10.1111/1475-6765.12402 · Cited by 59

Temporal stability in predictive process monitoring

Teinemaa, I., Dumas, M., Leontjeva, A., & Maggi, F. M. (2018) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-018-0575-9 · Cited by 58

METTLE: A METamorphic Testing approach to assessing and validating unsupervised machine LEarning systems

(7794023), X. X., (7695560), Z. Z., (9865748), T. C., (4702669), Y. L., (9827297), P. P., & (7787783), B. X. (2020) · Figshare · Cited by 57

Comparison of new hyperspectral index and machine learning models for prediction of winter wheat leaf water content

Zhang, J., Zhang, W., Xiong, S., Song, Z., Tian, W., Shi, L., & Ma, X. (2021) · Plant Methods · doi:10.1186/s13007-021-00737-2 · Cited by 57

Fire now, fire later: alarm-based systems for prescriptive process monitoring

Fahrenkrog-Petersen, S. A., Tax, N., Teinemaa, I., Dumas, M., de Leoni, M., Maggi, F. M., & Weidlich, M. (2021) · Knowledge and Information Systems · doi:10.1007/s10115-021-01633-w · Cited by 56

How to read and review papers on machine learning and artificial intelligence in radiology: a survival guide to key methodological concepts

Koçak, B., Kuş, E. A., & Kılıçkesmez, Ö. (2020) · European Radiology · doi:10.1007/s00330-020-07324-4 · Cited by 57

A Novel Hybrid Fuel Consumption Prediction Model for Ocean-Going Container Ships Based on Sensor Data

Hu, Z., Zhou, T., Osman, M. T., Li, X., Yong-xin, J., & Zhen, R. (2021) · Journal of Marine Science and Engineering · doi:10.3390/jmse9040449 · Cited by 55

Identification of DNA motifs that regulate DNA methylation

Wang, M., Zhang, K., Ngo, V., Liu, C., Fan, S., Whitaker, J. W., Chen, Y., Ai, R., Chen, Z., Wang, J., et al. (2019) · Nucleic Acids Research · doi:10.1093/nar/gkz483 · Cited by 57

Lessons and tips for designing a machine learning study using EHR data

Arbet, J., Brokamp, C., Meinzen-Derr, J., Trinkley, K. E., & Spratt, H. M. (2021) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.513 · Cited by 46

Machine learning for construction crew productivity prediction using daily work reports

Sadatnya, A., Sadeghi, N., Sabzekar, S., Khanjani, M., Tak, A. N., & Taghaddos, H. (2023) · Automation in Construction · doi:10.1016/j.autcon.2023.104891 · Cited by 56

CRISPRO: identification of functional protein coding sequences based on genome editing dense mutagenesis

Schoonenberg, V. A. C., Cole, M. A., Yao, Q., Macias-Treviño, C., Sher, F., Schupp, P. G., Canver, M. C., Maeda, T., Pinello, L., & Bauer, D. E. (2018) · Genome biology · doi:10.1186/s13059-018-1563-5 · Cited by 49

Detection of Bark Beetle Disturbance at Tree Level Using UAS Multispectral Imagery and Deep Learning

Minařík, R., Langhammer, J., & Lendzioch, T. (2021) · Remote Sensing · doi:10.3390/rs13234768 · Cited by 47

Alarm-Based Prescriptive Process Monitoring

Teinemaa, I., Tax, N., de Leoni, M., Dumas, M., & Maggi, F. M. (2018) · Lecture notes in business information processing · doi:10.1007/978-3-319-98651-7_6 · Cited by 46

Machine learning applied to electronic health record data in home healthcare: A scoping review

Hobensack, M., Song, J., Scharp, D., Bowles, K. H., & Topaz, M. (2022) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2022.104978 · Cited by 49

Do it right the first time: Vehicle routing with home delivery attempt predictors

Lim, S. F. W. T., Wang, Q., & Webster, S. (2022) · Production and Operations Management · doi:10.1111/poms.13926 · Cited by 48

Leveraging High-Resolution Satellite Imagery and Gradient Boosting for Invasive Weed Mapping

Shendryk, Y., Rossiter‐Rachor, N. A., Setterfield, S. A., & Levick, S. R. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3013663 · Cited by 42

Database, Features, and Machine Learning Model to Identify Thermally Driven Metal–Insulator Transition Compounds

Georgescu, A. B., Ren, P., Toland, A. R., Zhang, S., Miller, K. D., Apley, D. W., Olivetti, E., Wagner, N., & Rondinelli, J. M. (2021) · Chemistry of Materials · doi:10.1021/acs.chemmater.1c00905 · Cited by 42

Evaluation of stacked ensemble model performance to predict clinical outcomes: A COVID-19 study

Kablan, R., Miller, H. A., Suliman, S. H., & Frieboes, H. B. (2023) · International Journal of Medical Informatics · doi:10.1016/j.ijmedinf.2023.105090 · Cited by 41

Raman Spectroscopy and Machine Learning for IDH Genotyping of Unprocessed Glioma Biopsies

Sciortino, T., Secoli, R., d’Amico, E., Moccia, S., Nibali, M. C., Gay, L. G., Rossi, M., Pecco, N., Castellano, A., Momi, E. D., et al. (2021) · Cancers · doi:10.3390/cancers13164196 · Cited by 41

Passive mobile sensing and psychological traits for large scale mood prediction

Spathis, D., Servia-Rodriguez, S., Farrahi, K., Mascolo, C., & Rentfrow, J. (2019) · Proceedings of the 13th EAI International Conference on Pervasive Computing Technologies for Healthcare · doi:10.1145/3329189.3329213 · Cited by 40

Preparing Next-Generation Scientists for Biomedical Big Data: Artificial Intelligence Approaches

Moore, J. H., Boland, M. R., Cámara, P. G., Chervitz, H., Gonzalez‐Hernandez, G., Himes, B. E., Kim, D., Mowery, D. L., Ritchie, M. D., Shen, L., et al. (2019) · Personalized Medicine · doi:10.2217/pme-2018-0145 · Cited by 40

Machine Learning for SociologyPreprint

Molina, M. D., & Garip, F. (2019) · SocArXiv (OSF Preprints) · doi:10.31235/osf.io/a6r9g · Cited by 47

Predicting process performance: A white‐box approach based on process models

Verenich, I., Dumas, M., Rosa, M. L., & Nguyen, H. (2019) · Journal of Software Evolution and Process · doi:10.1002/smr.2170 · Cited by 39

Dynamic connectivity predicts acute motor impairment and recovery post-stroke

Bonkhoff, A. K., Rehme, A. K., Hensel, L., Tscherpel, C., Volz, L. J., Espinoza, F. A., Gazula, H., Vergara, V. M., Fink, G. R., Calhoun, V. D., et al. (2021) · Brain Communications · doi:10.1093/braincomms/fcab227 · Cited by 46

Harnessing the power of machine learning for crop improvement and sustainable production

Khatibi, S. M. H., & Ali, J. (2024) · Frontiers in Plant Science · doi:10.3389/fpls.2024.1417912 · Cited by 43

Identification of potentially undiagnosed patients with nontuberculous mycobacterial lung disease using machine learning applied to primary care data in the UK

Doyle, O., van der Laan, R., Obradović, M., McMahon, P., Daniels, F. A., Pitcher, A., & Loebinger, M. R. (2020) · European Respiratory Journal · doi:10.1183/13993003.00045-2020 · Cited by 37

Fairness of Machine Learning Algorithms for Predicting Foregone Preventive Dental Care for Adults

Schuch, H. S., Borba, M. F., Silva, G. F. D. S., Kawachi, I. I., Filho, A. D. P. C., & Elani, H. W. (2023) · JAMA Network Open · doi:10.1001/jamanetworkopen.2023.41625 · Cited by 41

Discussing the spectrum of physics-enhanced machine learning: a survey on structural mechanics applications

Haywood-Alexander, M., Liu, W., Bacsa, K., Lai, Z., & Chatzi, E. (2024) · Data-Centric Engineering · doi:10.1017/dce.2024.33 · Cited by 44

Can we automate expert-based journal rankings? Analysis of the Finnish publication indicator

Saarela, M., & Kärkkäinen, T. (2020) · Journal of Informetrics · doi:10.1016/j.joi.2020.101008 · Cited by 37

Specific histone modifications associate with alternative exon selection during mammalian development

Hu, Q., Greene, C. S., & Heller, E. A. (2020) · Nucleic Acids Research · doi:10.1093/nar/gkaa248 · Cited by 36

Predicting the naturalistic course in anxiety disorders using clinical and biological markers: a machine learning approach

Bokma, W. A., Zhutovsky, P., Giltay, E. J., Schoevers, R. A., Penninx, B. W. J. H., van Balkom, A. J. L. M., Batelaan, N. M., & van Wingen, G. A. (2020) · Psychological Medicine · doi:10.1017/s0033291720001658 · Cited by 36

Atlas of Transcription Factor Binding Sites from ENCODE DNase Hypersensitivity Data across 27 Tissue Types

Funk, C. C., Casella, A. M., Jung, S. C., Richards, M. A., Rodriguez, A., Shannon, P. T., Donovan-Maiye, R., Heavner, B., Chard, K., Xiao, Y., et al. (2020) · Cell Reports · doi:10.1016/j.celrep.2020.108029 · Cited by 34

Prediction of the functional impact of missense variants in BRCA1 and BRCA2 with BRCA-ML

Hart, S. N., Polley, E. C., Shimelis, H., Yadav, S., & Couch, F. J. (2020) · npj Breast Cancer · doi:10.1038/s41523-020-0159-x · Cited by 34

Boosted decision trees in the era of new physics: a smuon analysis case study

Cornell, A., Doorsamy, W., Fuks, B., Harmsen, G., & Mason, L. (2021) · Journal of High Energy Physics · doi:10.1007/jhep04(2022)015 · Cited by 34

A machine learning approach to identifying patients with pulmonary hypertension using real-world electronic health records

Kogan, E., Didden, E., Lee, E., Nnewihe, A. N., Stamatiadis, D., Mataraso, S. J., Quinn, D. A., Rosenberg, D., Chehoud, C., & Bridges, C. R. (2022) · International Journal of Cardiology · doi:10.1016/j.ijcard.2022.12.016 · Cited by 34

Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in dilated cardiomyopathy with heart failure

Zhu, Y., Yang, X., & Zu, Y. (2022) · Frontiers in Cell and Developmental Biology · doi:10.3389/fcell.2022.1089915 · Cited by 34

Artificial intelligence in medicine: past, present, and future

Gennatas, E. D., & Chen, J. H. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00001-6 · Cited by 32

Machine learning in the coagulation and hemostasis arena: an overview and evaluation of methods, review of literature, and future directions

Rashidi, H. H., Bowers, K., & Gil, M. R. (2023) · Journal of Thrombosis and Haemostasis · doi:10.1016/j.jtha.2022.12.019 · Cited by 33

Sequence Multi-task Learning to Forecast Mental Wellbeing from Sparse Self-reported Data

Spathis, D., Servia-Rodriguez, S., Farrahi, K., Mascolo, C., & Rentfrow, J. (2019) · Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3292500.3330730 · Cited by 30

Ensemble learning for detecting gene-gene interactions in colorectal cancer

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