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

Stable isotope and trace element analyses with non‐linear machine‐learning data analysis improved coffee origin classification and marker selection

Sim, J., McGoverin, C., Oey, I., Frew, R. D., & Kebede, B. (2023) · Journal of the Science of Food and Agriculture · doi:10.1002/jsfa.12546 · Cited by 25

Predicting critical state after COVID-19 diagnosis: Model development using a large US electronic health record dataset

Rinderknecht, M. D., & Klopfenstein, Y. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00482-9 · Cited by 23

Transforming Stability into Change: How the Media Select and Report Opinion Polls

Larsen, E. G., & Fazekas, Z. (2019) · The International Journal of Press/Politics · doi:10.1177/1940161219864295 · Cited by 24

Development, implementation, and prospective validation of a model to predict 60-day end-of-life in hospitalized adults upon admission at three sites

Major, V. J., & Aphinyanaphongs, Y. (2020) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-020-01235-6 · Cited by 24

Methods for Addressing Missingness in Electronic Health Record Data for Clinical Prediction Models: Comparative Evaluation

Digitale, J. C., Franzon, D. E., Pletcher, M. J., McCulloch, C. E., & Gennatas, E. D. (2025) · JMIR Medical Informatics · doi:10.2196/79307 · Cited by 30

A machine learning and geostatistical hybrid method to improve spatial prediction accuracy of soil potentially toxic elements

Molla, A., Zhang, W., Zuo, S., Ren, Y., & Han, J. (2022) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-022-02284-1 · Cited by 23

Forecasting small area populations with long short-term memory networks

Grossman, I., Wilson, T., & Temple, J. (2023) · Socio-Economic Planning Sciences · doi:10.1016/j.seps.2023.101658 · Cited by 27

Big Data Analytics and Deep Learning in Bioinformatics With Hadoop

Armoogum, S., & Li, X. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-816718-2.00009-9 · Cited by 24

Artificial Intelligence for Risk Assessment on Primary Prevention of Coronary Artery Disease

Chen, S., Loguercio, S., Chen, K., Lee, S. E., Park, J., Liu, S., Sadaei, H. J., & Torkamani, A. (2023) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-023-00731-4 · Cited by 26

Predicting Inhibition of CDK2 with SAnDReS: The Application of Machine Learning to Navigate the Scoring Function Space

de Azevedo, W. F. (2024) · Current Medicinal Chemistry · doi:10.2174/0109298673313727240819070317 · Cited by 22

Predictive Analytics on Genomic Data with High-Performance Computing

Leung, C. K.-S., Sarumi, O. A., & Zhang, C. Y. (2020) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm49941.2020.9312982 · Cited by 22

Evaluating recommender systems for AI-driven biomedical informatics

Cava, W. L., Williams, H., Fu, W., Vitale, S., Srivatsan, D., & Moore, J. H. (2020) · Bioinformatics · doi:10.1093/bioinformatics/btaa698 · Cited by 28

Urban soil management in the strategies for adaptation to climate change of cities in the Tropical Andes

Bonilla‐Bedoya, S., Herrera, M. Á., Yánez, A. V., Salazar, L., Žalakevičiūtė, R., Mejía, D., & López-Ulloa, M. (2022) · Geoderma · doi:10.1016/j.geoderma.2022.115840 · Cited by 21

A machine learning model on Real World Data for predicting progression to Acute Respiratory Distress Syndrome (ARDS) among COVID-19 patients

Lazzarini, N., Filippoupolitis, A., Manzione, P., & Eleftherohorinou, H. (2022) · PLoS ONE · doi:10.1371/journal.pone.0271227 · Cited by 20

Predicting salvage laryngectomy in patients treated with primary nonsurgical therapy for laryngeal squamous cell carcinoma using machine learning

Smith, J. B., Shew, M. A., Karadaghy, O. A., Nallani, R., Sykes, K. J., Gan, G. N., Brant, J. A., & Bur, A. M. (2020) · Head & Neck · doi:10.1002/hed.26246 · Cited by 17

An Entropy-Based Architecture for Detection of Sepsis in Newborn Cry Diagnostic Systems

Khalilzad, Z., Kheddache, Y., & Tadj, C. (2022) · Entropy · doi:10.3390/e24091194 · Cited by 18

Interpretability and Explainability in Machine Learning

Tsang, W. K., & Benoit, D. F. (2022) · Intelligent systems reference library · doi:10.1007/978-3-031-11593-6_5 · Cited by 19

Machine learning clinical prediction models for acute kidney injury: the impact of baseline creatinine on prediction efficacy

Rahimi, A. K., Ghadimi, M., van der Vegt, A. H., Canfell, O. J., Pole, J. D., Sullivan, C. M., & Shrapnel, S. (2023) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-023-02306-0 · Cited by 21

Prototype Cross Platform Oriented on Cybersecurity, Virtual Connectivity, Big Data and Artificial Intelligence Control

Massaro, A. S., Gargaro, M., Dipierro, G., Galiano, A. M., & Buonopane, S. (2020) · IEEE Access · doi:10.1109/access.2020.3034399 · Cited by 18

Clastic facies classification using machine learning-based algorithms: A case study from Rawat Basin, Sudan

Babai, A. M. A., Ehinola, O. A., Gebbayin, O. I. M. F. A., & Ibrahim, M. A. E. (2024) · Energy Geoscience · doi:10.1016/j.engeos.2024.100353 · Cited by 19

TAWSEEM: A Deep-Learning-Based Tool for Estimating the Number of Unknown Contributors in DNA Profiling

Alotaibi, H., Alsolami, F. J., Abozinadah, E., & Mehmood, R. (2022) · Electronics · doi:10.3390/electronics11040548 · Cited by 18

Deep Learning to Predict the Feasibility of Priority-Based Ethernet Network Configurations

Mai, T. L., & Navet, N. (2021) · ACM Transactions on Cyber-Physical Systems · doi:10.1145/3468890 · Cited by 15

Valproic acid-induced changes of 4D nuclear morphology in astrocyte cells

Kalinin, A. A., Hou, X., Ade, A. S., Fon, G.-V., Meixner, W., Higgins, G. A., Sexton, J. Z., Wan, X., Dinov, I. D., O’Meara, M. J., et al. (2021) · Molecular Biology of the Cell · doi:10.1091/mbc.e20-08-0502 · Cited by 19

Enhancing Burp Suite with Machine Learning Extension for Vulnerability Assessment of Web Applications

Thaqi, R., Vishi, K., & Rexha, B. (2022) · Journal of Applied Security Research · doi:10.1080/19361610.2022.2096387 · Cited by 17

Artificial Intelligence Based Approaches to Identify Molecular Determinants of Exceptional Health and Life Span-An Interdisciplinary Workshop at the National Institute on Aging

Moore, J. H., Raghavachari, N., & Speakers, W. (2019) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2019.00012 · Cited by 19

Predicting structural class for protein sequences of 40% identity based on features of primary and secondary structure using Random Forest algorithm

Apurva, M., & Mazumdar, H. S. (2019) · Computational Biology and Chemistry · doi:10.1016/j.compbiolchem.2019.107164 · Cited by 17

Evaluation of a genetic risk score for severity of COVID-19 using human chromosomal-scale length variation

Toh, C., & Brody, J. P. (2020) · Human Genomics · doi:10.1186/s40246-020-00288-y · Cited by 17

Using machine learning for the personalised prediction of revision endoscopic sinus surgery

Nuutinen, M., Haukka, J. K., Virkkula, P., Torkki, P. M., & Toppila-Salmi, S. K. (2022) · PLoS ONE · doi:10.1371/journal.pone.0267146 · Cited by 16

Optimization of Cervical Cancer Screening: A Stacking-Integrated Machine Learning Algorithm Based on Demographic, Behavioral, and Clinical Factors

Sun, L., Yang, L., Liu, X., Tang, L., Zeng, Q., Gao, Y., Chen, Q., Liu, Z., & Peng, B. (2022) · Frontiers in Oncology · doi:10.3389/fonc.2022.821453 · Cited by 17

A Machine Learning–Based Prognostic Model for the Prediction of Early Death After Traumatic Brain Injury: Comparison with the Corticosteroid Randomization After Significant Head Injury (CRASH) Model

Lee, S. H., Lee, C. H., Hwang, S. H., & Kang, D.-H. (2022) · World Neurosurgery · doi:10.1016/j.wneu.2022.06.130 · Cited by 17

Motivation for using data-driven algorithms in research: A review of machine learning solutions for image analysis of micrographs in neuroscience

Thiele, F., Windebank, A. J., & Siddiqui, A. M. (2023) · Journal of Neuropathology & Experimental Neurology · doi:10.1093/jnen/nlad040 · Cited by 20

Identifying the predictors of avoidable emergency department attendance after contact with the NHS 111 phone service: analysis of 16.6 million calls to 111 in England in 2015–2017

Egan, M., Murar, F., Lawrence, J., & Burd, H. (2020) · BMJ Open · doi:10.1136/bmjopen-2019-032043 · Cited by 16

Condition Monitoring of Slow-speed Gear Wear using a Transmission Error-based Approach with Automated Feature Selection

Sendlbeck, S., Fimpel, A., Siewerin, B. J., Otto, M. K., & Stahl, K. (2021) · International Journal of Prognostics and Health Management · doi:10.36001/ijphm.2021.v12i2.3026 · Cited by 15

Supervised machine learning for microbiomics: Bridging the gap between current and best practices

Dudek, N. K., Chakhvadze, M., Kobakhidze, S., Kantidze, O. L., & Gankin, Y. V. (2024) · Machine Learning with Applications · doi:10.1016/j.mlwa.2024.100607 · Cited by 16

PROVIDENT: Development and Validation of a Machine Learning Model to Predict Neighborhood-level Overdose Risk in Rhode Island

Allen, B., Schell, R. C., Jent, V. A., Krieger, M. S., Pratty, C., Hallowell, B. D., Goedel, W. C., Basta, M., Yedinak, J. L., Li, Y., et al. (2024) · Epidemiology · doi:10.1097/ede.0000000000001695 · Cited by 14

Comparison of machine learning approaches for structure–function modeling in glaucoma

Wong, D. W. K., Chua, J., Bujor, I. A., Chong, R. S., Nongpiur, M. E., Vithana, E. N., Husain, R., Aung, T., Popa‐Cherecheanu, A., & Schmetterer, L. F. (2022) · Annals of the New York Academy of Sciences · doi:10.1111/nyas.14844 · Cited by 12

Non-linear machine learning coupled near infrared spectroscopy enhanced model performance and insights for coffee origin traceability

Sim, J., McGoverin, C., Oey, I., Frew, R. D., & Kebede, B. (2024) · Journal of Near Infrared Spectroscopy · doi:10.1177/09670335241269014 · Cited by 13

Urban soils as a spatial indicator of quality for urban socio-ecological systems

Bonilla‐Bedoya, S., López-Ulloa, M., Mora, A., Pezzopane, J. E. M., Salazar, L., & Herrera, M. Á. (2021) · Journal of Environmental Management · doi:10.1016/j.jenvman.2021.113556 · Cited by 13

Two phase feature-ranking for new soil dataset for Coxiella burnetii persistence and classification using machine learning models

Ahmad, F., Khan, M. U. G., Tahir, A., Tipu, M. Y., Rabbani, M., & Shabbir, M. Z. (2023) · Scientific Reports · doi:10.1038/s41598-022-26956-8 · Cited by 14

Automated feature engineering for automated machine learning

de Winter, C., Frăsincar, F., de Peuter, B., Matsiiako, V., Ido, E., & Klinkhamer, J. (2025) · Knowledge-Based Systems · doi:10.1016/j.knosys.2025.113671 · Cited by 12

Prediction of Attention-Deficit/Hyperactivity Disorder Diagnosis Using Brief, Low-Cost Clinical Measures: A Competitive Model Evaluation

Mooney, M. A., Neighbor, C., Karalunas, S. L., Dieckmann, N. F., Nikolas, M. A., Nousen, E. K., Tipsord, J. M., Song, X. B., & Nigg, J. T. (2022) · Clinical Psychological Science · doi:10.1177/21677026221120236 · Cited by 18

Osteoporosis Prediction Using Machine-Learned Optical Bone Densitometry Data

Miura, K., Tanaka, S. M., Chotipanich, C., Chobpenthai, T., Jantarato, A., & Khantachawana, A. (2023) · Annals of Biomedical Engineering · doi:10.1007/s10439-023-03387-8 · Cited by 14

Soil sampling design matters - Enhancing the efficiency of digital soil mapping at the field scale

Žížala, D., Princ, T., Skála, J., Juřicová, A., Lukas, V., Bohovic, R., Zádorová, T., & Minařík, R. (2024) · Geoderma Regional · doi:10.1016/j.geodrs.2024.e00874 · Cited by 12

Development of a Frailty Detection Model Using Machine Learning with the Korean Frailty and Aging Cohort Study Data

Koo, D., Lee, A. R., Lee, E., & Kim, I. K. (2022) · Healthcare Informatics Research · doi:10.4258/hir.2022.28.3.231 · Cited by 12

Approximation of SHAP Values for Randomized Tree Ensembles

Loecher, M., Lai, D., & Qi, W. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14463-9_2 · Cited by 12

TMCrys: predict propensity of success for transmembrane protein crystallization

Varga, J. K., & Tusnády, G. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty342 · Cited by 11

Prediction of Coronary Artery Disease using Electrocardiography: A Machine Learning Approach

PHADKE, G., Rajati, M. R., & Phadke, L. (2020) · International Conference on Machine Learning and Cybernetics · doi:10.1109/icmlc51923.2020.9469585 · Cited by 11

Predicting protein interaction network perturbation by alternative splicing with semi-supervised learning

Narykov, O., Johnson, N. T., & Korkin, D. (2021) · Cell Reports · doi:10.1016/j.celrep.2021.110045 · Cited by 11

A data-driven machine learning approach for brain-computer interfaces targeting lower limb neuroprosthetics

Dillen, A., Lathouwers, E., Miladinović, A., Marušič, U., Ghaffari, F., Romain, O., MEEUSEN, R. R., & Pauw, K. D. (2022) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2022.949224 · Cited by 12

Efficient Machine Learning Models for the Uplift Behavior of Helical Anchors in Dense Sand for Wind Energy Harvesting

Wang, L., Wu, M., Chen, H., Hao, D., Tian, Y., & Qi, C. (2022) · Applied Sciences · doi:10.3390/app122010397 · Cited by 11

From Research to Diagnostic Application of Raman Spectroscopy in Neurosciences: Past and Perspectives

Klamminger, G. G., Frauenknecht, K. B. M., Mittelbronn, M. G. A., & Borgmann, F. B. K. (2022) · Free Neuropathology · doi:10.17879/freeneuropathology-2022-4210 · Cited by 10

Ensemble learning-assisted prediction of prolonged hospital length of stay after spine correction surgery: a multi-center cohort study

Li, W., Zhang, Y., Zhou, X., Quan, X., Chen, B., Hou, X., Xu, Q., He, W., Chen, L., Liu, X., et al. (2024) · Journal of Orthopaedic Surgery and Research · doi:10.1186/s13018-024-04576-4 · Cited by 11

Big Data and Machine Learning

Listl, S., & Filho, A. D. P. C. (2020) · Textbooks in contemporary dentistry · doi:10.1007/978-3-030-50123-5_23 · Cited by 11

Tackling the infodemic during a pandemic: A comparative study on algorithms to deal with thematically heterogeneous fake news

Vasist, P. N., & Sebastian, M. P. (2022) · International Journal of Information Management Data Insights · doi:10.1016/j.jjimei.2022.100133 · Cited by 11

Multishell diffusion MRI reveals whole‐brain white matter changes in HIV

Minosse, S., Picchi, E., Conti, A., Giuliano, F. D., Ciò, F. D., Sarmati, L., Teti, E., Santis, S. D., Andreoni, M., Floris, R., et al. (2023) · Human Brain Mapping · doi:10.1002/hbm.26448 · Cited by 9

AID4I: An Intrusion Detection Framework for Industrial Internet of Things Using Automated Machine Learning

Sezgin, A., & Boyacı, A. (2023) · Computers, materials & continua/Computers, materials & continua (Print) · doi:10.32604/cmc.2023.040287 · Cited by 13

Optimized feature selection using gray wolf and particle swarm algorithms for corn seed image classification

Yasin, E. T., Ropelewska, E., Kursun, R., Çınar, İ., Taşpınar, Y. S., Yaşar, A., Mirjalili, S., & Köklü, M. (2025) · Journal of Food Composition and Analysis · doi:10.1016/j.jfca.2025.107738 · Cited by 11

A comparative analysis of machine learning classifiers for predicting protein-binding nucleotides in RNA sequences

Agarwal, A., Singh, K., Kant, S., & Bahadur, R. P. (2022) · Computational and Structural Biotechnology Journal · doi:10.1016/j.csbj.2022.06.036 · Cited by 10

Debiasing MDI Feature Importance and SHAP Values in Tree Ensembles

Loecher, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-14463-9_8 · Cited by 10

Model-Based Machine Learning for Transportation

Peled, I., Rodrigues, F., & Pereira, F. C. (2018) · Elsevier eBooks · doi:10.1016/b978-0-12-812970-8.00007-5 · Cited by 10

Towards an efficient prognostic model for fetal state assessment

Neto, M. G. D. S., Madeiro, J. P. D. V., Marques, J. A. L., & Gomes, D. G. (2021) · Measurement · doi:10.1016/j.measurement.2021.110034 · Cited by 10

BiLSTM-CNN Hyperparameter Optimization for Speech Emotion and Stress Recognition

Gumelar, A. B., Yuniarno, E. M., Adi, D. P., Sooai, A. G., Sugiarto, I., & Purnomo, M. H. (2021) · International Electronics Symposium (IES) · doi:10.1109/ies53407.2021.9594024 · Cited by 10

Hyperboost: Hyperparameter Optimization by Gradient Boosting surrogate modelsPreprint

van Hoof, J., & Vanschoren, J. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.02289 · Cited by 10

Conformance-oriented Predictive Process Monitoring in BPaaS Based on Combination of Neural Networks

Wang, J., Chang, V. I. C., Yu, D., Liu, C., Ma, X., & Yu, D. (2022) · Journal of Grid Computing · doi:10.1007/s10723-022-09613-2 · Cited by 10

Novel Hybridized Computational Paradigms Integrated with Five Stand-Alone Algorithms for Clinical Prediction of HCV Status among Patients: A Data-Driven Technique

Madaki, Z., Abacıoğlu, N., Usman, A. G., Taner, N., Şehirli, A. Ö., & Abba, S. I. (2022) · Life · doi:10.3390/life13010079 · Cited by 9

Data-Driven Consumer Debt Collection via Machine Learning and Approximate Dynamic ProgrammingPreprint

van de Geer, R., Wang, Q., & Bhulai, S. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3250755 · Cited by 9

Genetic risk score for ovarian cancer based on chromosomal-scale length variation

Toh, C., & Brody, J. P. (2021) · BioData Mining · doi:10.1186/s13040-021-00253-y · Cited by 10

A machine learning approach to identifying important features for achieving step thresholds in individuals with chronic stroke

Miller, A. E., Russell, E., Reisman, D. S., Kim, H. E., & Dinh, V. (2022) · PLoS ONE · doi:10.1371/journal.pone.0270105 · Cited by 8

Rain Predictive Model using Machine learning Techniques

Muneer, M. S., Mustafa, S. M. N., Zehra, S. S., & Maqsood, H. (2023) · International Multi-disciplinary Conference in Emerging Research Trends (IMCERT) · doi:10.1109/imcert57083.2023.10075275 · Cited by 8

Peer Review #1 of "Random forest as a generic framework for predictive modeling of spatial and spatio-temporal variables (v0.1)"

(2018) · PeerJ · doi:10.7287/peerj.5518v0.1/reviews/1 · Cited by 8

Reiterative modeling of combined transcriptomic and proteomic features refines and improves the prediction of early recurrence in squamous cell carcinoma of head and neck

Salehi, A., Wang, L., Coates, P. J., Spaak, L. N., Gu, X., Sgaramella, N., & Nylander, K. (2022) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2022.105991 · Cited by 7

Leveraging Airborne LiDAR Data and Gradient Boosting for Mapping the Density of Different Sized Trees

Shendryk, Y., & Gorrod, E. J. (2020) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2020.3046303 · Cited by 7

Integrative Taxonomy of Armeria Taxa (Plumbaginaceae) Endemic to Sardinia and Corsica

Tiburtini, M., Bacchetta, G., Sarigu, M., Cambria, S., Caputo, P., Luca, D. D., Domina, G., Turini, A., & Peruzzi, L. (2023) · Plants · doi:10.3390/plants12112229 · Cited by 7

Understanding biochemical design principles with ensembles of canonical non-linear models

Bromig, L., Kremling, A., & Marı́n-Sanguino, A. (2020) · PLoS ONE · doi:10.1371/journal.pone.0230599 · Cited by 7

Identification of Patients with Nontraumatic Intracranial Hemorrhage Using Administrative Claims Data

Sangal, R. B., Fodeh, S. J., Taylor, A., Rothenberg, C., Finn, E. B., Sheth, K. N., Matouk, C. C., Ulrich, A. S., Parwani, V. L., Sather, J., et al. (2020) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2020.105306 · Cited by 7

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

Asgari, E., Garakani, K., McHardy, A. C., & Mofrad, M. R. K. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/255018 · Cited by 7

Optuna Based Hyperparameter Tuning for Improving the Performance Prediction Mortality and Hospital Length of Stay for Stroke Patients

Purwwokerto, U. A., Tikaningsih, A., Lestari, P., Nurhopipah, A., Tahyudin, I., Winarto, E., & Hassa, N. (2024) · Telematika · doi:10.35671/telematika.v17i1.2816 · Cited by 6

Identifying Infant Body Position from Inertial Sensors with Machine Learning: Which Parameters Matter?

Duda‐Goławska, J., Rogowski, A., Laudańska, Z., Żygierewicz, J., & Tomalski, P. (2024) · Sensors · doi:10.3390/s24237809 · Cited by 6

Prediction of Lumbar Drainage-Related Meningitis Based on Supervised Machine Learning Algorithms

Wang, P., Cheng, S., Li, Y., Liu, L., Liu, J., Zhao, Q., & Luo, S. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.910479 · Cited by 6

Predicting Unplanned 7-day Intensive Care Unit Readmissions with Machine Learning Models for Improved Discharge Risk Assessment.

Shi, K., Ho, V. T., Song, J. J., Bechler, K., & Chen, J. H. (2022) · PubMed Central · Cited by 6

Machine-Learning Models With Multiple Imputation With Sequential Nearest Neighbors Imputation for Predicting the Prognosis of Idiopathic Sudden Sensorineural Hearing Loss Patients

Jin, Y., Li, M., Li, M., Fan, H., Gong, H., Chen, W., Zhang, M., Yu, Y., Luo, W., & Zhang, X. (2025) · Ear and Hearing · doi:10.1097/aud.0000000000001711 · Cited by 5

MMMF: Multimodal Multitask Matrix Factorization for Classification and Feature Selection

Hwang, J., & Lee, H. (2022) · IEEE Access · doi:10.1109/access.2022.3221089 · Cited by 5

New hydro-pedotransfer functions for Austrian soil mapping applications

Darmann, F., Schwaighofer, I., Kumpan, M., Weninger, T., & Strauß, P. (2024) · Geoderma Regional · doi:10.1016/j.geodrs.2024.e00875 · Cited by 5

Random Forest Model and Sample Explainer for Non-experts in Machine Learning – Two Case Studies

Petković, D., Alavi, A., Cai, D., & Wong, M. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-68796-0_5 · Cited by 5

Mood Prediction Based on Calendar Events Using Multitask Learning

Tateyama, N., Fukui, R., & Warisawa, S. (2022) · IEEE Access · doi:10.1109/access.2022.3193778 · Cited by 5

Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients

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