Iterative random forests to discover predictive and stable high-order interactions

Random Forests

Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538

A decision-theoretic generalization of on-line learning and an application to boosting

Freund, Y., & Schapire, R. (1997) · European Conference on Computational Learning Theory · doi:10.1007/3-540-59119-2_166 · Cited by 23,988

Classification and Regression Trees.

Gordon, A. D., Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (1984) · Biometrics · doi:10.2307/2530946 · Cited by 23,867

Classification and Regression by randomForest

Liaw, A. I., & Wiener, M. C. (2007) · Cited by 18,594

An integrated encyclopedia of DNA elements in the human genome

The ENCODE Project Consortium (2012) · Nature · doi:10.1038/nature11247 · Cited by 18,072

Bagging predictors

Breiman, L. (1996) · Machine Learning · doi:10.1007/bf00058655 · Cited by 16,325

Classification And Regression Trees

Breiman, L., Friedman, J. H., Olshen, R. A., & Stone, C. J. (2017) · doi:10.1201/9781315139470 · Cited by 11,417

Mutations affecting segment number and polarity in Drosophila

Nüsslein‐Volhard, C., & Wieschaus, E. F. (1980) · Nature · doi:10.1038/287795a0 · Cited by 4,370

A non-radioactive in situ hybridization method for the localization of specific RNAs in Drosophila embryos reveals translational control of the segmentation gene hunchback

Tautz, D., & Pfeifle, C. (1989) · Chromosoma · doi:10.1007/bf00291041 · Cited by 2,518

ChIP-seq guidelines and practices of the ENCODE and modENCODE consortia

Landt, S. G., Marinov, G. K., Kundaje, A., Kheradpour, P., Pauli, F., Batzoglou, S., Bernstein, B. E., Bickel, P., Brown, J. B., Cayting, P., et al. (2012) · Genome Research · doi:10.1101/gr.136184.111 · Cited by 2,033

Fast Interpretable Greedy-Tree Sums

Tan, Y. S., Singh, C., Nasseri, K., Agarwal, A., Duncan, J., Ronen, O., Epland, M. B., Kornblith, A. E., & Yu, B. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2310151122 · Cited by 50

Refining interaction search through signed iterative Random ForestsPreprint

Kumbier, K., Basu, S., Brown, J. B., Celniker, S., & Yu, B. (2018) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/467498 · Cited by 18

Attacking the opioid epidemic: determining the epistatic and pleiotropic genetic architectures for chronic pain and opioid addiction

Joubert, W. D., Weighill, D. A., Kainer, D., Climer, S., Justice, A. C., Fagnan, K., & Jacobson, D. (2018) · IEEE International Conference on High Performance Computing, Data, and Analytics · doi:10.5555/3291656.3291732 · Cited by 20

Predicting sludge generation patterns and carbon reduction potential under Shared Socioeconomic Pathways

Zhang, O., Gao, L., Li, W., & Xiao, L. (2022) · Journal of Environmental Management · doi:10.1016/j.jenvman.2022.116088 · Cited by 17

Understanding the role of neutral species by means of high-order interaction in the rock-paper-scissors dynamics

Lu, Y., Wang, X., Du, C., Wang, Y., Geng, Y., Ши, Л., & Park, J. (2024) · Physical review. E · doi:10.1103/physreve.109.014313 · Cited by 17

Centromeres are hotspots for chromosomal inversions and breeding traits in mango

Wilkinson, M. J., McLay, K., Kainer, D., Elphinstone, C., Dillon, N., Webb, M., Wijesundara, U. K., Ali, A., Bally, I. S. E., Munyengwa, N., et al. (2024) · New Phytologist · doi:10.1111/nph.20252 · Cited by 17

Using Explainable Artificial Intelligence to Discover Interactions in an Ecological Model for Obesity

Allen, B., Lane, M., Steeves, E. A., & Raynor, H. A. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19159447 · Cited by 16

d-Alanine Affects the Circadian Clock to Regulate Glucose Metabolism in the Kidney

Sakai, S., Tanaka, Y., Tsukamoto, Y., Kimura‐Ohba, S., Hesaka, A., Hamase, K., Hsieh, C.-L., Kawakami, E., Ono, H., Yokote, K., et al. (2023) · Kidney360 · doi:10.34067/kid.0000000000000345 · Cited by 16

Reusability First: Toward FAIR Workflows

Wolf, M., Logan, J. S., Mehta, K. R., Jacobson, D., Cashman, M., Walker, A. M., Eisenhauer, G., Widener, P. M., & Cliff, A. M. (2021) · IEEE International Conference on Cluster Computing (CLUSTER) · doi:10.1109/cluster48925.2021.00053 · Cited by 16

Sharp Analysis of a Simple Model for Random ForestsPreprint

Klusowski, J. M. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.02587 · Cited by 20

Decision tree boosted varying coefficient models

Zhou, Y., & Hooker, G. (2022) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-022-00863-y · Cited by 15

Genetics of varicose veins reveals polygenic architecture and genetic overlap with arterial and venous disease

Levin, M. G., Huffman, J. E., Verma, A., Sullivan, K. A., Rodriguez, A., Kainer, D., Garvin, M. R., Lane, M. J., Cashman, M., Miller, J., et al. (2023) · Nature Cardiovascular Research · doi:10.1038/s44161-022-00196-5 · Cited by 14

Systems Analysis of Gut Microbiome Influence on Metabolic Disease in HIV-Positive and High-Risk Populations

Armstrong, A. J. S., Quinn, K. D., Fouquier, J., Li, S. X., Schneider, J. M., Nusbacher, N. M., Doenges, K., Fiorillo, S. P., Marden, T. J., HIGGINS, J. A., et al. (2021) · mSystems · doi:10.1128/msystems.01178-20 · Cited by 15

SHAFF: Fast and consistent SHApley eFfect estimates via random ForestsPreprint

Bénard, C., Gérard Biau, A., da Veiga, S., & Scornet, E. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2105.11724 · Cited by 21

Provable Boolean interaction recovery from tree ensemble obtained via random forests

Behr, M., Wang, Y., Li, X., & Yu, B. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2118636119 · Cited by 14

Dissecting Breeders’ Sense via Explainable Machine Learning Approach: Application to Fruit Peelability and Hardness in Citrus

Minamikawa, M. F., Nonaka, K., Hamada, H., Shimizu, T., & Iwata, H. (2022) · Frontiers in Plant Science · doi:10.3389/fpls.2022.832749 · Cited by 14

Prenatal metal exposures and childhood gut microbial signatures are associated with depression score in late childhood

Midya, V., Nagdeo, K. P., Lane, J. M., Torres-Olascoaga, L. A., Torres-Calapiz, M., Gennings, C., Horton, M. K., Téllez‐Rojo, M. M., Wright, R. O., Arora, M., et al. (2024) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2024.170361 · Cited by 14

Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature ImportancePreprint

Carletti, M., Terzi, M., & Susto, G. A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2007.11117 · Cited by 14

Artificial Intelligence for Earth System Predictability (AI4ESP) (2021 Workshop Report)

Hickmon, N. L., Varadharajan, C., Hoffman, F. M., Wainwright, H. M., & Collis, S. (2022) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.2172/1888810 · Cited by 16

Identifying Politically Connected Firms: A Machine Learning Approach

Titl, V., Mazrekaj, D., & Schiltz, F. (2021) · Oxford Bulletin of Economics and Statistics · doi:10.1111/obes.12586 · Cited by 16

Interaction Selection and Prediction Performance in High-Dimensional Data: A Comparative Study of Statistical and Tree-Based Methods

Nzekwe, C. J., Kim, S., & Mostafa, S. A. (2024) · Journal of Data Science · doi:10.6339/24-jds1127 · Cited by 13

Association between Exposure to Metals during Pregnancy, Childhood Gut Microbiome, and Risk of Intestinal Inflammation in Late Childhood

Midya, V., Agrawal, M., Lane, J. M., Gennings, C., Tarassishin, L., Torres-Olascoaga, L. A., Eggers, J., Gregory, J. K., Picker, M., Peter, I., et al. (2024) · Environment & Health · doi:10.1021/envhealth.4c00125 · Cited by 13

Incorporation of Data-Mined Knowledge into Black-Box SVM for Interpretability

Chen, S., Gao, C., & Zhang, P. (2022) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/3548775 · Cited by 13

Dissecting Trichoderma antagonism: Role of strain identity, volatiles, biomass, and morphology in suppressing cacao pathogens

Baek, I., Bhatt, J., Jang, J. H., Lim, S., Lovelace, A. H., Cha, M., Lakshman, D. K., Kim, M. S., Meinhardt, L. W., Park, S., et al. (2025) · Biological Control · doi:10.1016/j.biocontrol.2025.105807 · Cited by 17

Drivers, predictors, and probabilities of plant extinctions in South Africa

der Colff, D. V., Kumschick, S., Foden, W., Raimondo, D. C., Botella, C., von Staden, L., & Wilson, J. R. (2023) · Biodiversity and Conservation · doi:10.1007/s10531-023-02696-7 · Cited by 13

INGOT-DR: an interpretable classifier for predicting drug resistance in M. tuberculosis

Zabeti, H., Dexter, N., Safari, A. H., Sedaghat, N., Libbrecht, M. W., & Chindelevitch, L. (2021) · Algorithms for Molecular Biology · doi:10.1186/s13015-021-00198-1 · Cited by 11

The Power of ANN-Random Forest Algorithm in Human Activities Recognition Using IMU Data

Nia, N. G., Amiri, A., Nasab, A., Kaplanoğlu, E., & Liang, Y. (2023) · IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI ...) · doi:10.1109/bhi58575.2023.10313507 · Cited by 12

The Fault Diagnosis of Rolling Bearing Based on Variational Mode Decomposition and Iterative Random Forest

Qin, X., Guo, J., Dong, X., & Guo, Y. F. (2020) · Shock and Vibration · doi:10.1155/2020/1576150 · Cited by 12

Signed iterative random forests to identify enhancer-associated transcription factor bindingPreprint

Kumbier, K., Basu, S., Frise, E., Celniker, S., Brown, J. B., Celniker, S., & Yu, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.07287 · Cited by 12

Research on the impact of climate change on food security in Africa

Liu, J., Wu, J., Jiang, D., Chen, S., Hao, M., Ding, F., Wu, G., & Liang, H. (2025) · Scientific Reports · doi:10.1038/s41598-025-14560-5 · Cited by 11

Running Ensemble Workflows at Extreme Scale: Lessons Learned and Path Forward

Mehta, K. R., Cliff, A. M., Suter, F., Walker, A. M., Wolf, M., Jacobson, D., & Klasky, S. (2022) · IEEE International Conference on eScience · doi:10.1109/escience55777.2022.00042 · Cited by 11

Detecting Shape-Based Interactions Among Environmental Chemicals Using an Ensemble of Exposure-Mixture Regression and Interpretable Machine Learning Tools

Midya, V., & Gennings, C. (2023) · Statistics in Biosciences · doi:10.1007/s12561-023-09405-6 · Cited by 11

Identifying FUS amyotrophic lateral sclerosis disease signatures in patient dermal fibroblasts

Kumbier, K., Roth, M. A., Li, Z., Lazzari-Dean, J. R., Waters, C., Hammerlindl, S., Rinaldi, C., Huang, P., Korben, V. A., Phatnani, H. P., et al. (2024) · Developmental Cell · doi:10.1016/j.devcel.2024.05.011 · Cited by 10

Beyond Importance Scores: Interpreting Tabular ML by Visualizing Feature Semantics

Ghorbani, A., Berenbaum, D., Ivgi, M., Dafna, Y., & Zou, J. (2021) · Information · doi:10.3390/info13010015 · Cited by 10

Soil Metabolomics Predict Microbial Taxa as Biomarkers of Moisture Status in Soils from a Tidal Wetland

Chowdhury, T. R., Bramer, L., Brown, J., Kim, Y., Zink, E., Metz, T., McCue, L. A., Diefenderfer, H. L., & Bailey, V. (2022) · Microorganisms · doi:10.3390/microorganisms10081653 · Cited by 10

Epistasis regulates genetic control of cardiac hypertrophy

Wang, Q., Tang, T. M., Youlton, M., Weldy, C. S., Kenney, A., Ronen, O., Hughes, J. W., Chin, E. T., Sutton, S. C., Agarwal, A., et al. (2025) · Nature Cardiovascular Research · doi:10.1038/s44161-025-00656-8 · Cited by 13

Post-hoc Interpretation of Clinical Pathways Clustering using Bayesian Inference

Balabaeva, K., & Kovalchuk, S. V. (2020) · Procedia Computer Science · doi:10.1016/j.procs.2020.11.028 · Cited by 10

Epigenome‐wide three‐way interaction study identifies a complex pattern between TRIM27 , KIAA0226 , and smoking associated with overall survival of early‐stage NSCLC

Ji, X., Lin, L., Fan, J., Li, Y., Wei, Y., Shen, S., Su, L., Shafer, A., Bjaanæs, M. M., Karlsson, A., et al. (2021) · Molecular Oncology · doi:10.1002/1878-0261.13167 · Cited by 11

Improving Deep Forest by Exploiting High-order Interactions

Chen, Y.-H., Lyu, S.-H., & Jiang, Y. Y. (2021) · Proceedings · doi:10.1109/icdm51629.2021.00118 · Cited by 11

The Holdout Randomization Test for Feature Selection in Black Box Models

Tansey, W., Veitch, V., Zhang, H., Rabadán, R., & Blei, D. M. (2021) · Figshare · doi:10.6084/m9.figshare.14798983 · Cited by 10

Artificial intelligence to investigate metabolomics data for precision medicine

Shenouda, A., Senthilkumar, S., Mourad, Y., Xie, J., Peker, E., Zeeshan, S., & Ahmed, Z. (2026) · Metabolomics · doi:10.1007/s11306-026-02401-z · Cited by 9

Application of Artificial Neural Networks in the Problems of the Patient’s Condition Diagnosis in Medical Monitoring Systems

Strilets, V., Bakumenko, N., Chernysh, S., Ugryumov, M., & Donets, V. (2020) · Advances in intelligent systems and computing · doi:10.1007/978-3-030-37618-5_16 · Cited by 9

On the drivers of potential customers' interest in long‐term care insurance: Evidence from Switzerland

Fuino, M., Montero, A. U., & Wagner, J. (2022) · Risk Management and Insurance Review · doi:10.1111/rmir.12218 · Cited by 9

Interpreting tree ensemble machine learning models with endoR

Ruaud, A., Pfister, N., Ley, R. E., & Youngblut, N. D. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010714 · Cited by 9

Coordinating directional switches in pigeon flocks: the role of nonlinear interactions

Chen, D., Sun, Y., Shao, G., Yu, W., Zhang, H., & Lin, W. J. (2021) · Royal Society Open Science · doi:10.1098/rsos.210649 · Cited by 10

A behavioral intervention to increase preschool attendance in Uruguay

Ajzenman, N., Luna, L. B., Hernández-Agramonte, J. M., Bóo, F. L., Alfaro, M. P., Vásquez‐Echeverría, A., & Díaz, M. M. (2022) · Journal of Development Economics · doi:10.1016/j.jdeveco.2022.102984 · Cited by 9

Disentangled Attribution Curves for Interpreting Random Forests and Boosted TreesPreprint

Devlin, S., Singh, C., Murdoch, W. J., & Yu, B. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.07631 · Cited by 9

The Effect of Information Disclosure on Industry Payments to PhysiciansPreprint

Guo, T., Sriram, S., & Manchanda, P. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.3064769 · Cited by 9

Opportunities and Challenges for AI-Based Analysis of RWD in Pharmaceutical R&D: A Practical Perspective

Behr, M., Burghaus, R., Diedrich, C., & Lippert, J. (2023) · Künstliche Intell. · doi:10.1007/s13218-023-00809-6 · Cited by 8

Seminar: Scalable Preprocessing Tools for Exposomic Data Analysis

Grady, S. K., Dojcsak, L., Harville, E. W., Wallace, M. E., Vilda, D., Donneyong, M. M., Hood, D. B., Valdez, R. B., Ramesh, A., Im, W., et al. (2023) · Environmental Health Perspectives · doi:10.1289/ehp12901 · Cited by 8

Error-controlled non-additive interaction discovery in machine learning models

Chen, W., Jiang, Y., Noble, W. S., & Lu, Y. Y. (2025) · Nature Machine Intelligence · doi:10.1038/s42256-025-01086-8 · Cited by 8

Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts

Pavicic, M., Walker, A. M., Sullivan, K. A., Lagergren, J. H., Cliff, A. M., Romero, J., Streich, J., Garvin, M. R., Workgroup, M. S. E., Program, T. M. V., et al. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1178633 · Cited by 10

Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model

Nassani, R., Bokhari, Y., & Alrfaei, B. M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0287448 · Cited by 8

Predicting the spatial variation in cost-efficiency for agricultural greenhouse gas mitigation programs in the U.S.

Cameron-Harp, M. V., Hendricks, N. P., & Potter, N. A. (2024) · Carbon Balance and Management · doi:10.1186/s13021-024-00252-6 · Cited by 8

Persistence and heterogeneity of the effects of educating mothers to improve child immunisation uptake: Experimental evidence from Uttar Pradesh in India

O’Neill, S., Grieve, R., Singh, K., Dutt, V., & Powell‐Jackson, T. (2024) · Journal of Health Economics · doi:10.1016/j.jhealeco.2024.102899 · Cited by 7

Interpretability via Random Forests

Bénard, C., da Veiga, S., & Scornet, E. (2022) · Springer eBooks · doi:10.1007/978-3-031-12402-0_3 · Cited by 9

Role models, aspirations and desire to migrate

Mesplé‐Somps, S., & Nilsson, B. (2023) · Journal of Economic Behavior & Organization · doi:10.1016/j.jebo.2023.06.013 · Cited by 8

Tree-Weighting for Multi-Study Ensemble Learners

Ramchandran, M., Patil, P., & Parmigiani, G. (2019) · Pacific Symposium on Biocomputing (PSB) · doi:10.1142/9789811215636_0040 · Cited by 8

High-dimensional iterative causal forest (hdiCF) for subgroup identification using health care claims data

Wang, T., Pate, V., Wyss, R., Buse, J. B., Kosorok, M. R., & Stürmer‎, T. (2024) · American Journal of Epidemiology · doi:10.1093/aje/kwae322 · Cited by 9

Analyses of GWAS signal using GRIN identify additional genes contributing to suicidal behavior

Sullivan, K. A., Lane, M. J., Cashman, M., Miller, J. I., Pavicic, M., Walker, A. M., Cliff, A. M., Romero, J., Qin, X., Lindquist, J., et al. (2024) · Communications Biology · doi:10.1038/s42003-024-06943-7 · Cited by 11

Akkermansia muciniphila attenuates association between specific metal exposures during pregnancy and depressive symptoms in late childhood

Midya, V., Nagdeo, K. P., Lane, J. M., Torres-Olascoaga, L. A., Martínez, G. G., Horton, M. K., McRae, N., Lopez, I., Landero, J., Gennings, C., et al. (2024) · iScience · doi:10.1016/j.isci.2024.111335 · Cited by 7

The Data Science Process: One Culture

Yu, B., & Barter, R. (2020) · Journal of the American Statistical Association · doi:10.1080/01621459.2020.1762615 · Cited by 7

Incorporating water quality into land use scenario analysis with random forest models

Goodspeed, R., Wang, R., Lizundia, C., Du, L., & Jaipuria, S. (2022) · Environment and Planning B Urban Analytics and City Science · doi:10.1177/23998083221138842 · Cited by 7

Learning high-order interactions for polygenic risk prediction

Massi, M. C., Franco, N. R., Manzoni, A., Paganoni, A. M., Park, H. A., Hoffmeister, M., Brenner, H., Chang-Claude, J. C., Ieva, F., & Zunino, P. (2023) · PLoS ONE · doi:10.1371/journal.pone.0281618 · Cited by 7

Identifying genetic determinants of complex phenotypes from whole genome sequence data

Long, G. S., Hussen, M., Dench, J., & Aris‐Brosou, S. (2019) · BMC Genomics · doi:10.1186/s12864-019-5820-0 · Cited by 9

Learning epistatic polygenic phenotypes with Boolean interactions

Behr, M., Kumbier, K., Córdova‐Palomera, A., Aguirre, M., Ronen, O., Ye, C., Ashley, E. A., Butte, A. J., Arnaout, R., Brown, B., et al. (2024) · PLoS ONE · doi:10.1371/journal.pone.0298906 · Cited by 7

An experimental manipulation of the value of effortPreprint

Lin, H., Westbrook, A., Fan, F., & Inzlicht, M. (2021) · doi:10.31234/osf.io/gnk4m · Cited by 7

An interpretable machine learning technique to identify the key meteorological factors influencing the incidence of wheat Fusarium head blight in Korea

Lee, N. S., Yang, J., Jung, J., Kim, Y.-H., & Kim, K. (2025) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2025.110355 · Cited by 6

Predictability and stability testing to assess clinical decision instrument performance for children after blunt torso trauma

Kornblith, A. E., Singh, C., Devlin, G., Addo, N., Streck, C. J., Holmes, J. F., Kuppermann, N., Grupp‐Phelan, J., Fineman, J. R., Butte, A. J., et al. (2022) · PLOS Digital Health · doi:10.1371/journal.pdig.0000076 · Cited by 6

Application of machine learning to discover interactions predictive of dietary lapses

Sala, M., Taylor, A., Crochiere, R. J., Zhang, F., & Forman, E. M. (2022) · Applied Psychology Health and Well-Being · doi:10.1111/aphw.12432 · Cited by 5

Neural correlates of negative life events and their relationships with alcohol and cannabis use initiation

Zhao, Y., Potenza, M. N., Tapert, S. F., & Paulus, M. P. (2023) · Dialogues in Clinical Neuroscience · doi:10.1080/19585969.2023.2252437 · Cited by 6

Higher-order neurodynamical equation for simplex prediction

Wang, Z., Chen, J., Gong, M., & Shao, Z. (2024) · Neural Networks · doi:10.1016/j.neunet.2024.106185 · Cited by 6

A Sparse Bayesian Learning Method for Structural Equation Model-Based Gene Regulatory Network Inference

Li, Y., Liu, D., Chu, J., Zhu, Y., Liu, J., & Cheng, X. (2020) · IEEE Access · doi:10.1109/access.2020.2976743 · Cited by 6

A novel dimension reduction algorithm based on weighted kernel principal analysis for gene expression data

Liu, W. B., Liang, S., & Qin, X. (2021) · PLoS ONE · doi:10.1371/journal.pone.0258326 · Cited by 6

Abnormal weather and loan bias: Evidence from China's FinTech credit market

Hao, X., Chen, F., & Chen, Z. (2023) · Environmental Impact Assessment Review · doi:10.1016/j.eiar.2023.107164 · Cited by 6

TiSAn: estimating tissue-specific effects of coding and non-coding variants

Vervier, K., & Michaelson, J. J. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty301 · Cited by 6

Causal Inference and Policy Evaluation Without a Control GroupPreprint

Cerqua, A., Menchetti, F., & Letta, M. (2025) · SSRN Electronic Journal · doi:10.2139/ssrn.5105143 · Cited by 6

A measurement study of the environmental quality and medical expenditures of elderly individuals: causal inference based on machine learning

Zhang, Y., Chen, S., & Liu, D. (2024) · Archives of Public Health · doi:10.1186/s13690-024-01386-2 · Cited by 7

Pure interaction effects unseen by Random Forests

Blum, R., Hiabu, M., Mammen, E., & Meyer, J. T. (2025) · Computational Statistics & Data Analysis · doi:10.1016/j.csda.2025.108237 · Cited by 5

Prevalence of malnutrition and associated factors in Chinese children and adolescents aged 3–14 years using machine learning algorithms

Zheng, F., Chen, K., Zhang, X., Wang, Q., Zhang, Z., & Niu, W. (2025) · Journal of Global Health · doi:10.7189/jogh.15.04204 · Cited by 7

Machine learning for prediction of schizophrenia based on identifying the primary and interaction effects of minor physical anomalies

Jeng, S., Tu, M.-J., Lin, C.-W., Jang, F.-L., Tseng, H., Jang, F.-L., Lu, M.-K., Chen, P.-S., Huang, C., Chang, W.-H., et al. (2024) · Journal of Psychiatric Research · doi:10.1016/j.jpsychires.2024.02.032 · Cited by 5

Hereditary and breastfeeding factors are positively associated with the aetiology of mammary gland hyperplasia: a case–control study

Gao, H., Yang, C., Fan, J., Li, L., & Pang, D. (2020) · International Health · doi:10.1093/inthealth/ihaa028 · Cited by 5

Four Markers Useful for the Distinction of Intrauterine Growth Restriction in Sheep

Wang, W., Chen, S., Qiao, L., Zhang, S., Liu, Q., Yang, K., Pan, Y., Liu, J., & Liu, W. (2023) · Animals · doi:10.3390/ani13213305 · Cited by 5

INGOT-DR: An Interpretable Classifier for Predicting Drug Resistance in M. Tuberculosis

Zabeti, H., Dexter, N., Safari, A. H., Sedaghat, N., Libbrecht, M. W., & Chindelevitch, L. (n.d.) · Summit (Simon Fraser University) · Cited by 5

AI-driven discovery of blood xenobiotic biomarkers in neovascular age-related macular degeneration using iterative random forests

Künzel, S. E., Frentzel, D. P., Flesch, L. T. M., Knecht, V. A., Rübsam, A., Dreher, F., Schütte, M., Dubrac, A., Lange, B. M. H., Yaspo, M., et al. (2024) · Graefe s Archive for Clinical and Experimental Ophthalmology · doi:10.1007/s00417-024-06538-2 · Cited by 4

Lessons from a Human-in-the-Loop Machine Learning Approach for Identifying Vacant, Abandoned, and Deteriorated Properties in Savannah, Georgia

Liang, X., Brainerd, B., Hicks, T., & Andris, C. (2024) · Journal of Planning Education and Research · doi:10.1177/0739456x241273945 · Cited by 4

Enhancement of persistence in the rock-paper-scissors dynamics through higher-order interactions

Lu, Y., Dai, H., Tan, H., Duan, X., Ши, Л., & Park, J. (2024) · Applied Mathematics and Computation · doi:10.1016/j.amc.2024.129083 · Cited by 4

Investigating the determinants of homestay satisfaction on Airbnb using multiple techniques

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