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Molecular classification of cancer: class discovery and class prediction by gene expression monitoring

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Combining logistic regression-based hybrid optimized machine learning algorithms with sensitivity analysis to achieve robust landslide susceptibility mapping

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A Machine Learning Analysis of Health Records of Patients With Chronic Kidney Disease at Risk of Cardiovascular Disease

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Urban local surface temperature prediction using the urban gray-green space landscape and vegetation indices

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Combining data envelopment analysis and Random Forest for selecting optimal locations of solar PV plants

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Maize Cropping Systems Mapping Using RapidEye Observations in Agro-Ecological Landscapes in Kenya

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Tracking Major Sources of Water Contamination Using Machine Learning

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Propagation of Meteorological Drought to Agricultural and Hydrological Droughts in the Tropical Lancang–Mekong River Basin

Feng, G., Chen, Y., Mansaray, L. R., Xu, H.-F., Shi, A., & Chen, Y. (2023) · Remote Sensing · doi:10.3390/rs15245678 · Cited by 40

Prediction of chlorophyll-a as an indicator of harmful algal blooms using deep learning with Bayesian approximation for uncertainty assessment

Busari, I., Sahoo, D., & Jana, R. B. (2024) · Journal of Hydrology · doi:10.1016/j.jhydrol.2024.130627 · Cited by 38

An evaluation of random forest based input variable selection methods for one month ahead streamflow forecasting

Fang, W., Ren, K., Liu, T., Shang, J., Jia, S., Jiang, X., & Zhang, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-81502-y · Cited by 41

Land Cover Characterization in West Sudanian Savannas Using Seasonal Features from Annual Landsat Time Series

Liu, J., Heiskanen, J., Aynekulu, E., Maeda, E. E., & Pellikka, P. (2016) · Remote Sensing · doi:10.3390/rs8050365 · Cited by 38

Predicting fishiness off-flavour and identifying compounds of lipid oxidation in dairy powders by SPME-GC/MS and machine learning

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Environmental factors influencing snowfall and snowfall prediction in the Tianshan Mountains, Northwest China

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Benefits of dimension reduction in penalized regression methods for high-dimensional grouped data: a case study in low sample size

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An Integrative Machine Learning Method to Improve Fault Detection and Productivity Performance in a Cyber-Physical System

Chiu, M., Tsai, C.-D., & Li, T.-L. (2019) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4045663 · Cited by 39

Assessing the biophysical and social drivers of burned area distribution at the local scale

Oliveira, S., & Zêzere, J. L. (2020) · Journal of Environmental Management · doi:10.1016/j.jenvman.2020.110449 · Cited by 43

An Optimized Object-Based Random Forest Algorithm for Marsh Vegetation Mapping Using High-Spatial-Resolution GF-1 and ZY-3 Data

Lou, P., Fu, B., He, H., Li, Y., Tang, T., Lin, X. C., Fan, D., & Gao, E. (2020) · Remote Sensing · doi:10.3390/rs12081270 · Cited by 38

Using Sensor Data to Detect Lameness and Mastitis Treatment Events in Dairy Cows: A Comparison of Classification Models

Post, C., Rietz, C., Büscher, W., & Müller, U. (2020) · Sensors · doi:10.3390/s20143863 · Cited by 41

Improving random forest algorithm by Lasso method

Wang, H., & Wang, G. (2020) · Journal of Statistical Computation and Simulation · doi:10.1080/00949655.2020.1814776 · Cited by 38

Multi-scale habitat selection and impacts of climate change on the distribution of four sympatric meso-carnivores using random forest algorithm

Rather, T. A., Kumar, S., & Khan, J. A. (2020) · Ecological Processes · doi:10.1186/s13717-020-00265-2 · Cited by 38

Lipidomics Prediction of Parkinson’s Disease Severity: A Machine-Learning Analysis

Avisar, H., Guardia‐Laguarta, C., Área-Gómez, E., Surface, M., Chan, A. K., Alcalay, R. N., & Lerner, B. (2021) · Journal of Parkinson s Disease · doi:10.3233/jpd-202476 · Cited by 38

Exploiting centimetre resolution of drone-mounted sensors for estimating mid-late season above ground biomass in rice

Adeluyi, O., Harris, A., Foster, T., & Clay, G. D. (2021) · European Journal of Agronomy · doi:10.1016/j.eja.2021.126411 · Cited by 38

Prediction and evaluation of energy and exergy efficiencies of a nanofluid-based photovoltaic-thermal system with a needle finned serpentine channel using random forest machine learning approach

Yuanlei, S., Brumerčík, F., Chun-sheng, Y., Głowacz, A., Ma, Z., Siarry, P., Sułowicz, M., Gupta, M. K., & Li, Z. (2023) · Engineering Analysis with Boundary Elements · doi:10.1016/j.enganabound.2023.03.009 · Cited by 38

Observation‐Constrained Projection of Flood Risks and Socioeconomic Exposure in China

Kang, S., Yin, J., Gu, L., Yang, Y., Liu, D., & Slater, L. J. (2023) · Earth s Future · doi:10.1029/2022ef003308 · Cited by 38

Variable selection using support vector regression and random forests: A comparative study

Ishak, A. B. (2016) · Intelligent Data Analysis · doi:10.3233/ida-150795 · Cited by 38

Classification of Southern Ocean krill and icefish echoes using random forests

Fallon, N. G., Fielding, S., & Fernandes, P. G. (2016) · ICES Journal of Marine Science · doi:10.1093/icesjms/fsw057 · Cited by 38

Identification of Bamboo Species Based on Extreme Gradient Boosting (XGBoost) Using Zhuhai-1 Orbita Hyperspectral Remote Sensing Imagery

Zhou, G., Ni, Z., Zhao, Y., & Luan, J. (2022) · Sensors · doi:10.3390/s22145434 · Cited by 37

Real-Time Wildfire Detection Algorithm Based on VIIRS Fire Product and Himawari-8 Data

Zhang, D., Huang, C., Gu, J., Hou, J., Zhang, Y., Han, W., Dou, P., & Feng, Y. (2023) · Remote Sensing · doi:10.3390/rs15061541 · Cited by 37

Global deep learning model for delineation of optically shallow and optically deep water in Sentinel-2 imagery

Richardson, G., Foreman, N., Knudby, A. J., Wu, Y., & Lin, Y. (2024) · Remote Sensing of Environment · doi:10.1016/j.rse.2024.114302 · Cited by 37

A novel two-stage feature selection method based on random forest and improved genetic algorithm for enhancing classification in machine learning

Ding, J., Du, J., Wang, H., & Xiao, S. (2025) · Scientific Reports · doi:10.1038/s41598-025-01761-1 · Cited by 38

High and Medium Resolution Satellite Imagery to Evaluate Late Holocene Human–Environment Interactions in Arid Lands: A Case Study from the Central Sahara

Biagetti, S., Merlo, S., Adam, E., Lobo, A., Conesa, F. C., Knight, J., Bekrani, H., Crema, E. R., Alcaina-Mateos, J., & Madella, M. (2017) · Remote Sensing · doi:10.3390/rs9040351 · Cited by 39

An Ensemble-Based Malware Detection Model Using Minimum Feature Set

Zelinka, I., & Amer, E. (2019) · MENDEL · doi:10.13164/mendel.2019.2.001 · Cited by 37

National Scale 3D Mapping of Soil pH Using a Data Augmentation Approach

Roudier, P., Burge, O. R., Richardson, S. J., McCarthy, J. K., Grealish, G., & Ausseil, A.-G. (2020) · Remote Sensing · doi:10.3390/rs12182872 · Cited by 37

Topography, Climate and Fire History Regulate Wildfire Activity in the Alaskan Tundra

Masrur, A., Taylor, A. H., Harris, L. B., Barnes, J. L., & Petrov, A. N. (2022) · Journal of Geophysical Research Biogeosciences · doi:10.1029/2021jg006608 · Cited by 38

Can We Predict Functional Outcome in Neonates with Hypoxic Ischemic Encephalopathy by the Combination of Neuroimaging and Electroencephalography?

Nanavati, T., Seemaladinne, N., Regier, M., Yossuck, P., & Pergami, P. (2015) · Pediatrics & Neonatology · doi:10.1016/j.pedneo.2014.12.005 · Cited by 37

Oil in the Sahara: mapping anthropogenic threats to Saharan biodiversity from space

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Measuring regional resilience towards fossil fuel supply constraints. Adaptability and vulnerability in socio-ecological Transformations-the case of Austria

Exner, A., Politti, E., Schriefl, E., Erker, S., Stangl, R., Baud, S., Warmuth, H., Matzenberger, J., Kranzl, L., Paulesich, R., et al. (2016) · Energy Policy · doi:10.1016/j.enpol.2015.12.031 · Cited by 37

A fuzzy transition based approach for fault severity prediction in helical gearboxes

Cerrada, M., Li, C., Sánchez, R., Pacheco, F., Cabrera, D. R., & de Oliveira, J. V. (2016) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2016.12.017 · Cited by 37

An Identification Method for Spring Maize in Northeast China Based on Spectral and Phenological Features

Tang, K., Zhu, W., Zhan, P., & Ding, S. (2018) · Remote Sensing · doi:10.3390/rs10020193 · Cited by 37

A robust method for mapping soybean by phenological aligning of Sentinel-2 time series

Huang, X., Vrieling, A., Dou, Y., Belgiu, M., & Nelson, A. (2024) · ISPRS Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.isprsjprs.2024.10.015 · Cited by 36

Effects of forest management on ground beetle diversity in alpine beech ( Fagus sylvatica L.) stands

Negro, M., Vacchiano, G., Berretti, R., Chamberlain, D., Palestrini, C., Motta, R., & Rolando, A. (2014) · Forest Ecology and Management · doi:10.1016/j.foreco.2014.05.049 · Cited by 36

Quantitative structure-property relationship modelling of thermal decomposition temperatures of ionic liquids

Venkatraman, V., & Alsberg, B. K. (2016) · Journal of Molecular Liquids · doi:10.1016/j.molliq.2016.08.023 · Cited by 36

Perceived social support in adults with autism spectrum disorder and attention‐deficit/hyperactivity disorder

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Testing an optimality-based model of rooting zone water storage capacity in temperate forests

Speich, M., Lischke, H., & Zappa, M. (2018) · Hydrology and earth system sciences · doi:10.5194/hess-22-4097-2018 · Cited by 36

Cross-domain Meta-learning for Time-series Forecasting

Ali, A. R., Gabryś, B., & Budka, M. (2018) · Procedia Computer Science · doi:10.1016/j.procs.2018.07.204 · Cited by 36

Autoregressive Planet Search: Application to the Kepler Mission

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Application of feature selection methods and machine learning algorithms for saltmarsh biomass estimation using Worldview-2 imagery

Rasel, S. M. M., Chang, H., Ralph, T. J., Saintilan, N., & Diti, I. J. (2019) · Geocarto International · doi:10.1080/10106049.2019.1624988 · Cited by 38

Random-Forest (RF) and Support Vector Machine (SVM) Implementation for Analysis of Gene Expression Data in Chronic Kidney Disease (CKD)

Rustam, Z., Sudarsono, E., & Sarwinda, D. (2019) · IOP Conference Series Materials Science and Engineering · doi:10.1088/1757-899x/546/5/052066 · Cited by 36

Application of random forest classification to predict daily oviposition events in broiler breeders fed by precision feeding system

You, J., van der Klein, S. A. S., Lou, E. H. M., & Zuidhof, M. J. (2020) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2020.105526 · Cited by 36

Safeguarding Food Supply and Groundwater Safety for Maize Production in China

Ying, H., Xue, Y., Yan, K., Wang, Y., Yin, Y., Liu, Z., Zhang, Q., Tian, X., Li, Z., Liu, Y., et al. (2020) · Environmental Science & Technology · doi:10.1021/acs.est.9b05642 · Cited by 36

Urban Water Demand Modeling Using Machine Learning Techniques: Case Study of Fortaleza, Brazil

Carvalho, T. M. N., Filho, F. D. A. D. S., & Porto, V. C. (2020) · Journal of Water Resources Planning and Management · doi:10.1061/(asce)wr.1943-5452.0001310 · Cited by 36

Are OpenStreetMap building data useful for flood vulnerability modelling?

Cerri, M., Steinhausen, M., Kreibich, H., & Schröter, K. (2021) · Natural hazards and earth system sciences · doi:10.5194/nhess-21-643-2021 · Cited by 36

Effects of Bark Beetle Outbreaks on Forest Landscape Pattern in the Southern Rocky Mountains, U.S.A.

Rodman, K. C., Andrus, R. A., Butkiewicz, C., Chapman, T. B., Gill, N. S., Harvey, B. J., Kulakowski, D., Tutland, N. J., Veblen, T. T., & Hart, S. J. (2021) · Remote Sensing · doi:10.3390/rs13061089 · Cited by 40

Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids

Dickinson, E. R., Twining, J. P., Wilson, R. P., Stephens, P. A., Westander, J., Marks, N. J., & Scantlebury, D. M. (2021) · Movement Ecology · doi:10.1186/s40462-021-00265-7 · Cited by 38

Anomaly Detection in Dam Behaviour with Machine Learning Classification Models

Salazar, F., Conde, A., González, J. I., & Vicente, D. J. (2021) · Water · doi:10.3390/w13172387 · Cited by 36

Using machine learning and qualitative interviews to design a five-question survey module for women’s agency

Jayachandran, S., Biradavolu, M. R., & Cooper, J. E. (2022) · World Development · doi:10.1016/j.worlddev.2022.106076 · Cited by 40

Model-free variable selection in reproducing kernel Hilbert space

Yang, L., Lv, S., & Wang, J. (2016) · Journal of Machine Learning Research · doi:10.5555/2946645.3007035 · Cited by 36

The Assessment of Autonomic Nervous System Activity Based on Photoplethysmography in Healthy Young Men

Liu, B., Zhang, Z., Di, X., Wang, X., Xie, L., Xie, W., & Zhang, J. (2021) · Frontiers in Physiology · doi:10.3389/fphys.2021.733264 · Cited by 35

Application of machine learning in material corrosion research

Ma, S., Du, Y., Wang, S., & Su, Y. (2023) · Corrosion Reviews · doi:10.1515/corrrev-2022-0089 · Cited by 35

Development of an enhanced bidirectional recurrent neural network combined with time-varying filter-based empirical mode decomposition to forecast weekly reference evapotranspiration

Karbasi, M., Jamei, M., Ali, M., Malik, A., Chu, X., Farooque, A. A., & Yaseen‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Z. M. (2023) · Agricultural Water Management · doi:10.1016/j.agwat.2023.108604 · Cited by 35

Optimizing actual evapotranspiration simulation to identify evapotranspiration partitioning variations: A fusion of physical processes and machine learning techniques

Jiang, X., Wang, Y., Yinglan, A., Wang, G., Zhang, X., Ma, G., Duan, L., & Liu, K. (2024) · Agricultural Water Management · doi:10.1016/j.agwat.2024.108755 · Cited by 35

Prognosis of COVID-19 severity using DERGA, a novel machine learning algorithm

Asteris, P. G., Gandomi, A. H., Armaghani, D. J., Kokoris, S. I., Papandreadi, A. T., Roumelioti, A., Papanikolaou, S., Tsoukalas, M. Z., Triantafyllidis, L., Koutras, E. I., et al. (2024) · European Journal of Internal Medicine · doi:10.1016/j.ejim.2024.02.037 · Cited by 35

Prioritization of monitoring compounds from SNTS identified organic micropollutants in contaminated groundwater using a machine learning optimized ToxPi model

Ekpe, O. D., Moon, H., Pyo, J., & Oh, J. (2024) · Water Research · doi:10.1016/j.watres.2024.122824 · Cited by 35

Genome Wide Analysis of Flowering Time Trait in Multiple Environments via High-Throughput Genotyping Technique in Brassica napus L.

Li, L., Long, Y., Zhang, L., Dalton‐Morgan, J., Batley, J., Yu, L., Meng, J., & Li, M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0119425 · Cited by 38

The Role of Highly-Resolved Gust Speed in Simulations of Storm Damage in Forests at the Landscape Scale: A Case Study from Southwest Germany

Jung, C., Schindler, D., Albrecht, A. T., & Buchholz, A. (2016) · Atmosphere · doi:10.3390/atmos7010007 · Cited by 35

An effective implementation and assessment of a random forest classifier as a soil spatial predictive model

Shukla, G., Garg, R. D., Srivastava, H. S., & Garg, P. K. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2018.1430399 · Cited by 35

Detecting Recovery Problems Just in Time: Application of Automated Linguistic Analysis and Supervised Machine Learning to an Online Substance Abuse Forum

Kornfield, R., Sarma, P. K., Shah, D. V., Mctavish, F. M., Landucci, G., Pe‐Romashko, K., & Gustafson, D. H. (2018) · Journal of Medical Internet Research · doi:10.2196/10136 · Cited by 37

Multispectral, Aerial Disease Detection for Myrtle Rust (Austropuccinia psidii) on a Lemon Myrtle Plantation

Heim, R. H. J., Wright, I. J., Scarth, P. F., Carnegie, A. J., Taylor, D., & Oldeland, J. (2019) · Drones · doi:10.3390/drones3010025 · Cited by 35

Perceived Importance of Authentic Learning Factors in Designing Construction Safety Simulation Game-Based Assignment: Random Forest Approach

Lee, Y. Y. R., Samad, H., & Goh, Y. M. (2020) · Journal of Construction Engineering and Management · doi:10.1061/(asce)co.1943-7862.0001779 · Cited by 35

A feature-fusion framework of clinical, genomics, and histopathological data for METABRIC breast cancer subtype classification

El-Nabawy, A., El-Bendary, N., & Belal, N. A. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106238 · Cited by 35

Towards Question-based High-recall Information Retrieval

Zou, J., & Kanoulas, E. (2020) · ACM Transactions on Information Systems · doi:10.1145/3388640 · Cited by 35

Bronchial Aspirate-Based Profiling Identifies MicroRNA Signatures Associated With COVID-19 and Fatal Disease in Critically Ill Patients

Molinero, M., Benitez, I. D., González, J., Gort‐Paniello, C., Moncusí‐Moix, A., Rodríguez‐Jara, F., García‐Hidalgo, M. C., Torres, G., Vengoechea, J. J., Gómez, S., et al. (2022) · Frontiers in Medicine · doi:10.3389/fmed.2021.756517 · Cited by 35

Variation in and Factors Associated With US County-Level Cancer Mortality, 2008-2019

Dong, W., Bensken, W. P., Kim, U., Rose, J., Fan, Q., Schiltz, N. K., Berger, N. A., & Koroukian, S. M. (2022) · JAMA Network Open · doi:10.1001/jamanetworkopen.2022.30925 · Cited by 35

Estimation of Canopy Nitrogen Concentration Across C3 and C4 Grasslands Using WorldView-2 Multispectral Data

Adjorlolo, C., Mutanga, O., & Cho, M. A. (2014) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2014.2320601 · Cited by 35

Upland vegetation mapping using Random Forests with optical and radar satellite data

Barrett, B., Raab, C., Cawkwell, F., & Green, S. (2016) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.32 · Cited by 35

Granger-causal Attentive Mixtures of Experts: Learning Important Features with Neural NetworksPreprint

Schwab, P., Miladinovic, D., & Karlen, W. (2018) · Repository for Publications and Research Data (ETH Zurich) · doi:10.3929/ethz-b-000241126 · Cited by 35

Predicting mortality in hemodialysis patients using machine learning analysis

García-Montemayor, V., Martín‐Malo, A., Barbieri, C., Bellocchio, F., Soriano, S., de Mier, M. V. P.-R., Molina, I. R., Aljama, P., & Rodríguez, M. (2020) · Clinical Kidney Journal · doi:10.1093/ckj/sfaa126 · Cited by 34

Predicting post‐fire debris flow grain sizes and depositional volumes in the Intermountain West, United States

Wall, S., Murphy, B. P., Belmont, P., & Yocom, L. L. (2022) · Earth Surface Processes and Landforms · doi:10.1002/esp.5480 · Cited by 35

Prediction of the transient emission characteristics from diesel engine using temporal convolutional networks

Liao, J., Hu, J., Chen, P., Zhu, L., Wu, Y., Cai, Z., Wu, H., & Wang, M. (2023) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107227 · Cited by 34

optRF: Optimising random forest stability by determining the optimal number of trees

Lange, T. M., Gültaş, M., Schmitt, A. O., & Heinrich, F. (2025) · BMC Bioinformatics · doi:10.1186/s12859-025-06097-1 · Cited by 34

Predicting Tree Species Diversity Using Geodiversity and Sentinel-2 Multi-Seasonal Spectral Information

Chrysafis, I., Korakis, G., Kyriazopoulos, A. P., & Mallinis, G. (2020) · Sustainability · doi:10.3390/su12219250 · Cited by 34

GT-Finder: Classify the family of glucose transporters with pre-trained BERT language models

Shah, S. M. A., Taju, S. W., Ho, Q., Nguyen, T., & Ou, Y. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104259 · Cited by 34

Robust biomarker discovery for hepatocellular carcinoma from high-throughput data by multiple feature selection methods

Zhang, Z., & Liu, Z. (2021) · BMC Medical Genomics · doi:10.1186/s12920-021-00957-4 · Cited by 35

Prediction of Masked Hypertension and Masked Uncontrolled Hypertension Using Machine Learning

Hung, M.-H., Shih, L.-C., Wang, Y.-C., Leu, H., Huang, P.-H., Wu, T., Lin, S.-J., Pan, W., Chen, J.-W., & Huang, C.-C. (2021) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2021.778306 · Cited by 33

Machine-learning predicts time-series prognosis factors in metastatic prostate cancer patients treated with androgen deprivation therapy

Saito, S., Sakamoto, S., Higuchi, K., Sato, K., Zhao, X., Wakai, K., Kanesaka, M., Kamada, S., Takeuchi, N., Sazuka, T., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-32987-6 · Cited by 35

A New Data Analysis System to Quantify Associations between Biochemical Parameters of Chronic Kidney Disease-Mineral Bone Disease

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