Most-cited papers on Machine Learning Integration

Ranked by citation count via OpenAlex. Citations update on a rolling basis; list regenerates every 24h.

1. Machine learning-based integration develops an immune-derived lncRNA signature for improving outcomes in colorectal cancer

   2022 · Nature Communications · Liu, Zaoqu, Liu, Long, Weng, Siyuan et al.

   899 cited

2. A Survey on Data Collection for Machine Learning: A Big Data - AI Integration Perspective

   2019 · IEEE Transactions on Knowledge and Data Engineering · Roh, Yuji, Heo, Geon, Whang, Steven Euijong

   873 cited

3. Informed Machine Learning - A Taxonomy and Survey of Integrating Prior Knowledge into Learning Systems

   2021 · IEEE Transactions on Knowledge and Data Engineering · Rueden, Laura von, Mayer, Sebastian, Beckh, Katharina et al.

   773 cited

4. Hybrid integration of Multilayer Perceptron Neural Networks and machine learning ensembles for landslide susceptibility assessment at Himalayan area (India) using GIS

   2016 · CATENA · Pham, Binh Thai, Bui, Dieu Tien, Prakash, Indra et al.

   627 cited

5. Machine learning for integrating data in biology and medicine: Principles, practice, and opportunities

   2018 · Information Fusion · Žitnik, Marinka, Nguyen, Francis, Wang, Bo et al.

   626 cited

6. Integrating machine learning and multiscale modeling—perspectives, challenges, and opportunities in the biological, biomedical, and behavioral sciences

   2019 · npj Digital Medicine · Alber, Mark, Tepole, Adrián Buganza, Cannon, William R. et al.

   613 cited

7. Integration strategies of multi-omics data for machine learning analysis

   2021 · Computational and Structural Biotechnology Journal · Picard, Milan, Scott‐Boyer, Marie‐Pier, Bodein, Antoine et al.

   609 cited

8. Integrating Scientific Knowledge with Machine Learning for Engineering and Environmental Systems

   2022 · ACM Computing Surveys · Willard, Jared, Jia, Xiaowei, Xu, Shaoming et al.

   594 cited

9. Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches

   2019 · Agricultural and Forest Meteorology · Cai, Yaping, Guan, Kaiyu, Lobell, David B. et al.

   567 cited

10. Advanced machine learning in action: identification of intracranial hemorrhage on computed tomography scans of the head with clinical workflow integration

    2018 · npj Digital Medicine · Arbabshirani, Mohammad R., Fornwalt, Brandon K., Mongelluzzo, Gino et al.

    438 cited

11. A review on machine learning principles for multi-view biological data integration

    2016 · Briefings in Bioinformatics · Li, Yifeng, Wu, Fang‐Xiang, Ngom, Alioune

    435 cited

12. Machine Learning and Integrative Analysis of Biomedical Big Data

    2019 · Genes · Mirza, Bilal, Wang, Wei, Wang, Jie et al.

    380 cited

13. Machine learning-based prediction of drug–drug interactions by integrating drug phenotypic, therapeutic, chemical, and genomic properties

    2014 · Journal of the American Medical Informatics Association · Cheng, Feixiong, Zhao, Zhongming

    378 cited

14. Satellite-based soybean yield forecast: Integrating machine learning and weather data for improving crop yield prediction in southern Brazil

    2020 · Agricultural and Forest Meteorology · Schwalbert, Raí Augusto, Amado, Telmo Jorge Carneiro, Corassa, Geomar Mateus et al.

    375 cited

15. Drug repositioning: a machine-learning approach through data integration

    2013 · Journal of Cheminformatics · Napolitano, Francesco, Zhao, Yan, Moreira, Vânia M. et al.

    352 cited

16. Integrating Multi-Source Data for Rice Yield Prediction across China using Machine Learning and Deep Learning Approaches

    2020 · Agricultural and Forest Meteorology · Cao, Juan, Zhang, Zhao, Tao, Fulu et al.

    347 cited

17. A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia

    2017 · Nature Communications · Lee, Su‐In, Çelik, Safiye, Logsdon, Benjamin A. et al.

    340 cited

18. Multimodal data integration using machine learning improves risk stratification of high-grade serous ovarian cancer

    2022 · Nature Cancer · Boehm, Kevin, Aherne, Emily A., Ellenson, Lora H. et al.

    332 cited

19. Forecasting yield by integrating agrarian factors and machine learning models: A survey

    2018 · Computers and Electronics in Agriculture · Elavarasan, Dhivya, Vincent, P. M. Durai Raj, Sharma, Vishal et al.

    331 cited

20. Integration of high resolution remotely sensed data and machine learning techniques for spatial prediction of soil properties and corn yield

    2018 · Computers and Electronics in Agriculture · Khanal, Sami, Fulton, John P, Klopfenstein, Andrew et al.

    330 cited

21. Gravity Spy: integrating advanced LIGO detector characterization, machine learning, and citizen science

    2017 · Classical and Quantum Gravity · Zevin, M., Coughlin, Scott, Bahaadini, Sara et al.

    328 cited

22. Integration of convolutional neural network and conventional machine learning classifiers for landslide susceptibility mapping

    2020 · Computers & Geosciences · Fang, Zhice, Wang, Yi, Peng, Ling et al.

    314 cited

23. Integrating Physics-Based Modeling with Machine Learning: A Survey

    2020 · Willard, Jared, Jia, Xiaowei, Xu, Shaoming et al.

    307 cited

24. Machine learning for multi-omics data integration in cancer

    2022 · iScience · Cai, Zhaoxiang, Poulos, Rebecca C., Liu, Jia et al.

    292 cited

25. Slow pyrolysis as a platform for negative emissions technology: An integration of machine learning models, life cycle assessment, and economic analysis

    2020 · Energy Conversion and Management · Cheng, Fangwei, Luo, Hongxi, Colosi, Lisa M.

    249 cited

26. Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia

    2019 · Agricultural Systems · Feng, Puyu, Wang, Bin, Liu, De Li et al.

    236 cited

27. Integrating Machine Learning with Human Knowledge

    2020 · iScience · Deng, Changyu, Ji, Xunbi A., Rainey, Colton et al.

    232 cited

28. Integration of novel monitoring devices with machine learning technology for scalable cardiovascular management

    2020 · Nature Reviews Cardiology · Krittanawong, Chayakrit, Rogers, Albert J., Johnson, Kipp W. et al.

    224 cited

29. Identification of Three Rheumatoid Arthritis Disease Subtypes by Machine Learning Integration of Synovial Histologic Features and <scp>RNA</scp> Sequencing Data

    2018 · Arthritis & Rheumatology · Orange, Dana E., Agius, Phaedra, DiCarlo, Edward F. et al.

    211 cited

30. Integrative analysis from multi-center studies identities a consensus machine learning-derived lncRNA signature for stage II/III colorectal cancer

    2021 · EBioMedicine · Liu, Zaoqu, Guo, ChunGuang, Dang, Qin et al.

    211 cited