• A data science approach for the classification of low-grade and high-grade ovarian serous carcinomas.
  Lin Sangdi et al. BMC genomics 2018 Nov 19(1) 841
• Big-Data Analysis, Cluster Analysis, and Machine-Learning Approaches.
  Alonso-Betanzos Amparo et al. Advances in experimental medicine and biology 1065607-626
• Machine learning in suicide science: Applications and ethics.
  Linthicum Kathryn P et al. Behavioral sciences & the law 2019 Jan
• Machine Learning Models for Genetic Risk Assessment of Infants with Non-syndromic Orofacial Cleft.
  Zhang Shi-Jian et al. Genomics, proteomics & bioinformatics 2018 16(5) 354-364
• Predicting deterioration of ventricular function in patients with repaired tetralogy of Fallot using machine learning.
  Samad Manar D et al. European heart journal cardiovascular Imaging 2018 19(7) 730-738
• Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior.
  Velupillai Sumithra et al. Frontiers in psychiatry 2019 1036
• The use of machine learning in the study of suicidal and non-suicidal self-injurious thoughts and behaviors: A systematic review.
  Burke Taylor A et al. Journal of affective disorders 2019 245869-884
• Predicting lithium treatment response in bipolar patients using gender-specific gene expression biomarkers and machine learning.
  Eugene Andy R et al. F1000Research 2018 7474
• Big Data Cohort Extraction for Personalized Statin Treatment and Machine Learning.
  Adam Terrence J et al. Methods in molecular biology (Clifton, N.J.) 2019 1939255-272
• Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images.
  Nir Guy et al. JAMA network open 2019 Mar 2(3) e190442

more