Last Posted: Mar 18, 2019
- 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
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Disclaimer: Articles listed in the Public Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.
- Page last reviewed:Mar 1, 2018
- Page last updated:Mar 19, 2019
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