Identifying the candidate genes using co-expression, GO, and machine learning techniques for Alzheimer’s disease-Reference-Cited by-同舟云学术

Identifying the candidate genes using co-expression, GO, and machine learning techniques for Alzheimer’s disease

Published:2022-02-04 Issue:1 Volume:11 Page:
ISSN:2192-6662
Container-title:Network Modeling Analysis in Health Informatics and Bioinformatics
language:en
Short-container-title:Netw Model Anal Health Inform Bioinforma

Author:

Sahu Shailendra^ORCID,Dholaniya Pankaj Singh,Rani T. Sobha

Publisher

Springer Science and Business Media LLC

Subject

Urology

Link

https://link.springer.com/content/pdf/10.1007/s13721-021-00349-9.pdf

Reference43 articles.

1. Akoglu H (2018) User’s guide to correlation coefficients. Turk J Emerg Med 18(3):91–93

2. AL-Dlaeen D, Alashqur A(2014) Using decision tree classification to assist in the prediction of Alzheimer’s disease. 6th International conference on computer science and information technology (CSIT), Amman, pp. 122-126

3. Alzheimer’s Disease Fact Sheet, National Institute of Aging, U.S. Department of Health and Human Services. 2015. https://www.nia.nih.gov/health/alzheimers-disease-fact-sheet

4. Anand KS, Dhikav V (2012) Hippocampus in health and disease: an overview. Ann Indian Acad Neurol 15(4):239–46

5. Ashburner M et al (2000) Gene ontology: tool for the unification of biology. Nat Genet 25(1):25–9

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