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January 4, 2026Reconciling modern machine-learning practice and the classical bias–variance trade-off | PNAS
May 19, 2025“double descent”
Suffix Array and BWT Explaination
May 18, 2025The book with a nice explanation of suffix array and BWT is
Bioinformatics Algorithms: An Active Learning Approach by Phillip Compeau & Pavel Pevzner. https://www.bioinformaticsalgorithms.org/
A Step By Step Guide To Implement Naive Bayes In R | by Sahiti Kappagantula | Edureka | Medium
March 2, 2025Smith–Waterman algorithm – Wikipedia
February 19, 2025Navigating the pitfalls of applying machine learning in genomics
February 11, 2025Whalen, S., Schreiber, J., Noble, W. S., & Pollard, K. S. (2021). Navigating the pitfalls of applying machine learning in genomics. Nature Reviews Genetics, 23(3), 169–181.
https://doi.org/10.1038/s41576-021-00434-9
Suffix Array and BWT Explaination
February 11, 2025The book with a nice explanation of suffix array and BWT is
Bioinformatics Algorithms: An Active Learning Approach by Phillip Compeau & Pavel Pevzner. https://www.bioinformaticsalgorithms.org/
Principal component analysis | Nature Reviews Methods Primers
February 4, 2025https://www.nature.com/articles/s43586-022-00184-w
Greenacre, M., Groenen, P. J. F., Hastie, T., D’Enza, A. I., Markos, A., & Tuzhilina, E. (2022). Principal component analysis. Nature Reviews Methods Primers, 2(1).
https://doi.org/10.1038/s43586-022-00184-w
Network Analysis as a Grand Unifier in Biomedical Data Science | Annual Reviews
February 4, 2025https://www.annualreviews.org/content/journals/10.1146/annurev-biodatasci-080917-013444
McGillivray, P., Clarke, D., Meyerson, W., Zhang, J., Lee, D., Gu, M., Kumar, S., Zhou, H., & Gerstein, M. (2018). Network analysis as a grand unifier in biomedical data science. Annual Review of Biomedical Data Science, 1(1), 153–180.
https://doi.org/10.1146/annurev-biodatasci-080917-013444
Scale-free networks – PubMed
February 3, 2025https://pubmed.ncbi.nlm.nih.gov/12701331/
Barabási, A., & Bonabeau, E. (2003). Scale-Free networks. Scientific American, 288(5), 60–69.
https://doi.org/10.1038/scientificamerican0503-60
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