Titlebook: Statistical Modelling and Machine Learning Principles for Bioinformatics Techniques, Tools, and Appl; K. G. Srinivasa,G. M. Siddesh,S. R.

雪上轻舟飞过

K. G. Srinivasa,G. M. Siddesh,S. R. ManisekharCovers the latest research in the area of bioinformatics and machine learning.Highlights the application of knowledge from biological data in various domains.Is useful for researchers and academics in

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令人作呕

https://doi.org/10.1007/978-981-15-2445-5Bioinformatics; Machine Learning; Statistical Modelling; Genomics; Proteomics; Data Analytics; Structural

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Outmoded

镀金

Machine Learning for Bioinformaticsrform complex predictions on large datasets. ML is currently being applied in six key subfields of bioinformatics such as microarrays, evolution, systems biology, genomics, text mining, and proteomics. This chapter is composed of four sections. The first section will provide an outline of ML in bioi

EXTOL

Machine-Learning Algorithms for Feature Selection from Gene Expression Dataes which are most relevant to the problem being studied are first selected from the entire set of genes whose values are available to us, and this is known as feature selection. There are three categories of feature selection methods—filter, wrapper and embedded. Machine learning which involves lear

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Visualizing Codon Usage Within and Across Genomes: Concepts and Toolsn the early days of molecular biology, codon usage studies, in our opinion, suffer from underdevelopment of easy-to-use tools to analyze and visualize how codon sequence changes along the gene and across the homologous genes in course of evolution. In this review, we aim to describe main areas of co

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ORBIT