Machine Learning Tool Development And Use In Biological Information Decoding

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Sheetalrani R Kawale , kamalakar Ravindra Desai , Parismita Sarma , N. K. Darwante , C M Velu , Pundru Chandra Shaker Reddy


DNA, RNA, and proteins are the main molecules of life, and the varied roles that proteins play determine the phenotypes of living organisms. Since proteins are polymers made up of amino acid molecules, it is crucial to understand their many roles and features in order to comprehend life at the molecular level. Complete protein sequences for many species have been obtained thanks to recent developments in high throughput deep sequencing methods. Experimental approaches to functionally annotating proteins are time-consuming, labor-intensive, and expensive. As a result, only a fraction of the total sequenced proteins have been annotated experimentally. Instead of using experiments to determine how proteins should be categorised, we may utilise machine learning techniques to train computer models using annotated proteins and then use those models to classify freshly sequenced proteins into their respective categories. Significant biological knowledge and computing ability are necessary for using machine learning. Machine learning algorithms, on the other hand, are meant to construct models without any human intervention. However, this is true only for numerical training data sets, since the vast majority of biological data are textual or otherwise qualitative in nature. Specific algorithms are needed to transform biological data into machine readable forms. Therefore, experimentalists rely on computer professionals to create models using machine learning for their data. Due to the need for assistance from computer professionals, the time it takes to generate hypotheses and uncover new information has increased.

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