Bioinformatics Of Behavior Part 1
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Bioinformatics of Behavior: Part 1
This issue of International Review of Neurobiology is split over 2 volumes, bringing together cutting-edge research on Bioinformatics of Behavior. The 2 volumes review current knowledge and understanding, provide a starting point for researchers and practitioners entering the field, and build a platform for further research and discovery. - Leading authors review the state-of-the-art in their field of investigation, and provide their views and perspectives for future research - Chapters are extensively referenced to provide readers with a comprehensive list of resources on the topics covered - All chapters include comprehensive background information and are written in a clear form that is also accessible to the non-specialist
Bioinformatics of Behavior: Part 2
This issue of International Review of Neurobiology is split over 2 volumes, bringing together cutting-edge research on Bioinformatics of Behavior. The 2 volumes review current knowledge and understanding, provide a starting point for researchers and practitioners entering the field, and build a platform for further research and discovery. - Leading authors review the state-of-the-art in their field of investigation, and provide their views and perspectives for future research - Chapters are extensively referenced to provide readers with a comprehensive list of resources on the topics covered - All chapters include comprehensive background information and are written in a clear form that is also accessible to the non-specialist
Data Analytics in Bioinformatics
Author: Rabinarayan Satpathy
language: en
Publisher: John Wiley & Sons
Release Date: 2021-01-20
Machine learning techniques are increasingly being used to address problems in computational biology and bioinformatics. Novel machine learning computational techniques to analyze high throughput data in the form of sequences, gene and protein expressions, pathways, and images are becoming vital for understanding diseases and future drug discovery. Machine learning techniques such as Markov models, support vector machines, neural networks, and graphical models have been successful in analyzing life science data because of their capabilities in handling randomness and uncertainty of data noise and in generalization. Machine Learning in Bioinformatics compiles recent approaches in machine learning methods and their applications in addressing contemporary problems in bioinformatics approximating classification and prediction of disease, feature selection, dimensionality reduction, gene selection and classification of microarray data and many more.