Machine Learning and Deep Learning in Complex Biological and Genetic Phenomenon

Dear colleagues,

Biological systems are one of the most complex structures in nature. To comprehend the underlying physics of such complex systems and their behavior, often, researchers integrate different approaches including large scale user facilities (both experimental and computational). But as the process of data generation is becoming faster and easier, the meaningful extraction of features is also becoming more difficult. The primary reasons are the difficulty to deal with heterogeneous data, integration of large-scale dataset, elimination of noise or working with high dimensionality (otherwise known as 'curse of dimensionality') to name a few. In recent years, machine learning (ML) and deep learning (DL) algorithms are emerging as useful tools to tackle many of these problems such as protein folding, structure prediction, conformational dynamics, analysis of pathways, docking, system biology and so on.

While the application of ML or DL in the domain of biological science is still in its early stage, several new and exciting works are emerging with an exceptional pace. This Special Issue invites the presentation of new and novel implementations, methodologies and applications of ML and DL in the domain of biological sciences. In particular examples of used cases of ML/DL implementation in computational biology or bioinformatics are welcomed.

Dr. Debsindhu Bhowmik
Guest Editor

Manuscript Submission Information

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• machine learning
• deep learning
• AI
• biological sciences
• bioinformatics
• heterogeneous data
• omics
• dimensionality reduction
• noise elimination
• big data