Application of Machine Learning Algorithms for Biological Data and Biological Systems

Dear Colleagues,

Biological data are collected from a variety of sources, including humans, animals, and viruses. Research on these data has emerged in many fields, such as genetics, proteomics, and healthcare applications, to name a few. Presently, thanks to the development of computer technology, extracting useful data from raw biological data and deciphering intricate biological data has become more feasible through the application of machine learning techniques.

The purpose of this Special Issue is to publish recent developments in machine learning applications for biological data. We invite researchers to submit their research articles and reviews to this Special Issue. Example topics include (but are not limited to) the following, which relate machine learning applications to biological data:

• Use of machine learning algorithms for investigating biological systems;
• New machine learning algorithms;
• Genetics/genomics;
• Proteomics;
• Medical image analysis and diagnosis;
• Drug discovery/development;
• Healthcare applications.

Dr. Tong Gao
Guest Editor

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.