Advanced Applications with Data Mining Methods in Bioinformatics

Dear Colleagues,

Data mining methods in bioinformatics can be used to discover new gene regulatory networks, predict the functional characteristics of proteins, and learn about the evolution of species. These methods, such as clustering, classification, and association rule mining, can be used to analyze information on gene expression, DNA and protein sequences, and biological pathways. It enables us to discover significant biomarkers, genetic variations, and prospective therapeutic targets that may be crucial for the detection and treatment of diseases. 

High-throughput technologies such as genomics, transcriptomics, proteomics, and metabolomics have produced an exponential surge in data, necessitating the urgent need for sophisticated data mining algorithms that can efficiently examine these sizeable datasets. This Special Issue aims to highlight research that focuses on data mining algorithms that have the potential of analyzing bioinformatics data to derive insightful conclusions. It will target ways to improve accuracy, make algorithms scalable to process biomedical datasets, and enable successful integration with artificial intelligence (AI) pipelines, such as machine learning, deep learning, and natural language processing to gather more insightful information.

With the interdisciplinary nature of this Special Issue, we invite researchers from different areas of bioinformatics, mathematics, statistics, computer science, machine learning, and sub-disciplines of bioinformatics such as computational biology to submit manuscripts. Each submitted paper will be rigorously evaluated following the revision process routinely applied for the Mathematics journal. Topics include, but are not limited to, the following:

• Data mining techniques that can be used to analyze gene expression from microarray analysis.
• Pattern recognition in biological datasets, including biomedical images and text.
• Biomedical text generation and mining.
• Advancement in data mining techniques with applications in pathway analysis, metabolomics, transcriptomics, proteomics, etc.

Dr. Rahul Gomes
Prof. Dr. Anne M. Denton
Dr. Derek Gingerich
Dr. Abra Brisbin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Mathematics 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 2600 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.

• bioinformatics
• data mining
• machine learning
• deep learning
• data science
• genetics
• multi-omics