Special Issue "Machine Learning Supervised Algorithms in Bioinformatics"

A special issue of Genes (ISSN 2073-4425). This special issue belongs to the section "Bioinformatics".

Deadline for manuscript submissions: 15 January 2023 | Viewed by 67

Special Issue Editor

Dr. Tiziana Castrignanò
E-Mail Website
Guest Editor
Department of Ecology and Biology, University of Tuscia, San Camillo De Lellis, 01100 Viterbo, Italy
Interests: bioinformatics; computational biology; genomics; transcriptomics

Special Issue Information

Dear Colleagues,

With the advent of massive sequencing technologies and, more generally, increasingly sophisticated high-throughput experimental platforms, the amount of raw biological data produced in recent years has grown dramatically. The data deluge has led to the implementation and testing of powerful computational methods such as machine learning techniques to detect the complexity of biological data. Moreover, the contribution of omics technologies coupled with other heterogeneous data, such as phenotypic, structural and imaging data, helps to shed light on the enormous molecular complexity of living beings, although many aspects are still not characterized, since there are much missing data. Supervised machine learning algorithms that learn correlations between variables in annotated training data and use this information to predict inferred annotations for new data have already been used with great success in bioinformatics to predict biological events with greater reliability and accuracy than traditional algorithms.

This Special Issue is open to contributions concerning challenging research in different bioinformatics areas, (e.g., disease and health genomics, genomics and transcriptomics of both models and non-model organisms, eco-evolutionary genomics and phylogenomics, epigenomics, metagenomics, structural biology and bioimaging), addressed with supervised machine learning tools and algorithms.

This Special Issue on "Supervised Machine Learning Algorithms in Bioinformatics" aims to represent a wide range of topics related to theoretical, experimental, methodological or data contributions, or systematic reviews if they provide substantial contributions to the state-of-the-art. Topics include, but are not limited to, applications in bioinformatics of ML (machine learning) algorithms, such as SVM, KNN, regression or random forest and DL (deep learning) algorithms, such as, for example, RNN or CNN.

Dr. Tiziana Castrignanò
Guest Editor

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. Genes is an international peer-reviewed open access monthly 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.


  • bioinformatics
  • machine learning
  • computational biology
  • genomics
  • transcriptomics

Published Papers

This special issue is now open for submission, see below for planned papers.

Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Self-supervised learning of ligand binding;

Functional genomic analysis of variants associated to penetrance in 

COVID-19 host genetics of disease severity;

Detecting not transient epitranscriptome modifications using machine 

learning approaches




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