Special Issue "Bioinformatics of Sequencing Data: A Machine Learning Approach"

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

Deadline for manuscript submissions: 15 September 2022 | Viewed by 306

Special Issue Editor

Dr. Irina Mohorianu
E-Mail Website
Guest Editor
Bioinformatics/Scientific Computing, Core Bioinformatics Group, Wellcome-MRC Cambridge Stem Cell Institute, Cambridge CB2 0AW, UK
Interests: machine learning; bioinformatics; sequencing; multi-omics; spatial transcriptomics; clustering; classification; gene regulatory networks; small RNAs

Special Issue Information

Dear Colleagues,

Over the past few years, the deluge of sequencing data (bulk and single-cell, focusing on one or more modalities, retaining also spatial information) prompted us to look for more efficient approaches to summarize and synthesize biological signals. Machine learning methods, diverse and flexible yet robust, provide answers in terms of the optimized processing of large amounts of data and uncovering underlying signals that are hidden (e.g., masked by noise) in traditional approaches.

This Special Issue is open for cutting-edge research spanning the wide range of bioinformatics interests, from purely algorithmic to tightly embedded in the particularities of a data modality. Bold applications of machine learning approaches are welcome (unsupervised, e.g., clustering in single-cell data; supervised, e.g., classifiers/regression approaches aimed at improving predictions; semi-supervised methods to illustrate the handling of missing labels; or reinforcement learning when the online processing of information is required). Applications of extracting the essence quantified by sequencing experiments to the interpretation of biological phenomena (e.g., detailing gene regulatory networks) are also invited.

This Special Issue will both underline recent developments in the field (research papers) and summarize the next set of data-processing challenges which may be tackled using machine learning methods (review papers). Case studies are also welcome but should specifically address limitations/shortcomings of current computational approaches.

Dr. Irina Mohorianu
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.

Keywords

  • machine learning
  • high-throughput sequencing
  • multi-omics
  • (standard/spatial) transcriptomics
  • unsupervised learning (clustering)
  • supervised learning (classification/regression)
  • semi-supervised learning
  • reinforcement learning
  • gene regulatory networks

Published Papers

This special issue is now open for submission.
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