Special Issue "Recent Advances of Deep Learning and Machine Learning in Bioinformatics"

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Proteins and Proteomics".

Deadline for manuscript submissions: closed (27 May 2022) | Viewed by 2182

Special Issue Editors

Dr. Sandeep Kumar Dhanda
E-Mail Website
Guest Editor
Saint Jude Children’s Research Hospital, Memphis, TN 38105, USA
Interests: bioinformatics; immunology; vaccine design; oncology
Special Issues, Collections and Topics in MDPI journals
Dr. Ravindra Kumar
E-Mail Website
Guest Editor
National Cancer Institute, National Institute of Health, Rockville, MD 20850, USA
Interests: bioinformatics; machine learning; data mining; bioinformatics tool development; data visualization; prediction model development
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

A Special Issue on the topic "Recent advances of Deep learning and Machine learning in Bioinformatics" is being prepared for the journal Life. In recent years, machine learning is one of the most exciting tools that have entered the bioinformatics toolbox. The statistical method has already proved to be capable of considerably speeding up both fundamental and applied research in the field. At present, we are witnessing an explosion of works that develop and apply machine learning and deep learning to bioinformatics and computational biology. We begin a Special Issue which accepts the manuscript of the most recent research on this topic. We believe that this Special Issue will provide valuable insights and serve as a starting point for researchers to apply machine learning/deep learning approaches in bioinformatics studies.

In order to acknowledge the progress of this field, we are inviting manuscripts related to the recent advancement and application of machine learning and Deep learning in Bioinformatics.

The Special Issue will cover but not be limited to the following research topics:

  • Protein structure and function prediction
  • Next-generation sequencing
  • Integration of omics data
  • Prediction model
  • SARS-coV-2 and machine learning
  • Resources for advancement of the field
  • Review articles summarizing the progress of machine learning/Deep learning in Bioinformatics.

Dr. Sandeep Kumar Dhanda
Dr. Ravindra Kumar
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. Life 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 1800 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
  • deep learning
  • bioinformatics
  • data mining
  • omics
  • prediction model development

Published Papers (1 paper)

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Review

Review
The R Language: An Engine for Bioinformatics and Data Science
Life 2022, 12(5), 648; https://doi.org/10.3390/life12050648 - 27 Apr 2022
Cited by 2 | Viewed by 1262
Abstract
The R programming language is approaching its 30th birthday, and in the last three decades it has achieved a prominent role in statistics, bioinformatics, and data science in general. It currently ranks among the top 10 most popular languages worldwide, and its community [...] Read more.
The R programming language is approaching its 30th birthday, and in the last three decades it has achieved a prominent role in statistics, bioinformatics, and data science in general. It currently ranks among the top 10 most popular languages worldwide, and its community has produced tens of thousands of extensions and packages, with scopes ranging from machine learning to transcriptome data analysis. In this review, we provide an historical chronicle of how R became what it is today, describing all its current features and capabilities. We also illustrate the major tools of R, such as the current R editors and integrated development environments (IDEs), the R Shiny web server, the R methods for machine learning, and its relationship with other programming languages. We also discuss the role of R in science in general as a driver for reproducibility. Overall, we hope to provide both a complete snapshot of R today and a practical compendium of the major features and applications of this programming language. Full article
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