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Emerging Trends in Deep Learning for Data Mining in Bioinformatics Analysis

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 31 March 2025 | Viewed by 396

Special Issue Editors


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Guest Editor
Andalusian Center for Development Biology CABD, 41013 Sevilla, Spain
Interests: bioinformatics; machine learning; functional genomics

Special Issue Information

Dear Colleagues,

Bioinformatics has undergone a profound transformation in recent years, largely driven by the rapid accumulation of biological data across various scales and dimensions. This data deluge, comprising genomic sequences, protein structures, clinical records, and high-throughput experimental results, presents both unprecedented opportunities and formidable challenges. At the heart of this lies the need for advanced computational methods capable of extracting meaningful insights, predicting biological phenomena, and aiding biomedical research.

Motivated by these challenges, deep learning has emerged as a compelling approach.

In fact, deep learning, rooted in artificial neural networks inspired by the human brain, has risen to prominence due to its remarkable capacity to automatically learn hierarchical representations from raw data. In the context of bioinformatics, deep learning excels at deciphering intricate patterns, predicting biological outcomes, and extracting essential insights from multifaceted datasets. Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Graph Neural Networks (GNNs), and Transformerbased models have been adapted and extended to tackle diverse bioinformatics tasks, including genomic sequence analysis, protein structure prediction, biological networks, disease classification, and image analysis.

The topics of interests of this Special Issue are, but are not limited to:

  • Genomic data analysis.
  • Protein structures analysis.
  • Biological networks applications (e.g., protein–protein interaction networks).
  • Protein structure prediction and function prediction.
  • Disease prediction and diagnosis.
  • Drug discovery and pharmacology.
  • Clinical data (patient records and medical imaging) analysis.
  • Functional genomics and transcriptomics.
  • Biological image analysis (microscopy and radiology).
  • Convolutional Neural Networks (CNNs) for sequence and image data.
  • Recurrent Neural Networks (RNNs) for sequential data.
  • Graph Neural Networks (GNNs) for biological networks.
  • Transformer-based models for various bioinformatics tasks.
  • Transfer learning and pre-trained models.
  • Interpretability and explainability of deep learning models

Dr. Federico Divina
Dr. Pedro Manuel Martínez García
Dr. Miguel García-Torres
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. Applied Sciences 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.

Keywords

  • deep learning
  • data mining
  • genomic data
  • proteomic data
  • biological networks

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Published Papers

This special issue is now open for submission.
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