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Algorithms
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Advanced Research on Machine Learning Algorithms in Bioinformatics (2nd Edition)
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Advanced Research on Machine Learning Algorithms in Bioinformatics (2nd Edition)

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 341

Special Issue Editors

Dr. Roberto Pagliarini

E-Mail Website
Guest Editor
Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy
Interests: systems biology; computational biology; mathematical modelling
Prof. Dr. Carla Piazza

E-Mail Website
Guest Editor
Department of Mathematics, Computer Science and Physics, University of Udine, 33100 Udine, Italy
Interests: systems biology; hybrid automata; model checking; information flow security
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Epigenetic variation and, more generally, somatic mutations represent molecular components of biodiversity that directly link the genome to the environment. Recently, epigenetics has emerged as a promising approach for diagnosing several disorders. It could be an opportunity to uncover new mechanisms and therapeutic targets for cancer, and to analyze their links to metabolic dysregulation. The application of machine learning and automated reasoning techniques to mutational studies comprising large amounts of multi-omics data could significantly boost discovery and therapy development. For these reasons, we invite you to submit your latest research related to the development and application of artificial intelligence methods to this kind of problem to this Special Issue. It will focus on algorithms in the following areas:

  • Epigenomic and multi-omics data clustering;
  • Computational approaches to modeling and optimizing cancer treatment;
  • Patient-specific integrated network modeling;
  • Single-cell analysis in cancer genomics and epigenomics;
  • Modeling the evolutionary dynamics of cancer: from epigenetic regulation to cell population dynamics.

Dr. Roberto Pagliarini
Prof. Dr. Carla Piazza
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 250 words) can be sent to the Editorial Office for assessment.

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. Algorithms 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

  • artificial intelligence
  • computational biology
  • machine learning
  • genomics
  • data clustering

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Related Special Issue

Published Papers (1 paper)

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Research

16 pages, 1192 KB  
Article
Multi-Scale Feature Mixing of Language Model Embeddings for Enhanced Prediction of Submitochondrial Protein Localization
by Rong Wang, Menghua Wang, Yibo Wu, Lixiang Yang and Xiao Wang
Algorithms 2026, 19(3), 212; https://doi.org/10.3390/a19030212 - 11 Mar 2026
Viewed by 169
Abstract
Accurate prediction of submitochondrial localization is fundamental to understanding mitochondrial biogenesis and cellular metabolic pathways. While deep representations from pre-trained protein language models (pLMs) have significantly advanced the field, traditional global average pooling methods often fail to capture critical, localized N-terminal targeting signals, [...] Read more.
Accurate prediction of submitochondrial localization is fundamental to understanding mitochondrial biogenesis and cellular metabolic pathways. While deep representations from pre-trained protein language models (pLMs) have significantly advanced the field, traditional global average pooling methods often fail to capture critical, localized N-terminal targeting signals, particularly in long sequences where these motifs are mathematically diluted. To resolve this “signal dilution” bottleneck, we developed a multi-scale architecture that explicitly integrates high-resolution N-terminal features with global evolutionary context derived from ESM-2 embeddings. The proposed framework utilizes an orthogonal mixing strategy consisting of Token-mixing and Channel-mixing. Token-mixing is specifically designed to detect spatial rhythmic patterns across residue positions, while Channel-mixing refines the biochemical signatures within the latent feature space. Extensive benchmarking across diverse datasets demonstrates that our approach effectively maintains signal integrity. Compared to existing state-of-the-art methods, the model achieves a superior overall Generalized Correlation Coefficient (GCC) of 0.7443 on the SM424-18 dataset and 0.7878 on the SubMitoPred dataset, outperforming the latest benchmarks by 9.4% and 16.1%, respectively. Furthermore, on the independent M983 test set, our method maintained a high GCC of 0.6945, demonstrating a 9.9% improvement relative to the state-of-the-art methods. This robust and efficient framework provides a high-precision tool for large-scale mitochondrial proteomics. Full article
(This article belongs to the Special Issue Advanced Research on Machine Learning Algorithms in Bioinformatics (2nd Edition))
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