Bioinformatics and Computational Biology for Cancer Prediction and Prognosis, 2nd Edition

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

Deadline for manuscript submissions: 5 May 2025 | Viewed by 1362

Special Issue Editors


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Guest Editor
Department of Computer Science, Eastern Connecticut State University, Willimantic, CT, USA
Interests: bioinformatics and computational biology; cancer bioinformatics
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Bioinformatics tools play a vital role in understanding the biological complexity of cancer through the extraction of meaningful information from a large volume of diverse datasets. Of utmost importance are tools for data analysis, visualization, and interpretation that would aid in the attainment of personalized medicine based on omics (genomic, transcriptomic, or proteomic) data, as well as on images and texts.

This Special Issue aims to provide an overview of new and current bioinformatics tools for cancer prediction and prognosis. Contributions may describe novel approaches, or the application of new and existing ones, that aid in the identification of diagnostic, prognostic, or predictive cancer biomarkers; that identify potential therapeutic targets and important cancer-related pathways; or that otherwise provide valuable insight into cancer biology and treatment. To make progress in the field of cancer bioinformatics, contributions by experts in the field in the form of research papers and critical reviews are welcome.

Dr. Garrett M. Dancik
Dr. Spiros Vlahopoulos
Guest Editors

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Keywords

  • cancer bioinformatics
  • biomarkers
  • biostatistics
  • genomic sequencing
  • image recognition
  • databases

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Published Papers (1 paper)

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Review

23 pages, 1673 KiB  
Review
Multiomics with Evolutionary Computation to Identify Molecular and Module Biomarkers for Early Diagnosis and Treatment of Complex Disease
by Han Cheng, Mengyu Liang, Yiwen Gao, Wenshan Zhao and Wei-Feng Guo
Genes 2025, 16(3), 244; https://doi.org/10.3390/genes16030244 - 20 Feb 2025
Viewed by 696
Abstract
It is important to identify disease biomarkers (DBs) for early diagnosis and treatment of complex diseases in personalized medicine. However, existing methods integrating intelligence technologies and multiomics to predict key biomarkers are limited by the complex dynamic characteristics of omics data, making it [...] Read more.
It is important to identify disease biomarkers (DBs) for early diagnosis and treatment of complex diseases in personalized medicine. However, existing methods integrating intelligence technologies and multiomics to predict key biomarkers are limited by the complex dynamic characteristics of omics data, making it difficult to meet the high-precision requirements for biomarker characterization in large dimensions. This study reviewed current analysis methods of evolutionary computation (EC) by considering the essential characteristics of DB identification problems and the advantages of EC, aiming to explore the complex dynamic characteristics of multiomics. In this study, EC-based biomarker identification strategies were summarized as evolutionary algorithms, swarm intelligence and other EC methods for molecular and module DB identification, respectively. Finally, we pointed out the challenges in current research and future research directions. This study can enrich the application of EC theory and promote interdisciplinary integration between EC and bioinformatics. Full article
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