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Biomedicines
Special Issues
AI/Machine Learning-Driven Multi-Omics Research in Oncology
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AI/Machine Learning-Driven Multi-Omics Research in Oncology

A special issue of Biomedicines (ISSN 2227-9059). This special issue belongs to the section "Cancer Biology and Oncology".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 741

Special Issue Editors

Dr. Wenjun Mao

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Guest Editor
Department of Thoracic Surgery, The Affiliated Wuxi People’s Hospital of Nanjing Medical University, Wuxi, China
Interests: tumor immune microenvironment; tumor immuno-therapy; tumor biology; bioinformatics; application of biomaterials in medical field; AI
Dr. Renjun Gu

E-Mail
Guest Editor
1. School of Chinese Medicine, Nanjing University of Chinese Medicine, Nanjing, China
2. School of Integrated Chinese and Western Medicine, Nanjing University of Chinese Medicine, Nanjing, China
Interests: tumor marker; metabolism; evidence-based Medicine
Dr. Yize Mao

E-Mail
Guest Editor
Department of Pancreatobiliary Surgery, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, China
Interests: Immune; tumor marker; artificial intelligence

Special Issue Information

Dear Colleagues,

The integration of high-throughput biotechnologies and advanced computational biology is fundamentally reshaping oncology research. This Special Issue, entitled "AI/Machine Learning-Driven Multi-Omics Research in Oncology," is dedicated to exploring the transformative potential at the nexus of these disciplines. It seeks to showcase pioneering studies that harness artificial intelligence and machine learning to decode the complex, multi-layered biology of cancer.

As a systems-level disease, cancer arises from intricate interactions across genomic, transcriptomic, proteomic, epigenomic, and metabolomic dimensions. While multi-omics profiling offers an unprecedented, holistic perspective on tumor biology, the volume, heterogeneity, and complexity of the resulting data pose significant analytical challenges. Conventional statistical approaches are often inadequate for detecting the subtle, non-linear patterns and interactions inherent in such datasets.

This is where AI and machine learning emerge as essential. This Special Issue will highlight research employing advanced computational methods to integrate and interpret multi-omics data. We welcome contributions that not only introduce innovative algorithms but also yield meaningful biological insights and clinical applications. Key topics include the identification of novel biomarkers and therapeutic targets, refinement of molecular subtyping, elucidation of drug resistance mechanisms, and progress in personalized treatment strategies.

By compiling groundbreaking work in this rapidly evolving domain, the Special Issue aims to catalyze the next wave of advances in precision oncology. We invite submissions that illustrate how AI-powered multi-omics analysis can lead to a deeper understanding of cancer biology and, ultimately, to improved patient outcomes.

Dr. Wenjun Mao
Dr. Renjun Gu
Dr. Yize Mao
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. Biomedicines 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 2600 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
  • machine learning
  • multi-omics
  • oncology
  • precision medicine
  • biomarker discovery
  • cancer genomics
  • deep learning

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Published Papers (1 paper)

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Research

26 pages, 5074 KB  
Article
Wavelet-Enhanced CNN for Breast Ultrasound Classification Under Speckle Noise
by Ratapong Onjun, Tanakorn Sritarapipat and Sayan Kaennakham
Biomedicines 2026, 14(5), 1151; https://doi.org/10.3390/biomedicines14051151 - 19 May 2026
Viewed by 330
Abstract
Background/Objectives: Ultrasound is widely used for breast cancer screening and diagnosis, particularly in low- and middle-income settings, but its diagnostic reliability is often compromised by speckle noise that degrades lesion margins and tissue texture. This study proposes a compact convolutional neural network architecture [...] Read more.
Background/Objectives: Ultrasound is widely used for breast cancer screening and diagnosis, particularly in low- and middle-income settings, but its diagnostic reliability is often compromised by speckle noise that degrades lesion margins and tissue texture. This study proposes a compact convolutional neural network architecture that replaces standard max or average pooling layers with wavelet-based pooling using Symlet families, and optionally includes wavelet-domain preprocessing to suppress input noise. Methods: We conducted 108 experiments across six pooling configurations (avg, max, Sym2 ± preprocessing, Sym4 + preprocessing, Sym6 + preprocessing), two network depths, three batch sizes, and three simulated speckle levels (0%, 10%, 20%). Results: The proposed wavelet-based pooling framework showed consistently stronger in-domain performance than conventional pooling strategies across clean and speckle-corrupted settings, with the Sym2 + preprocessing configuration giving the best overall results. The model achieved 93.90% accuracy and 98.89% ROC AUC under clean internal test conditions and maintained stable performance under increased simulated noise levels. However, external validation on the independent BrEaST-Lesions-USG dataset revealed substantial performance degradation, with accuracy decreasing to 53.97% and ROC AUC to 0.4713, indicating limited cross-dataset generalization. Conclusions: These findings suggest that wavelet pooling is an effective architectural modification for improving in-domain robustness under controlled perturbation, although additional strategies are still required before reliable real-world deployment can be claimed. Full article
(This article belongs to the Special Issue AI/Machine Learning-Driven Multi-Omics Research in Oncology)
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