Network: Computation in Neural Systems

AI with Applications in Bioinformatics and Biomedicine

Advances in multi-omics profiling and biomedical imaging have led to an unprecedented volume of complex biological data across dynamic cellular scales. Traditional statistical frameworks often fail to extract meaningful signals from these high-dimensional datasets. Artificial intelligence (AI) provides essential computational methodologies to resolve these analytical challenges. Modern machine learning paradigms, ranging from deep generative architectures to large-scale foundation models, excel at modeling non-linear relationships, sparse representations, and complex cellular heterogeneity. By integrating raw sequence data, single-cell multi-omics, and complex protein structures, AI approaches offer a systematic framework to decode cellular mechanisms. Overall, these computational advances bridge the gap between high-throughput data collection and molecular biology, providing mechanistic insights into disease progression, regulatory networks, and dynamic biological systems.

Despite continuous progress, translating massive biological data into biomedical discoveries remains constrained by key methodological bottlenecks. High-throughput datasets naturally present severe challenges, including extreme sparsity, low signal-to-noise ratios, and severe batch effects arising from heterogeneous experimental platforms. Furthermore, the limited interpretability of many deep learning architectures presents a major barrier to validating computational predictions in biological and clinical settings. Overcoming these limitations is crucial for accelerating precision oncology, drug target identification, and therapeutic design. Developing robust, interpretable, and domain-informed AI frameworks is necessary to ensure that computational models generate reliable, reproducible hypotheses. Addressing these computational hurdles will improve predictive accuracy in basic biomedical research while laying the foundation for the safe and trustworthy adoption of AI tools in clinical workflows and therapeutic development.

Topics of Interest

This Article Collection aims to gather computational strategies and artificial intelligence methodologies tailored to tackle challenges in bioinformatics and biomedicine. We invite original research, methodologies, reviews, and short communications spanning a broad range of topics. Relevant areas of interest include: single-cell omics data integration, molecular representation learning, AI-driven drug discovery, structural bioinformatics, therapeutic peptide design, perturbation response prediction, and interpretable machine learning for biomarker identification. We particularly encourage submissions that introduce novel neural network architectures, generative models, multi-modal representation learning frameworks, or standardized benchmark datasets. By bridging computational innovation with biological validation, this collection seeks to foster interdisciplinary dialogue and advance the application of intelligent computing across the life sciences.

5 keywords

1. Artificial Intelligence
2. Bioinformatics
3. Multi-omics Analysis
4. Biomedical Image Analysis
5. Drug Discovery

Guest Advisor

Leyi Wei is a Professor at Macao Polytechnic University. His Research focuses on developing AI and machine learning methods for bioinformatics. His team works on single-cell analysis, drug design and genomic sequence prediction. For further details about Professor Wei's professional profile, kindly refer to the links: https://mpusite.mpu.edu.mo/esca/en/weileyi.php, and https://orcid.org/0000-0003-1444-190X.

Disclosure Statement: Professor Leyi Wei has no conflicts of interest to disclose.

All manuscripts submitted to this Article Collection will undergo a full peer-review; the Guest Advisor for this Collection will not be handling the manuscripts (unless they are an Editorial Board member).

Please review the journal scope and author submission instructions prior to submitting a manuscript.

The deadline for submitting manuscripts is the 28th February 2027.

To submit your papers to this Article Collection, please:

1. Check "yes" for the question, "Are you submitting your paper for a specific special issue or article collection?"
2. Select the Article Collection "AI with Applications in Bioinformatics and Biomedicine" from the drop-down menu under the question, "Special Issue or Article Collection Name."

Please contact Jieyu Zheng at Jieyu.Zheng@tandf.co.uk  with any questions relating to this Article Collection.

Article Publishing Charges (APCs)

Open Select Journals that publish open access require payment of an article publishing charge (APC); this includes manuscripts submitted to an Article Collection. APCs vary between journals and may depend on the article type. APCs may also differ from the standard APC if your institution or research funder has an open access publishing agreement or an open access membership with Taylor & Francis.