Computer Analysis for Molecular Pathological Research
A special issue of International Journal of Molecular Sciences (ISSN 1422-0067). This special issue belongs to the section "Molecular Informatics".
Deadline for manuscript submissions: 20 October 2025 | Viewed by 1477
Special Issue Editor
Interests: machine learning; neural network; artificial intelligence; immuno-oncology; immuno-informatics; cancer neo antigens discovery; computational cancer genomics; molecular dynamics; AI digital pathology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The advancements in computing technologies and the rapid development of increasingly sophisticated AI applications have highlighted the growing need for artificial intelligence in the molecule medical and biological fields. Recent literature is increasingly enriched with applications aimed at processing and analyzing big data derived from molecule pathological biospecimens. Once perceived as a field with limited innovation, molecule pathology is now experiencing an intense period of insights, discoveries, and improvements in diagnostic accuracy thanks to machine learning and the collaboration of scientists from exact sciences who are increasingly focusing on applying AI algorithms in molecule pathology.
With this in mind, I am pleased to introduce this particular issue that is dedicated to those who wish to contribute to developing computer analysis in molecule pathology. This special issue will cover multiple areas of interest, ranging from biomedical image recognition and classification by convolutional neural networks to foundation models and multimodal learning for integrating pathology data with genomic and proteomic sequencing data and identifying new biomarkers, both for diagnosis and as therapeutic targets.
I warmly invite you to participate in this unique Special Issue, which has the potential to shape the future of molecule pathology significantly.
Sincerely,
Dr. Luca Zammataro
Guest Editor
Manuscript Submission Information
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Keywords
- machine learning
- artifical intelligence
- large language models
- pathology
- digital pathology
- multimodal learning
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