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Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy.

Frontiers in cellular and infection microbiology
Messenger RNA (mRNA) vaccines offer an adaptable and scalable platform for cancer immunotherapy, requiring optimal design to elicit a robust and targeted immune response. Recent advancements in bioinformatics and artificial intelligence (AI) have sig...

An in-depth review of AI-powered advancements in cancer drug discovery.

Biochimica et biophysica acta. Molecular basis of disease
The convergence of artificial intelligence (AI) and genomics is redefining cancer drug discovery by facilitating the development of personalized and effective therapies. This review examines the transformative role of AI technologies, including deep ...

Current availability of radiotherapy devices in Peru and artificial intelligence-based analysis for constructing a nationwide implementation plan.

Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
PURPOSE: We provide for the first time a comprehensive situational diagnosis and propose an artificial intelligence (AI)-assisted nationwide plan of implementation, attending the most urgent needs.

Implementation of artificial intelligence approaches in oncology clinical trials: A systematic review.

Artificial intelligence in medicine
INTRODUCTION: There is a growing interest in leveraging artificial intelligence (AI) technologies to enhance various aspects of clinical trials. The goal of this systematic review is to assess the impact of implementing AI approaches on different asp...

AI-generated cancer prevention influencers can target risk groups on social media at low cost.

European journal of cancer (Oxford, England : 1990)
BACKGROUND: This study explores the potential of Artificial Intelligence (AI)-generated social media influencers to disseminate cancer prevention messages. Utilizing a Generative AI (GenAI) application, we created a virtual persona, "Wanda", to promo...

Biologically relevant integration of transcriptomics profiles from cancer cell lines, patient-derived xenografts, and clinical tumors using deep learning.

Science advances
Cell lines and patient-derived xenografts are essential to cancer research; however, the results derived from such models often lack clinical translatability, as they do not fully recapitulate the complex cancer biology. Identifying preclinical model...

Simplifying clinical use of TCGA molecular subtypes through machine learning models.

Cancer cell
In this issue of Cancer Cell, Ellrott et al. present machine learning models to classify samples into The Cancer Genome Atlas molecular subtypes using compact sets of genomic features. These validated, ready-to-use models are publicly available, alth...

MOCapsNet: Multiomics Data Integration for Cancer Subtype Analysis Based on Dynamic Self-Attention Learning and Capsule Networks.

Journal of chemical information and modeling
: With the rapid development of the accumulation of large-scale multiomics data sets, integrating various omics data to provide a thorough study from multiple perspectives can significantly provide stronger support for precise treatment of diseases. ...

Differences in technical and clinical perspectives on AI validation in cancer imaging: mind the gap!

European radiology experimental
Good practices in artificial intelligence (AI) model validation are key for achieving trustworthy AI. Within the cancer imaging domain, attracting the attention of clinical and technical AI enthusiasts, this work discusses current gaps in AI validati...

When multiple instance learning meets foundation models: Advancing histological whole slide image analysis.

Medical image analysis
Deep multiple instance learning (MIL) pipelines are the mainstream weakly supervised learning methodologies for whole slide image (WSI) classification. However, it remains unclear how these widely used approaches compare to each other, given the rece...