RNA-Seq and XAI Can be Used as Tools to Aid Pathologists in the Process of Cancer Diagnosis

Area of Science:

• Genomics and Computational Biology
• Cancer Research
• Bioinformatics

Background:

• RNA sequencing (RNA-Seq) offers precise and scalable transcriptome analysis.
• Cancer research benefits from RNA-Seq in identifying genes, isoforms, and variants.
• Tumor heterogeneity requires advanced analytical approaches for diagnostics and therapies.

Purpose of the Study:

• To explore the integration of RNA sequencing technologies and artificial intelligence for cancer research.
• To enhance the analysis of tumor heterogeneity and gene expression profiles.
• To optimize diagnostic and therapeutic strategies for personalized cancer treatment.

Main Methods:

• Utilizing RNA sequencing platforms (Illumina, PacBio, Oxford Nanopore) for gene expression and transcriptome analysis.
• Applying bioinformatics tools for detecting gene expression signatures, alternative splicing, and non-coding RNAs.
• Integrating explainable AI (XAI) models (LIME, SHAP) for reliable interpretation of transcriptomic data and identification of key genes.

Main Results:

• RNA-Seq combined with bioinformatics tools facilitates comprehensive analysis of transcriptomic data.
• Explainable AI enhances the interpretability of findings, aiding clinical decision-making.
• The integration of short- and long-read RNA-Seq with AI identifies critical isoforms and splicing events in cancer.

Conclusions:

• The synergy between advanced RNA sequencing and AI is crucial for addressing cancer heterogeneity.
• This integrated approach optimizes diagnostic and prognostic capabilities.
• It paves the way for developing personalized cancer therapies by characterizing genetic alterations and their impact on the tumor microenvironment.