Research Articles
Tumor Mutational Burden Measurement by NGS: A Comprehensive Guide for Cancer Researchers and Drug Developers
This article provides a comprehensive examination of Tumor Mutational Burden (TMB) as a predictive biomarker for immunotherapy response, focusing on Next-Generation Sequencing (NGS) methodologies.
Next-Generation Sequencing in Immuno-Oncology: Unlocking Biomarker Discovery for Precision Cancer Therapies
This article provides a comprehensive overview of how Next-Generation Sequencing (NGS) is revolutionizing biomarker discovery in immuno-oncology.
Liquid Biopsy and Circulating Tumor DNA: A Revolutionary Tool for Cancer Detection and Monitoring
This article provides a comprehensive overview of the transformative role of liquid biopsy and circulating tumor DNA (ctDNA) analysis in modern oncology.
Next-Generation Sequencing in Precision Oncology: A Comprehensive Review of Technologies, Clinical Applications, and Future Directions
Next-generation sequencing (NGS) has fundamentally transformed precision oncology by enabling comprehensive genomic profiling of tumors, thus guiding diagnosis, prognostication, and personalized treatment selection.
Comprehensive Genomic Profiling in Oncology: From Diagnostic Revolution to Therapeutic Precision
Comprehensive genomic profiling (CGP) represents a paradigm shift in cancer diagnostics, moving beyond single-gene testing to simultaneously analyze hundreds of cancer-related genes and genomic signatures.
DNA Sequence Representation Methods: A Comparative Analysis for Biomedical Research and Clinical Applications
This article provides a comprehensive comparative analysis of DNA sequence representation methods, tracing their evolution from foundational computational techniques to advanced AI-driven models.
Filter vs. Wrapper Feature Selection: A Comparative Guide for Biomedical Data Analysis
This article provides a comprehensive comparative analysis of filter and wrapper feature selection methods, tailored for researchers and professionals in drug development and biomedical sciences.
Cross-Validation Strategies for Genomic Cancer Classifiers: A Guide for Robust Model Development
This article provides a comprehensive guide to cross-validation (CV) strategies for developing and validating machine learning models in genomic cancer classification.
Optimizing Cancer Driver Gene Discovery: A Comprehensive Evaluation of Feature Selection Methods for Precision Oncology
The identification of cancer driver genes is fundamental to understanding oncogenesis and developing targeted therapies.
From Data to Diagnosis: A Roadmap for Clinically Validating cfDNA Machine Learning Models
The integration of machine learning (ML) with cell-free DNA (cfDNA) analysis holds transformative potential for non-invasive cancer detection, therapy selection, and monitoring.