DISCOVERY OF NOVEL DISEASE BIOMARKERS AND THERAPEUTICS USING MACHINE LEARNING MODELS

In the fields of computational biology and bioinformatics, the identification of novel disease biomarkers and therapeutic strategies, especially for cancer diseases, remains a crucial challenge. The advancement in computer science, particularly machine learning techniques, has greatly empowered the study of computational biology and bioinformatics for their unprecedented prediction power. This thesis explores how to utilize classic and advanced machine learning models to predict prognostic pathways, biomarkers, and therapeutics associated with cancers.

Firstly, this thesis presents a comprehensive overview of computational biology and bioinformatics, covering past milestones to current groundbreaking advancements, providing context for the research. A centerpiece of this thesis is the introduction of the Pathway Ensemble Tool and Benchmark, an original methodology designated for the unbiased discovery of cancer-related pathways and biomarkers. This toolset not only enhances the identification of crucial prognostic components distinguishing clinical outcomes in cancer patients but also guides the development of targeted drug treatments based on these signatures. Inspired by Benchmark, we extended the methodology to single-cell technologies and proposed scBenchmark and PathPCA, which provides insights into the potential and limitations of how novel techniques can benefit biomarker and therapeutic discovery. Next, the research progresses to the development of DREx, a deep learning model trained on large-scale transcriptome data, for predicting gene expression responses to drug treatments across multiple cell lines. DREx highlights the potential of advanced machine learning models in drug repurposing using a genomics-centric approach, which could significantly enhance the efficiency of initial drug selection.

The thesis concludes by summarizing these findings and highlighting their importance in advancing cancer-related biomarkers and drug discovery. Various computational predictions in this work have already been experimentally validated, showcasing the real-life impact of these methodologies. By integrating machine learning models with computational biology and bioinformatics, this research pioneers new standards for novel biomarker and therapeutics discovery.