Publication: Integrated Bioinformatics And Machine Learning Approach To Identify Chemotherapy Resistance Biomarkers And Develop Prognostic Models In Breast Cancer
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Date
2025-09
Authors
Zhang, Hao
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Abstract
Chemoresistance remains a critical challenge in breast cancer treatment, limiting therapeutic efficacy and compromising long-term survival. This thesis adopts a multi-stage, integrative approach to address this issue, beginning with a bibliometric analysis to map global research trends and highlight methodological priorities in chemoresistance studies. Building upon these insights, large-scale multi-omics datasets representative of big data, including transcriptomic profiles from TCGA and GEO, were systematically analysed using bioinformatics techniques such as differential expression analysis and weighted gene co-expression network analysis (WGCNA). Subsequently, a machine learning–based feature selection strategy using least absolute shrinkage and selection operator (LASSO) regression was applied to identify the most predictive genes, followed by multivariate Cox proportional hazards regression analysis to construct a robust prognostic model.
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Keywords
Integrated Bioinformatics And Machine Learning Approach To Identify Chemotherapy Resistance Biomarkers , Develop Prognostic Models In Breast Cancer