Bioinformatics and Machine Learning for Cancer Biology
Bioinformatics and Machine Learning for Cancer Biology is a cutting-edge reference that bridges computational science and oncology, providing researchers, data scientists, and biomedical professionals with the latest tools and methodologies to analyze complex cancer datasets. Available now in Pakistan through BooksHub.pk, this book delivers an in-depth exploration of how bioinformatics and AI-powered machine learning models are transforming cancer research, diagnostics, and treatment strategies.
Covering everything from genomic data analysis to predictive modeling, the book highlights how advanced algorithms can be applied to uncover molecular mechanisms, identify biomarkers, and develop personalized medicine approaches. It integrates theory with practical applications, ensuring that readers gain both conceptual understanding and hands-on knowledge for implementing computational pipelines in cancer biology research.
Key Features of “Bioinformatics and Machine Learning for Cancer Biology”:
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Comprehensive Scope: Detailed coverage of genomics, transcriptomics, proteomics, and epigenomics in cancer research.
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Machine Learning Applications: Practical guidance on supervised, unsupervised, and deep learning models for cancer data analysis.
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Data Integration & Visualization: Methods to combine multi-omics datasets for holistic cancer system biology studies.
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Predictive Modeling: Approaches for building AI-driven models to predict cancer progression, treatment response, and patient survival.
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Case Studies: Real-world examples demonstrating successful bioinformatics and machine learning applications in oncology.
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Reproducible Workflows: Step-by-step computational pipelines using open-source tools and cloud-based platforms.
