Electronic Resource

Bioinformatics and Machine Learning for Cancer Biology

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Cancer is a leading cause of death worldwide, claiming millions of lives each year. Cancer biology is an essential research field to understand how cancer develops, evolves, and responds to therapy. By taking advantage of a series of “omics” technologies (e.g., genomics, transcriptomics, and epigenomics), computational methods in bioinformatics and machine learning can help scientists and researchers to decipher the complexity of cancer heterogeneity, tumorigenesis, and anticancer drug discovery. Particularly, bioinformatics enables the systematic interrogation and analysis of cancer from various perspectives, including genetics, epigenetics, signaling networks, cellular behavior, clinical manifestation, and epidemiology. Moreover, thanks to the influx of next-generation sequencing (NGS) data in the postgenomic era and multiple landmark cancer-focused projects, such as The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC), machine learning has a uniquely advantageous role in boosting data-driven cancer research and unraveling novel methods for the prognosis, prediction, and treatment of cancer.

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Detail Information

Series Title

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Call Number

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Publisher

Basel : MDPI - Multidisciplinary Digital Publishing Institute., 2022

Collation

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Language

English

ISBN/ISSN

9783036548142

Classification

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Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

Breast Cancer
tumor mutational burden
DNA damage repair genes
immunotherapy
biomarker
biomedical informatics
estrogen receptor alpha

Specific Detail Info

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Statement of Responsibility

Shibiao Wan, Yiping Fan, Chunjie Jiang, Shengli Li,

Other Information

Cataloger

Candra

Source

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Validator

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Digital Object Identifier (DOI)

https://doi.org/10.3390/books978-3-0365-4813-5

Journal Volume

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Journal Issue

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Subtitle

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Parallel Title

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