Bioinformatics and Machine Learning for Cancer Biology
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 epigeno...
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| description | 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. |
| doi_str_mv | 10.3390/books978-3-0365-4813-5 |
| format | Book |
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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.</description><identifier>ISBN: 9783036548142</identifier><identifier>ISBN: 9783036548135</identifier><identifier>ISBN: 3036548149</identifier><identifier>ISBN: 3036548130</identifier><identifier>DOI: 10.3390/books978-3-0365-4813-5</identifier><language>eng</language><publisher>Basel: MDPI - Multidisciplinary Digital Publishing Institute</publisher><subject>Annexin family ; architectural distortion ; ARIMA ; bidirectional long short-term memory neural network ; Biology, life sciences ; biomarker ; biomarker identification ; biomedical informatics ; bladder cancer ; bladder urothelial carcinoma ; Book Industry Communication ; breast cancer ; cancer ; CCLE ; checkpoint ; CPA4 ; ctDNA ; deep learning ; DEGs ; depth-wise convolutional neural network ; diagnosis ; DNA damage repair genes ; drug resistance ; drug-drug interaction networks ; estrogen receptor alpha ; filtering ; forecasting ; Google Trends ; image processing ; immune cells ; immunotherapy ; incidence ; liquid biopsy ; machine learning ; major histocompatibility complex ; mammography ; Mathematics & science ; metastasis ; modeling ; molecular docking ; mortality ; multi-omics analysis ; n/a ; NGS ; NNAR ; ovarian cancer ; papillary thyroid cancer (PTCa) ; persistent organic pollutants ; prediction ; prognostic signature ; proteomics ; PUS7 ; R Shiny application ; Reference, information & interdisciplinary subjects ; Research & information: general ; RMGs ; RNA-seq ; Romania ; sitagliptin ; survival analysis ; T cell exhaustion ; T-cell acute lymphoblastic leukemia ; TBATS ; therapeutic target ; thyroid cancer (THCA) ; thyroidectomy ; transcriptomics ; tumor mutational burden ; VAF ; variable selection ; variant calling</subject><creationdate>2022</creationdate><tpages>196</tpages><format>196</format><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>306,780,784,786,27925,55310</link.rule.ids></links><search><contributor>Li, Shengli</contributor><contributor>Fan, Yiping</contributor><contributor>Wan, Shibiao</contributor><contributor>Jiang, Chunjie</contributor><title>Bioinformatics and Machine Learning for Cancer Biology</title><description>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.</description><subject>Annexin family</subject><subject>architectural distortion</subject><subject>ARIMA</subject><subject>bidirectional long short-term memory neural network</subject><subject>Biology, life sciences</subject><subject>biomarker</subject><subject>biomarker identification</subject><subject>biomedical informatics</subject><subject>bladder cancer</subject><subject>bladder urothelial carcinoma</subject><subject>Book Industry Communication</subject><subject>breast cancer</subject><subject>cancer</subject><subject>CCLE</subject><subject>checkpoint</subject><subject>CPA4</subject><subject>ctDNA</subject><subject>deep learning</subject><subject>DEGs</subject><subject>depth-wise convolutional neural network</subject><subject>diagnosis</subject><subject>DNA damage repair genes</subject><subject>drug resistance</subject><subject>drug-drug interaction networks</subject><subject>estrogen receptor alpha</subject><subject>filtering</subject><subject>forecasting</subject><subject>Google Trends</subject><subject>image processing</subject><subject>immune cells</subject><subject>immunotherapy</subject><subject>incidence</subject><subject>liquid biopsy</subject><subject>machine learning</subject><subject>major histocompatibility complex</subject><subject>mammography</subject><subject>Mathematics & science</subject><subject>metastasis</subject><subject>modeling</subject><subject>molecular docking</subject><subject>mortality</subject><subject>multi-omics analysis</subject><subject>n/a</subject><subject>NGS</subject><subject>NNAR</subject><subject>ovarian cancer</subject><subject>papillary thyroid cancer (PTCa)</subject><subject>persistent organic pollutants</subject><subject>prediction</subject><subject>prognostic signature</subject><subject>proteomics</subject><subject>PUS7</subject><subject>R Shiny application</subject><subject>Reference, information & interdisciplinary subjects</subject><subject>Research & information: general</subject><subject>RMGs</subject><subject>RNA-seq</subject><subject>Romania</subject><subject>sitagliptin</subject><subject>survival analysis</subject><subject>T cell exhaustion</subject><subject>T-cell acute lymphoblastic leukemia</subject><subject>TBATS</subject><subject>therapeutic target</subject><subject>thyroid cancer (THCA)</subject><subject>thyroidectomy</subject><subject>transcriptomics</subject><subject>tumor mutational burden</subject><subject>VAF</subject><subject>variable selection</subject><subject>variant calling</subject><isbn>9783036548142</isbn><isbn>9783036548135</isbn><isbn>3036548149</isbn><isbn>3036548130</isbn><fulltext>true</fulltext><rsrctype>book</rsrctype><creationdate>2022</creationdate><recordtype>book</recordtype><sourceid>V1H</sourceid><recordid>eNotj8tKBDEURAMiKGN_wYDkB6JJbjqPpTa-oMWNsx7yuD0THRPpduPfG3VWBaeKKoqQS8GvABy_DrW-L85YBoyD7pmyAlh_QrrG4Jc0oOQZ6ZbljXMuHbfaiHOib3PNZarzh__KcaG-JPrs4z4XpCP6ueSyo82mgy8RZ9rih7r7viCnkz8s2B11RTb3d6_DIxtfHp6Gm5HtJUhg0fZRoJCm7U8QhEGUIkUupdXahWCaq5PB6CFpa4LrwUnrlQsyWASAFVn_91b_iWWbqv87unVGCQU_qCRF7Q</recordid><startdate>2022</startdate><enddate>2022</enddate><general>MDPI - Multidisciplinary Digital Publishing Institute</general><scope>V1H</scope></search><sort><creationdate>2022</creationdate><title>Bioinformatics and Machine Learning for Cancer Biology</title></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-h2323-c85c1e127654f3b17ee21dc0228669bb7c1e6d7eca3d687b953928a49b2b8e333</frbrgroupid><rsrctype>books</rsrctype><prefilter>books</prefilter><language>eng</language><creationdate>2022</creationdate><topic>Annexin family</topic><topic>architectural distortion</topic><topic>ARIMA</topic><topic>bidirectional long short-term memory neural network</topic><topic>Biology, life sciences</topic><topic>biomarker</topic><topic>biomarker identification</topic><topic>biomedical informatics</topic><topic>bladder cancer</topic><topic>bladder urothelial carcinoma</topic><topic>Book Industry Communication</topic><topic>breast cancer</topic><topic>cancer</topic><topic>CCLE</topic><topic>checkpoint</topic><topic>CPA4</topic><topic>ctDNA</topic><topic>deep learning</topic><topic>DEGs</topic><topic>depth-wise convolutional neural network</topic><topic>diagnosis</topic><topic>DNA damage repair genes</topic><topic>drug resistance</topic><topic>drug-drug interaction networks</topic><topic>estrogen receptor alpha</topic><topic>filtering</topic><topic>forecasting</topic><topic>Google Trends</topic><topic>image processing</topic><topic>immune cells</topic><topic>immunotherapy</topic><topic>incidence</topic><topic>liquid biopsy</topic><topic>machine learning</topic><topic>major histocompatibility complex</topic><topic>mammography</topic><topic>Mathematics & science</topic><topic>metastasis</topic><topic>modeling</topic><topic>molecular docking</topic><topic>mortality</topic><topic>multi-omics analysis</topic><topic>n/a</topic><topic>NGS</topic><topic>NNAR</topic><topic>ovarian cancer</topic><topic>papillary thyroid cancer (PTCa)</topic><topic>persistent organic pollutants</topic><topic>prediction</topic><topic>prognostic signature</topic><topic>proteomics</topic><topic>PUS7</topic><topic>R Shiny application</topic><topic>Reference, information & interdisciplinary subjects</topic><topic>Research & information: general</topic><topic>RMGs</topic><topic>RNA-seq</topic><topic>Romania</topic><topic>sitagliptin</topic><topic>survival analysis</topic><topic>T cell exhaustion</topic><topic>T-cell acute lymphoblastic leukemia</topic><topic>TBATS</topic><topic>therapeutic target</topic><topic>thyroid cancer (THCA)</topic><topic>thyroidectomy</topic><topic>transcriptomics</topic><topic>tumor mutational burden</topic><topic>VAF</topic><topic>variable selection</topic><topic>variant calling</topic><toplevel>online_resources</toplevel><collection>DOAB: Directory of Open Access Books</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Li, Shengli</au><au>Fan, Yiping</au><au>Wan, Shibiao</au><au>Jiang, Chunjie</au><format>book</format><genre>book</genre><ristype>BOOK</ristype><btitle>Bioinformatics and Machine Learning for Cancer Biology</btitle><date>2022</date><risdate>2022</risdate><isbn>9783036548142</isbn><isbn>9783036548135</isbn><isbn>3036548149</isbn><isbn>3036548130</isbn><abstract>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.</abstract><cop>Basel</cop><pub>MDPI - Multidisciplinary Digital Publishing Institute</pub><doi>10.3390/books978-3-0365-4813-5</doi><tpages>196</tpages><oa>free_for_read</oa></addata></record> |
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| source | DOAB: Directory of Open Access Books |
| subjects | Annexin family architectural distortion ARIMA bidirectional long short-term memory neural network Biology, life sciences biomarker biomarker identification biomedical informatics bladder cancer bladder urothelial carcinoma Book Industry Communication breast cancer cancer CCLE checkpoint CPA4 ctDNA deep learning DEGs depth-wise convolutional neural network diagnosis DNA damage repair genes drug resistance drug-drug interaction networks estrogen receptor alpha filtering forecasting Google Trends image processing immune cells immunotherapy incidence liquid biopsy machine learning major histocompatibility complex mammography Mathematics & science metastasis modeling molecular docking mortality multi-omics analysis n/a NGS NNAR ovarian cancer papillary thyroid cancer (PTCa) persistent organic pollutants prediction prognostic signature proteomics PUS7 R Shiny application Reference, information & interdisciplinary subjects Research & information: general RMGs RNA-seq Romania sitagliptin survival analysis T cell exhaustion T-cell acute lymphoblastic leukemia TBATS therapeutic target thyroid cancer (THCA) thyroidectomy transcriptomics tumor mutational burden VAF variable selection variant calling |
| title | Bioinformatics and Machine Learning for Cancer Biology |
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