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...
Na minha lista:
| Formato: | Online |
|---|---|
| Idioma: | inglês |
| Publicado em: |
MDPI - Multidisciplinary Digital Publishing Institute
2023
|
| Assuntos: | |
| Acesso em linha: | ONIX_20230220_9783036548142_17 |
| Tags: |
Sem tags, seja o primeiro a adicionar uma tag!
|
| _version_ | 1869516610393341952 |
|---|---|
| collection | Directory of Open Access Books |
| 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. |
| format | Online |
| id | doab-20.500.12854ir-97414 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-974142024-03-28T03:33:14Z Bioinformatics and Machine Learning for Cancer Biology Wan, Shibiao Fan, Yiping Jiang, Chunjie Li, Shengli tumor mutational burden DNA damage repair genes immunotherapy biomarker biomedical informatics breast cancer estrogen receptor alpha persistent organic pollutants drug-drug interaction networks molecular docking NGS ctDNA VAF liquid biopsy filtering variant calling DEGs diagnosis ovarian cancer PUS7 RMGs CPA4 bladder urothelial carcinoma immune cells T cell exhaustion checkpoint architectural distortion image processing depth-wise convolutional neural network mammography bladder cancer Annexin family survival analysis prognostic signature therapeutic target R Shiny application RNA-seq proteomics multi-omics analysis T-cell acute lymphoblastic leukemia CCLE sitagliptin thyroid cancer (THCA) papillary thyroid cancer (PTCa) thyroidectomy metastasis drug resistance n/a biomarker identification transcriptomics machine learning prediction variable selection major histocompatibility complex bidirectional long short-term memory neural network deep learning cancer incidence mortality modeling forecasting Google Trends Romania ARIMA TBATS NNAR thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences 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. 2023-02-20T16:43:20Z 2023-02-20T16:43:20Z 2022 book ONIX_20230220_9783036548142_17 9783036548142 9783036548135 https://directory.doabooks.org/handle/20.500.12854/97414 eng image/jpeg Attribution 4.0 International https://mdpi.com/books/pdfview/book/5918 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-0365-4813-5 10.3390/books978-3-0365-4813-5 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783036548142 9783036548135 196 Basel open access |
| spellingShingle | tumor mutational burden DNA damage repair genes immunotherapy biomarker biomedical informatics breast cancer estrogen receptor alpha persistent organic pollutants drug-drug interaction networks molecular docking NGS ctDNA VAF liquid biopsy filtering variant calling DEGs diagnosis ovarian cancer PUS7 RMGs CPA4 bladder urothelial carcinoma immune cells T cell exhaustion checkpoint architectural distortion image processing depth-wise convolutional neural network mammography bladder cancer Annexin family survival analysis prognostic signature therapeutic target R Shiny application RNA-seq proteomics multi-omics analysis T-cell acute lymphoblastic leukemia CCLE sitagliptin thyroid cancer (THCA) papillary thyroid cancer (PTCa) thyroidectomy metastasis drug resistance n/a biomarker identification transcriptomics machine learning prediction variable selection major histocompatibility complex bidirectional long short-term memory neural network deep learning cancer incidence mortality modeling forecasting Google Trends Romania ARIMA TBATS NNAR thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences Bioinformatics and Machine Learning for Cancer Biology |
| title | Bioinformatics and Machine Learning for Cancer Biology |
| title_full | Bioinformatics and Machine Learning for Cancer Biology |
| title_fullStr | Bioinformatics and Machine Learning for Cancer Biology |
| title_full_unstemmed | Bioinformatics and Machine Learning for Cancer Biology |
| title_short | Bioinformatics and Machine Learning for Cancer Biology |
| title_sort | bioinformatics and machine learning for cancer biology |
| topic | tumor mutational burden DNA damage repair genes immunotherapy biomarker biomedical informatics breast cancer estrogen receptor alpha persistent organic pollutants drug-drug interaction networks molecular docking NGS ctDNA VAF liquid biopsy filtering variant calling DEGs diagnosis ovarian cancer PUS7 RMGs CPA4 bladder urothelial carcinoma immune cells T cell exhaustion checkpoint architectural distortion image processing depth-wise convolutional neural network mammography bladder cancer Annexin family survival analysis prognostic signature therapeutic target R Shiny application RNA-seq proteomics multi-omics analysis T-cell acute lymphoblastic leukemia CCLE sitagliptin thyroid cancer (THCA) papillary thyroid cancer (PTCa) thyroidectomy metastasis drug resistance n/a biomarker identification transcriptomics machine learning prediction variable selection major histocompatibility complex bidirectional long short-term memory neural network deep learning cancer incidence mortality modeling forecasting Google Trends Romania ARIMA TBATS NNAR thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences |
| topic_facet | tumor mutational burden DNA damage repair genes immunotherapy biomarker biomedical informatics breast cancer estrogen receptor alpha persistent organic pollutants drug-drug interaction networks molecular docking NGS ctDNA VAF liquid biopsy filtering variant calling DEGs diagnosis ovarian cancer PUS7 RMGs CPA4 bladder urothelial carcinoma immune cells T cell exhaustion checkpoint architectural distortion image processing depth-wise convolutional neural network mammography bladder cancer Annexin family survival analysis prognostic signature therapeutic target R Shiny application RNA-seq proteomics multi-omics analysis T-cell acute lymphoblastic leukemia CCLE sitagliptin thyroid cancer (THCA) papillary thyroid cancer (PTCa) thyroidectomy metastasis drug resistance n/a biomarker identification transcriptomics machine learning prediction variable selection major histocompatibility complex bidirectional long short-term memory neural network deep learning cancer incidence mortality modeling forecasting Google Trends Romania ARIMA TBATS NNAR thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GP Research and information: general thema EDItEUR::P Mathematics and Science::PS Biology, life sciences |
| url | ONIX_20230220_9783036548142_17 |