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The Cancer Genome Atlas (TCGA): TCGA is a comprehensive database of genomic and clinical data from various cancer types, including ovarian cancer. It provides a wealth of data for cancer subtype classification and outlier detection. You can access the TCGA dataset through the Genomic Data Commons (GDC) portal (https://portal.gdc.cancer.gov/).
\n** ## Papers and Research:**
\nThe Cancer Genome Atlas (TCGA): TCGA is a comprehensive database of genomic and clinical data from various cancer types, including ovarian cancer. It provides a wealth of data for cancer subtype classification and outlier detection. You can access the TCGA dataset through the Genomic Data Commons (GDC) portal (https://portal.gdc.cancer.gov/).
\nHope you like it and benefit from this.
\n
The Cancer Genome Atlas (TCGA): TCGA is a comprehensive database of genomic and clinical data from various cancer types, including ovarian cancer. It provides a wealth of data for cancer subtype classification and outlier detection. You can access the TCGA dataset through the Genomic Data Commons (GDC) portal (https://portal.gdc.cancer.gov/).
\n** ## Papers and Research:**
\nThe Cancer Genome Atlas (TCGA): TCGA is a comprehensive database of genomic and clinical data from various cancer types, including ovarian cancer. It provides a wealth of data for cancer subtype classification and outlier detection. You can access the TCGA dataset through the Genomic Data Commons (GDC) portal (https://portal.gdc.cancer.gov/).
\nHope you like it and benefit from this.
\n
Your valuable content is really a treasure of knowledge read again and again and bookmarked ๐ @kalilurrahman
", "rawMarkdown": "Your valuable content is really a treasure of knowledge read again and again and bookmarked ๐ @kalilurrahman ", "votes": 1 }, { "id": 2474690, "postDate": "2023-10-09T11:43:31.447Z", "content": "Your comments are welcome!
", "rawMarkdown": "Your comments are welcome!" } ], "comments": [ { "id": 2475205, "author_name": "Tariq Mahmood", "author_url": "", "post_date": "2023-10-09T17:37:14.267000", "content": "Your valuable content is really a treasure of knowledge read again and again and bookmarked ๐ @kalilurrahman
", "votes": 1, "replies": [] }, { "id": 2474690, "author_name": "Kalilur Rahman", "author_url": "", "post_date": "2023-10-09T11:43:31.447000", "content": "Your comments are welcome!
", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "2472219": "# Best Source to begin with \n\n**The Cancer Genome Atlas (TCGA)**: TCGA is a comprehensive database of genomic and clinical data from various cancer types, including ovarian cancer. It provides a wealth of data for cancer subtype classification and outlier detection. You can access the TCGA dataset through the Genomic Data Commons (GDC) portal ([https://portal.gdc.cancer.gov/](https://portal.gdc.cancer.gov/)).\n\n** ## Papers and Research:**\n\n- [CA125 and Ovarian Cancer](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7763876/): A Comprehensive Review\n- [Ovarian Cancer by Taruna Arora; Sanjana Mullangi; Manidhar Reddy Lekkala](https://www.ncbi.nlm.nih.gov/books/NBK567760/).\n- [High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints](https://www.mdpi.com/1422-0067/20/4/952)\n- [Machine learning applications in cancer prognosis and prediction](https://www.sciencedirect.com/science/article/pii/S2001037014000464)\n- [Ovarian Cancer subtype classification and outlier detection](https://zenodo.org/record/7844718)\n- [A Gene Selection Method Based on Outliers for Breast Cancer Subtype Classification](https://pubmed.ncbi.nlm.nih.gov/34860652/ )# Best Source to begin with \n\n**The Cancer Genome Atlas (TCGA)**: TCGA is a comprehensive database of genomic and clinical data from various cancer types, including ovarian cancer. It provides a wealth of data for cancer subtype classification and outlier detection. You can access the TCGA dataset through the Genomic Data Commons (GDC) portal ([https://portal.gdc.cancer.gov/](https://portal.gdc.cancer.gov/)).\n\n## Papers and Research:\n\n- [**CA125 and Ovarian Cancer**](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7763876/): A Comprehensive Review\n- [**Ovarian Cancer by Taruna Arora; Sanjana Mullangi; Manidhar Reddy Lekkala**](https://www.ncbi.nlm.nih.gov/books/NBK567760/).\n- [**High-Grade Serous Ovarian Cancer: Basic Sciences, Clinical and Therapeutic Standpoints**](https://www.mdpi.com/1422-0067/20/4/952)\n- [**Machine learning applications in cancer prognosis and prediction**](https://www.sciencedirect.com/science/article/pii/S2001037014000464)\n- [**Ovarian Cancer subtype classification and outlier detection**](https://zenodo.org/record/7844718)\n- [**A Gene Selection Method Based on Outliers for Breast Cancer Subtype Classification](https://pubmed.ncbi.nlm.nih.gov/34860652/ )- [A Gene Selection Method Based On Outliers for Breast Cancer Subtype Classification**](https://pubmed.ncbi.nlm.nih.gov/34860652)\n- [**Google Scholar Research Papers**](https://scholar.google.co.in/scholar?q=Cancer+Subtype+Classification+and+Outlier+Detection&hl=en&as_sdt=0&as_vis=1&oi=scholart)\n\n\n## Some good Papers and Research with relevant summary for this competition\n\n- [\"**Identification of cancer outliers via statistical analysis of high-dimensional data**\"](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3531916/) - : This paper focuses on outlier detection methods in cancer genomics. It introduces statistical approaches to identify outliers in high-dimensional genomic data and discusses their potential applications in cancer research.\n- [**\"Outlier analysis for gene expression data\" by Tarca AL et al. (2006)**\"](https://link.springer.com/content/pdf/10.1007/BF02944782.pdf :) - This research article presents a comprehensive overview of outlier detection methods applied to gene expression data. It discusses various statistical techniques and computational approaches for identifying outliers in gene expression datasets.\n- [**\"Identification of ovarian cancer subtypes in high-grade serous carcinoma using genomic data and immunohistochemistry\"**](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8036744/) by Cancer Genome Atlas Research Network et al. (2014): This study utilizes genomic data and immunohistochemistry to classify high-grade serous ovarian carcinoma into different subtypes. It provides insights into the molecular characteristics of each subtype and their clinical implications.\n\n\n## Additional Resources:\n\n- [**The Cancer Genome Atlas (TCGA) website**](https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga): It provides access to a wide range of cancer genomics data, including ovarian cancer. \n- [**PubMed (https://pubmed.ncbi.nlm.nih.gov/)**](https://pubmed.ncbi.nlm.nih.gov): PubMed is a vast database of scientific articles and research papers. You can search for specific keywords like \"ovarian cancer subtype classification\" or \"cancer outlier detection\" to find relevant publications in the field.\n\n_________________\nHope you like it and benefit from this. \n_________________\n_________________\n### Best wishes for the competition \n[](https://postimg.cc/jWTcdJ2m)\n", "2475205": "Your valuable content is really a treasure of knowledge read again and again and bookmarked ๐ @kalilurrahman ", "2474690": "Your comments are welcome!" } }