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      AI-driven analysis establishes the single base substitution signatures as personalized prognostic predictors for five-year survival of gastric cancer


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          COSMIC: the Catalogue Of Somatic Mutations In Cancer

          Abstract COSMIC, the Catalogue Of Somatic Mutations In Cancer (https://cancer.sanger.ac.uk) is the most detailed and comprehensive resource for exploring the effect of somatic mutations in human cancer. The latest release, COSMIC v86 (August 2018), includes almost 6 million coding mutations across 1.4 million tumour samples, curated from over 26 000 publications. In addition to coding mutations, COSMIC covers all the genetic mechanisms by which somatic mutations promote cancer, including non-coding mutations, gene fusions, copy-number variants and drug-resistance mutations. COSMIC is primarily hand-curated, ensuring quality, accuracy and descriptive data capture. Building on our manual curation processes, we are introducing new initiatives that allow us to prioritize key genes and diseases, and to react more quickly and comprehensively to new findings in the literature. Alongside improvements to the public website and data-download systems, new functionality in COSMIC-3D allows exploration of mutations within three-dimensional protein structures, their protein structural and functional impacts, and implications for druggability. In parallel with COSMIC’s deep and broad variant coverage, the Cancer Gene Census (CGC) describes a curated catalogue of genes driving every form of human cancer. Currently describing 719 genes, the CGC has recently introduced functional descriptions of how each gene drives disease, summarized into the 10 cancer Hallmarks.
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            Random forest: a classification and regression tool for compound classification and QSAR modeling.

            A new classification and regression tool, Random Forest, is introduced and investigated for predicting a compound's quantitative or categorical biological activity based on a quantitative description of the compound's molecular structure. Random Forest is an ensemble of unpruned classification or regression trees created by using bootstrap samples of the training data and random feature selection in tree induction. Prediction is made by aggregating (majority vote or averaging) the predictions of the ensemble. We built predictive models for six cheminformatics data sets. Our analysis demonstrates that Random Forest is a powerful tool capable of delivering performance that is among the most accurate methods to date. We also present three additional features of Random Forest: built-in performance assessment, a measure of relative importance of descriptors, and a measure of compound similarity that is weighted by the relative importance of descriptors. It is the combination of relatively high prediction accuracy and its collection of desired features that makes Random Forest uniquely suited for modeling in cheminformatics.
              • Record: found
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              Is Open Access

              Whole-genome sequencing reveals novel tandem-duplication hotspots and a prognostic mutational signature in gastric cancer

              Genome-wide analysis of genomic signatures might reveal novel mechanisms for gastric cancer (GC) tumorigenesis. Here, we analysis structural variations (SVs) and mutational signatures via whole-genome sequencing of 168 GCs. Our data demonstrates diverse models of complex SVs operative in GC, which lead to high-level amplification of oncogenes. We find varying proportion of tandem-duplications (TDs) among individuals and identify 24 TD hotspots involving well-established cancer genes such as CCND1, ERBB2 and MYC. Specifically, we nominate a novel hotspot involving the super-enhancer of ZFP36L2 presents in approximately 10% GCs from different cohorts, the oncogenic role of which is further confirmed by experimental data. In addition, our data reveal a mutational signature, specifically occurring in noncoding region, significantly enriched in tumors with cadherin 1 mutations, and associated with poor prognoses. Collectively, our data suggest that TDs might serve as an important mechanism for cancer gene activation and provide a novel signature for stratification.

                Author and article information

                Genes Dis
                Genes Dis
                Genes & Diseases
                Chongqing Medical University
                13 July 2023
                July 2024
                13 July 2023
                : 11
                : 4
                : 101030
                [a ]School of Biomedical Engineering, The Sixth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong 511436, China
                [b ]College of Mathematics and Systems Science, Guangdong Polytechnic Normal University, Guangzhou, Guangdong 510665, China
                [c ]The Second Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong 510260, China
                [d ]Center for Learning Sciences and Technologies, The Chinese University of Hong Kong, Shatin, New Territories 999077, China
                [e ]Affiliated Stomatology Hospital of Guangzhou Medical University, Guangzhou Medical University, Guangzhou, Guangdong 510182, China
                [f ]International School of Photonics, Cochin University of Science and Technology, Kochi, Kerala 682022, India
                Author notes
                []Corresponding author. The Sixth Affiliated Hospital, School of Biomedical Engineering, Guangzhou Medical University, Xinzao, Panyu District, Guangzhou, Guangdong 511436, China. lychris@ 123456sina.com
                [∗∗ ]Corresponding author. mature303@ 123456126.com

                These authors contributed equally to this work.

                S2352-3042(23)00298-2 101030
                © 2023 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd.

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                : 12 January 2023
                : 25 May 2023
                Rapid Communication


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