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42 followers · 126 following

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Achievement: Arctic Code Vault ContributorAchievement: Pull Shark

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Achievement: Arctic Code Vault ContributorAchievement: Pull Shark

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vd4mmind/README.md

👋 Hi, I’m Vivek Das and you can find me at: X https://twitter.com/ivivek87 or in LinkedIn https://www.linkedin.com/in/vivek-das-phd-m-sc-b1110b25/

  • 👀 I work on finding therapeutic target or biomarkers (prognostic/predictive) for multiple cardiometabolomic (e.g. Chronic Kidney, Athersclerosis, Diabetes, Liver, Neurodegenrative, etc.) diseases combining data-driven and knowledge-driven avenues by leveraging the integreation of bulk and single cell multi-omics data alongside clinical data from human observational cohorts and clinical trials

  • 🌱 I currently collaborate, lead studies or projects and team of exceptional clinical data scientistists and bioinformaticians involving single-cell transcriptomcis, spatial transcriptomics, proteomics, metabolomics, multi-omics data integration, clinical data integration to achieve the above

  • 💻 I also worked on omics projects involving preclinical data with interventional designs e.g. knockout or drug treated at single cell or bulk multi-omics level

  • 💞️ I also collaborate on developing applied machine learning models using publicly available data with curious students or researchers and contribute to open science and open source modeling. I am fascinated by the potential of high-dimnesional and high-throughput biomedical data.

  • 📚 To read more about our works kindly check out Google Scholar page at: https://scholar.google.it/citations?user=l_Aj58cAAAAJ&hl=en

  • 📫 vivekdas.0687@gmail.com

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  1. multiOmicsIntegration multiOmicsIntegration Public template

    Github Repository for multiOmics Integration project involving genomics, epigenomics and transcriptomics

    Jupyter Notebook 10 7

  2. RNASeq RNASeq Public

    This project consists of various scripts that were used for analysis of RNA-Seq data. The scripts are not customised but basal level which one can use to fit into their suitable experimental design…

    Shell 5 1

  3. mutation_analysis_scripts mutation_analysis_scripts Public

    day to day activity for mutation data

    Shell 2 5

  4. deeplearner87/CASSL deeplearner87/CASSL Public

    A cell-type annotation method for single cell transcriptomics data using semi-supervised learning

    Jupyter Notebook 4 1

  5. albanobel/Deconvolution-for-ST-in-Cardiorenal-Disease albanobel/Deconvolution-for-ST-in-Cardiorenal-Disease Public

    Python

  6. AKITA_LCM_RNASeq_Treatment AKITA_LCM_RNASeq_Treatment Public

    This study includes 54 mouse kidney samples stratified into 9 groups based on disease, treatment, and genotype explanatory variables. The table below summarizes the experimental design.

    HTML