Metabolic subtyping of tumors from gene expression data
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Updated
May 30, 2024 - HTML
Metabolic subtyping of tumors from gene expression data
This project is a comparative study of Autoencoder (AE) and Principal Component Analysis (PCA) for dimensionality reduction in gene expression data. It aims to understand the unique capabilities and applications of both methods in handling high-dimensional biological data.
This is the R code to extract Gene Expression Profiles of particular genes from GEO (Gene Expression Omnibus) datasets.
An R-shiny package to analyse and visualise fungal gene expression data
Implementation code for evaluating COMET approach on various simulation datasets
Repository of my internship in the RDDS lab of the University of Trento
A workflow to understand the changes in expression levels of epithelial cells in smokers.
An interactive Streamlit app for processing and visualizing GEO data from NCBI's Gene Expression Omnibus.
CocoTF: a workflow to identify context specific co-occurring TF motifs using bedtools, Homer and R packages.
Agglomerative based clustering on gene expression dataset
Predict the samplesizes for certain errorrates for statistical classifiers
A biclustering library with datasets, evaluation measures and a benchmarking framework
Workflow for retrieving spatial gene-expression data from the Allen Institute's Mouse Brain Atlas
Shiny app that recopilates all gene expression of zebra fish and informs about the tissue and developmental stage in which the gene is expressed.
Differential expression analysis using R 🧬
Implementation of SRIQ clustering
PCA: Exploring transcriptome-wide changes using python (pandas/scikit-learn/matplotlib) 🧬 💻 👩🔬 🖥️
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