Reconstruct a Transcriptional Regulatory Network using the principle of Maximum Entropy.
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Updated
Mar 30, 2017 - Julia
Reconstruct a Transcriptional Regulatory Network using the principle of Maximum Entropy.
Gene expression experiments using Python and R
Homework Machine Learning
An R package to create gene expression atlases from bulk RNA-seq data on NCBI SRA
Single Cell Expression Atlas t-SNE plot
I am going to collect and sort out the data of Asian and White patients who have the Liver Hepatocellular Carcinoma disease, and compare the gene expression between these two kinds of patients.
Master thesis on "Microarray data analysis in prediction of breast cancer metastasis" - synced from Overleaf
Different methods for optimizing state-of-the-art feature selection methods namely SVMRFE, HSICLASSO, and mRMR.
BSc thesis: "Convolutional Neural Networks and their Application in Cancer Diagnosis based on RNA-Sequencing"
Bulk Rna-seq Analysis
schematic of gene expression during adult and fetal erythropoiesis
Source code for "Molecular mechanisms implicated in myogenic differentiation of human alveolar mucosa derived cells" paper
A basic analysis of the gene expressions in the gravier dataset.
An open platform which provides information about miRNAs and genes from different popular databases
🐳 Docker image for CirComPara
Similarity Weighted Nonnegative Embedding (SWNE), a method for visualizing high dimensional datasets
ROSeq - A rank based approach to modeling gene expression with filtered and normalized read count matrix. Takes in the complete filtered and normalized read count matrix, the location of the two sub-populations and the number of cores to be used.
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