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.
Downloading and processing pipelines used for single cell gene expression assays of human tumor biopsies
Master thesis on "Microarray data analysis in prediction of breast cancer metastasis" - synced from Overleaf
schematic of gene expression during adult and fetal erythropoiesis
Ensemble of convolutional neural networks for transcriptional classification
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.
Gene Expression analysis with BIG-DE
Generate expression matrix from microarray data derived from BXD quadriceps
Selection of genes to be cultured and studied on-board for the AcubeSAT nanosatellite project. Microarray data analysis in R. Meta-analysis in R. Literature review and more
Brain Tissue Gene Expression
Predicting gene expression using promoter sequence.
Functions that implement the algorithms described in the preprint "Normalization and gene selection for single-cell RNA-seq UMI data using sampling-adjusted sums of squares of Pearson residuals with a Poisson model" and Jupyter notebooks that reproduce the results in the preprint and its Supplement 1
This is a group project
NanoString classifier based on NGS training set
Code for my master thesis: "Microarray data analysis in prediction of breast cancer metastasis"
Analysis of DNA methylation and gene expression for thioredoxin system genes in Wilson disease
Get a gene expression table from transcript compatibility counts
Stage classification of liver cancer patients from their gene expression profile by using Machine Learning algorithms. This was a Kaggle challenge. There is the competition link https://www.kaggle.com/c/clsp/leaderboard
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