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.
"DNA methylation and gene expression integration in cardiovascular disease"
TRANSCRIPTOMICS & METABOLOMICS: Gene expression modules vs. metabolites modules correlation using WGCNA R package.
MassGEM_Plot can visualize the expression of a large number of genes at the same time
The provided set of bash scripts constitutes a comprehensive RNA-seq analysis workflow, facilitating the processing and analysis of high-throughput sequencing data.
To perform RNA-Seq data analysis and calculate length-scaled transcripts per million (TPM) values using the Salmon tool and the GenomicFeatures package in R.
R script for predicting the ubiquitin by gene expression matrix
Data science project work with genomics/biological data
Co-expression networks for gene correlation analysis.
An R package to impute miRNA activity using protein-coding gene expression
End-to-end ensemble model that integrates several neural networks trained on distinct features with attention mechanism.
An optimal experimental design framework for accelerating knowledge discovery using gene expression data
Code to reproduce analyses in Iron Responsive Element (IRE)-mediated responses to iron dyshomeostasis in Alzheimer’s disease (Hin et al.)
Code for the preprint "A small fraction of progenitors differentiate into mature adipocytes due to constraints on the cell structure change"
Acute Myeloid Leukemia Risk Group Prediction from Gene Expression Data with Feed-Forward Neural Networks
Differential Gene Expression (DGE) Analysis in Curated Microarray Data of Breast Cancer Subtypes
This project provides the classification of DNA sequences for Breast cancer prediction which into promoter regions associated. Using machine learning and deep learning techniques, I analyze and try to predict sequence data for negative and positive answers in cancer prediction.
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