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.
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.
Active modules for multilayer weighted gene co-expression networks: a continuous optimization approach
A gene expression scatter plot analysis tool that also checks category-specific correlations.
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