mehranpiran

NLP-models

Different sequence and seq-to-seq models are built in this repository. One aim is to fit these models to biomedical OMICs data for the purpose of nucleotide / amino acid / token prediction, given previous sequences. Another aim is to infer gene regulatory network and cell communication by considering each single cell or gene as a token.

Graph-neural-network-models

A GNN model was trained on drug combinations to classify new combinations as HIV inhibitors. Another model was trained on the MNIST dataset of handwritten digits.

Single-cell-Spatial-transcriptomics-analysis

Coherency-between-signaling-gene-pairs-and-transcriptomic-data-

Dynamic-modeling-for-cell-communication

Control engineering of different steady states for epithelial to mesenchymal transition gene regulatory network using logical modelling with GINsim and EpiLog

Whole-Exome-Sequencing-Germline-Variant-Calling

ChIP-seq-Data-Analysis

Meta-Analysis

Pathway-Mining

mehranpiran/NLP-models

Different sequence and seq-to-seq models are built in this repository. One aim is to fit these models to biomedical OMICs data for the purpose of nucleotide / amino acid / token prediction, given previous sequences. Another aim is to infer gene regulatory network and cell communication by considering each single cell or gene as a token.

mehranpiran/Graph-neural-network-models

A GNN model was trained on drug combinations to classify new combinations as HIV inhibitors. Another model was trained on the MNIST dataset of handwritten digits.

mehranpiran/Dynamic-modeling-for-cell-communication

Control engineering of different steady states for epithelial to mesenchymal transition gene regulatory network using logical modelling with GINsim and EpiLog

mehranpiran/Single-cell-Spatial-transcriptomics-analysis

mehranpiran/Whole-Exome-Sequencing-Germline-Variant-Calling

mehranpiran/ChIP-seq-Data-Analysis

mehranpiran/Coherency-between-signaling-gene-pairs-and-transcriptomic-data-

mehranpiran/Pathway-Mining

mehranpiran/Meta-Analysis