shilpasy

Analysis-of-differential-gene-expression-with-DESeq-in-R-Notebook

"Analysis of differential gene expression with DESeq in R Notebook"

Autoencoder-Bioinformatics-examples

Collection of examples and applications demonstrating the use of autoencoders in bioinformatics

binder-framework

A sample repo on deploying to Binder

Building-and-Interpreting-Random-Forest-based-Cell-Penetrating-Peptide-Prediction-Model

Targeting intracellular pathways with peptide drugs is becoming increasingly desirable but often limited in application due to their poor cell permeability. Understanding cellular permeability of peptides remains a major challenge with very little structure-activity relationship known. Fortunately, there exist a class of peptides called Cell-Penetrating Peptides (CPPs), which have the ability to cross cell membranes and are also capable of delivering biologically active cargo into cells. Discovering patterns that make peptides cell-permeable have a variety of applications in drug delivery. In the current study, we build prediction models for CPPs exploring features covering a range of properties based on amino acid sequences, using Random forest classifiers which are often more interpretable than other ensemble machine learning algorithms. While obtaining prediction accuracies of ~96%, we also interpret our prediction models using TreeInterpreter, LIME and SHAP to decipher the contributions of important features and optimal feature space for CPP class. We propose that our work might offer an intuitive guide for incorporating features that impart cell-penetrability into the design of novel CPPs.

citeseq_autoencoder

Integrative analysis of single-cell multi-omics data using deep learning

CrimeCategoryPrediction_Capstone

The data was obtained from Torontopolice of Ontario, Canada. This dataset includes all Major Crime Indicators (MCI) occurrences by reported date and various other details related to location, day and time of the crime. Based on this feature, I attempt to predict which the category of crime

Data-manipulation-and-visualization-of-gene-expression-data-from-NCBI-GEO

Pixel_Quest_CIFAR-10_TensorFlow

Projects_partof_DataScienceFellowship_Python

single-cell-RNAseq_Data_analysis_Rnotebooks

In this tutorial, we will be looking at a dataset of PBMCs. PBMCs are like the army of immune cells that live in our blood and fight off any invaders like viruses or bacteria. We will look at standard preprocesing workflow of sc-RNAseq data, normalization, clustering,

shilpasy/Building-and-Interpreting-Random-Forest-based-Cell-Penetrating-Peptide-Prediction-Model

Targeting intracellular pathways with peptide drugs is becoming increasingly desirable but often limited in application due to their poor cell permeability. Understanding cellular permeability of peptides remains a major challenge with very little structure-activity relationship known. Fortunately, there exist a class of peptides called Cell-Penetrating Peptides (CPPs), which have the ability to cross cell membranes and are also capable of delivering biologically active cargo into cells. Discovering patterns that make peptides cell-permeable have a variety of applications in drug delivery. In the current study, we build prediction models for CPPs exploring features covering a range of properties based on amino acid sequences, using Random forest classifiers which are often more interpretable than other ensemble machine learning algorithms. While obtaining prediction accuracies of ~96%, we also interpret our prediction models using TreeInterpreter, LIME and SHAP to decipher the contributions of important features and optimal feature space for CPP class. We propose that our work might offer an intuitive guide for incorporating features that impart cell-penetrability into the design of novel CPPs.

shilpasy/Analysis-of-differential-gene-expression-with-DESeq-in-R-Notebook

"Analysis of differential gene expression with DESeq in R Notebook"

shilpasy/citeseq_autoencoder

Integrative analysis of single-cell multi-omics data using deep learning

shilpasy/CrimeCategoryPrediction_Capstone

The data was obtained from Torontopolice of Ontario, Canada. This dataset includes all Major Crime Indicators (MCI) occurrences by reported date and various other details related to location, day and time of the crime. Based on this feature, I attempt to predict which the category of crime

shilpasy/NLP_DiscoveryHub

shilpasy/Projects_partof_DataScienceFellowship_Python

shilpasy/Autoencoder-Bioinformatics-examples

Collection of examples and applications demonstrating the use of autoencoders in bioinformatics

shilpasy/binder-framework

A sample repo on deploying to Binder

shilpasy/BioEmbed-HuggingFace-Projects

shilpasy/Data-manipulation-and-visualization-of-gene-expression-data-from-NCBI-GEO

shilpasy/DataCampProjects

shilpasy/flask-framework

Basic template for using Flask on Render

shilpasy/MemSatSVM

Membrane helix prediction with SVMs

shilpasy/Pixel_Quest_CIFAR-10_TensorFlow

shilpasy/single-cell-RNAseq_Data_analysis_Rnotebooks

In this tutorial, we will be looking at a dataset of PBMCs. PBMCs are like the army of immune cells that live in our blood and fight off any invaders like viruses or bacteria. We will look at standard preprocesing workflow of sc-RNAseq data, normalization, clustering,

shilpasy/pepmlm

Target Sequence-Conditioned Generation of Peptide Binders via Masked Language Modeling

shilpasy/TE-LFS-pipeline

shilpasy/Timeseries_healthcare_projects

shilpasy/Unsupervised-Adventures