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jesse-islam's Projects

basicgameingadot icon basicgameingadot

makes a basic game in godot following tutorial: https://www.youtube.com/watch?v=Mc13Z2gboEk

basicgodotgame icon basicgodotgame

creates a simple game in godot enginer following this tutorial series https://www.youtube.com/watch?v=Mc13Z2gboEk

basicneuralnetworkpackage icon basicneuralnetworkpackage

Implementing a basic neural network from scratch, by translating a tutorial from Javier Ideami on Medium. https://towardsdatascience.com/coding-a-2-layer-neural-network-from-scratch-in-python-4dd022d19fd2

cbnn icon cbnn

R package for case-base neural networks:

cbnnmanuscript icon cbnnmanuscript

Code required for simulations, casestudies, and paper that generates CBNN manuscript

clustalsort icon clustalsort

Sorts the output of clustal given strings starting with xx- or x-

genpipesextension icon genpipesextension

I extended genpipes by creating a script to work with the output folder structure. We get a few transcript and gene expression counts which we can use for analysis. uses txImporter, featurecounts and stringtie's prepDE.py

heartbeastarpgtutorialgodot icon heartbeastarpgtutorialgodot

All the code needed to follow and develop the game by heartbeast following this tutorial series: https://www.youtube.com/watch?v=mAbG8Oi-SvQ

riskregression icon riskregression

R package for risk regression and prediction with censored data

rnaseq-qc-tools-genpipes icon rnaseq-qc-tools-genpipes

a set of functions to streamline the analysis of RNAseq using deseq2. We base the code here off of the output of genpipes

rnaseqqcgenpipes icon rnaseqqcgenpipes

set of tools to unify the output of genpipes, and extend its functionality to include quality control assessments

rpkg icon rpkg

Sahir's R package creation tutorial

survivalmlpusingcasebase-ai-genomics icon survivalmlpusingcasebase-ai-genomics

This repository was created for the WINTER 2020 AI-genomics course given by district 3. the purpose of this project is to create a novel approach to survival analysis using casebase sampling. Casebase sampling reformulates how survival analysis is done, making existing methods like logistic regression feasible in a survival setting.

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