Homework 2 for MCS 5603 Intro to Bioinformatics. Written in Python.
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Updated
Oct 16, 2014 - Python
Homework 2 for MCS 5603 Intro to Bioinformatics. Written in Python.
Deep learning neural network to analyse variant-call data, with a Bayesian network to rank functionally important genes
Machine learning methods for the prediction of pathology in protein mutations, as in the PMut predictor.
Enhanced protein mutational sampling using time-lagged variational autoencoders
A genetic algorithm that attempts to get a swarm of dots from the start point to the end goal in the least number of steps
Creation of a simple genetic algorithm in order to find the phrase "methinks it is like a weasel", with and without the use of crossover
PyTorch Implementation of DeepSequence
Assignments from Professor Roman Yampolskiy's Artificial Intelligence class.
Utility to generate codon mutations of a given coding DNA sequence
A re-implementation in Python3 of the open source project MutationFinder for gene mutation detection in free text.
Short scripts to demonstrate data available from MolecularMatch API (api key needed). Data includes clinical trials, drugs, publications, molecular information, bioinformatics, report generation and more.
Predicting the effect of mutations on protein stability and protein-protein interaction affinity.
Automated analysis tool for mutations in promoters, transcription factor binding sites, coding regions and protein domains in the context of gene regulatory networks.
SARS-Cov-2 Recombinant Finder for fasta sequences
MSc Bioinformatics with Systems Biology Dissertation
Chromosomal mutations associated with nitrofurantoin resistance in Escherichia coli
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