krishnanlab/talks

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Created about 6 years ago
Updated over 5 years ago

krishnanlab/talks

krishnanlab

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Slides and materials from talks, workshops, seminars, etc.

PecanPy

A fast, parallelized, memory efficient, and cache-optimized Python implementation of node2vec

txt2onto

Code for classifying unstructured text to tissue ontology terms using natural language processing and machine learning.

teaching

Courses & workshops led by members of the Krishnan Lab.

Jupyter Notebook

PyGenePlexus

A network based gene classification library to generate genome wide predictions about genes that are functionally similar to the input gene list.

obnb

A Python toolkit for setting up benchmarking dataset using biomedical networks

RNAseq_coexpression

In-depth analysis of normalization and transformation methods for building coexpression networks from RNA-seq data.

GenePlexus

Data, results, and code accompanying the manuscript: "Supervised learning is an accurate method for network-based gene classification"

PecanPy_benchmarks

Expresto

Data, results, and code accompanying the manuscript: "A Flexible, Interpretable, and Accurate Approach for Imputing the Expression of Unmeasured Genes" "

Jupyter Notebook

pydab

A _blazingly fast_ Python package for modifying and converting DAB files

node2vecplus_benchmarks

MCC-F1-Curve-and-Metrics

MCC-F1 curve: a performance evaluation technique for binary classification

Jupyter Notebook

geneplexus-app-v2

GenePlexus App - backend (cloud functions) and frontend

cone

COntext-specific Network Embedding

obnbench

Benchmarking repository for OBNB

ASD-gene-prediction-Krishnan2016

Weighted Learning on ASD data

txt2onto2.0

Public repo for txt2onto2.0

geneplexus_app

GenePlexusZoo-Mancusript

Repository Accompanying the manuscript J"oint representation of molecular networks from multiple species improves gene classification"

Jupyter Notebook

chronic-inflammation

Source code for "A network-based approach for isolating the chronic inflammation gene signatures underlying complex diseases towards finding new treatment opportunities"