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Ming Hao's Projects

imbalanced-learn icon imbalanced-learn

Python module to perform under sampling and over sampling with various techniques.

imgsvd icon imgsvd

Shiny Web Application for Image Compression via SVD

imix icon imix

Integrative genomics using a multivariate mixture model framework

iml icon iml

iml: interpretable machine learning R package

immcp icon immcp

Candidate Prescriptions Discovery Based on Pathway Fingerprints

implicit icon implicit

Fast Python Collaborative Filtering for Implicit Datasets

implicit-cf icon implicit-cf

A Python implementation of Hu et al. (2008)'s "Collaborative Filtering for Implicit Feedback Datasets"

implicit-mf icon implicit-mf

Implicit matrix factorization as outlined in http://yifanhu.net/PUB/cf.pdf.

implicit-sgd icon implicit-sgd

Using stochastic gradient descent (SGD) with explicit and implicit updates to fit large-scale statistical models.

implyr icon implyr

SQL backend to dplyr for Impala

incidence2 icon incidence2

Compute and visualise incidence (reworking of the original incidence package)

indigo icon indigo

Universal cheminformatics libraries, utilities and database search tools

influence_boosting icon influence_boosting

Supporting code for the paper "Finding Influential Training Samples for Gradient Boosted Decision Trees"

influencer icon influencer

Software tools to quantify structural importance of nodes in a network

influential icon influential

Identification and Classification of the Most Influential Nodes

inprna icon inprna

A tool for insight into protein-nucleic acids interaction information

inrg_cmap icon inrg_cmap

Iterative Network Guided Connectivity Mapping http://www.ebi.ac.uk/~iorio/PLoS_ONE_Submission

installing-r-with-anaconda icon installing-r-with-anaconda

Biological researchers, bioinformatics professionals, and biostatisticians should install R via Anaconda to gain access to the Bioconda package channel to enable easy dependency management and reproduce-ability.

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