Computational Biology and Computational Learning @ UCI

Login: uci-cbcl

Company: null

Location: Irvine, CA, USA

email: uci-cbcl@googlegroups.com

Blog: http://cbcl.ics.uci.edu

Members

  1. Daniel Newkirk
  2. Jake Biesinger
  3. Yifei Chen
  4. Yi Li
  5. Yuanfeng Wang
  6. null

Repositories

AREM
AREM -- Aligning Reads by Expectation-Maximization
ChestXRay
null
consensus-search
Simple consensus search script as used in our *genesis* Xenopus paper
CoreNLP
Stanford CoreNLP: A Java suite of core NLP tools.
DanQ
A hybrid convolutional and recurrent neural network for predicting the function of DNA sequences
DeepCADD
null
DeepCons
Understanding sequence conservation with deep learning
DeepEM-for-Weakly-Supervised-Detection
MICCAI18 DeepEM: Deep 3D ConvNets with EM for Weakly Supervised Pulmonary Nodule Detection
DeepLung
WACV18 paper "DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification"
D-GEX
Deep learning for gene expression inference
EXTREME
An online EM implementation of the MEME model for fast motif discovery in large ChIP-Seq and DNase-Seq Footprinting data
FactorNet
A deep learning package for predicting TF binding
GBMCI
The implementation of gradient boosting machine for concordance index learning.
genomix
Parallel genome assembly using Hyracks
HLA-bind
Amino acid embedding and Convolutional Neural Network for HLA Class I-peptide binding prediction
HTS-waterworks
A Ruffus pipeline for Analysis of High-Throughput Sequencing data
mdos
MDOS motif discovery using orthologous sequences (alignment independent)
MixClone
A mixture model for inferring tumor subclonal populations
polyAcode
null
PyLOH
Deconvolving tumor purity and ploidy by integrating copy number alterations and loss of heterozygosity
Rainfall
null
TEMT
Transcripts abundances estimation from heterogeneous tissue sample of RNA-Seq data (TEMT)
tree-hmm
Tree hidden Markov model for learning epigenetic states in multiple cell types

This work is supported by the National Institutes of Health's National Center for Advancing Translational Sciences, Grant Number U24TR002306. This work is solely the responsibility of the creators and does not necessarily represent the official views of the National Institutes of Health.