dyxstat

awesome-semi-supervised-learning

:scroll: An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.

bash-tutorial

Bash 教程

Bayesian-Active-Learning-Pytorch

Implementation of Bayesian NNs in Pytorch (https://arxiv.org/pdf/1703.02910.pdf) (With some help from https://github.com/Riashat/Deep-Bayesian-Active-Learning/))

HiCBin

HiCBin: binning metagenomic contigs and recovering metagenome-assembled genomes using Hi-C contact maps

HiCzin

HiCzin: Normalizing metagenomic Hi-C data and detecting spurious contacts using zero-inflated negative binomial regression

HiFine

HiFine: integrating Hi-c-based and shotgun-based methods to reFine binning of metagenomic contigs

ImputeCC

ImputeCC enhances integrative Hi-C-based metagenomic binning through constrained random-walk-based imputation

MetaCC

MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data

Unlabeled_HiCzin

Unlabeled version of HiCzin exployed in the published paper

ViralCC

ViralCC: leveraging metagenomic proximity-ligation to retrieve complete viral genomes

dyxstat/HiCBin

HiCBin: binning metagenomic contigs and recovering metagenome-assembled genomes using Hi-C contact maps

dyxstat/ViralCC

ViralCC: leveraging metagenomic proximity-ligation to retrieve complete viral genomes

dyxstat/MetaCC

MetaCC allows scalable and integrative analyses of both long-read and short-read metagenomic Hi-C data

dyxstat/HiCzin

HiCzin: Normalizing metagenomic Hi-C data and detecting spurious contacts using zero-inflated negative binomial regression

dyxstat/HiFine

HiFine: integrating Hi-c-based and shotgun-based methods to reFine binning of metagenomic contigs

dyxstat/ImputeCC

ImputeCC enhances integrative Hi-C-based metagenomic binning through constrained random-walk-based imputation

dyxstat/Unlabeled_HiCzin

Unlabeled version of HiCzin exployed in the published paper

dyxstat/awesome-semi-supervised-learning

:scroll: An up-to-date & curated list of awesome semi-supervised learning papers, methods & resources.

dyxstat/bash-tutorial

Bash 教程

dyxstat/Bayesian-Active-Learning-Pytorch

Implementation of Bayesian NNs in Pytorch (https://arxiv.org/pdf/1703.02910.pdf) (With some help from https://github.com/Riashat/Deep-Bayesian-Active-Learning/))

dyxstat/bin3C

Extract metagenome-assembled genomes (MAGs) from metagenomic data using Hi-C.

dyxstat/CheckM

Assess the quality of microbial genomes recovered from isolates, single cells, and metagenomes

dyxstat/CheckM2

Assessing the quality of metagenome-derived genome bins using machine learning

dyxstat/database

dyxstat/DeepDecon

DeepDecon: A Deep-learning Method for Estimating Cell Fractions in Bulk RNA-seq Data with Applications to AM

dyxstat/dyxstat.github.io

Github Pages template for academic personal websites, forked from mmistakes/minimal-mistakes

dyxstat/glmmTMB

glmmTMB

dyxstat/hellinger-distance-criterion

Random Forest model using Hellinger Distance as split criterion

dyxstat/MAGpy

Snakemake pipeline for downstream analysis of metagenome-assembled genomes (MAGs) (pronounced mag-pie)

dyxstat/metaTOR

Metagenomic binning based on Hi-C data

dyxstat/metaWRAP

MetaWRAP - a flexible pipeline for genome-resolved metagenomic data analysis

dyxstat/minimap2

A versatile pairwise aligner for genomic and spliced nucleotide sequences

dyxstat/pharokka

fast phage annotation program

dyxstat/prokka

:zap: :aquarius: Rapid prokaryotic genome annotation

dyxstat/pytorch-vae

A Variational Autoencoder (VAE) implemented in PyTorch

dyxstat/Reproduce_MetaCC

dyxstat/Reproduce_ViralCC

Instruction of reproducing results in ViralCC paper

dyxstat/seqtk

Toolkit for processing sequences in FASTA/Q formats

dyxstat/vamb

Variational autoencoder for metagenomic binning

dyxstat/vRhyme

Binning Virus Genomes from Metagenomes