antonkratz
Anton Kratz, Ph.D. | Scientist | The Systems Biology Institute
The Systems Biology InstituteGotanda, Tokyo, Japan
Pinned Repositories
alignOntology
Code for aligning two ontologies. Originally used in "A gene ontology inferred from molecular networks" doi:10.1038/nbt.2463
CliXO-1.0
CliXO algorithm re-implemented
dbl-visualization
DCell
DCell browser and gene deletion simulator
ddot
Toolkit for constructing, analyzing, and visualizing data-driven ontologies
deiva_github
DEIVA prototype, implemented in Shiny
dotfiles
various configuration files including .gitconfig, .vimrc and .screenrc
DrugCell
A visible neural network model for drug response prediction
genome-research-edgeR-DESeq2
Generate edgeR- and DESeq2-flavored input files for DEIVA
antonkratz's Repositories
antonkratz/deiva_github
DEIVA prototype, implemented in Shiny
antonkratz/genome-research-edgeR-DESeq2
Generate edgeR- and DESeq2-flavored input files for DEIVA
antonkratz/alignOntology
Code for aligning two ontologies. Originally used in "A gene ontology inferred from molecular networks" doi:10.1038/nbt.2463
antonkratz/CliXO-1.0
CliXO algorithm re-implemented
antonkratz/dbl-visualization
antonkratz/DCell
DCell browser and gene deletion simulator
antonkratz/ddot
Toolkit for constructing, analyzing, and visualizing data-driven ontologies
antonkratz/dotfiles
various configuration files including .gitconfig, .vimrc and .screenrc
antonkratz/DrugCell
A visible neural network model for drug response prediction
antonkratz/HiSig
Enrichment analysis for nested and overlapping gene sets
antonkratz/hiview
HiView of DNA Damage Response Assemblies Map (DDRAM)
antonkratz/LDL
Learning Deep Learning
antonkratz/microbiome-metabolome-curated-data
AK fork of Muller, E., Algavi, Y.M. & Borenstein, E. The gut microbiome-metabolome dataset collection: a curated resource for integrative meta-analysis. npj Biofilms Microbiomes 8, 79 (2022). https://doi.org/10.1038/s41522-022-00345-5
antonkratz/nest_drugcell_sdp
antonkratz/nest_vnn
NeST-VNN repo
antonkratz/shap
A game theoretic approach to explain the output of any machine learning model.