Data and Software
MALANI (Machine Learning-Assisted Network Inference) is a hybrid computational platform that harnesses the power of both machine learning and network biology methodologies to provide new insights and improve understanding of complex biological systems.
Reference: Sci Rep. 2017 Aug 01.
MALANI source code can be downloaded at https://malani.hulilab.org.
P-Map (Phenotype mapping) is a network-based phenotype mapping approach to identify genes and regularory networks that modulate drug response phenotypes.
Reference: Sci Rep. 2016 Nov 14.
P-Map source code can be downloaded at https://github.com/HuLiLab/P-Map.
NetDecoder is a network biology computational platform to dissect context-specific biological networks and gene activities. NetDecoder provides freely available source code and web portal resource for researchers to explore genome-wide context-dependent information flow profiles and key genes using pairwise phenotypic comparative analyses. NetDecoder also allows researchers to prioritize drug targets for genes that affect pathological contexts.
Reference: Nucleic Acids Res. 2016 Mar 14.
NetDecoder web interface and other materials are available at the website portal.
NetDecoder source code can be downloaded at https://github.com/HuLiLab/NetDecoder.
For support of NetDecoder, please subscribe to our web forum.
CellNet is a network biology-based computational platform that more accurately assesses the fidelity of cellular engineering than existing methodologies and generates hypotheses for improving cell derivations.
Reference: Cell. 2014 Aug 14;158(4):903-15.;
Cell. 2014 Aug 14;158(4):889-902.
CellNet web interface and other materials are available at the website portal.
StemSite is a database of regulators network of the developmental origin of mouse hematopoietic stem cells.
Reference: Cell Stem Cell. 2012 Nov 2; 11(5):701-14.
StemSite Database is available here.
MNI (Mode-of-action by Network Inference) is a reverse engineering network biology algorithm to identify the gene targets and key mediators of a biomedical phenotype based on transcriptome data.
Reference: Nat Biotechnol. 2005 Mar;23(3):377-83.
MNI source code can be downloaded here.
CLR (Context Likelihood of Relatedness) is an network biology algorithm to reverse-engineer and infer regulatory interactions between master regulators and their targets using a compendium of transcriptome profiles.
Reference: PLoS Biol 5(1): e8.
CLR source code can be downloaded here.
GEDI (Gene Expression Dynamics Inspector) is a computational program that opens a new perspective to the analysis of transcriptome data. By treating each high-dimensional sample, such as one transcriptome experiment, as an object, it accentuates and visualize the genome-wide response of a tissue or a patient and treats it as an integrated biological entity. GEDI honors the new spirit of a system-level approach in biology and unites a novel holistic perspective with the traditional gene-centered approach in molecular biology.
Reference: Bioinformatics. 2003 Nov 22;19(17):2321-2.
GEDI source code can be downloaded here.
For general questions on GEDI source code, please contact Hu Li.