Data and Software
PERsonalized MUtation evaluaTOR (PERMUTOR) is a novel computational pipeline which collects potent disease gene cooperative pathways to envision individualized disease etiology and therapies. Our algorithm constructs individualized disease networks and modules de novo which enable us to elucidate the importance of mutated genes in specific patients and to understand the synthetic penetrance of these genes across patients.
Individualized module disruption enables us to devise customized singular and combinatorial target therapies which were highly varied across patients demonstrating the need for precision therapeutics pipelines. As the first analysis of de novo individualized disease networks and modules, we illustrate the power of individualized disease modules for precision medicine by providing deep novel insights on the activity of diseased genes in individuals.
Reference: Genome Res. 2021.
PERMUTOR source code are availalbe for public accessing Li lab GitHub.
Artificial neural network (ANN) was initially created to model how human brain works. Over past few decades, ANN has evolved into numerous sophisticated algorithms with proven outstanding performance in various recognition tasks.
Artificial Neural Network Encoder (ANNE) is a novel weight engineering deep machine learning method that harness the power of autoencoder and demonstrated that it is possible to decode meaningful information encoded in ANN models trained for specific tasks.
We applied ANNE on breast cancer gene expression data with known clinical properties as case studies. Our work illustrates the trained autoencoder models are indeed information encoders that meaningful gene-gene associations with numerous supported evidences can be retrieved.
ANNE opens a new avenue in machine intelligence that ANN models will no longer perceived as tools to perform recognition tasks but as powerful tools to extract meaningful information embedded within the sea of high dimensional data.
ANNE source code are availalbe for public accessing Li lab GitHub.
Machine Learning, Feature Selection, Applications
Machine learning methods and feature selection approaches for predicting specific Pharmacodynamic, Pharmacokinetic or Toxicological properties of pharmaceutical agents are useful for facilitating new drug discovery and development.
Reference: J Pharm Sci. 2007.;
Drug Development Research. 2006.;
J Mol Graph Model. 2006.;
J Chem Inf Model. 2005.
EDDI (Expression Dosage Dependent Inferelator) is a machine learning and systems biology approach to characterize dosage-based gene dependencies.
Reference: J Bioinform Syst Biol. 2021.
EDDI source code are availalbe for public accessing Li lab GitHub.
ASTAR-Seq is an automated method with high sensitivity, assay for single-cell transcriptome and accessibility regions for simultaneous measurement of whole-cell transcriptome and chromatin accessibility within the same single cell.
Reference: Genome Research. 2020 July;
Science Advances 2020 September.
RSI (Regulostat Inferelator ) is a novel computational algorithm to decipher intrinsic molecular devices called regulostats that predetermine cellular phenotypic responses.
Reference: Nucleic Acids Res. 2019 May
RSI web interface and source code are availalbe at the RSI website portal Li lab GitHub.
DPYD-Varifier (DPYD Gene-specific variant classifier) is a highly accurate in silico classifier to predict the functional impact of DPYD variants on DPD activity. DPYD-Varifier have great potential to systems pharmacology and individualize medicine and improve the clinical decision-making process.
Reference: Clin Pharmacol Ther. 2018 Jan 12.
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 Li lab GitHub.
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 Li lab GitHub..
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 Li lab GitHub.
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.
Highly efficient reprogramming to pluripotency and directed differentiation of human cells with synthetic modified mRNA.
Reference: Cell Stem Cell. 2010.
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.
Reference: Sci Transl Med. 2014 Jan 1;6(217):217ra2.
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), developed by Dr. Ingber's Lab, 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 Dr. Donald Ingber or Hu Li.
Pathway Modelling and Simulation
One of the most commonly used approaches to model biological systems is that of ODEs. In general, a differential equation can be used to describe the chemical reaction rate that depends on the change of participating species over time. The temporal dynamic behavior of molecular species in the biological signaling pathway network can be captured by a set of coupled ODEs.
Reference: Bioinformatics. 2009.;
FEBS Lett. 2008.