Uncovering cancer gene regulation by accurate regulatory network inference from uninformative data

Abstract

The interactions among the components of a living cell that constitute the gene regulatory network (GRN) can be inferred from perturbation-based gene expression data. Such networks are useful for providing mechanistic insights of a biological system. In order to explore the feasibility and quality of GRN inference at a large scale, we used the L1000 data where ~1000 genes have been perturbed and their expression levels have been quantified in 9 cancer cell lines. We found that these datasets have a very low signal-to-noise ratio (SNR) level causing them to be too uninformative to infer accurate GRNs. We developed a gene reduction pipeline in which we eliminate uninformative genes from the system using a selection criterion based on SNR, until reaching an informative subset. The results show that our pipeline can identify an informative subset in an overall uninformative dataset, allowing inference of accurate subset GRNs. The accurate GRNs were functionally characterized and potential novel cancer-related regulatory interactions were identified.

Repurposing Multiple-Molecule Drugs for COVID-19-Associated Acute Respiratory Distress Syndrome and Non-Viral Acute Respiratory Distress Syndrome via a Systems Biology Approach and a DNN-DTI Model Based on Five Drug Design Specifications

“…The candidate drugs of drug targets predicted by the DNN-DTI model were further filtered by the following five drug design specifications. For drug regulation ability, we downloaded Phase I L1000 level 5 datasets (GSE92742) from the Broad Institute Library of integrated Cellular Signatures (LINCS) [ 144 , 145 ]. This dataset includes the moderated Z-scores (MODZS) from differential gene analysis of 12 , 328 genes for 19 , 811 perturbagens (small molecule) treatments across 76 human cell lines corresponding with 45 , 956 expression signatures.…”

Identifying Drug Targets of Oral Squamous Cell Carcinoma through a Systems Biology Method and Genome-Wide Microarray Data for Drug Discovery by Deep Learning and Drug Design Specifications

“…To measure the regulatory ability of drug candidates, available regulatory capacity data were downloaded from the L1000 level5 dataset [ 57 , 58 ], which contains 978 genes treated with 19,811 small molecule compounds in 78 different cell lines. In the accommodation ability data, positive values indicate up-regulations and negative values indicate down-regulations.…”

COVID-19-related Versus Non-Viral Acute Respiratory Distress Syndrome: Comparison of Upper Airway Molecular Pathway and Drug Discovery Design based on Systems Biology and Deep Learning Methods

“…The Candidate drugs predicted by DNN-DTI model would be further filtered by the following drug design specifications. For drug regulation ability, we download Phase I L1000 level5 datasets (GSE92742) from Broad Institute Library of integrated Cellular Signatures (LINCS) [26][27]. This dataset includes moderated Z-scores (MODZS) from differential gene analysis of 12328 genes for 19811 perturbagens (small molecule) treatments across 76 human cell lines corresponding with 45956 expression signatures.…”