Transcription factors (TFs) and microRNAs (miRNAs) form a gene regulatory network (GRN) at the transcriptional and post-transcriptional level in living cells. However, this network has not been well characterized, especially in regards to the mutual regulations between TFs and miRNAs in cancers. In this study, we collected those regulations inferred by ChIP-Seq or CLIP-Seq to construct the GRN ...

The current study aimed to identify key genes in glaucoma based on a benchmarked dataset and gene regulatory network (GRN). Local and global noise was added to the gene expression dataset to produce a benchmarked dataset. Differentially-expressed genes (DEGs) between patients with glaucoma and normal controls were identified utilizing the Linear Models for Microarray Data (Limma) package based ...

Granger causality (GC), based on a vector autoregressive model, is one of the most popular methods in uncovering the temporal dependencies among time series. The original Granger model is able to detect only linear causal dependencies and many approaches were recently developed to extend it to the non-linear modeling. The method Copula-Granger from Bahadori and Liu in 2012 introduces non-linear...

Abstract Transcription factors (TFs) and their target gene regulatory networks (GRNs) control immune cell fate decisions during differentiation responses to environmental cues signals. To better understand how GRN outcomes are dysregulated in disease settings, we developed MANGO (Multiomics Aided Neural Graph Ontology), a computational method that integrates single expression chromatin accessib...

A central challenge of developmental and evolutionary biology is to understand the transformation of genetic information into morphology. Elucidating the connections between genes and anatomy will require model morphogenetic processes that are amenable to detailed analysis of cell/tissue behaviors and to systems-level approaches to gene regulation. The formation of the calcified endoskeleton of...

A central challenge of developmental and evolutionary biology is to understand the transformation of genetic information into morphology. Elucidating the connections between genes and anatomy will require model morphogenetic processes that are amenable to detailed analysis of cell/tissue behaviors and to systems-level approaches to gene regulation. The formation of the calcified endoskeleton of...

Elucidating gene regulatory network (GRN) from large scale experimental data remains a central challenge in systems biology. Recently, numerous techniques, particularly consensus driven approaches combining different algorithms, have become a potentially promising strategy to infer accurate GRNs. Here, we develop a novel consensus inference algorithm, TopkNet that can integrate multiple algorit...

Model Checking is one of the formal verification methods which can be used to infer parameters of a Gene Regulatory Network (GRN) using discrete formalism of René Thomas. However, the sequential approach for identification of these logical parameters is computationally intensive and takes lot of processing time, depending on number of genes and range of their expression levels in a network. In ...

RATIONALE The cardiac gene regulatory network (GRN) is controlled by transcription factors and signaling inputs, but network logic in development and it unraveling in disease is poorly understood. In development, the membrane-tethered signaling ligand Neuregulin (Nrg)1, expressed in endocardium, is essential for ventricular morphogenesis. In adults, Nrg1 protects against heart failure and can i...

Identifying cancer type-specific genes that define cell states is important to develop effective therapies for patients and methods detection, early diagnosis, prevention. While molecular mechanisms drive malignancy have been identified various cancers, the identification of cell-type defining transcription factors (TFs) distinguish normal cells from has not fully elucidated. Here, we utilized ...