Dynamically characterizing individual clinical change by the steady state of disease-associated pathway

Abstract

BackgroundAlong with the development of precision medicine, individual heterogeneity is attracting more and more attentions in clinical research and application. Although the biomolecular reaction seems to be some various when different individuals suffer a same disease (e.g. virus infection), the final pathogen outcomes of individuals always can be mainly described by two categories in clinics, i.e. symptomatic and asymptomatic. Thus, it is still a great challenge to characterize the individual specific intrinsic regulatory convergence during dynamic gene regulation and expression. Except for individual heterogeneity, the sampling time also increase the expression diversity, so that, the capture of similar steady biological state is a key to characterize individual dynamic biological processes.ResultsAssuming the similar biological functions (e.g. pathways) should be suitable to detect consistent functions rather than chaotic genes, we design and implement a new computational framework (ABP: Attractor analysis of Boolean network of Pathway). ABP aims to identify the dynamic phenotype associated pathways in a state-transition manner, using the network attractor to model and quantify the steady pathway states characterizing the final steady biological sate of individuals (e.g. normal or disease). By analyzing multiple temporal gene expression datasets of virus infections, ABP has shown its effectiveness on identifying key pathways associated with phenotype change; inferring the consensus functional cascade among key pathways; and grouping pathway activity states corresponding to disease states.ConclusionsCollectively, ABP can detect key pathways and infer their consensus functional cascade during dynamical process (e.g. virus infection), and can also categorize individuals with disease state well, which is helpful for disease classification and prediction.

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

“…In the study, the authors integrate 10 different types of biological networks such as drug-disease, drug-side effects, drug-target, and seven drug-drug networks. The results concluded that deepDR predicted approved drugs such as risperidone and aripiprazole for the treatment of Alzheimer's disease (AD), whereas methylphenidate and pergolide for treatment of Parkinson's disease (PD) [ 291 ]. Likewise, Chen et al 2020 constructed an AI-based novel algorithm called as iDrug ( https://github.com/Case-esaC/iDrug ) for the integration of drug repositioning and drug-target prediction through cross-network embedding.…”

“…Similarly, X. Zeng et al . 2019 developed a DL-based drug repurposing tool, called deepDR ( https://github.com/ChengF-Lab/deepDR ), which is used to find new repurposed drugs for AD and PD [ 291 ]. Furthermore, [ 474 ] proposed telmisartan as potential repurposed drug for AD by using a genetic network-driven classification model.…”

Longitudinal pathway analysis using structural information with case studies in early type 1 diabetes

“…There are few methods that allow the pathway analysis of longitudinal data. They include Attractor analysis of Boolean network of Pathway (ABP) (Sun et al, 2019), Longitudinal Linear Combination Test (LLCT) (Khodayari Moez et al, 2019), Time-Course Gene Set Analysis (TcGSA) (Hejblum et al, 2015), and Gene Set Enrichment Analysis (GSEA) for time series (Subramanian et al 2005). ABP allows sophisticated post-processing of sample-specific pathway scores, but it has not been implemented into an R package, which hinders the ease of its usage.…”

Multiple-Tissue and Multilevel Analysis on Differentially Expressed Genes and Differentially Correlated Gene Pairs for HFpEF

“…The methods used in systems biology and high-throughput techniques have successfully reconstructed various disease-related networks for pathological conditions, such as cancer, type 2 diabetes mellitus (T2DM), and influenza. These methods enable the integration and interpretation of functional genomic datasets and the identification of novel biomarkers or modules, which can aid in the elucidation of the molecular mechanisms of diseases ( Tang et al, 2018 ; Sun et al, 2019b ; Wu Y. et al, 2020 ). In particular, multilevel analysis based on tissue-related networks can systemically reveal the pathophysiology of the disease through the integration of several target tissues and the identification of key pathways or biomarkers ( Knaack et al, 2014 ; Sun et al, 2019a ).…”