PWN: enhanced random walk on a warped network for disease target prioritization.

BACKGROUND: Extracting meaningful information from unbiased high-throughput data has been a challenge in diverse areas. Specifically, in the early stages of drug discovery, a considerable amount of data was generated to understand disease biology when identifying disease targets. Several random walk-based approaches have been applied to solve this problem, but they still have limitations. Therefore, we suggest a new method that enhances the effectiveness of high-throughput data analysis with random walks. RESULTS: We developed a new random walk-based algorithm named prioritization with a warped network (PWN), which employs a warped network to achieve enhanced performance. Network warping is based on both internal and external features: graph curvature and prior knowledge. CONCLUSIONS: We showed that these compositive features synergistically increased the resulting performance when applied to random walk algorithms, which led to PWN consistently achieving the best performance among several other known methods. Furthermore, we performed subsequent experiments to analyze the characteristics of PWN.

Heter-LP: A heterogeneous label propagation algorithm and its application in drug repositioning.

Drug repositioning offers an effective solution to drug discovery, saving both time and resources by finding new indications for existing drugs. Typically, a drug takes effect via its protein targets in the cell. As a result, it is necessary for drug development studies to conduct an investigation into the interrelationships of drugs, protein targets, and diseases. Although previous studies have made a strong case for the effectiveness of integrative network-based methods for predicting these interrelationships, little progress has been achieved in this regard within drug repositioning research. Moreover, the interactions of new drugs and targets (lacking any known targets and drugs, respectively) cannot be accurately predicted by most established methods. In this paper, we propose a novel semi-supervised heterogeneous label propagation algorithm named Heter-LP, which applies both local and global network features for data integration. To predict drug-target, disease-target, and drug-disease associations, we use information about drugs, diseases, and targets as collected from multiple sources at different levels. Our algorithm integrates these various types of data into a heterogeneous network and implements a label propagation algorithm to find new interactions. Statistical analyses of 10-fold cross-validation results and experimental analyses support the effectiveness of the proposed algorithm.

Computational biology approaches for drug repurposing.

The drug discovery and development (DDD) process greatly relies on the data available in various forms to generate hypotheses for novel drug design. The complex and heterogeneous nature of biological data makes it difficult to utilize or gather meaningful information as such. Computational biology techniques have provided us with opportunities to better understand biological systems through refining and organizing large amounts of data into actionable and systematic purviews. The drug repurposing approach has been utilized to overcome the expansive time periods and costs associated with traditional drug development. It deals with discovering new uses of already approved drugs that have an established safety and efficacy profile, thereby, requiring them to go through fewer development phases. Thus, drug repurposing through computational biology provides a systematic approach to drug development and overcomes the constraints of traditional processes. The current chapter covers the basics, approaches and tools of computational biology that can be employed to effectively develop repurposing profile of already approved drug molecules.

Text Mining Protocol to Retrieve Significant Drug-Gene Interactions from PubMed Abstracts.

Genes and proteins form the basis of all cellular processes and ensure a smooth functioning of the human system. The diseases caused in humans can be either genetic in nature or may be caused due to external factors. Genetic diseases are mainly the result of any anomaly in gene/protein structure or function. This disruption interferes with the normal expression of cellular components. Against external factors, even though the immunogenicity of every individual protects them to a certain extent from infections, they are still susceptible to other disease-causing agents. Understanding the biological pathway/entities that could be targeted by specific drugs is an essential component of drug discovery. The traditional drug target discovery process is time-consuming and practically not feasible. A computational approach could provide speed and efficiency to the method. With the presence of vast biomedical literature, text mining also seems to be an obvious choice which could efficiently aid with other computational methods in identifying drug-gene targets. These could aid in initial stages of reviewing the disease components or can even aid parallel in extracting drug-disease-gene/protein relationships from literature. The present chapter aims at finding drug-gene interactions and how the information could be explored for drug interaction.

An Integrative Network Approach to Identify Common Genes for the Therapeutics in Tuberculosis and Its Overlapping Non-Communicable Diseases.

Tuberculosis (TB) is the leading cause of death from a single infectious agent. The estimated total global TB deaths in 2019 were 1.4 million. The decline in TB incidence rate is very slow, while the burden of noncommunicable diseases (NCDs) is exponentially increasing in low- and middle-income countries, where the prevention and treatment of TB disease remains a great burden, and there is enough empirical evidence (scientific evidence) to justify a greater research emphasis on the syndemic interaction between TB and NCDs. The current study was proposed to build a disease-gene network based on overlapping TB with NCDs (overlapping means genes involved in TB and other/s NCDs), such as Parkinson's disease, cardiovascular disease, diabetes mellitus, rheumatoid arthritis, and lung cancer. We compared the TB-associated genes with genes of its overlapping NCDs to determine the gene-disease relationship. Next, we constructed the gene interaction network of disease-genes by integrating curated and experimentally validated interactions in humans and find the 13 highly clustered modules in the network, which contains a total of 86 hub genes that are commonly associated with TB and its overlapping NCDs, which are largely involved in the Inflammatory response, cellular response to cytokine stimulus, response to cytokine, cytokine-mediated signaling pathway, defense response, response to stress and immune system process. Moreover, the identified hub genes and their respective drugs were exploited to build a bipartite network that assists in deciphering the drug-target interaction, highlighting the influential roles of these drugs on apparently unrelated targets and pathways. Targeting these hub proteins by using drugs combination or drug repurposing approaches will improve the clinical conditions in comorbidity, enhance the potency of a few drugs, and give a synergistic effect with better outcomes. Thus, understanding the Mycobacterium tuberculosis (Mtb) infection and associated NCDs is a high priority to contain its short and long-term effects on human health. Our network-based analysis opens a new horizon for more personalized treatment, drug-repurposing opportunities, investigates new targets, multidrug treatment, and can uncover several side effects of unrelated drugs for TB and its overlapping NCDs.

Network pharmacology analysis of the mechanism of our hospital's experiential prescription in the treatment of Guillain Barré syndrome.

BACKGROUND: This study aimed to explore the active ingredients and potential mechanism of our hospital's Guillain-Barré syndrome (GBS) experiential prescription in the treatment of GBS based on network pharmacology. METHODS: The traditional Chinese medicine system pharmacology (TCMSP) database was used to screen the active ingredients of the eight traditional Chinese medicines (TCMs) of the GBS-experiential prescription, and the Online Mendelian Inheritance in Man (OMIM), GeneCards, and MalaCards databases were used to obtain GBS-related gene targets. The common targets of the experiential prescriptions and GBS-related gene targets were acquired and imported into the STRING database to obtain the protein interaction relationship. Gene oncology (GO) function and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to predict the major mechanism of this prescription. RESULTS: The formula contained at least 154 potential active ingredients and a total of 4,270 unique targets, among which a total of 158 GBS-related disease targets and 70 common targets were found. The key targets included EGFR (Epidermal Growth Factor Receptor), TNF (Tumor Necrosis Factor), ITGAL (Integrin Subunit Alpha L), and CEBPA (CCAAT/Enhancer-Binding Protein Alpha), CPT2 (Carnitine Palmitoyltransferase 2), CRP (C-reactive protein), ICAM1 (Intercellular Adhesion Molecule 1), IL6 (interleukin 6), and PECAM1 (Platelet and Endothelial Cell Adhesion Molecule 1), CREBBP (CREB Binding Protein), etc. The GO enrichment analysis results revealed 116 terms, and the KEGG signaling pathway enrichment analysis results yielded 61 pathways, including influenza A, hepatitis B, malaria, etc. CONCLUSIONS: The development of GBS and the mechanism underlying the effects of the GBS-experiential prescription have common and complex targets, which are worthy of in-depth exploration.

Role of RhoA-ROCK signaling in Parkinson's disease.

Parkinson's disease (PD) is a complex and widespread neurodegenerative disease characterized by depletion of midbrain dopaminergic (DA) neurons. Key issues are the development of therapies that can stop or reverse the disease progression, identification of dependable biomarkers, and better understanding of the pathophysiological mechanisms of PD. RhoA-ROCK signals appear to have an important role in PD symptoms, making it a possible approach for PD treatment strategies. Activation of RhoA-ROCK (Rho-associated coiled-coil containing protein kinase) appears to stimulate various PD risk factors including aggregation of alpha-synuclein (αSyn), dysregulation of autophagy, and activation of apoptosis. This manuscript reviews current updates about the biology and function of the RhoA-ROCK pathway and discusses the possible role of this signaling pathway in causing the pathogenesis of PD. We conclude that inhibition of the RhoA-ROCK signaling pathway may have high translational potential and could be a promising therapeutic target in PD.

Heter-LP: A Heterogeneous Label Propagation Method for Drug Repositioning.

Using existing drugs for diseases which are not developed for their treating (drug repositioning) provides a new approach to developing drugs at a lower cost, faster, and more secured. We proposed a method for drug repositioning which can predict simple and complex relationships between drugs, drug targets, and diseases. Since biological networks typically present a suitable model for relationships between different biological concepts, our primary approach is to analyze graphs and complex networks in the study of drugs and their therapeutic effects. Given the nature of existing data, the use of semi-supervised learning methods is crucial. So, in our research, we have developed a label propagation method to predict drug-target, drug-disease, and disease-target interactions (Heter-LP), which integrates various data sources at different levels. The predicted interactions are the most prominent relationships among the millions of relationships suggested to the related researchers for further investigation. The main advantages of Heter-LP are the effective integration of input data, eliminating the need for negative samples, and the use of local and global features together. The main steps of this research are as follows. The first step is the construction of a heterogeneous network as a data modeling task, in which data are collected and prepared. The second step is predicting potential interactions. We present a new label propagation algorithm for heterogeneous networks, which consists of two parts, one mapping and the other an iterative method for determining the final labels of the entire network vertices. Finally, for evaluation, we calculated the AUC and AUPR with tenfold cross-validation and compared the results with the best available methods for label propagation in heterogeneous networks and drug repositioning. Also, a series of experimental evaluations and some specific case studies have been presented. The result of the AUC and AUPR for Heter-LP was much higher than the average of the best available methods.