A Computing System for Discovering Causal Relationships Among Human Genes to Improve Drug Repositioning

Abstract

The automatic discovery of causal relationships among human genes can shed light on gene regulatory processes and guide drug repositioning. To this end, a computationally-heavy method for causal discovery is distributed on a volunteer computing grid and, taking advantage of variable subsetting and stratification, proves to be useful for expanding local gene regulatory networks. The input data are purely observational measures of transcripts expression in human tissues and cell lines collected within the FANTOM project. The system relies on the BOINC platform and on optimized client code. The functional relevance of results, measured by analyzing the annotations of the identified interactions, increases significantly over the simple Pearson correlation between the transcripts. Additionally, in 82% of cases networks significantly overlap with known protein-protein interactions annotated in biological databases. In the two case studies presented, this approach has been used to expand the networks of genes associated with two severe human pathologies: prostate cancer and coronary artery disease. The method identified respectively 22 and 36 genes to be evaluated as novel targets for already approved drugs, demonstrating the effective applicability of the approach in pipelines aimed to drug repositioning.

RETRACTED ARTICLE: DLMBHCO: design of an augmented bioinspired deep learning-based multidomain body parameter analysis via heterogeneous correlative body organ analysis

“…In recent years, owing to research that combines classic convoluted neural networks with self-attention approaches (Blanzieri 2021 ; Mahmood 2020 ; Yang et al 2021 ), the performance of networks has greatly increased, resulting in considerable gains. The conventional U-shaped skip connection that was used for medical image categorization has reportedly been superseded by the fusion addition attention gates skip connection, as stated in the research that has been published (Patel et al 2021 ; Lee 2020 ; Pu 2022 ).…”

Vitis OneGenE: A Causality-Based Approach to Generate Gene Networks in Vitis vinifera Sheds Light on the Laccase and Dirigent Gene Families

“…The expansion of each gene of the Vitis vinifera genome originated from the run of the algorithms of the OneGenE system [18,38]. Each expansion procedure consisted of 2000 iterations of our C++ implementation (https://bitbucket.org/francesco-asnicar/pc-boinc/, last accessed on 16 June 2020) of the PC-algorithm skeleton procedure [39] (α = 0.05) to 29 sets of 1000 variables (genes), which included the gene to be expanded, and a random subset of 999 genes sampled without replacement.…”

“…Each expansion procedure consisted of 2000 iterations of our C++ implementation (https://bitbucket.org/francesco-asnicar/pc-boinc/, last accessed on 16 June 2020) of the PC-algorithm skeleton procedure [39] (α = 0.05) to 29 sets of 1000 variables (genes), which included the gene to be expanded, and a random subset of 999 genes sampled without replacement. The blocks scheme of the OneGenE architecture is shown in Blanzieri et al 2020, Figure 1 [18]. The input data were extracted from the 28,013 × 1131 normalized expression data matrix initially obtained from the VESPUCCI repository [1], filtered, and preprocessed [16].…”

“…OneGenE systematically runs-single gene NES 2 RA expansions with fixed parameters and combines them afterward to simulate LGN expansions. In this way, the single-gene expansions can be used multiple times, thus effectively reducing the computational effort [18].…”

Volunteer Computing Project SiDock@home for Virtual Drug Screening Against SARS-CoV-2

“…TN-Grid project [33], in its sub-project gene@home, explores human gene regulatory networks for receptor proteins of SARS-CoV-2 and other viruses. To date, results include the expansion of the networks of genes associated with two non-viral diseases, identification of 22 and 36 genes to be evaluated as novel targets for already approved drugs [2].…”