scholarly journals Genome-wide discovery of hidden genes mediating known drug-disease association

2019 ◽  
Author(s):  
Hua Yu ◽  
Lu Lu ◽  
Ming Chen ◽  
Chen Li ◽  
Jin Zhang
Keyword(s):  
Interaction Network ◽  
Drug Repurposing ◽  
Minimum Cost ◽  
Disease Association ◽  
Computational Tool ◽  
Minimum Cost Flow ◽  
Functional Interaction ◽  
Disease Pathogenesis ◽  
Link Type ◽  
Genome Wide

AbstractIdentifying of hidden genes mediating Known Drug-Disease Association (KDDA) is of great significance for understanding disease pathogenesis and guiding drug repurposing. Here, we present a novel computational tool, called KDDANet, for systematic and accurate uncovering hidden genes mediating KDDA from the perspective of genome-wide gene functional interaction network. By implementing minimum cost flow optimization, combined with depth first searching and graph clustering on a unified flow network model, KDDANet outperforms existing methods in both sensitivity and specificity of identifying genes in mediating KDDA. Case studies on Alzheimer’s disease (AD) and obesity uncover the mechanistic relevance of KDDANet predictions. Furthermore, when applied with multiple types of cancer-omics datasets, KDDANet not only recapitulates known genes mediating KDDAs related to cancer, but also uncovers novel candidates that offer new biological insights. Importantly, KDDANet can be used to discover the shared genes mediating multiple KDDAs. KDDANet can be accessed at http://www.kddanet.cn and the code can be freely downloaded at https://github.com/huayu1111/KDDANet/.

scholarly journals Genome-wide discovery of hidden genes mediating known drug-disease association using KDDANet

2021 ◽  
Vol 6 (1) ◽  
Author(s):  
Hua Yu ◽  
Lu Lu ◽  
Ming Chen ◽  
Chen Li ◽  
Jin Zhang
Keyword(s):  
State Of The Art ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Gene Interaction ◽  
Disease Association ◽  
Computational Tool ◽  
Gene Interaction Network ◽  
Disease Pathogenesis ◽  
Link Type ◽  
Genome Wide

AbstractMany of genes mediating Known Drug-Disease Association (KDDA) are escaped from experimental detection. Identifying of these genes (hidden genes) is of great significance for understanding disease pathogenesis and guiding drug repurposing. Here, we presented a novel computational tool, called KDDANet, for systematic and accurate uncovering the hidden genes mediating KDDA from the perspective of genome-wide functional gene interaction network. KDDANet demonstrated the competitive performances in both sensitivity and specificity of identifying genes in mediating KDDA in comparison to the existing state-of-the-art methods. Case studies on Alzheimer’s disease (AD) and obesity uncovered the mechanistic relevance of KDDANet predictions. Furthermore, when applied with multiple types of cancer-omics datasets, KDDANet not only recapitulated known genes mediating KDDAs related to cancer, but also revealed novel candidates that offer new biological insights. Importantly, KDDANet can be used to discover the shared genes mediating multiple KDDAs. KDDANet can be accessed at http://www.kddanet.cn and the code can be freely downloaded at https://github.com/huayu1111/KDDANet.

scholarly journals DTI-Voodoo: machine learning over interaction networks and ontology-based background knowledge predicts drug–target interactions

2021 ◽  
Author(s):  
Tilman Hinnerichs ◽  
Robert Hoehndorf
Keyword(s):  
Drug Target ◽  
Drug Targets ◽  
Interaction Network ◽  
Drug Repurposing ◽  
Computational Method ◽  
Interaction Networks ◽  
Supplementary Information ◽  
Prediction Methods ◽  
Link Type ◽  
Molecular Features

AbstractMotivationIn silico drug–target interaction (DTI) prediction is important for drug discovery and drug repurposing. Approaches to predict DTIs can proceed indirectly, top-down, using phenotypic effects of drugs to identify potential drug targets, or they can be direct, bottom-up and use molecular information to directly predict binding potentials. Both approaches can be combined with information about interaction networks.ResultsWe developed DTI-Voodoo as a computational method that combines molecular features and ontology-encoded phenotypic effects of drugs with protein–protein interaction networks, and uses a graph convolutional neural network to predict DTIs. We demonstrate that drug effect features can exploit information in the interaction network whereas molecular features do not. DTI-Voodoo is designed to predict candidate drugs for a given protein; we use this formulation to show that common DTI datasets contain intrinsic biases with major affects on performance evaluation and comparison of DTI prediction methods. Using a modified evaluation scheme, we demonstrate that DTI-Voodoo improves significantly over state of the art DTI prediction methods.AvailabilityDTI-Voodoo source code and data necessary to reproduce results are freely available at https://github.com/THinnerichs/DTI-VOODOO.Supplementary informationSupplementary data are available at https://github.com/ THinnerichs/DTI-VOODOO.

scholarly journals Genetic and functional interaction network analysis reveals global enrichment of regulatory T cell genes influencing basal cell carcinoma susceptibility

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Christelle Adolphe ◽  
Angli Xue ◽  
Atefeh Taherian Fard ◽  
Laura A. Genovesi ◽  
Jian Yang ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Network Analysis ◽  
Basal Cell Carcinoma ◽  
Regulatory Network ◽  
Interaction Network ◽  
Susceptibility Genes ◽  
Genome Wide Association ◽  
Functional Interaction ◽  
Functional Interaction Network ◽  
Genome Wide

Abstract Background Basal cell carcinoma (BCC) of the skin is the most common form of human cancer, with more than 90% of tumours presenting with clear genetic activation of the Hedgehog pathway. However, polygenic risk factors affecting mechanisms such as DNA repair and cell cycle checkpoints or which modulate the tumour microenvironment or host immune system play significant roles in determining whether genetic mutations culminate in BCC development. We set out to define background genetic factors that play a role in influencing BCC susceptibility via promoting or suppressing the effects of oncogenic drivers of BCC. Methods We performed genome-wide association studies (GWAS) on 17,416 cases and 375,455 controls. We subsequently performed statistical analysis by integrating data from population-based genetic studies of multi-omics data, including blood- and skin-specific expression quantitative trait loci and methylation quantitative trait loci, thereby defining a list of functionally relevant candidate BCC susceptibility genes from our GWAS loci. We also constructed a local GWAS functional interaction network (consisting of GWAS nearest genes) and another functional interaction network, consisting specifically of candidate BCC susceptibility genes. Results A total of 71 GWAS loci and 46 functional candidate BCC susceptibility genes were identified. Increased risk of BCC was associated with the decreased expression of 26 susceptibility genes and increased expression of 20 susceptibility genes. Pathway analysis of the functional candidate gene regulatory network revealed strong enrichment for cell cycle, cell death, and immune regulation processes, with a global enrichment of genes and proteins linked to TReg cell biology. Conclusions Our genome-wide association analyses and functional interaction network analysis reveal an enrichment of risk variants that function in an immunosuppressive regulatory network, likely hindering cancer immune surveillance and effective antitumour immunity.

scholarly journals Genomics-driven drug discovery based on disease-susceptibility genes

2021 ◽  
Vol 41 (1) ◽  
Author(s):  
Kyuto Sonehara ◽  
Yukinori Okada
Keyword(s):  
Drug Discovery ◽  
Drug Targets ◽  
Disease Susceptibility ◽  
Mendelian Randomization ◽  
Association Studies ◽  
Drug Repurposing ◽  
Susceptibility Genes ◽  
Genome Wide Association Studies ◽  
Genome Wide ◽  
Disease Associations

AbstractGenome-wide association studies have identified numerous disease-susceptibility genes. As knowledge of gene–disease associations accumulates, it is becoming increasingly important to translate this knowledge into clinical practice. This challenge involves finding effective drug targets and estimating their potential side effects, which often results in failure of promising clinical trials. Here, we review recent advances and future perspectives in genetics-led drug discovery, with a focus on drug repurposing, Mendelian randomization, and the use of multifaceted omics data.

scholarly journals Genome-wide identification of the GATA transcription factor family and their expression patterns under temperature and salt stress in Aspergillus oryzae

AMB Express ◽  
2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Chunmiao Jiang ◽  
Gongbo Lv ◽  
Jinxin Ge ◽  
Bin He ◽  
Zhe Zhang ◽  
...  
Keyword(s):  
Salt Stress ◽  
Expression Patterns ◽  
Interaction Network ◽  
Protein Protein Interaction ◽  
Genome Wide ◽  
Gata Transcription Factor ◽  
Starting Point ◽  
Evolutionary Features ◽  
First Time

AbstractGATA transcription factors (TFs) are involved in the regulation of growth processes and various environmental stresses. Although GATA TFs involved in abiotic stress in plants and some fungi have been analyzed, information regarding GATA TFs in Aspergillusoryzae is extremely poor. In this study, we identified and functionally characterized seven GATA proteins from A.oryzae 3.042 genome, including a novel AoSnf5 GATA TF with 20-residue between the Cys-X2-Cys motifs which was found in Aspergillus GATA TFs for the first time. Phylogenetic analysis indicated that these seven A. oryzae GATA TFs could be classified into six subgroups. Analysis of conserved motifs demonstrated that Aspergillus GATA TFs with similar motif compositions clustered in one subgroup, suggesting that they might possess similar genetic functions, further confirming the accuracy of the phylogenetic relationship. Furthermore, the expression patterns of seven A.oryzae GATA TFs under temperature and salt stresses indicated that A. oryzae GATA TFs were mainly responsive to high temperature and high salt stress. The protein–protein interaction network of A.oryzae GATA TFs revealed certain potentially interacting proteins. The comprehensive analysis of A. oryzae GATA TFs will be beneficial for understanding their biological function and evolutionary features and provide an important starting point to further understand the role of GATA TFs in the regulation of distinct environmental conditions in A.oryzae.

scholarly journals A Generalized Minimum Cost Flow Model for Multiple Emergency Flow Routing

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jianxun Cui ◽  
Shi An ◽  
Meng Zhao
Keyword(s):  
Flow Model ◽  
Time Window ◽  
Programming Model ◽  
Real Life ◽  
Minimum Cost ◽  
Emergency Evacuation ◽  
Mixed Integer ◽  
Fixed Cost ◽  
Minimum Cost Flow ◽  
Cost Flow

During real-life disasters, that is, earthquakes, floods, terrorist attacks, and other unexpected events, emergency evacuation and rescue are two primary operations that can save the lives and property of the affected population. It is unavoidable that evacuation flow and rescue flow will conflict with each other on the same spatial road network and within the same time window. Therefore, we propose a novel generalized minimum cost flow model to optimize the distribution pattern of these two types of flow on the same network by introducing the conflict cost. The travel time on each link is assumed to be subject to a bureau of public road (BPR) function rather than a fixed cost. Additionally, we integrate contraflow operations into this model to redesign the network shared by those two types of flow. A nonconvex mixed-integer nonlinear programming model with bilinear, fractional, and power components is constructed, and GAMS/BARON is used to solve this programming model. A case study is conducted in the downtown area of Harbin city in China to verify the efficiency of proposed model, and several helpful findings and managerial insights are also presented.

Minimum cost flow problem with conflicts

Networks ◽  
2021 ◽  
Author(s):  
Zeynep Şuvak ◽  
İ. Kuban Altınel ◽  
Necati Aras
Keyword(s):  
Flow Problem ◽  
Minimum Cost ◽  
Minimum Cost Flow ◽  
Minimum Cost Flow Problem ◽  
Cost Flow

scholarly journals Genome-wide mapping of unexplored essential regions in the Saccharomyces cerevisiae genome: evidence for hidden synthetic lethal combinations in a genetic interaction network

2014 ◽  
Vol 42 (15) ◽  
pp. 9838-9853 ◽  
Author(s):  
Saeed Kaboli ◽  
Takuya Yamakawa ◽  
Keisuke Sunada ◽  
Tao Takagaki ◽  
Yu Sasano ◽  
...  
Keyword(s):  
Saccharomyces Cerevisiae ◽  
Genetic Interaction ◽  
Interaction Network ◽  
Essential Genes ◽  
Genetic Interactions ◽  
Lethal Gene ◽  
Synthetic Lethal ◽  
Genome Wide ◽  
A Genome ◽  
Wide Scale

Abstract Despite systematic approaches to mapping networks of genetic interactions in Saccharomyces cerevisiae, exploration of genetic interactions on a genome-wide scale has been limited. The S. cerevisiae haploid genome has 110 regions that are longer than 10 kb but harbor only non-essential genes. Here, we attempted to delete these regions by PCR-mediated chromosomal deletion technology (PCD), which enables chromosomal segments to be deleted by a one-step transformation. Thirty-three of the 110 regions could be deleted, but the remaining 77 regions could not. To determine whether the 77 undeletable regions are essential, we successfully converted 67 of them to mini-chromosomes marked with URA3 using PCR-mediated chromosome splitting technology and conducted a mitotic loss assay of the mini-chromosomes. Fifty-six of the 67 regions were found to be essential for cell growth, and 49 of these carried co-lethal gene pair(s) that were not previously been detected by synthetic genetic array analysis. This result implies that regions harboring only non-essential genes contain unidentified synthetic lethal combinations at an unexpectedly high frequency, revealing a novel landscape of genetic interactions in the S. cerevisiae genome. Furthermore, this study indicates that segmental deletion might be exploited for not only revealing genome function but also breeding stress-tolerant strains.

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