scholarly journals Application of augmented topic model to predicting biomarkers and therapeutic targets using multiple human disease-omics datasets

2021 ◽  
Author(s):  
Thomas N Sato ◽  
Satoshi Kozawa ◽  
Kyoji Urayama ◽  
Kengo Tejima ◽  
Hotaka Doi ◽  
...  
Keyword(s):  
Human Disease ◽  
Cross Validation ◽  
Topic Model ◽  
Therapeutic Targets ◽  
Human Diseases ◽  
Single Type ◽  
Omics Data ◽  
Model Framework ◽  
Therapeutic Development ◽  
Validation Scheme

Human diseases are multifactorial - hence it is important to characterize diseases on the basis of multiple disease-omics. However, the capability of the existing methods is largely limited to classifying diseases based on a single type or a few closely related omics data. Herein, we report a topic model framework that allows for characterizing diseases according to their multiple omics data. We also show that this method can be utilized to predict potential biomarkers and/or therapeutic targets. In this study, we illustrate a computational concept of this augmented topic model and demonstrate its prediction performance by a leave one-disease features out cross-validation scheme. Furthermore, we exploit this method together with human disease tissue/organ-transcriptome data and identify putative biomarkers and/or therapeutic targets across 79 diseases. In conclusion, this method and the prediction framework shown reported herein provide important tools for understanding complex human diseases and also facilitate diagnostic and/or therapeutic development.

Prediction of Nitration Sites Based on FCBF Method and Stacking Ensemble Model

2021 ◽  
Vol 18 ◽  
Author(s):  
Min Liu ◽  
Lu Zhang ◽  
Xinyi Qin ◽  
Tao Huang ◽  
Ziwei Xu ◽  
...  
Keyword(s):  
Prediction Model ◽  
Protein Sequence ◽  
Cross Validation ◽  
Transplant Rejection ◽  
Sampling Technique ◽  
Single Type ◽  
Post Translational Modification ◽  
Tyrosine Residues ◽  
Under Sampling ◽  
Fusion Features

Background: Nitration is one of the important Post-Translational Modification (PTM) occurring on the tyrosine residues of proteins. The occurrence of protein tyrosine nitration under disease conditions is inevitable and represents a shift from the signal transducing physiological actions of -NO to oxidative and potentially pathogenic pathways. Abnormal protein nitration modification can lead to serious human diseases, including neurodegenerative diseases, acute respiratory distress, organ transplant rejection and lung cancer. Objective: It is necessary and important to identify the nitration sites in protein sequences. Predicting that which tyrosine residues in the protein sequence are nitrated and which are not is of great significance for the study of nitration mechanism and related diseases. Methods: In this study, a prediction model of nitration sites based on the over-under sampling strategy and the FCBF method was proposed by stacking ensemble learning and fusing multiple features. Firstly, the protein sequence sample was encoded by 2701-dimensional fusion features (PseAAC, PSSM, AAIndex, CKSAAP, Disorder). Secondly, the ranked feature set was generated by the FCBF method according to the symmetric uncertainty metric. Thirdly, in the process of model training, use the over- and under- sampling technique was used to tackle the imbalanced dataset. Finally, the Incremental Feature Selection (IFS) method was adopted to extract an optimal classifier based on 10-fold cross-validation. Results and Conclusion: Results show that the model has significant performance advantages in indicators such as MCC, Recall and F1-score, no matter in what way the comparison was conducted with other classifiers on the independent test set, or made by cross-validation with single-type feature or with fusion-features on the training set. By integrating the FCBF feature ranking methods, over- and under- sampling technique and a stacking model composed of multiple base classifiers, an effective prediction model for nitration PTM sites was build, which can achieve a better recall rate when the ratio of positive and negative samples is highly imbalanced.

scholarly journals Identification of COVID-19 prognostic markers and therapeutic targets through meta-analysis and validation of Omics data from nasopharyngeal samples

EBioMedicine ◽  
2021 ◽  
Vol 70 ◽  
pp. 103525
Author(s):  
Abhijith Biji ◽  
Oyahida Khatun ◽  
Shachee Swaraj ◽  
Rohan Narayan ◽  
Raju S. Rajmani ◽  
...  
Keyword(s):  
Prognostic Markers ◽  
Meta Analysis ◽  
Therapeutic Targets ◽  
Omics Data

scholarly journals Reinforcement Learning to Boost Molecular Docking upon Protein Conformational Ensemble

2021 ◽  
Author(s):  
Bin Chong ◽  
Yingguang Yang ◽  
Zi-Le Wang ◽  
Han Xing ◽  
Zhirong Liu
Keyword(s):  
Molecular Docking ◽  
Reinforcement Learning ◽  
Intrinsically Disordered Proteins ◽  
Therapeutic Targets ◽  
Human Diseases ◽  
Disordered Proteins ◽  
Conformational Ensemble ◽  
Intrinsically Disordered

Intrinsically disordered proteins (IDPs) widely involve in human diseases and are thus attractive therapeutic targets. In practice, however, it is computationally prohibitive to dock large ligand libraries to thousands and...

scholarly journals Application of microRNA Database Mining in Biomarker Discovery and Identification of Therapeutic Targets for Complex Disease

2020 ◽  
Vol 4 (1) ◽  
pp. 5
Author(s):  
Jennifer L. Major ◽  
Rushita A. Bagchi ◽  
Julie Pires da Silva
Keyword(s):  
Complex Disease ◽  
Biomarker Discovery ◽  
Ischemia Reperfusion Injury ◽  
Ischemia Reperfusion ◽  
Expression Patterns ◽  
Therapeutic Targets ◽  
Human Diseases ◽  
Mirna Expression Data ◽  
Artery Disease ◽  
Underlying Mechanisms

Over the past two decades, it has become increasingly evident that microRNAs (miRNA) play a major role in human diseases such as cancer and cardiovascular diseases. Moreover, their easy detection in circulation has made them a tantalizing target for biomarkers of disease. This surge in interest has led to the accumulation of a vast amount of miRNA expression data, prediction tools, and repositories. We used the Human microRNA Disease Database (HMDD) to discover miRNAs which shared expression patterns in the related diseases of ischemia/reperfusion injury, coronary artery disease, stroke, and obesity as a model to identify miRNA candidates for biomarker and/or therapeutic intervention in complex human diseases. Our analysis identified a single miRNA, hsa-miR-21, which was casually linked to all four pathologies, and numerous others which have been detected in the circulation in more than one of the diseases. Target analysis revealed that hsa-miR-21 can regulate a number of genes related to inflammation and cell growth/death which are major underlying mechanisms of these related diseases. Our study demonstrates a model for researchers to use HMDD in combination with gene analysis tools to identify miRNAs which could serve as biomarkers and/or therapeutic targets of complex human diseases.

scholarly journals TarGo: network based target gene selection system for human disease related mouse models

2019 ◽  
Vol 35 (1) ◽  
Author(s):  
Daejin Hyung ◽  
Ann-Marie Mallon ◽  
Dong Soo Kyung ◽  
Soo Young Cho ◽  
Je Kyung Seong
Keyword(s):  
Mouse Models ◽  
Network Topology ◽  
Human Disease ◽  
Target Gene ◽  
Gene Selection ◽  
Genetically Engineered ◽  
Human Diseases ◽  
Selection System ◽  
Genetically Engineered Mouse Models ◽  
Disease Signatures

Abstract Genetically engineered mouse models are used in high-throughput phenotyping screens to understand genotype-phenotype associations and their relevance to human diseases. However, not all mutant mouse lines with detectable phenotypes are associated with human diseases. Here, we propose the “Target gene selection system for Genetically engineered mouse models” (TarGo). Using a combination of human disease descriptions, network topology, and genotype-phenotype correlations, novel genes that are potentially related to human diseases are suggested. We constructed a gene interaction network using protein-protein interactions, molecular pathways, and co-expression data. Several repositories for human disease signatures were used to obtain information on human disease-related genes. We calculated disease- or phenotype-specific gene ranks using network topology and disease signatures. In conclusion, TarGo provides many novel features for gene function prediction.

Using What We Already Have: Uncovering New Drug Repurposing Strategies in Existing Omics Data

2020 ◽  
Vol 60 (1) ◽  
pp. 333-352 ◽  
Author(s):  
Jill M. Pulley ◽  
Jillian P. Rhoads ◽  
Rebecca N. Jerome ◽  
Anup P. Challa ◽  
Kevin B. Erreger ◽  
...  
Keyword(s):  
Decision Making ◽  
Drug Development ◽  
Drug Safety ◽  
Human Disease ◽  
Drug Repurposing ◽  
Drug Efficacy ◽  
Omics Data ◽  
New Drug ◽  
Cost Efficient

The promise of drug repurposing is to accelerate the translation of knowledge to treatment of human disease, bypassing common challenges associated with drug development to be more time- and cost-efficient. Repurposing has an increased chance of success due to the previous validation of drug safety and allows for the incorporation of omics. Hypothesis-generating omics processes inform drug repurposing decision-making methods on drug efficacy and toxicity. This review summarizes drug repurposing strategies and methodologies in the context of the following omics fields: genomics, epigenomics, transcriptomics, proteomics, metabolomics, microbiomics, phenomics, pregomics, and personomics. While each omics field has specific strengths and limitations, incorporating omics into the drug repurposing landscape is integral to its success.

scholarly journals Tissue resident regulatory T cells: novel therapeutic targets for human disease

2015 ◽  
Vol 12 (5) ◽  
pp. 543-552 ◽  
Author(s):  
Xiaohui Zhou ◽  
Jiayou Tang ◽  
Hao Cao ◽  
Huimin Fan ◽  
Bin Li
Keyword(s):  
T Cells ◽  
Regulatory T Cells ◽  
Human Disease ◽  
Therapeutic Targets ◽  
Novel Therapeutic

scholarly journals WMGHMDA: a novel weighted meta-graph-based model for predicting human microbe-disease association on heterogeneous information network

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Yahui Long ◽  
Jiawei Luo
Keyword(s):  
Computational Methods ◽  
Cross Validation ◽  
Search Algorithm ◽  
Human Diseases ◽  
Information Network ◽  
Bipartite Network ◽  
Heterogeneous Information Network ◽  
Similarity Network ◽  
Heterogeneous Information ◽  
Disease Associations

Abstract Background An increasing number of biological and clinical evidences have indicated that the microorganisms significantly get involved in the pathological mechanism of extensive varieties of complex human diseases. Inferring potential related microbes for diseases can not only promote disease prevention, diagnosis and treatment, but also provide valuable information for drug development. Considering that experimental methods are expensive and time-consuming, developing computational methods is an alternative choice. However, most of existing methods are biased towards well-characterized diseases and microbes. Furthermore, existing computational methods are limited in predicting potential microbes for new diseases. Results Here, we developed a novel computational model to predict potential human microbe-disease associations (MDAs) based on Weighted Meta-Graph (WMGHMDA). We first constructed a heterogeneous information network (HIN) by combining the integrated microbe similarity network, the integrated disease similarity network and the known microbe-disease bipartite network. And then, we implemented iteratively pre-designed Weighted Meta-Graph search algorithm on the HIN to uncover possible microbe-disease pairs by cumulating the contribution values of weighted meta-graphs to the pairs as their probability scores. Depending on contribution potential, we described the contribution degree of different types of meta-graphs to a microbe-disease pair with bias rating. Meta-graph with higher bias rating will be assigned greater weight value when calculating probability scores. Conclusions The experimental results showed that WMGHMDA outperformed some state-of-the-art methods with average AUCs of 0.9288, 0.9068 ±0.0031 in global leave-one-out cross validation (LOOCV) and 5-fold cross validation (5-fold CV), respectively. In the case studies, 9, 19, 37 and 10, 20, 45 out of top-10, 20, 50 candidate microbes were manually verified by previous reports for asthma and inflammatory bowel disease (IBD), respectively. Furthermore, three common human diseases (Crohn’s disease, Liver cirrhosis, Type 1 diabetes) were adopted to demonstrate that WMGHMDA could be efficiently applied to make predictions for new diseases. In summary, WMGHMDA has a high potential in predicting microbe-disease associations.

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