Gene network biological validity based on gene-gene interaction relevance.
Ontology highlight
ABSTRACT:
SUBMITTER: Gomez-Vela F
PROVIDER: S-EPMC4175387 | biostudies-other | 2014
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Background: Complex biological networks control fundamental processes such as reproduction. The relationship of network structure to biological function was investigated in a global gene interaction network developed from ovary tissues of the model fish, fathead minnow (Pimephales promelas). Results: A global ovary gene interaction network was inferred from gene expression in ovaries of fathead minnow representing 288 different exposure conditions and 1,472 microarrays. More than 74 percent of the interactions in two subnetworks and 37% of the connections in a third subnetwork were also present as known connections found in curated databases and the literature. Subnetworks within the network were enriched in specific pathways and key ovarian functions. The network location of differentially expressed genes from different ovary stages was consistent with known functional changes at those stages. The global network provide additional insight into gene function when gene directly linked to differentially expressed genes are considered in enrichment analysis. Analysis of the impact of bisphenol A on ovarian gene expression demonstrated that the network provides an informative frame work in which to investigate mechanisms by which chemical effects occur. Conclusions: Our results suggest that the ovary transcriptional network is composed of several connected subnetworks where each is enriched in specific functions. Construction of a global gene regulatory network from multiple experiments provides valuable insight into the function of genes and the impact of chemicals on biological systems. total RNA from the ovary tissue of treated or control fish labeled in single color was hybridized to Agilent fathead minnow microarray (design 019597)
2016-07-06 | E-GEOD-70936 | biostudies-arrayexpress
Project description:Background: Complex biological networks control fundamental processes such as reproduction. The relationship of network structure to biological function was investigated in a global gene interaction network developed from ovary tissues of the model fish, fathead minnow (Pimephales promelas). Results: A global ovary gene interaction network was inferred from gene expression in ovaries of fathead minnow representing 288 different exposure conditions and 1,472 microarrays. More than 74 percent of the interactions in two subnetworks and 37% of the connections in a third subnetwork were also present as known connections found in curated databases and the literature. Subnetworks within the network were enriched in specific pathways and key ovarian functions. The network location of differentially expressed genes from different ovary stages was consistent with known functional changes at those stages. The global network provide additional insight into gene function when gene directly linked to differentially expressed genes are considered in enrichment analysis. Analysis of the impact of bisphenol A on ovarian gene expression demonstrated that the network provides an informative frame work in which to investigate mechanisms by which chemical effects occur. Conclusions: Our results suggest that the ovary transcriptional network is composed of several connected subnetworks where each is enriched in specific functions. Construction of a global gene regulatory network from multiple experiments provides valuable insight into the function of genes and the impact of chemicals on biological systems.
2016-07-06 | GSE70936 | GEO
Project description:BACKGROUND:In recent years, biological interaction networks have become the basis of some essential study and achieved success in many applications. Some typical networks such as protein-protein interaction networks have already been investigated systematically. However, little work has been available for the construction of gene functional similarity networks so far. In this research, we will try to build a high reliable gene functional similarity network to promote its further application. RESULTS:Here, we propose a novel method to construct and refine the gene functional similarity network. It mainly contains three steps. First, we establish an integrated gene functional similarity networks based on different functional similarity calculation methods. Then, we construct a referenced gene-gene association network based on the protein-protein interaction networks. At last, we refine the spurious edges in the integrated gene functional similarity network with the help of the referenced gene-gene association network. Experiment results indicate that the refined gene functional similarity network (RGFSN) exhibits a scale-free, small world and modular architecture, with its degrees fit best to power law distribution. In addition, we conduct protein complex prediction experiment for human based on RGFSN and achieve an outstanding result, which implies it has high reliability and wide application significance. CONCLUSIONS:Our efforts are insightful for constructing and refining gene functional similarity networks, which can be applied to build other high quality biological networks.
| S-EPMC5751769 | biostudies-literature
Project description:Transcriptomic profiling of metastasis tissue samples obtained from prostate cancer patients was performed using Clariom D human arrays.
2021-11-23 | GSE189343 | GEO
Project description:Drug repositioning is the identification of interactions between drugs and target proteins in pharmaceutical sciences. Traditional large-scale validation through chemical experiments is time-consuming and expensive, while drug repositioning can drastically decrease the cost and duration taken by traditional drug development. With the rapid advancement of high-throughput technologies and the explosion of various biological and medical data, computational drug repositioning methods have been used to systematically identify potential drug-target interactions. Some of them are based on a particular class of machine learning algorithms called kernel methods. In this paper, we propose a new machine learning prediction method combining multiple kernels into a tripartite heterogeneous drug-target-disease interaction spaces in order to integrate multiple sources of biological information simultaneously. This novel network algorithm extends the traditional drug-target interaction bipartite graph to the third disease layer. Meanwhile, Gaussian kernel functions on heterogeneous networks and the regularized least square method of the Kronecker product are used to predict new drug-target interactions. The values of AUPR (area under the precision-recall curve) and AUC (the area under the receiver operating characteristic curve) of the proposed algorithm are significantly improved. Especially, the AUC values are improved to 0.99, 0.99, 0.97, and 0.96 on four benchmark data sets. These experimental results substantiate that the network topology can be used for predicting drug-target interactions.
| S-EPMC7860102 | biostudies-literature
Project description:DNA microarray technologies are used extensively to profile the expression levels of thousands of genes under various conditions, yielding extremely large data-matrices. Thus, analyzing this information and extracting biologically relevant knowledge becomes a considerable challenge. A classical approach for tackling this challenge is to use clustering (also known as one-way clustering) methods where genes (or respectively samples) are grouped together based on the similarity of their expression profiles across the set of all samples (or respectively genes). An alternative approach is to develop biclustering methods to identify local patterns in the data. These methods extract subgroups of genes that are co-expressed across only a subset of samples and may feature important biological or medical implications. In this study we evaluate 13 biclustering and 2 clustering (k-means and hierarchical) methods. We use several approaches to compare their performance on two real gene expression data sets. For this purpose we apply four evaluation measures in our analysis: (1) we examine how well the considered (bi)clustering methods differentiate various sample types; (2) we evaluate how well the groups of genes discovered by the (bi)clustering methods are annotated with similar Gene Ontology categories; (3) we evaluate the capability of the methods to differentiate genes that are known to be specific to the particular sample types we study and (4) we compare the running time of the algorithms. In the end, we conclude that as long as the samples are well defined and annotated, the contamination of the samples is limited, and the samples are well replicated, biclustering methods such as Plaid and SAMBA are useful for discovering relevant subsets of genes and samples.
| S-EPMC3961251 | biostudies-literature
Project description:The number of hyperthyroidism patients is increasing these years. As a disease that can lead to cardiovascular disease, it brings great potential health risks to humans. Since hyperthyroidism can induce the occurrence of many diseases, studying its genetic factors will promote the early diagnosis and treatment of hyperthyroidism and its related diseases. Previous studies have used genome-wide association analysis (GWAS) to identify genes related to hyperthyroidism. However, these studies only identify significant sites related to the disease from a statistical point of view and ignore the complex regulation relationship between genes. In addition, mutation is not the only genetic factor of causing hyperthyroidism. Identifying hyperthyroidism-related genes from gene interactions would help researchers discover the disease mechanism. In this paper, we purposed a novel machine learning method for identifying hyperthyroidism-related genes based on gene interaction network. The method, which is called "RW-RVM," is a combination of Random Walk (RW) and Relevance Vector Machines (RVM). RW was implemented to encode the gene interaction network. The features of genes were the regulation relationship between genes and non-coding RNAs. Finally, multiple RVMs were applied to identify hyperthyroidism-related genes. The result of 10-cross validation shows that the area under the receiver operating characteristic curve (AUC) of our method reached 0.9, and area under the precision-recall curve (AUPR) was 0.87. Seventy-eight novel genes were found to be related to hyperthyroidism. We investigated two genes of these novel genes with existing literature, which proved the accuracy of our result and method.
| S-EPMC8365469 | biostudies-literature
Project description:The measurement of mobility is essential to both aging research and clinical practice. A newly developed self-report measure of mobility, the mobility assessment tool-short form (MAT-sf), uses video animations to improve measurement accuracy/precision. Using a large baseline data set, we recalibrated the items, evaluated the extent to which older patients' self-efficacy (i.e., confidence) for walking was related to MAT-sf scores beyond their actual 400-m walk time, and assessed the relationship of the MAT-sf with body mass index and other clinical variables.The analyses employed baseline data from the Lifestyle Interventions and Independence for Elders Study.Item recalibration demonstrated that the MAT-sf scoring algorithm was robust. In an analysis with 400-m walk time and self-efficacy regressed on the MAT-sf, both variables shared unique variance with the MAT-sf (p < .001). The MAT-sf was inversely related to several comorbidities, most notably hypertension and arthritis (p < .001), and scores were lowest when body mass index ? 35 kg/m(2). Finally, MAT-sf scores were directly related to Short Physical Performance Battery scores, inversely related to difficulty with activities of daily living (p < .001) and higher for men than for women (p < .001).The findings extend the validity and clinical utility of this innovative tool for assessing self-reported mobility in older adults. Longitudinal data on the MAT-sf from the Lifestyle Interventions and Independence for Elders Study will enable us to evaluate the relative contributions of self-report and performance-based measures of mobility on important health outcomes.
| S-EPMC3814234 | biostudies-literature
Project description:BENviewer is a brand-new online gene interaction network visualization server based on graph embedding models. With mature graph embedding algorithms applied on several interaction network databases, it provides human-friendly 2D visualization based on more than 2000 biological pathways, and these results present not only genes involved but also tightness of interactions in an intuitive way. As a unique visualization server introducing graph embedding application for the first time, it is expected to help researchers gain deeper insights into biological networks beyond generating results explainable by existing knowledge. Additionally, operation flow for users is simplified to greater extent in its current version; meanwhile URL optimization contributes to data acquisition in batch for further analysis. BENviewer is freely available at http://www.bmeonline.cn/BENviewer, besides it is open-sourced at https://github.com/SKLB-lab/BENviewer, http://benviewer.bmeonline.cn.
| S-EPMC8163240 | biostudies-literature
Project description:BackgroundThe use of predictive gene signatures to assist clinical decision is becoming more and more important. Deep learning has a huge potential in the prediction of phenotype from gene expression profiles. However, neural networks are viewed as black boxes, where accurate predictions are provided without any explanation. The requirements for these models to become interpretable are increasing, especially in the medical field.ResultsWe focus on explaining the predictions of a deep neural network model built from gene expression data. The most important neurons and genes influencing the predictions are identified and linked to biological knowledge. Our experiments on cancer prediction show that: (1) deep learning approach outperforms classical machine learning methods on large training sets; (2) our approach produces interpretations more coherent with biology than the state-of-the-art based approaches; (3) we can provide a comprehensive explanation of the predictions for biologists and physicians.ConclusionWe propose an original approach for biological interpretation of deep learning models for phenotype prediction from gene expression data. Since the model can find relationships between the phenotype and gene expression, we may assume that there is a link between the identified genes and the phenotype. The interpretation can, therefore, lead to new biological hypotheses to be investigated by biologists.
| S-EPMC7643315 | biostudies-literature