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Consistency of biological networks inferred from microarray and sequencing data

Veronica Vinciotti, Ernst C. Wit, Rick Jansen, Eco J. C. N. de Geus, Brenda W. J. H. Penninx, Dorret I. Boomsma, Peter A. C. 't Hoen
2016 BMC Bioinformatics  
Availability of data and materials Gene expression data used for this study are available at dbGaP, accession number phs000486.v1.p1 (http://www.ncbi.nlm.nih.gov/projects/gap/cgibin/study.cgi?  ...  microarray data [7] and networks from next generation sequencing data, which are discrete, e.g  ...  In particular, [29] studied the consistency of clustering methods on microarray and RNA-seq data and [11] studied the consistency of coexpression networks on microarray and RNA-seq data, where the  ... 
doi:10.1186/s12859-016-1136-0 pmid:27342572 pmcid:PMC4919861 fatcat:qu3rdkxzzfgrpe7dv5jko6sfg4

A framework for integrative analysis of transcriptional and post-transcriptional gene regulation

Hasan Ogul, Giray S. Ozcan
2013 2013 7th International Conference on Application of Information and Communication Technologies  
The framework uses paired samples of mRNA and microRNA expressions and their sequence data to produce low-level regulatory circuits in addition to the coregulated entities of mRNAs, microRNAs and transcription  ...  The experiments performed on a real cancer dataset reveal that several biologically meaningful clusters and motifs can be inferred.  ...  The second problem is usually referred as inferring gene regulatory network from gene expression data.  ... 
doi:10.1109/icaict.2013.6722723 fatcat:xpxv7ls7tfgmtoaajeqm66bskq

Input Dataset Survey of In-Silico Tools for Inference and Visualization of Gene Regulatory Networks (GRN)

Taiwo Adigun
2015 Computational Biology and Bioinformatics  
Dataset from microarray and ChIP-Chip experiments are hybridization-based while RNA-seq and ChIP-seq are sequence-based.  ...  We consider four omic datasets and two of their sources for the purpose of this review. The biological data source technologies are hybridization-based, and sequence-based.  ...  Grn Input Data The analysis of large datasets of information derived from various biological experiments plays a vital role in functional genomics, and a good inference of gene regulatory network is one  ... 
doi:10.11648/j.cbb.20150306.11 fatcat:x7ogjrlg35cbdpwrlk6eh5uc2q

Inferring gene correlation networks from transcription factor binding sites

Ghasem Mahdevar, Abbas Nowzari-Dalini, Mehdi Sadeghi
2013 Genes & Genetic Systems  
According to the results, this method works well on biological data and its outcome is comparable to the methods that use microarray as input.  ...  This paper presents a novel method for inferring correlation networks, networks constructed by connecting coexpressed genes, through predicting co-expression level from genes promoter's sequences.  ...  Results show that the accuracy and efficiency of this method in inferring correlation networks, merely, from promoters is almost equivalent to the accuracy of methods that use microarray data as input,  ... 
doi:10.1266/ggs.88.301 pmid:24694393 fatcat:qxtv7wcswzecbpmjidltizzxfu

Pan- and core- gene association networks: Integrative approaches to understanding biological regulation

Warodom Wirojsirasak, Saowalak Kalapanulak, Treenut Saithong, Enrique Hernandez-Lemus
2019 PLoS ONE  
We showed the overall performance of pan- and core-GANs was more robust to the number of data points in gene expression data compared to the GANs inferred from individual datasets.  ...  The core-GAN represents the consensus associations of genes that were inferred from all individual networks.  ...  Somkid Bumee for providing some data information and suggestions.  ... 
doi:10.1371/journal.pone.0210481 pmid:30625202 pmcid:PMC6326509 fatcat:nam2f7vnwreqjnw7hrbhsly67u

Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency

Hsiang-Yuan Yeh, Shih-Wu Cheng, Yu-Chun Lin, Cheng-Yu Yeh, Shih-Fang Lin, Von-Wun Soo
2009 BMC Medical Genomics  
Conclusions: We provide a computational framework to reconstruct the genetic regulatory network from the microarray data using biological knowledge and constraint-based inferences.  ...  We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method.  ...  doesn't fit the real networks inferred from microarray data.  ... 
doi:10.1186/1755-8794-2-70 pmid:20025723 pmcid:PMC2805685 fatcat:yl3pwgecqja4bb6vvsndma6u64

A comparative analytical assay of gene regulatory networks inferred using microarray and RNA-seq datasets

Fereshteh Izadi, Hamid Najafi Zarrini, Nadali Babaeian Jelodar
2016 Bioinformation  
Omics data explosion generated from high-throughput genomic assays such as microarray and RNA-Seq technologies and the emergence of a number of pre-processing methods demands suitable guidelines to determine  ...  Topological analysis showed that GRNs inferred from two platforms were similar in several of topological features although we observed more connectivity in RNA-Seq derived genes network.  ...  Nooshin Omranian, scientific staff in Systems Biology and Mathematical Modelling Group, Max Planck Institute for Molecular Plant Physiology, Potsdam, Germany for her precious assistances.  ... 
doi:10.6026/97320630012340 pmid:28293077 pmcid:PMC5320930 fatcat:wkfa3nveyngvbpjkfffos4ikhi

Transcriptional Networks of Microglia in Alzheimer's Disease and Insights into Pathogenesis

Chew, Petretto
2019 Genes  
Given the rapid technological advancements in transcriptional profiling, spanning from microarrays to single-cell RNA sequencing (scRNA-seq), tools used for mapping gene expression networks have evolved  ...  Lastly, we discuss examples of how transcriptional network analysis provides new insights into AD mechanisms and pathogenesis.  ...  As for DE analysis in single-cell data, gene network inference from scRNA-seq data is therefore substantially different from data generated by microarray or bulk RNA-seq.  ... 
doi:10.3390/genes10100798 pmid:31614849 pmcid:PMC6826883 fatcat:va3mecrwirdpxficc5tnz76ola

Exploiting big biology: Integrating large-scale biological data for function inference

E. M. Marcotte
2001 Briefings in Bioinformatics  
Some of the fastest growing sets of data are measurements of gene expression, comparable in quantity only to gene sequences and the vast biological literature.  ...  Both gene expression data and sequence data offer hints as to the functions of thousands of newly discovered genes, but neither give complete answers.  ...  At the most basic level, functional inferences from sequence data come in two general avours: homology-and non-homologybased inferences.  ... 
doi:10.1093/bib/2.4.363 pmid:11808748 fatcat:ug55ja7ddjd3lim73fvecjb4cy

Unraveling transcriptional regulatory programs by integrative analysis of microarray and transcription factor binding data

Huai Li, Ming Zhan
2008 Computer applications in the biosciences : CABIOS  
Method: Here, we present a new methodology that integrates microarray and TF binding data for unraveling transcriptional regulatory networks.  ...  The TF-target gene relationships are derived from ChIP-chip and other TF binding data.  ...  The consistency of our findings from the inferred network with experimental data and predictions by others support the validity of our methods in inferring the regulatory networks.  ... 
doi:10.1093/bioinformatics/btn332 pmid:18586698 pmcid:PMC2519161 fatcat:g5l7bd22jvbw3hkqjvkwdlskpm

Defining the protein interaction network of human malaria parasite Plasmodium falciparum

Abhinay Ramaprasad, Arnab Pain, Timothy Ravasi
2012 Genomics  
This review aims to discuss the computational approaches used till date to construct a malaria protein interaction network and to catalog the functional predictions and biological inferences made from  ...  analysis of the PPI network.  ...  An example of a network inferred from this data is shown in Fig. 3 . Sensitivity of around 21% was obtained and surprisingly almost 95% of the "hypothetical proteins" were linked to known proteins.  ... 
doi:10.1016/j.ygeno.2011.11.006 pmid:22178265 fatcat:774lkkr73fetnbs3bu26rqp6zi

From Point B To Point A: Applying Toxicogenomics to Biological Inference

Kris Freeman
2005 Environmental Health Perspectives  
The latter is called biological inference-the highly iterative process of inferring cause-and-effect relationships from toxicogenomics data, using computation efforts linked to mathematics.  ...  One of the simplest models, Boolean networks, can capture multivariate gene relationships that can be inferred from measurement data.  ... 
doi:10.1289/ehp.113-a388 pmid:15929880 pmcid:PMC1257622 fatcat:gp3vpxtw6zbc7fgegnm4jgfl3m

Network constrained clustering for gene microarray data

D. Zhu, A. O Hero, H. Cheng, R. Khanna, A. Swaroop
2005 Bioinformatics  
Based on the construction of co-expression networks that consists of both significantly linear and nonlinear gene associations together with controlled biological and statistical significance, we can make  ...  Many bioinformatics problems can be tackled from a fresh angle offered by the network perspective.  ...  Inferring gene pathway from microarray is a relatively recent area in microarray data analysis. The gene pathway is a sequence of gene interactions leading to a specific biological endpoint function.  ... 
doi:10.1093/bioinformatics/bti655 pmid:16141248 fatcat:jciyev4gi5h5hnu7u2b2qo6dte

Reverse engineering of gene regulatory networks

K.-H. Cho, H.-S. Choi, S.H. Jung, J. Kim, J.-R. Kim, S.-M. Choo
2007 IET Systems Biology  
Various approaches of inferring GRNs from gene expression profiles and biological information, including machine learning approaches, have been reviewed, with a brief introduction of DNA microarray experiments  ...  In this regard, reconstructing gene regulatory networks (GRNs) forms the basis for the dynamical analysis of gene interactions and related effects on cellular control pathways.  ...  The procedure of inferring a DAG and CPD from an input data D is as follows.  ... 
doi:10.1049/iet-syb:20060075 pmid:17591174 fatcat:cdwoq3udmndalmd5yluxy4dx5y

Machine learning in bioinformatics

Pedro Larrañaga, Borja Calvo, Roberto Santana, Concha Bielza, Josu Galdiano, Iñaki Inza, José A. Lozano, Rubén Armañanzas, Guzmán Santafé, Aritz Pérez, Victor Robles
2006 Briefings in Bioinformatics  
It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization  ...  Applications in genomics, proteomics, systems biology, evolution and text mining are also shown.  ...  This work was partly supported by the University of the Basque Country, the Basque Government and the Ministry of Education and Science under grants 9/UPV 00140. 226  ... 
doi:10.1093/bib/bbk007 pmid:16761367 fatcat:4oss26occvhkjnetcr3sesnkcu
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