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About 6,374 papers for “high-throughput data integration”

Integration of high throughput data to detect groups of glioblastoma multiforme

Castells Domingo, Xavier · QRU Quaderns de Recerca en Urbanisme · 2013

The discovery of new subtypes of glioblastoma (Gb), grade IV glioma,\n\t\t\t\t based on high throughput molecular data, such as gene-expression and methylation\n\t\t\t\t microarrays, have become a challenging research eld for the scienti c community dedicated to neuro-oncology. The current diagnostic tools are not accurate enough and \n\t\t\t\t molecular signatures from high throughput data holds great promise to improve the patient's outcome. The detection of such sigatures requires a detailed statistical analysis of data to ensure their robustness. This study has compared the ablity to detect reliable signatures by separately using three different types of high throughput data (gene-expression, methylation and microRNA) and by integrating all the data types mentioned. A resampling approach was carried out based on multiple factor analysis and consensus clustering.. The objective of this study is to find signatures using different types of omics data in order to identify the most likely subtypes of glioblastomas (Gb). This study will be performed using data downloaded from the TCGA database, mainly consisting on 3 datasets: Gene expression, Methylation and miRNA microarrays. The application of appropriate methos will allow to achieve an improvement of the results respect to the results obtained using only gene expression microarray data

Cited by 0Open access

hipFG: High-throughput harmonization and integration pipeline for functional genomics data

Jeffrey Cifello, Pavel P. Kuksa, Naveensri Saravanan et al. · bioRxiv (Cold Spring Harbor Laboratory) · 2023

Preparing functional genomic (FG) data with diverse assay types and file formats for integration into analysis workflows that interpret genome-wide association and other studies is a significant and time-consuming challenge. Here we introduce hipFG, an automatically customized pipeline for efficient and scalable normalization of heterogenous FG data collections into standardized, indexed, rapidly searchable analysis-ready datasets while accounting for FG datatypes (e.g., chromatin interactions, genomic intervals, quantitative trait loci).

Cited by 0Open access

L-PBF High-Throughput Data Pipeline Approach for Multi-modal Integration

Kristen J. Hernandez, Thomas G. Ciardi, Rachel Yamamoto et al. · Integrating materials and manufacturing innovation · 2024

Abstract Metal-based additive manufacturing requires active monitoring solutions for assessing part quality. Multiple sensors and data streams, however, generate large heterogeneous data sets that are impractical for manual assessment and characterization. In this work, an automated pipeline is developed that enables feature extraction from high-speed camera video and multi-modal data analysis. The framework removes the need for manual assessment through the utilization of deep learning techniques and training models in a weakly supervised paradigm. We demonstrate this pipeline’s capability over 700,000 high-speed camera frames. The pipeline successfully extracts melt pool and spatter geometries and links them to corresponding pyrometry, radiography, and processparameter information. 715 individual prints are examined to reveal melt pool areas that exceeds 0.07 mm 2 and pyrometry signal over a threshold (375 pyrometry units) were more likely to have defects. These automated processes enable massive throughput of characterization techniques.

Cited by 5Open access

Integration and cross‐validation of high‐throughput gene expression data: comparing heterogeneous data sets

Vincent Detours, Jacques E. Dumont, Hugues Bersini et al. · FEBS Letters · 2003

Data analysis--not data production--is becoming the bottleneck in gene expression research. Data integration is necessary to cope with an ever increasing amount of data, to cross-validate noisy data sets, and to gain broad interdisciplinary views of large biological data sets. New Internet resources may help researchers to combine data sets across different gene expression platforms. However, noise and disparities in experimental protocols strongly limit data integration. A detailed review of four selected studies reveals how some of these limitations may be circumvented and illustrates what can be achieved through data integration.

Cited by 35

Data Integration of High-Throughput Proteomic and Transcriptomic Data based on Public Database Knowledge

Astrid Wachter · 2017

With the advance of high-throughput methods enabling deep characterization of the cell on different cellular layers, ideas to combine different data types for inference of regulatory processes have emerged. Such integration promises an improved molecular understanding of physiological and pathophysiological mechanisms, which aids in the identification of drug targets and in the design of therapies. Current integration approaches are based on the idea of reducing false negatives by reinforcing concordant information between datasets. In most cases optimized for a specific integration setting and data structure, these approaches are rarely accompanied by bioinformatic tools enabling researchers to work on their own datasets. In this thesis I present the public knowledge guided integration of phosphoproteomic, transcriptomic and proteomic time series datasets on the basis of signaling pathways. This integration allows to follow signaling cascades, to identify feedback regulation mechanisms and to observe the coordination of molecular processes in the cell by monitoring temporal variation upon external perturbation. To extract these cellular characteristics the cellular layers on which the individual datasets have been generated are taken into consideration. Separate downstream and upstream analyses of phosphoproteome and transcriptome data, respectively, and subsequent intersection analysis are coupled with a combination of network reconstruction and inference methods. Graphical consensus networks and co-regulation patterns can be extracted by this cross-platform analysis. Moreover, it provides high flexibility in terms of high-throughput platforms used for data generation as analysis is based on preprocessed datasets. On the examples of epidermal growth factor signaling and B cell receptor signaling we were able to show that the results gained by this integration method confirm known regulatory patterns but also point to interactions that were not described previously in these contexts. This is demonstrated by performing a response-specific analysis instead of the typical layer-specific analysis. Limitations of the approach described here are linked to database-bias and -dependency, to the low temporal resolution of high-throughput measurements and to data standardization. While overcoming these issues constitutes a challenge for the whole systems biology community, the integration approach itself can be optimized in future by working with refined disease-specific and tissue-specific signaling pathway models and database entries. The presented integration method was implemented as R software package 'pwOmics' and made available to other researchers.

Cited by 0Open access

Systems Analysis of Biliary Atresia Through Integration of High-Throughput Biological Data

Jun Min, Mylarappa Ningappa, Juhoon So et al. · Frontiers in Physiology · 2020

Biliary atresia (BA), blockage of the proper bile flow due to loss of extrahepatic bile ducts, is a rare, complex disease of the liver and the bile ducts with unknown etiology. Despite ongoing investigations to understand its complex pathogenesis, BA remains the most common cause of liver failure requiring liver transplantation in children. To elucidate underlying mechanisms, we analyzed the different types of high-throughput genomic and transcriptomic data collected from the blood and liver tissue samples of children suffering from BA. Through use of a novel integrative approach, we identified potential biomarkers and over-represented biological functions and pathways to derive a comprehensive network showing the dysfunctional mechanisms associated with BA. One of the pathways highlighted in the integrative network was hypoxia signaling. Perturbation with hypoxia inducible factor activator, dimethyloxalylglycine, induced the biliary defects of BA in a zebrafish model, serving as a validation for our studies. Our approach enables a systems-level understanding of human BA biology that is highlighted by the interaction between key biological functions such as fibrosis, inflammation, immunity, hypoxia, and development.

Cited by 7Open access

A semi-automated data integration workflow to enable high-throughput crop modelling

Benjamin M. L. Leroy, Sebastian Burkhart, Lara Kempf et al. · Gesellschaft für Informatik (GI) · 2026

Crop simulation models are important tools for yield projection and analysis but are rarely used for real-time decision support due to persistent bottlenecks in data management and processing workflows to generate input datasets. We present a blueprint for a semi-automated data management workflow that enables high-throughput simulation, integrating diverse source data into a rapid, scalable pipeline. The modular workflow leverages established standards in geospatial science, agronomy, and crop modeling to assemble high-quality inputs for crop models. The blueprint outlines the technical requirements to operationalize crop models, enabling broader applications as decision-support tools.

Cited by 0Open access

Integration of high-throughput proteomic data and complementary omics layers with PriOmics

Robin Kosch, Katharina Limm, Annette M. Staiger et al. · Genome Research · 2025

High-throughput bottom-up proteomic data cover thousands of proteins and related co- and post-translational modifications (CTMs/PTMs). Yet, it remains an open question how to holistically explore such data and their relationship to complementary omics/phenotypic information. Graphical models are particularly suited to study molecular networks and underlying regulatory mechanisms, as they can distinguish direct from indirect relationships, aside from their generalizability to diverse data types. Here, we propose PriOmics to integrate proteomic data with complementary omics and phenotypic data. PriOmics models intensities of individual proteotypic peptides and incorporates their protein affiliation as prior knowledge to resolve statistical relationships between proteins and CTMs/PTMs. This is verified in simulation studies, which also demonstrate that PriOmics can disentangle regulatory effects of protein modifications from those of respective protein abundances. These findings are substantiated in a diffuse large B cell lymphoma (DLBCL) data set in which we integrate SWATH-MS-based proteomics with transcriptomic and phenotypic data.

Cited by 0Open access

Dengue infection atlas - A systems biology approach using high-throughput data integration

Prasad Siddavatam · Purdue e-Pubs (Purdue University System) · 2012

Dengue virus (DENV) causes millions of infections around the world and is classified as an emerging pathogen in the USA. Though some of the steps in Dengue virus infection and replication are known, understanding of molecular mechanisms is still elusive. An unpublished microarray gene expression data from Dengue infected HuH7 liver cells at 18 hr and 24 hr p.i. was analyzed to identify differentially expressed genes (DEGs). Transcription of 30 genes was found to be perturbed (26 induced and four down-regulated). Pathway and functional analysis of the DEGs, strongly suggests that the Endoplasmic Reticulum plays an important role in Dengue virus infection. Integration of high-throughput data, DEGs, human proteins interacting with Dengue proteins (DHPINs) and human genes required for Dengue replication (DRNAi), confirms the significance of the Endoplasmic reticulum-associated protein-degradation pathway in DENV infection. DEGs identified in this work did not show extensive overlap with previously reported DEGs. Application of consistent analytical and statistical techniques to the gene expression data from ten DENV infection studies did not substantially improve the amount of overlap between the reported DEGs. Meta-analysis of the combined data using a leave-out dataset cross-validation approach, however, found a total of 1,568 differentially expressed genes. In analyses that include time points early in infection, pathways related to cancer and cell junction are enriched in DEGs, but in analyses in which early time-points are excluded, metabolic and cell signaling related pathways are enriched. This confirms that there is a progressive shift in the effect of the virus on cellular gene expression as infection progresses. Topological profiling, with respect to the human protein-interaction network (HPIN), of DEGs, DRNAi, and DHPINs from DENV shows that DEGs and DHPINs prefer proteins with high degree and betweenness centrality measures but DRNAi do not show a similar trend. However, DENV does not target proteins with high clustering coefficient. DHPINs do not interact with hub proteins (top 5% degree proteins) but rather interact with bottleneck proteins (top 5% betweenness centrality proteins). The combination of these studies substantially extends our understanding of the effects of Dengue virus on host molecular processes.

Cited by 0

Conceptual overview of the integration of data from high-tech animal instrumentation with high-throughput sequencing data.

Aaron B. A. Shafer (102435), Joseph M. Northrup (117628), Martin Wikelski (4024) et al. · Figshare · 2016

<p>The top section highlights that tissue collection for sequencing and instrumentation fitting naturally occur at the same time. The two side boxes show the separate analysis streams—ecological (left) and genomics (right). Integrative approaches making use of both data types are listed in the middle. Image contributors: raccoon in trap by Woodstream Corporation; map of habitat use was generated in ArcMap by Kevin White (Alaska Department of Fish & Game); radio-collar from LOTEK WIRELESS Inc.</p>

Cited by 0Open access

Application of supervised classification methods and integration of heterogeneous data for high-throughput transcriptomic data

Vincent Guillemot · HAL (Le Centre pour la Communication Scientifique Directe) · 2010

Les methodes d'apprentissage supervise sont appliquees depuis recemment a des jeux de donnees de puces a ADN, afin d'une part d'extraire des genes impliques dans les differences entre les classes d'individus etudies et d'autre part de construire une fonction de classification permettant de predire la classe d'un nouvel individu. Ces donnees de puces a ADN peuvent etre accompagnees d'une information precieuse decrivant les interactions entre les variables (les genes). Cette information est regroupee sous la forme de reseaux de regulations genetiques (RRG). L'objectif de la these est de realiser l'integration de l'information contenue dans ces RRGs dans une methode de classification supervisee binaire. Nous proposons une nouvelle methode, graph Constrained Discriminant Analysis (gCDA), basee sur l'analyse discriminante de Fisher. Les methodes de la litterature se proposent d'implementer la contrainte suivante : les genes qui sont voisins dans le RRG doivent avoir des poids proches, voire identiques, dans la fonction de classification. A contrepoint de ces methodes, gCDA est basee sur l'estimation regularisee des matrices de variance covariance qui sont utilisees dans l'analyse discriminante de Fisher. Les estimateurs utilises dans gCDA prennent en compte l'information contenue dans les RRGs disponibles a priori grâce aux proprietes des modeles graphiques gaussiens. gCDA est comparee aux methodes de la litterature sur des donnees simulees, donnees pour lesquelles le graphe sous-jacent est parfaitement connu. Dans le cas de donnees reelles, le graphe sous-jacent decrivant les interactions entre variables n'est pas connu. Nous nous sommes donc egalement interesses a des methodes permettant d'inferer de tels graphes a partir de donnees transcriptomiques. Enfin, des resultats sont obtenus sur trois jeux de donnees reelles. Les RRG ont ete inferes soit sur des jeux de donnees de meme nature mais independants (c'est-a-dire concernant des individus qui ne sont pas utilises pour en classification), soit sur une partie independante du jeu de donnees etudie. Nous montrons une amelioration notable des performances de classification sur ces jeux de donnees lorsque gCDA est utilisee par rapport a l'utilisation des methodes de la litterature decrites dans la deuxieme partie.

Cited by 0Open access

Integration of Dosimetry, Exposure, and High-Throughput Screening Data in Chemical Toxicity Assessment

Barbara A. Wetmore, John F. Wambaugh, Stephen Ferguson et al. · Toxicological Sciences · 2011

High-throughput in vitro toxicity screening can provide an efficient way to identify potential biological targets for chemicals. However, relying on nominal assay concentrations may misrepresent potential in vivo effects of these chemicals due to differences in bioavailability, clearance, and exposure. Hepatic metabolic clearance and plasma protein binding were experimentally measured for 239 ToxCast Phase I chemicals. The experimental data were used in a population-based in vitro-to-in vivo extrapolation model to estimate the daily human oral dose, called the oral equivalent dose, necessary to produce steady-state in vivo blood concentrations equivalent to in vitro AC(50) (concentration at 50% of maximum activity) or lowest effective concentration values across more than 500 in vitro assays. The estimated steady-state oral equivalent doses associated with the in vitro assays were compared with chronic aggregate human oral exposure estimates to assess whether in vitro bioactivity would be expected at the dose-equivalent level of human exposure. A total of 18 (9.9%) chemicals for which human oral exposure estimates were available had oral equivalent doses at levels equal to or less than the highest estimated U.S. population exposures. Ranking the chemicals by nominal assay concentrations would have resulted in different chemicals being prioritized. The in vitro assay endpoints with oral equivalent doses lower than the human exposure estimates included cell growth kinetics, cytokine and cytochrome P450 expression, and cytochrome P450 inhibition. The incorporation of dosimetry and exposure provide necessary context for interpretation of in vitro toxicity screening data and are important considerations in determining chemical testing priorities.

Cited by 406

Integration and analysis of multi-type high-throughput data for biomolecular knowledge discovery

Erik Bongcam‐Rudloff · EMBnet journal · 2012

Erik Bongcam-Rudloff received his doctorate in medical sciences from Uppsala University, Sweden. He is now the Director of the SLU Global Bioinformatics Centre (SGBC) at the Swedish University of Agricultural Sciences. Erik was the chairman of EMBnet (2003-2010), a science-based group of worldwide collaborating bioinformatics nodes. He also coordinated the "Test Cases" work package 4 in the FP6 EMBRACE project (2005-2010). The goal of WP4 was to collect test cases from the scientific community to identify real research problems and to provide solutions for content and tool integration. Bongcam-Rudloff is today Chair of SeqAhead, a Biomedical European COST Action: "Next Generation Sequencing Data Analysis Network" and Coordinator of ALLBIO, a FP7 project: "Broadening the Bioinformatics Infrastructure to unicellular, animal, and plant science". He is also the founder of eBioinformatics.org the creators of eBiotools, eBioKit, eBioX and eBioKit. His main research deals with development of bioinformatics solutions for the Life Sciences community. In this talk he will discuss the new techniques that are now driving the generation of knowledge (especially in biomedicine and molecular life sciences) to new dimensions eg. NGS. He will also discuss the new opportunities in human and non-human research that these techniques create but also the new challenges in the design of ontologies for data and methods, and choosing common interoperability standards.

Cited by 0Open access