Knowledge and Theme Discovery across Very Large Biological Data Sets Using Distributed Queries: A Prototype Combining Unstructured and Structured Data

Abstract Background In recent years, to investigate challenging bioinformatics problems, the utilization of multiple genomic and proteomic sources has become immensely popular among researchers. One such issue is feature or gene selection and identifying relevant and non-redundant marker genes from high dimensional gene expression data sets. In that context, designing an efficient feature selection algorithm exploiting knowledge from multiple potential biological resources may be an effective way to understand the spectrum of cancer or other diseases with applications in specific epidemiology for a particular population. Results In the current article, we design the feature selection and marker gene detection as a multi-view multi-objective clustering problem. Regarding that, we propose an Unsupervised Multi-View Multi-Objective clustering-based gene selection approach called UMVMO-select. Three important resources of biological data (gene ontology, protein interaction data, protein sequence) along with gene expression values are collectively utilized to design two different views. UMVMO-select aims to reduce gene space without/minimally compromising the sample classification efficiency and determines relevant and non-redundant gene markers from three cancer gene expression benchmark data sets. Conclusion A thorough comparative analysis has been performed with five clustering and nine existing feature selection methods with respect to several internal and external validity metrics. Obtained results reveal the supremacy of the proposed method. Reported results are also validated through a proper biological significance test and heatmap plotting.

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Data‐Driven simulation of inelastic materials using structured data sets and tangential transition rules

PAMM ◽

10.1002/pamm.202000241 ◽

2021 ◽

Vol 20 (1) ◽

Author(s):

Kerem Ciftci ◽

Klaus Hackl

Keyword(s):

Structured Data ◽

Data Driven ◽

Data Sets ◽

Transition Rules

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BioNetLink - An Architecture for Working with Network Data

Journal of Integrative Bioinformatics ◽

10.1515/jib-2014-241 ◽

2014 ◽

Vol 11 (2) ◽

pp. 68-79

Author(s):

Matthias Klapperstück ◽

Falk Schreiber

Keyword(s):

Experimental Data ◽

Biological Networks ◽

Regulatory Networks ◽

Large Data ◽

Biological Data ◽

Network Data ◽

Data Sets ◽

Dynamic Views ◽

Gene Regulatory ◽

Multiple Network

Summary The visualization of biological data gained increasing importance in the last years. There is a large number of methods and software tools available that visualize biological data including the combination of measured experimental data and biological networks. With growing size of networks their handling and exploration becomes a challenging task for the user. In addition, scientists also have an interest in not just investigating a single kind of network, but on the combination of different types of networks, such as metabolic, gene regulatory and protein interaction networks. Therefore, fast access, abstract and dynamic views, and intuitive exploratory methods should be provided to search and extract information from the networks. This paper will introduce a conceptual framework for handling and combining multiple network sources that enables abstract viewing and exploration of large data sets including additional experimental data. It will introduce a three-tier structure that links network data to multiple network views, discuss a proof of concept implementation, and shows a specific visualization method for combining metabolic and gene regulatory networks in an example.

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iSEE: Interactive SummarizedExperiment Explorer

F1000Research ◽

10.12688/f1000research.14966.1 ◽

2018 ◽

Vol 7 ◽

pp. 741 ◽

Cited By ~ 26

Author(s):

Kevin Rue-Albrecht ◽

Federico Marini ◽

Charlotte Soneson ◽

Aaron T.L. Lun

Keyword(s):

High Throughput ◽

Software Package ◽

Biological Data ◽

Data Exploration ◽

Data Sets ◽

Proteomics Data ◽

Code Tracking ◽

Dynamic Linking ◽

Interactive Visualisation ◽

Visual Interface

Data exploration is critical to the comprehension of large biological data sets generated by high-throughput assays such as sequencing. However, most existing tools for interactive visualisation are limited to specific assays or analyses. Here, we present the iSEE (Interactive SummarizedExperiment Explorer) software package, which provides a general visual interface for exploring data in a SummarizedExperiment object. iSEE is directly compatible with many existing R/Bioconductor packages for analysing high-throughput biological data, and provides useful features such as simultaneous examination of (meta)data and analysis results, dynamic linking between plots and code tracking for reproducibility. We demonstrate the utility and flexibility of iSEE by applying it to explore a range of real transcriptomics and proteomics data sets.

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Denoising large-scale biological data using network filters

10.21203/rs.3.rs-66071/v2 ◽

2021 ◽

Author(s):

Andrew J Kavran ◽

Aaron Clauset

Keyword(s):

Large Scale ◽

Synthetic Data ◽

Interaction Network ◽

Learning Task ◽

Biological Data ◽

Data Sets ◽

Proteomics Data ◽

Life History Variation ◽

Wide Range ◽

Underlying Processes

Abstract Background: Large-scale biological data sets are often contaminated by noise, which can impede accurate inferences about underlying processes. Such measurement noise can arise from endogenous biological factors like cell cycle and life history variation, and from exogenous technical factors like sample preparation and instrument variation.Results: We describe a general method for automatically reducing noise in large-scale biological data sets. This method uses an interaction network to identify groups of correlated or anti-correlated measurements that can be combined or “ﬁltered” to better recover an underlying biological signal. Similar to the process of denoising an image, a single network ﬁlter may be applied to an entire system, or the system may be ﬁrst decomposed into distinct modules and a diﬀerent ﬁlter applied to each. Applied to synthetic data with known network structure and signal, network ﬁlters accurately reduce noise across a wide range of noise levels and structures. Applied to a machine learning task of predicting changes in human protein expression in healthy and cancerous tissues, network ﬁltering prior to training increases accuracy up to 43% compared to using unﬁltered data.Conclusions: Network ﬁlters are a general way to denoise biological data and can account for both correlation and anti-correlation between diﬀerent measurements. Furthermore, we ﬁnd that partitioning a network prior to ﬁltering can signiﬁcantly reduce errors in networks with heterogenous data and correlation patterns, and this approach outperforms existing diﬀusion based methods. Our results on proteomics data indicate the broad potential utility of network ﬁlters to applications in systems biology.

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Improving the Quality of Linked Data Using Statistical Distributions

Information Retrieval and Management ◽

10.4018/978-1-5225-5191-1.ch074 ◽

2018 ◽

pp. 1638-1664 ◽

Cited By ~ 1

Author(s):

Heiko Paulheim ◽

Christian Bizer

Keyword(s):

Knowledge Base ◽

Linked Data ◽

Relational Databases ◽

Knowledge Bases ◽

Structured Data ◽

Data Sources ◽

Data Sets ◽

Statistical Distributions ◽

The Web

Linked Data on the Web is either created from structured data sources (such as relational databases), from semi-structured sources (such as Wikipedia), or from unstructured sources (such as text). In the latter two cases, the generated Linked Data will likely be noisy and incomplete. In this paper, we present two algorithms that exploit statistical distributions of properties and types for enhancing the quality of incomplete and noisy Linked Data sets: SDType adds missing type statements, and SDValidate identifies faulty statements. Neither of the algorithms uses external knowledge, i.e., they operate only on the data itself. We evaluate the algorithms on the DBpedia and NELL knowledge bases, showing that they are both accurate as well as scalable. Both algorithms have been used for building the DBpedia 3.9 release: With SDType, 3.4 million missing type statements have been added, while using SDValidate, 13,000 erroneous RDF statements have been removed from the knowledge base.

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Analysis and Integration of Biological Data

Knowledge Discovery Practices and Emerging Applications of Data Mining - Advances in Data Mining and Database Management ◽

10.4018/978-1-60960-067-9.ch014 ◽

2010 ◽

pp. 287-314

Author(s):

Diego Milone ◽

Georgina Stegmayer ◽

Matías Gerard ◽

Laura Kamenetzky ◽

Mariana López ◽

...

Keyword(s):

Data Integration ◽

New Technologies ◽

Biological Significance ◽

Neural Model ◽

Biological Data ◽

Data Sets ◽

Clustering Methods ◽

Gene Expressions ◽

Internal Coherence ◽

Biological Data Integration

The volume of information derived from post genomic technologies is rapidly increasing. Due to the amount of involved data, novel computational methods are needed for the analysis and knowledge discovery into the massive data sets produced by these new technologies. Furthermore, data integration is also gaining attention for merging signals from different sources in order to discover unknown relations. This chapter presents a pipeline for biological data integration and discovery of a priori unknown relationships between gene expressions and metabolite accumulations. In this pipeline, two standard clustering methods are compared against a novel neural network approach. The neural model provides a simple visualization interface for identification of coordinated patterns variations, independently of the number of produced clusters. Several quality measurements have been defined for the evaluation of the clustering results obtained on a case study involving transcriptomic and metabolomic profiles from tomato fruits. Moreover, a method is proposed for the evaluation of the biological significance of the clusters found. The neural model has shown a high performance in most of the quality measures, with internal coherence in all the identified clusters and better visualization capabilities.

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