Formalization and Semantic Integration of Heterogeneous Omics Annotations for Exploratory Searches

2020 ◽  
Vol 15 ◽  
Author(s):  
Omer Irshad ◽  
Muhammad Usman Ghani Khan
Keyword(s):  
Data Integration ◽  
Semantic Integration ◽  
Biological Data ◽  
Cellular System ◽  
Formal Specifications ◽  
Integration Model ◽  
Geographically Dispersed ◽  
Proposed Model ◽  
Data Heterogeneity ◽  
Biological Entities

Aim: To facilitate researchers and practitioners for unveiling the mysterious functional aspects of human cellular system through performing exploratory searching on semantically integrated heterogeneous and geographically dispersed omics annotations. Background: Improving health standards of life is one of the motives which continuously instigates researchers and practitioners to strive for uncovering the mysterious aspects of human cellular system. Inferring new knowledge from known facts always requires reasonably large amount of data in well-structured, integrated and unified form. Due to the advent of especially high throughput and sensor technologies, biological data is growing heterogeneously and geographically at astronomical rate. Several data integration systems have been deployed to cope with the issues of data heterogeneity and global dispersion. Systems based on semantic data integration models are more flexible and expandable than syntax-based ones but still lack aspect-based data integration, persistence and querying. Furthermore, these systems do not fully support to warehouse biological entities in the form of semantic associations as naturally possessed by the human cell. Objective: To develop aspect-oriented formal data integration model for semantically integrating heterogeneous and geographically dispersed omics annotations for providing exploratory querying on integrated data. Method: We propose an aspect-oriented formal data integration model which uses web semantics standards to formally specify its each construct. Proposed model supports aspect-oriented representation of biological entities while addressing the issues of data heterogeneity and global dispersion. It associates and warehouses biological entities in the way they relate with Result: To show the significance of proposed model, we developed a data warehouse and information retrieval system based on proposed model compliant multi-layered and multi-modular software architecture. Results show that our model supports well for gathering, associating, integrating, persisting and querying each entity with respect to its all possible aspects within or across the various associated omics layers. Conclusion: Formal specifications better facilitate for addressing data integration issues by providing formal means for understanding omics data based on meaning instead of syntax

scholarly journals Methods for biological data integration: perspectives and challenges

2015 ◽  
Vol 12 (112) ◽  
pp. 20150571 ◽  
Author(s):  
Vladimir Gligorijević ◽  
Nataša Pržulj
Keyword(s):  
Data Integration ◽  
Heterogeneous Data ◽  
Biological Data ◽  
Data Types ◽  
Specific Data ◽  
Advantages And Disadvantages ◽  
Technological Advances ◽  
Biological Data Integration ◽  
Biological Entities ◽  
Further Development

Rapid technological advances have led to the production of different types of biological data and enabled construction of complex networks with various types of interactions between diverse biological entities. Standard network data analysis methods were shown to be limited in dealing with such heterogeneous networked data and consequently, new methods for integrative data analyses have been proposed. The integrative methods can collectively mine multiple types of biological data and produce more holistic, systems-level biological insights. We survey recent methods for collective mining ( integration ) of various types of networked biological data. We compare different state-of-the-art methods for data integration and highlight their advantages and disadvantages in addressing important biological problems. We identify the important computational challenges of these methods and provide a general guideline for which methods are suited for specific biological problems, or specific data types. Moreover, we propose that recent non-negative matrix factorization-based approaches may become the integration methodology of choice, as they are well suited and accurate in dealing with heterogeneous data and have many opportunities for further development.

scholarly journals Multimedia Quality Integration Using Piecewise Function

2011 ◽  
Vol 1 ◽  
pp. 375-380
Author(s):  
Shu Ai Wan ◽  
Kai Fang Yang ◽  
Hai Yong Zhou
Keyword(s):  
Visual Quality ◽  
Experimental Results ◽  
Integration Model ◽  
Quality Performance ◽  
Piecewise Function ◽  
Proposed Model ◽  
Constant Coefficients ◽  
Theoretical Analyses ◽  
Different Levels

In this paper the important issue of multimedia quality evaluation is concerned, given the unimodal quality of audio and video. Firstly, the quality integration model recommended in G.1070 is evaluated using experimental results. Theoretical analyses aide empirical observations suggest that the constant coefficients used in the G.1070 model should actually be piecewise adjusted for different levels of audio and visual quality. Then a piecewise function is proposed to perform multimedia quality integration under different levels of the audio and visual quality. Performance gain observed from experimental results substantiates the effectiveness of the proposed model.

Semantic Interation, Text Mining, Tools and Technologies

2015 ◽  
pp. 47-63
Author(s):  
Chandrakant Ekkirala
Keyword(s):  
Text Mining ◽  
Data Integration ◽  
Semantic Integration ◽  
Semantic Technologies ◽  
Integration Technique ◽  
Semantic Web Technologies ◽  
Web Technologies ◽  
Periodic Data ◽  
Integration Techniques ◽  
Mining Algorithms

Semantic technologies have gained prominence over the last several years. Semantic technologies are explored in detail and semantic integration of data will be outlined. The various data integration techniques and approaches will also be touched upon. Text Mining, different associated algorithms and the various tools and technologies used in text mining will be enumerated in detail. The chapter will have the following sections – 1. Data Integration Techniques • Data Integration Technique – Extraction, Transformation and Loading (ETL) • Data Integration Technique – Data Federation 2. Data Integration Approaches • Need Based Data Integration • Periodic Data Integration • Continuous Data Integration 3. Semantic Integration 4. Semantic Technologies 5. Semantic Web Technologies 6. Text Mining 7. Text Mining Algorithms 8. Tools and Technologies for Text Mining

Analysis and Integration of Biological Data

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.

Simulating E-business Innovation Process Improvement with Virtual Teams Across Europe and Asia

2011 ◽  
pp. 1023-1043
Author(s):  
Kenneth D. Strang ◽  
Cliff E.L. Chan
Keyword(s):  
Virtual Teams ◽  
New Product Development ◽  
Quality Function Deployment ◽  
Phase 1 ◽  
Innovation Process ◽  
New Product ◽  
Geographically Dispersed ◽  
Proposed Model ◽  
The Voice

In this article, E-business new product development innovation processes were studied at four enterprises across Europe and Asia. E-entrepreneurship innovation was improved using a quality of idea priority model. The conventional quality function deployment phase 1 matrix was revised to increase the voice of customers and engineer quality of idea decision-making. The proposed model was simulated with geographically dispersed virtual teams (based on production data). Statistical analyses were applied to test the hypothesis that an improved innovation process could better discriminate between new product return on investment pass or fail probability.

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