Data Mining in Gene Expression Analysis

2008 ◽  
pp. 1643-1673
Author(s):  
Jilin Han ◽  
Le Gruenwald ◽  
Tyrrell Conway
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Biological Information ◽  
Future Research ◽  
Gene Expression Data Analysis ◽  
Experimental Conditions ◽  
Single Experiment ◽  
Important Approach ◽  
Future Research Directions ◽  
Gene Expression Levels

The study of gene expression levels under defined experimental conditions is an important approach to understand how a living cell works. High-throughput microarray technology is a very powerful tool for simultaneously studying thousands of genes in a single experiment. This revolutionary technology results in an extensive amount of data, which raises an important question: how to extract meaningful biological information from these data? In this chapter, we survey data mining techniques that have been used for clustering, classification and association rules for gene expression data analysis. In addition, we provide a comprehensive list of currently available commercial and academic data mining software together with their features. Lastly, we suggest future research directions.

Data Mining in Gene Expression Analysis

2011 ◽  
pp. 375-418
Author(s):  
Jilin Han ◽  
Le Gruenwald ◽  
Tyrrell Conway
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Biological Information ◽  
Future Research ◽  
Gene Expression Data Analysis ◽  
Experimental Conditions ◽  
Single Experiment ◽  
Important Approach ◽  
Future Research Directions ◽  
Gene Expression Levels

The study of gene expression levels under defined experimental conditions is an important approach to understand how a living cell works. High-throughput microarray technology is a very powerful tool for simultaneously studying thousands of genes in a single experiment. This revolutionary technology results in an extensive amount of data, which raises an important question: how to extract meaningful biological information from these data? In this chapter, we survey data mining techniques that have been used for clustering, classification and association rules for gene expression data analysis. In addition, we provide a comprehensive list of currently available commercial and academic data mining software together with their features. Lastly, we suggest future research directions.

Information Extraction from Microarray Data

2014 ◽  
Vol 25 (1) ◽  
pp. 29-58 ◽  
Author(s):  
Alessandro Fiori ◽  
Alberto Grand ◽  
Giulia Bruno ◽  
Francesco Gavino Brundu ◽  
Domenico Schioppa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Microarray Data ◽  
Regulatory Networks ◽  
Molecular Data ◽  
Experimental Conditions ◽  
Single Experiment ◽  
Building Models ◽  
Critical Issues ◽  
Highly Correlated

Nowadays, a huge amount of high throughput molecular data are available for analysis and provide novel and useful insights into complex biological systems, through the acquisition of a high-resolution picture of their molecular status in defined experimental conditions. In this context, microarrays are a powerful tool to analyze thousands of gene expression values with a single experiment. A number of approaches have been developed to detecting genes highly correlated to diseases, selecting genes that exhibit a similar behavior under specific conditions, building models to predict disease outcome based on genetic profiles, and inferring regulatory networks. This paper discusses popular and recent data mining techniques (i.e., Feature Selection, Clustering, Classification, and Association Rule Mining) applied to microarray data. The main characteristics of microarray data and preprocessing procedures are presented to understand the critical issues introduced by gene expression values analysis. Each technique is analyzed, and relevant examples of pertinent literature are reported. Moreover, real use cases exploiting analytic pipelines that use these methods are also introduced. Finally, future directions of data mining research on microarray data are envisioned.

Information Extraction from Microarray Data

2016 ◽  
pp. 1180-1211 ◽  
Author(s):  
Alessandro Fiori ◽  
Alberto Grand ◽  
Giulia Bruno ◽  
Francesco Gavino Brundu ◽  
Domenico Schioppa ◽  
...  
Keyword(s):  
Gene Expression ◽  
Data Mining ◽  
Microarray Data ◽  
Regulatory Networks ◽  
Molecular Data ◽  
Experimental Conditions ◽  
Single Experiment ◽  
Building Models ◽  
Critical Issues ◽  
Highly Correlated

Nowadays, a huge amount of high throughput molecular data are available for analysis and provide novel and useful insights into complex biological systems, through the acquisition of a high-resolution picture of their molecular status in defined experimental conditions. In this context, microarrays are a powerful tool to analyze thousands of gene expression values with a single experiment. A number of approaches have been developed to detecting genes highly correlated to diseases, selecting genes that exhibit a similar behavior under specific conditions, building models to predict disease outcome based on genetic profiles, and inferring regulatory networks. This paper discusses popular and recent data mining techniques (i.e., Feature Selection, Clustering, Classification, and Association Rule Mining) applied to microarray data. The main characteristics of microarray data and preprocessing procedures are presented to understand the critical issues introduced by gene expression values analysis. Each technique is analyzed, and relevant examples of pertinent literature are reported. Moreover, real use cases exploiting analytic pipelines that use these methods are also introduced. Finally, future directions of data mining research on microarray data are envisioned.

scholarly journals Validation of Reference Genes for Studying Different Abiotic Stresses in oat (Avena sativa L.) by RT-qPCR

Plants ◽  
2021 ◽  
Vol 10 (7) ◽  
pp. 1272
Author(s):  
Judit Tajti ◽  
Magda Pál ◽  
Tibor Janda
Keyword(s):  
Gene Expression ◽  
Avena Sativa ◽  
Abiotic Stresses ◽  
Reference Genes ◽  
Nutritional Value ◽  
Tissue Type ◽  
Future Research ◽  
Experimental Conditions ◽  
Expression Studies ◽  
Gene Expression Studies

Oat (Avena sativa L.) is a widely cultivated cereal with high nutritional value and it is grown mainly in temperate regions. The number of studies dealing with gene expression changes in oat continues to increase, and to obtain reliable RT-qPCR results it is essential to establish and use reference genes with the least possible influence caused by experimental conditions. However, no detailed study has been conducted on reference genes in different tissues of oat under diverse abiotic stress conditions. In our work, nine candidate reference genes (ACT, TUB, CYP, GAPD, UBC, EF1, TBP, ADPR, PGD) were chosen and analysed by four statistical methods (GeNorm, Normfinder, BestKeeper, RefFinder). Samples were taken from two tissues (leaves and roots) of 13-day-old oat plants exposed to five abiotic stresses (drought, salt, heavy metal, low and high temperatures). ADPR was the top-rated reference gene for all samples, while different genes proved to be the most stable depending on tissue type and treatment combinations. TUB and EF1 were most affected by the treatments in general. Validation of reference genes was carried out by PAL expression analysis, which further confirmed their reliability. These results can contribute to reliable gene expression studies for future research in cultivated oat.

Learning Analytics

2018 ◽  
pp. 5158-5168
Author(s):  
Constanţa-Nicoleta Bodea ◽  
Maria-Iuliana Dascalu ◽  
Radu Ioan Mogos ◽  
Stelian Stancu
Keyword(s):  
Data Mining ◽  
Learning Analytics ◽  
Educational Data Mining ◽  
Future Research ◽  
Educational Systems ◽  
Main Section ◽  
Research Directions ◽  
Data Intensive ◽  
Academic Analytics ◽  
Future Research Directions

Reinforcement of the technology-enhanced education transformed education into a data-intensive domain. As in many other data-intensive domains, the interest for data analysis through various analytics is growing. The article starts by defining LA, with relevant views on the literature. A discussion about the relationships between LA, educational data mining and academic analytics is included in the background section. In the main section of the article, the learning analytics, as an emerging trend in the educational systems is describe, by discussing the main issues, controversies, problems on this topic. Final part of the article presents the future research directions and the conclusion.

Metric Methods in Data Mining

2008 ◽  
pp. 849-879
Author(s):  
Dan A. Simovici
Keyword(s):  
Data Mining ◽  
Metric Space ◽  
Training Data ◽  
Future Research ◽  
Open Problems ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Future Research Directions ◽  
Major Data ◽  
Geometric Study

This chapter presents data mining techniques that make use of metrics defined on the set of partitions of finite sets. Partitions are naturally associated with object attributes and major data mining problem such as classification, clustering, and data preparation benefit from an algebraic and geometric study of the metric space of partitions. The metrics we find most useful are derived from a generalization of the entropic metric. We discuss techniques that produce smaller classifiers, allow incremental clustering of categorical data and help user to better prepare training data for constructing classifiers. Finally, we discuss open problems and future research directions.

Unit of Analysis in Digitally-Enabled Electronic Procurement Research

2020 ◽  
pp. 83-103
Author(s):  
Md Mahbubur Rahim ◽  
Maryam Jabberzadeh ◽  
Nergiz Ilhan
Keyword(s):  
Data Mining ◽  
Cloud Computing ◽  
Critical Issue ◽  
Future Research ◽  
Research Directions ◽  
Research Issues ◽  
Unit Of Analysis ◽  
Electronic Procurement ◽  
Future Research Directions ◽  
Methodological Concern

E-procurement systems that have been in place for over a decade have begun incorporating digital tools like big data, cloud computing, internet of things, and data mining. Hence, there exists a rich literature on earlier e-procurement systems and advanced digitally-enabled e-procurement systems. Existing literature on these systems addresses many research issues (e.g., adoption) associated with e-procurement. However, one critical issue that has so far received no rigorous attention is about “unit of analysis,” a methodological concern of importance, for e-procurement research context. Hence, the aim of this chapter is twofold: 1) to discuss how the notion of “unit of analysis” has been conceptualised in the e-procurement literature and 2) to discuss how its use has been justified by e-procurement scholars to address the research issues under investigation. Finally, the chapter provides several interesting findings and outlines future research directions.

Computational and Data Mining Perspectives on HIV/AIDS in Big Data Era

2022 ◽  
pp. 1477-1503
Author(s):  
Ali Al Mazari
Keyword(s):  
Data Mining ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Healthcare Sector ◽  
Future Research ◽  
Research Directions ◽  
Scientific Disciplines ◽  
Future Research Directions ◽  
Hiv Aids

HIV/AIDS big data analytics evolved as a potential initiative enabling the connection between three major scientific disciplines: (1) the HIV biology emergence and evolution; (2) the clinical and medical complex problems and practices associated with the infections and diseases; and (3) the computational methods for the mining of HIV/AIDS biological, medical, and clinical big data. This chapter provides a review on the computational and data mining perspectives on HIV/AIDS in big data era. The chapter focuses on the research opportunities in this domain, identifies the challenges facing the development of big data analytics in HIV/AIDS domain, and then highlights the future research directions of big data in the healthcare sector.

Data Mining Analytics for Crime Security Investigation and Intrusion Detection

2016 ◽  
pp. 212-244
Author(s):  
Boutheina Fessi ◽  
Yacine Djemaiel ◽  
Noureddine Boudriga
Keyword(s):  
Data Mining ◽  
Dynamic Environments ◽  
Future Research ◽  
Cyber Crime ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Crime Investigation ◽  
Security Investigation ◽  
Future Research Directions ◽  
Digital Investigation

This chapter provides a review about the usefulness of applying data mining techniques to detect intrusion within dynamic environments and its contribution in digital investigation. Numerous applications and models are described based on data mining analytics. The chapter addresses also different requirements that should be fulfilled to efficiently perform cyber-crime investigation based on data mining analytics. It states, at the end, future research directions related to cyber-crime investigation that could be investigated and presents new trends of data mining techniques that deal with big data to detect attacks.

Data Mining Analytics for Crime Security Investigation and Intrusion Detection

2020 ◽  
pp. 700-725
Author(s):  
Boutheina A. Fessi ◽  
Yacine Djemaiel ◽  
Noureddine Boudriga
Keyword(s):  
Data Mining ◽  
Dynamic Environments ◽  
Future Research ◽  
Cyber Crime ◽  
Research Directions ◽  
Data Mining Techniques ◽  
Crime Investigation ◽  
Security Investigation ◽  
Future Research Directions ◽  
Digital Investigation

This chapter provides a review about the usefulness of applying data mining techniques to detect intrusion within dynamic environments and its contribution in digital investigation. Numerous applications and models are described based on data mining analytics. The chapter addresses also different requirements that should be fulfilled to efficiently perform cyber-crime investigation based on data mining analytics. It states, at the end, future research directions related to cyber-crime investigation that could be investigated and presents new trends of data mining techniques that deal with big data to detect attacks.

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