Biological Data Mining:

Researchers use transcriptomics analyses for biological data mining, interpretation, and presentation. Galaxy-based tools are utilized to analyze various complex disease transcriptomic data to understand the pathogenesis of the disease, which are user-friendly. This work provides simple methods for differential expression analysis and analysis of these results in gene ontology and pathway enrichment tools like David, WebGestalt. This method is very effective in better analysis and understanding the transcriptomic data. Transcriptomics analysis has been made on rheumatoid arthritis sra data. Rheumatoid arthritis (RA) is a systemic autoimmune disease. T cells and autoantibodies mediate the pathogenesis. This article discusses the genes which are differentially expressed between the healthy (n=50) and diseased (n=51) and the functions of those genes in the pathogenesis of RA.

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An Overview of Biological Data Mining

Biotechnology ◽

10.4018/978-1-5225-8903-7.ch005 ◽

2019 ◽

pp. 120-139

Author(s):

Seetharaman Balaji

Keyword(s):

Data Mining ◽

Information Retrieval ◽

Biological Networks ◽

Web Mining ◽

Biological Data ◽

Biological Research ◽

Digital Repository ◽

Domain Specific ◽

Novice Learner ◽

Integration Data

The largest digital repository of information, the World Wide Web keeps growing exponentially and calls for data mining services to provide tailored web experiences. This chapter discusses the overview of information retrieval, knowledge discovery and data mining. It reviews the different stages of data mining and introduces the wide spread biological databanks, their explosion, integration, data warehousing, information retrieval, text mining, text repositories for biological research publications, domain specific search engines, web mining, biological networks and visualization, ontology and systems biology. This chapter also illustrates some technical jargon with picture analogy for a novice learner to understand the concepts clearly.

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Biological Data Mining

Data Warehousing and Mining ◽

10.4018/978-1-59904-951-9.ch099 ◽

2008 ◽

pp. 1696-1705

Author(s):

George Tzanis ◽

Christos Berberidis ◽

Ioannis Vlahavas

Keyword(s):

Data Mining ◽

New Technologies ◽

Noisy Data ◽

Large Data ◽

Biological Data ◽

Customer Relationship ◽

Mass Storage ◽

Stock Management ◽

Management Fraud ◽

Storage Media

At the end of the 1980s, a new discipline named data mining emerged. The introduction of new technologies such as computers, satellites, new mass storage media, and many others have lead to an exponential growth of collected data. Traditional data analysis techniques often fail to process large amounts of, often noisy, data efficiently in an exploratory fashion. The scope of data mining is the knowledge extraction from large data amounts with the help of computers. It is an interdisciplinary area of research that has its roots in databases, machine learning, and statistics and has contributions from many other areas such as information retrieval, pattern recognition, visualization, parallel and distributed computing. There are many applications of data mining in the real world. Customer relationship management, fraud detection, market and industry characterization, stock management, medicine, pharmacology, and biology are some examples (Two Crows Corporation, 1999).

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An Overview of Biological Data Mining

Library and Information Services for Bioinformatics Education and Research - Advances in Library and Information Science ◽

10.4018/978-1-5225-1871-6.ch007 ◽

2017 ◽

pp. 130-154

Author(s):

Seetharaman Balaji

Keyword(s):

Data Mining ◽

Information Retrieval ◽

Biological Networks ◽

Web Mining ◽

Biological Data ◽

Biological Research ◽

Digital Repository ◽

Domain Specific ◽

Novice Learner ◽

Integration Data

The largest digital repository of information, the World Wide Web keeps growing exponentially and calls for data mining services to provide tailored web experiences. This chapter discusses the overview of information retrieval, knowledge discovery and data mining. It reviews the different stages of data mining and introduces the wide spread biological databanks, their explosion, integration, data warehousing, information retrieval, text mining, text repositories for biological research publications, domain specific search engines, web mining, biological networks and visualization, ontology and systems biology. This chapter also illustrates some technical jargon with picture analogy for a novice learner to understand the concepts clearly.

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Biological Data Mining

Encyclopedia of Database Technologies and Applications ◽

10.4018/978-1-59140-560-3.ch007 ◽

2005 ◽

pp. 35-41 ◽

Cited By ~ 9

Author(s):

George Tzanis ◽

Christos Berberidis ◽

Ioannis Vlahavas

Keyword(s):

Data Mining ◽

New Technologies ◽

Noisy Data ◽

Large Data ◽

Biological Data ◽

Customer Relationship ◽

Mass Storage ◽

Stock Management ◽

Management Fraud ◽

Storage Media

At the end of the 1980s, a new discipline named data mining emerged. The introduction of new technologies such as computers, satellites, new mass storage media, and many others have lead to an exponential growth of collected data. Traditional data analysis techniques often fail to process large amounts of, often noisy, data efficiently in an exploratory fashion. The scope of data mining is the knowledge extraction from large data amounts with the help of computers. It is an interdisciplinary area of research that has its roots in databases, machine learning, and statistics and has contributions from many other areas such as information retrieval, pattern recognition, visualization, parallel and distributed computing. There are many applications of data mining in the real world. Customer relationship management, fraud detection, market and industry characterization, stock management, medicine, pharmacology, and biology are some examples (Two Crows Corporation, 1999).

Download Full-text