Computational intelligence techniques for biological data mining: An overview

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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Hybrid Wavelet-Neuro-Fuzzy Systems of Computational Intelligence in Data Mining Tasks

Handbook of Research on Machine Learning Innovations and Trends - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-2229-4.ch035 ◽

2017 ◽

pp. 787-825

Author(s):

Yevgeniy Bodyanskiy ◽

Olena Vynokurova ◽

Oleksii Tyshchenko

Keyword(s):

Data Mining ◽

Neural Networks ◽

Artificial Neural Networks ◽

Learning Process ◽

Computational Intelligence ◽

Group Method ◽

Learning Criterion ◽

Adjustment Procedure ◽

Artificial Neural ◽

Input Variables

This work is devoted to synthesis of adaptive hybrid systems based on the Computational Intelligence (CI) methods (especially artificial neural networks (ANNs)) and the Group Method of Data Handling (GMDH) ideas to get new qualitative results in Data Mining, Intelligent Control and other scientific areas. The GMDH-artificial neural networks (GMDH-ANNs) are currently well-known. Their nodes are two-input N-Adalines. On the other hand, these ANNs can require a considerable number of hidden layers for a necessary approximation quality. Introduced Q-neurons can provide a higher quality using the quadratic approximation. Their main advantage is a high learning rate. Universal approximating properties of the GMDH-ANNs can be achieved with the help of compartmental R-neurons representing a two-input RBFN with the grid partitioning of the input variables' space. An adjustment procedure of synaptic weights as well as both centers and receptive fields is provided. At the same time, Epanechnikov kernels (their derivatives are linear to adjusted parameters) can be used instead of conventional Gauss functions in order to increase a learning process rate. More complex tasks deal with stochastic time series processing. This kind of tasks can be solved with the help of the introduced adaptive W-neurons (wavelets). Learning algorithms are characterized by both tracking and smoothing properties based on the quadratic learning criterion. Robust algorithms which eliminate an influence of abnormal outliers on the learning process are introduced too. Theoretical results are illustrated by multiple experiments that confirm the proposed approach's effectiveness.

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Cat Swarm Optimization Supported Data Mining

Intelligent Soft Computation and Evolving Data Mining ◽

10.4018/978-1-61520-757-2.ch002 ◽

2010 ◽

pp. 26-43

Author(s):

Pei-Wei Tsai ◽

Jeng-Shyang Pan ◽

Bin-Yih Liao ◽

Shu-Chuan Chu ◽

Mei-Chiao Lai

Keyword(s):

Data Mining ◽

Computational Intelligence ◽

Evolutionary Computing ◽

Swarm Optimization ◽

Cat Swarm Optimization

This chapter reviews the basic idea and processes in data mining and some algorithms within the field of evolutionary computing. The authors focus on introducing the algorithms of computational intelligence since they are useful tools for solving problems of optimization, data mining, and many kinds of industrial issues. A feasible model of combining computational intelligence with data mining is presented at the end of the chapter with the conclusions.

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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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