Computational Methods with Applications in Bioinformatics Analysis

The following sections are included:

• Preface
• Acknowledgment
• About the Authors
• List of Contributors
• Contents

The following sections are included:

• Introduction
• Literature Review
• Methods
• Experimental Results and Analysis
• Conclusion and Future Research Directions
• Acknowledgments
• References

The following sections are included:

• Introduction
• Preliminaries
• Methods
• Experimental Design
• Results and Discussion
• Conclusions and Future Research
• Acknowledgements
• References

Recently, the use of ensemble data matrix as a transformed space for classification has been put forward. Specific to the problem of predicting student dropout, the matrix generated as part of summarizing members in a cluster ensemble is investigated with a number of conventional classification methods. Despite the reported success in comparison to the case of original data and other attribute reduction techniques like PCA and KPCA, the study is limited to only one ensemble matrix that is created by the link-based ensemble approach or LCE. To provide an comparative review with respect to the aforementioned problem, this paper includes the experiments and findings obtained from the use of different graph-based ensemble algorithms as data transformation methods for microarray data classification. The empirical study can be hugely useful particularly for those working in bioinformatics, and generally applicable to any classification problem. Besides, the review initiates another interesting challenge for many researchers in the field of cluster ensemble to coupling their models with this hybrid, clustering-classification learning.

Biomedical intelligence (BMI) has been studied in solos, lacking a systematic methodology. Bioinformatics has been conceptualizing biological process in terms of genomics and applying computer science (derived from disciplines such as applied modeling, data mining, machine learning and statistics) to extract knowledge from biological data. Medical Informatics, on the other hand, has been developing health care applications based on clinical observations and applying computer science to extract knowledge and information to facilitate problem solving and decision marking In this chapter, we describe how semantic computing can enhance biological and medical intelligence. Specifically, we show how structured natural language (SNL) can express many problems in BMI with a finite number of sentence patterns, and show how biological analysis tools, OLAP, data mining and statistical analysis may be linked to solve problems related to biomedical data.

The following sections are included:

• Introduction
• The forces between proteins and nucleic acids
• Simulations on the interactions between proteins and nucleic acids
• References

The following sections are included:

• Introduction
• The conceptual framework of this study
• Discussion and Conclusion
• References

In the human genome, 10% of the nucleotide sequences were Alus which were retrotransposon-produced genomic repeats. Despite occasional evidence of Alu-induced genetic diseases, it remained a mystery whether these Alu elements play substantial physiological roles comparable with its proportion in the human genome. Recently, cytosolic sense- and antisense-Alu carrying mature RNAs, corresponding to more than 1300 protein coding and various non-coding genes, were shown to form a network of mutual regulations. Messenger RNA transcripts of genes pertinent to the immunological Th17 maturation, including CCL5, CCR6, IL23R, IL2RA, IL1R1, CD28 and REL, consistently carry Alu in the sense direction. On the other hand, other immunological genes such as CXCL16, IFNAR2, CD302, CDH1, IL28RA (a.k.a. IFNLR1) and JAK3, all carry Alus only in the antisense direction. The Alu sequences facilitated RNA-RNA interactions, resulting in a RNA regulatory network which enabled a computational modelling of cellular state transitions. The transition from naïve T cells to mature Th17 cells was largely controlled by the relative transcriptional rates of genes carrying sense and antisense Alus, which showed an inherent inverse relationship of levels at equilibrium.

The following sections are included:

• Introduction
• Previous researches using PCA for gene selection
• Principal component analysis based unsupervised feature extraction
• Identification of miRNA-mRNA interaction
• Integrated analysis of gene expression and promoter methylation
• Integration of more than two multi-omics data sets
• Integration of multiple gene expression data sets
• Conclusions
• References

The following sections are included:

• Introduction
• Methods
• Results
• Discussion
• References

In this chapter, we introduce the latest high-dimensional flow and mass cytometry technologies, and review state-of-the-art unsupervised clustering algorithms for this type of data. We continue to evaluate and compare three most recent algorithms on a mass cytometry data set, and discuss challenges that remain.

The following section is included:

• Index