Combinatorial Fusion Analysis: Methods and Practices of Combining Multiple Scoring Systems

Combinatorial Fusion Analysis: Methods and Practices of Combining Multiple Scoring Systems

D. Frank Hsu (Fordham University, USA), Yun-Sheng Chung (National Tsing Hua University, Taiwan) and Bruce S. Kristal (Burke Medical Research Institute and weill Medical College of Cornell University, USA)
Copyright: © 2006 |Pages: 31
DOI: 10.4018/978-1-59140-863-5.ch003
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Abstract

Combination methods have been investigated as a possible means to improve performance in multi-variable (multi-criterion or multi-objective) classification, prediction, learning, and optimization problems. In addition, information collected from multi-sensor or multi-source environment also often needs to be combined to produce more accurate information, to derive better estimation, or to make more knowledgeable decisions. In this chapter, we present a method, called Combinatorial Fusion Analysis (CFA), for analyzing combination and fusion of multiple scoring. CFA characterizes each Scoring system as having included a Score function, a Rank function, and a Rank/score function. Both rank combination and score combination are explored as to their combinatorial complexity and computational efficiency. Information derived from the scoring characteristics of each scoring system is used to perform system selection and to decide method combination. In particular, the rank/score graph defined by Hsu, Shapiro and Taksa (Hsu et al., 2002; Hsu & Taksa, 2005) is used to measure the diversity between scoring systems. We illustrate various applications of the framework using examples in information retrieval and biomedical informatics.

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Table of Contents
Acknowledgments
Hui-Huang Hsu
Chapter 1
Hui-Huang Hsu
Bioinformatics uses information technologies to facilitate the discovery of new knowledge in molecular biology. Among the information technologies... Sample PDF
Introduction to Data Mining in Bioinformatics
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Chapter 2
Li Liao
Recently, clustering and classification methods have seen many applications in bioinformatics. Some are simply straightforward applications of... Sample PDF
Hierarchical Profiling, Scoring and Applications in Bioinformatics
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Chapter 3
D. Frank Hsu, Yun-Sheng Chung, Bruce S. Kristal
Combination methods have been investigated as a possible means to improve performance in multi-variable (multi-criterion or multi-objective)... Sample PDF
Combinatorial Fusion Analysis: Methods and Practices of Combining Multiple Scoring Systems
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Chapter 4
Hsuan T. Chang
This chapter introduces various visualization (i.e., graphical representation) schemes of symbolic DNA sequences, which are basically represented by... Sample PDF
DNA Sequence Visualization
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Chapter 5
Simon Lin, Salvatore Mungal, Richard Haney, Edward F. Patz Jr., Patrick McConnell
This chapter provides a rudimentary review of the field of proteomics as it applies to mass spectrometry, data handling, and analysis. It points out... Sample PDF
Proteomics with Mass Spectrometry
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Chapter 6
Sven Rahmann, Tobias Muller, Thomas Dandekar, Matthias Wolf
The goal of phylogenetics is to reconstruct ancestral relationships between different taxa, e.g., different species in the tree of life, by means of... Sample PDF
Efficient and Robust Analysis of Large Phylogenetic Datasets
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Chapter 7
Tatsuya Akutsu
This chapter provides an overview of computational problems and techniques for protein threading. Protein threading is one of the most powerful... Sample PDF
Algorithmic Aspects of Protein Threading
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Chapter 8
Arpad Kelemen, Yulan Liang
Pattern differentiations and formulations are two main research tracks for heterogeneous genomic data pattern analysis. In this chapter, we develop... Sample PDF
Pattern Differentiations and Formulations for Heterogeneous Genomic Data through Hybrid Approaches
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Chapter 9
Vincent S. Tseng, Ching-Pin Kao
In recent years, clustering analysis has even become a valuable and useful tool for in-silico analysis of microarray or gene expression data.... Sample PDF
Paramaterless Clustering Techniques for Gene Expression Analysis
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Chapter 10
Junying Zhang
This chapter introduces gene selection approaches in microarray data analysis for two purposes: cancer classification and tissue heterogeneity... Sample PDF
Joint Discriminatory Gene Selection for Molecular Classification of Cancer
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Chapter 11
Takashi Kido
This chapter introduces computational methods for detecting complex disease loci with haplotype analysis. It argues that the haplotype analysis... Sample PDF
A Haplotype Analysis System for Genes Discovery of Common Diseases
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Chapter 12
Peng-Yeng yin, Shyong-Jian Shyu, Guan-Shieng Huang, Shuang-Te Liao
With the advent of new sequencing technology for biological data, the number of sequenced proteins stored in public databases has become an... Sample PDF
A Bayesian Framework for Improving Clustering Accuracy of Protein Sequences Based on Association Rules
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Chapter 13
Byung-Hoon Park, Phuongan Dam, Chongle Pan, Ying Xu, Al Geist, Grant Heffelfinger, Nagiza F. Samatova
Protein-protein interactions are fundamental to cellular processes. They are responsible for phenomena like DNA replication, gene transcription... Sample PDF
In Silico Recognition of Protein-Protein Interaction: Theory and Applications
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Chapter 14
Christopher Besemann, Anne Denton, Ajay Yekkirala, Ron Hutchison, Marc Anderson
In this chapter, we discuss the use of differential association rules to study the annotations of proteins in one or more interaction networks.... Sample PDF
Differential Association Rules: Understanding Annotations in Protein Interaction Networks
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Chapter 15
Francisco M. Couto, Mario J. Silva
This chapter introduces the use of Text Mining in scientific literature for biological research, with a special focus on automatic gene and protein... Sample PDF
Mining BioLiterature: Toward Automatic Annotation of Genes and Proteins
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Chapter 16
Kwangmin Choi, Sun Kim
Understanding the genetic content of a genome is a very important but challenging task. One of the most effective methods to annotate a genome is to... Sample PDF
Comparative Genome Annotation Systems
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