Luận án tiến sĩ: Phân loại motif DNA qua Naive Bayesian - Rodney Villalobos

Walden University SCHOOL OF MANAGEMENT This is to certify that the doctoral dissertation by Rodney V. Villalobos has been found to be complete and satisfactory in all respects, and that any and all revisions required by the review committee have been made. Review Committee Dr. Ruth Maurer, Committee Chairperson, Applied Management and Decision Sciences Faculty Dr.

Raghu Korrapati, Committee Member, Applied Management and Decision Sciences Faculty Dr. Louis Taylor, Committee Member, Applied Management and Decision Sciences Faculty Provost Denise DeZolt, Ph. Walden University 2007 Abstract Identification of Secondary and Tertiary Motifs in DNA Sequences through Naïve Bayesian Text Classification by Rodney V. Villalobos Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Applied Management and Decision Sciences.

Walden University February 2007 Abstract Faced with uncertain data and an unpredictable return on computational tool investment, researchers are opting for laboratory studies over in silico (computer based) studies. This study addressed the lack of efficiency in identifying motifs (biologically significant amino sequences) in deoxyribonucleic acid (DNA) sequences via naïve Bayesian text classification. DNA is a nucleic acid that carries genetic information in cells. A naïve Bayesian text classifier is a machine-learning tool that uses automated means of determining metadata and has been used to identify e-mail worms, viruses, and spam.

This quantitative study utilized a naïve Bayesian text classification algorithm as the primary data collection technique. The data were analyzed using the independent t test and the chi-square goodness of fit test to address the research questions. Based on the tests conducted, naïve Bayesian text classification is not effective in identifying and classifying motifs. The results do suggest that secondary and tertiary motifs can be found in DNA sequences using machine learning.

Given these 2 conclusions, the study adds to the area of research by furthering ways to help researchers handle large amounts of data that may point to more effective drugs, faster development of these drugs to the marketplace, and improvement to the care and cure of diseases. Identification of Secondary and Tertiary Motifs in DNA Sequences through Naïve Bayesian Text Classification by Rodney V. Villalobos Dissertation Submitted in Partial Fulfillment of the Requirements for the Degree of Doctor of Philosophy Applied Management and Decision Sciences. Walden University February 2007 UMI Number: 3247593 Copyright 2007 by Villalobos, Rodney V.

All rights reserved. UMI Microform 3247593 Copyright 2007 by ProQuest Information and Learning Company. All rights reserved. This microform edition is protected against unauthorized copying under Title 17, United States Code.

ProQuest Information and Learning Company 300 North Zeeb Road P. Box 1346 Ann Arbor, MI 48106-1346 TABLE OF CONTENTS CHAPTER 1: INTRODUCTION. 1 Statement of the Problem. 3 Background of the Problem.

5 Nature of the Study. 7 Purpose of the Study. 8 Definition of Terms. 12 Scope and Delimitations.

12 Significance of the Study. 13 Organization of the Study. 15 CHAPTER 2: Literature Review. 16 Overview of Computational Sequencing.

16 Supervised and Unsupervised Learning. 24 Baye's Rule and Naïve Bayesian Text Classification. 32 Research Design and Approach. 32 Target Population, Setting, and Sampling.

33 Algorithm and Treatment. 33 Reliability and Validity. 37 Data Collection and Analysis. 41 Description of the Sample.

51 ii CHAPTER 5: CONCLUSIONS, RECOMMENDATIONS, FURTHER RESEARCH. 59 Future of Motif Identification and Classification .84 iii LIST OF FIGURES Figure 1: Rearch focus. 2 iv LIST OF TABLES Table 1: Hypotheses and Related Tests. 39 Table 2: DNA Samples Used in the Study.

42 Table 3: Unindexed and Indexed DNA Sequences. 44 Table 4: Observed and Expected Indexed Motifs. 46 Table 5: Motif Categorization Results. 49 Table 6: Observed and Expected Individual Sequences.

51 v CHAPTER 1: INTRODUCTION Some of the most active research in statistics is Bayesian where determining prior events remains a valid approach to the discovery process used by scientists (Jaynes, 1979; Zhang, Mukherjee, Ghosh, & Wu, 2006). The largest and most inviting field for the application of statistical analysis is biology (Jaynes, 1979; Rodrigez-Esteban, Iossifov, & Rzhetsky, 2006). For example, the human genome is made up of 23 pairs of chromosomes, each containing molecules called deoxyribonucleic acid (DNA). DNA molecules are shaped in the form of a twisted ladder with sugar and phosphate molecular components forming the sides of the ladder and the pairs of nucleotide bases forming the ladder rungs.

The nucleotide bases are made up of guanine (G), adenine (A), cytosine (C), and thymine (T) (Mangalam et al. The human genome consists of approximately 3 billion base pairs of DNA making up nearly 100,000 genes (Rockett, 2000). Of the 3 billion base pairs that make up the human genome, only about 1% code for proteins (Swope, 2001). With the existence of varied heterogeneous remote and local data sources and the need for complex analyses of the data, several software platforms and application frameworks are needed to facilitate a better understanding of genomic data (Swope, 2001).

These will need to combine graphical interfaces and complex analytical and data- mining tools with Web-based access to one or more remote data sources on the Internet. The benefit will be to allow comparison of unknown protein sequences within a database of sequences from other organisms that are better understood. Through cross-over, mutation, and fitness, nature reuses what it has learned from the design of simple 2 organisms such as bacteria (Swope). From this knowledge, scientists are able to deduce the function of other proteins by searching for the similar genes or proteins in the databases of other genes and proteins worldwide.

Yet inferring biologically meaningful information from warehoused data requires sophisticated data-mining techniques (Cummings & Relman, 2000). This research focused on one such data-mining technique as illustrated in Figure 1. Figure 1: Research focus 3 To move in silico research in the direction of improved decision support, the ability to take raw data and determine relevant patterns is important in order to design potential cures for diseases by improving the drug development lifecycle. This study describes the effectiveness of using a naïve Bayesian text classifier to identify secondary and tertiary protein motifs in DNA sequences.

Bayes’s rule was used to determine data categories via probability. A naïve Bayesian text classifier is a machine-learning algorithm that uses an automated means of determining metadata about data. Categories are represented by a collection of motifs and their frequencies; frequency is the number of times each motif is identified in the data used to train the classifier. Statement of the Problem The problem addressed in this research focused on identification of secondary and tertiary protein motifs in DNA sequences via naïve Bayesian text classification.

Statistical hypothesis testing for decision making in in silico essentially involves class comparison, where several experimental groups are directly analyzed. Pattern recognition, on the other hand, involves class prediction where a range of supervised multivariate techniques is used or class discovery where unsupervised multivariate techniques are used. The problem with either approach is that biologists tend to want data analysis to be like “a laboratory protocol—a series of steps that, if followed faithfully, guarantee to produce the correct answer to their experimental question” (Morrison & Ellis, 2003, p. This, however, is not possible given that data analysis involves detecting and displaying those patterns that are present in the data without actually knowing what the patterns are in advance.

Here, trial and error play major roles in determining appropriate analyses and evaluating their results. 4 In pattern recognition and prediction, a different solution is required that uses logical analysis to determine prior states in the system under consideration (Jaynes, 1979). This is in part due to the inherent complexity involved in the data. Researchers frequently do not know which patterns to predict in the dataset and as a result are unable to be explicit about which patterns should be interpreted as biologically meaningful.

This process involves searching for patterns that might exist in the database which could then be interpreted post hoc for meaningfulness. In this case, the answer to the experimental question results not from the answer to an “explicit statistical question” but from the search patterns in the dataset (Morrison & Ellis, 2003, p. With the complexity of the data high, researchers are required to engage in data mining with a particular emphasis on pattern analysis. The problem with this approach is that there is no single technique that can be generally recommended to mine the data for patterns (Morrison & Ellis, 2003).

This leaves the in silico practice open to different methodologies and potential solutions where in silico uses an experimental techniques performed on a computer or via a computational simulation. Not knowing a priori which techniques are specific enough to find those patterns that might be in the data creates an inherent problem for researchers. The situation arises as a result of there being no single pattern that can be expected in the data. If there are many possible patterns in the data, then there must be many possible mathematical techniques for finding those patterns.

For example, “choosing the technique that is suggested to be best under the widest range of possible circumstances sounds like a reasonable criterion of choice, but this is not necessarily a good idea” (Morrison & Ellis, 2003, p. In this example, there is no question that the data will have their own 5 characteristics due to various circumstances involving the experimental conditions, the way the data are quantified, and even the experimental question being asked. The difficulty with relying on a rigid protocol is that there is no guarantee that the correct answer to an experimental question will be produced thereby causing the results to fall short of expectations (Morrison & Ellis). The study presented here describes the effectiveness of using a naïve Bayesian text classification algorithm to identify secondary and tertiary motifs in DNA sequences and addresses the lack of efficiency in identifying motifs in DNA sequences.

Background of the Problem Shah, Passovets, Kim, Ellrott, Wang, Vokler, LoCascio, Xu, and Xu (2003) found that technology and experimental techniques by themselves are not enough to keep pace with the production rate of protein sequences in order to analyze and predict the function of proteins. These high production rates of protein sequences that result from microarray experiments are generating large volumes of complex data in an effort to identify which genes might be overexpressed or underexpressed under experimental conditions. This effort, however, is only part of the story when it comes to the practice of in silico research. The other side involves the analysis of data, where preprocessing of the raw data for quality control is used in combination with standardization to ensure data uniformity throughout the dataset.

This is followed by formal quantitative analyses involving either statistical hypothesis testing or multivariate pattern recognition (Morrison & Ellis, 2003). For the general computational biologist, a pattern is predicted to occur in the dataset where the averages of the observations in two experimental groups are different 6 from each other and a single, repeatable mathematical test can be used to evaluate whether a pattern exists. The logic used in in silico research involves inductive arguments where the specific instance of the sample is used to generalize about the population. In other words, recognizing the virtue of deductive logic in experimentation requires inductive logic to analyze the data.

This approach is not without its problems. Inductive reasoning does not provide the formal proof needed as evidence in support of any one particular hypothesis. In addition, there is the issue where no matter how much evidence is gathered in support of a particular hypothesis; it is difficult to be certain that this same evidence would not equally support any number of unknown hypotheses (Morrison & Ellis, 2003). Given that large amounts of data are accumulated—in the range of hundreds of terabytes per day—regarding genetic information, it is important to make use of the available datato resolve pressing health issues (Guan & Bell, 2004).