How to Rank Answers in Text Mining

In this thesis, we mainly focus on case studies about answers. We present the methodology CEW-DTW and assess its performance about ranking quality. Based on the CEW-DTW, we improve this methodology by combining Kullback-Leibler divergence with CEW-DTW, since Kullback-Leibler divergence can check the difference of probability distributions in two sequences. However, CEW-DTW and KL-CEW-DTW do not care about the effect of noise and keywords from the viewpoint of probability distribution. Therefore, we develop a new methodology, the General Entropy, to see how probabilities of noise and keywords affect answer qualities. We firstly analyze some properties of the General Entropy, such as the value range of the General Entropy. Especially, we try to find an objective goal, which can be regarded as a standard to assess answers. Therefore, we introduce the maximum general entropy. We try to use the general entropy methodology to find an imaginary answer with the maximum entropy from the mathematical viewpoint (though this answer may not exist). This answer can also be regarded as an “ideal” answer. By comparing maximum entropy probabilities and global probabilities of noise and keywords respectively, the maximum entropy probability of noise is smaller than the global probability of noise, maximum entropy probabilities of chosen keywords are larger than global probabilities of keywords in some conditions. This allows us to determinably select the max number of keywords. We also use Amazon dataset and a small group of survey to assess the general entropy. Though these developed methodologies can analyze answer qualities, they do not incorporate the inner connections among keywords and noise. Based on the Markov transition matrix, we develop the Jump Probability Entropy. We still adapt Amazon dataset to compare maximum jump entropy probabilities and global jump probabilities of noise and keywords respectively. Finally, we give steps about how to get answers from Amazon dataset, including obtaining original answers from Amazon dataset, removing stopping words and collinearity. We compare our developed methodologies to see if these methodologies are consistent. Also, we introduce Wald–Wolfowitz runs test and compare it with developed methodologies to verify their relationships. Depending on results of comparison, we get conclusions about consistence of these methodologies and illustrate future plans

An Iterative Learning Algorithm for Deciphering Stegoscripts: a Grammatical Approach for Motif Discovery

Steganography, or information hiding, is to conceal the existence of messages so as to protect their confidentiality. We consider de-ciphering a stegoscript, a text with secret messages embedded within a covertext, and identifying the vocabularies used in the mes-sages, with no knowledge of the vocabularies and grammar in which the script was writ-ten. Our research was motivated by the prob-lem of identifying conserved non-coding func-tional elements (motifs) in regulatory regions of genome sequences, which we view as stego-scripts constructed by nature with a statis-tical model consisting of a dictionary and a grammar. We develop an iterative learning algorithm, WordSpy, to learn such a model from a stegoscript. The model then can be applied to identify the embedded secret mes-sages, i.e., the functional motifs. Our algo-rithm can successfully recover the most pos-sible text of the first ten chapters of a novel embedded in a stegoscript and identify the transcription factor binding motifs in the up-stream regions of ∼ 800 yeast genes

A steganalysis-based approach to comprehensive identification and characterization of functional regulatory elements

The comprehensive identification of cis-regulatory elements on a genome scale is a challenging problem. We develop a novel, steganalysis-based approach for genome-wide motif finding, called WordSpy, by viewing regulatory regions as a stegoscript with cis-elements embedded in 'background' sequences. We apply WordSpy to the promoters of cell-cycle-related genes of Saccharomyces cerevisiae and Arabidopsis thaliana, identifying all known cell-cycle motifs with high ranking. WordSpy can discover a complete set of cis-elements and facilitate the systematic study of regulatory networks

UV-B responsive microRNA genes in Arabidopsis thaliana

MicroRNAs (miRNAs) are small, non-coding RNAs that play critical roles in post-transcriptional gene regulation. In plants, mature miRNAs pair with complementary sites on mRNAs and subsequently lead to cleavage and degradation of the mRNAs. Many miRNAs target mRNAs that encode transcription factors; therefore, they regulate the expression of many downstream genes. In this study, we carry out a survey of Arabidopsis microRNA genes in response to UV-B radiation, an important adverse abiotic stress. We develop a novel computational approach to identify microRNA genes induced by UV-B radiation and characterize their functions in regulating gene expression. We report that in A. thaliana, 21 microRNA genes in 11 microRNA families are upregulated under UV-B stress condition. We also discuss putative transcriptional downregulation pathways triggered by the induction of these microRNA genes. Moreover, our approach can be directly applied to miRNAs responding to other abiotic and biotic stresses and extended to miRNAs in other plants and metazoans

WordSpy: identifying transcription factor binding motifs by building a dictionary and learning a grammar

Transcription factor (TF) binding sites or motifs (TFBMs) are functional cis-regulatory DNA sequences that play an essential role in gene transcriptional regulation. Although many experimental and computational methods have been developed, finding TFBMs remains a challenging problem. We propose and develop a novel dictionary based motif finding algorithm, which we call WordSpy. One significant feature of WordSpy is the combination of a word counting method and a statistical model which consists of a dictionary of motifs and a grammar specifying their usage. The algorithm is suitable for genome-wide motif finding; it is capable of discovering hundreds of motifs from a large set of promoters in a single run. We further enhance WordSpy by applying gene expression information to separate true TFBMs from spurious ones, and by incorporating negative sequences to identify discriminative motifs. In addition, we also use randomly selected promoters from the genome to evaluate the significance of the discovered motifs. The output from WordSpy consists of an ordered list of putative motifs and a set of regulatory sequences with motif binding sites highlighted. The web server of WordSpy is available at

Characterization and Identification of MicroRNA Core Promoters in Four Model Species

MicroRNAs are short, noncoding RNAs that play important roles in post-transcriptional gene regulation. Although many functions of microRNAs in plants and animals have been revealed in recent years, the transcriptional mechanism of microRNA genes is not well-understood. To elucidate the transcriptional regulation of microRNA genes, we study and characterize, in a genome scale, the promoters of intergenic microRNA genes in Caenorhabditis elegans, Homo sapiens, Arabidopsis thaliana, and Oryza sativa. We show that most known microRNA genes in these four species have the same type of promoters as protein-coding genes have. To further characterize the promoters of microRNA genes, we developed a novel promoter prediction method, called common query voting (CoVote), which is more effective than available promoter prediction methods. Using this new method, we identify putative core promoters of most known microRNA genes in the four model species. Moreover, we characterize the promoters of microRNA genes in these four species. We discover many significant, characteristic sequence motifs in these core promoters, several of which match or resemble the known cis-acting elements for transcription initiation. Among these motifs, some are conserved across different species while some are specific to microRNA genes of individual species