Yiou X.

Histone modifications provide an important layer of gene regulation in eukaryotes. In this paper, we propose an approach that identifies the histone modification patterns most relevant for specific biological functions, such as flowering in plants. We first propose a new pattern scoring method, which evaluates the importance of each combinatorial pattern of histone modifications; this is used along with logistic regression, Support Vector Machines, and naive Bayesian classifier algorithms to… Show more

Histone modifications provide an important layer of gene regulation in eukaryotes. In this paper, we propose an approach that identifies the histone modification patterns most relevant for specific biological functions, such as flowering in plants. We first propose a new pattern scoring method, which evaluates the importance of each combinatorial pattern of histone modifications; this is used along with logistic regression, Support Vector Machines, and naive Bayesian classifier algorithms to predict gene functions. This approach is shown to be successful in inferring significant patterns verified by independent gene function data, outperforming other pattern scores used in current histone modification analysis research. Show less

We propose a conjunction rule based classification technique that has good classification performance, is simple, automatically identifies important attributes, and is extremely fast. Due to these properties the classifier is most suitable for “big” streaming data. Empirical study, using multiple datasets, shows that time complexity, compared with other classifiers, is faster by several factors, especially for large number of attributes without sacrificing performance.

Abstract:
In recent years, with the advent of fast sequencing technology, the genomic database is growing rapidly. Researchers in the bioinformatics field are expecting faster and more accurate tools to effectively analyze the gigantic data sets. In the context of aptamer search, the goal is to search for the over-represented DNA sequences from the randomly generated aptamer libraries. Hash functions are widely used in substring comparison, sequence alignment and clustering tools. We have… Show more

Abstract:
In recent years, with the advent of fast sequencing technology, the genomic database is growing rapidly. Researchers in the bioinformatics field are expecting faster and more accurate tools to effectively analyze the gigantic data sets. In the context of aptamer search, the goal is to search for the over-represented DNA sequences from the randomly generated aptamer libraries. Hash functions are widely used in substring comparison, sequence alignment and clustering tools. We have developed a light-weight tool that takes advantage of the hash functions to reduce the size of genomic data and conducts η-neighbor searches on the centroid sequence. This greatly improves the efficiency of the search compared with existing tools. Furthermore, the prior calculation of hash values of η-neighbors decreases the searching overhead. In a dataset of 2.23 million sequences, the proposed algorithm accurately count the frequency of the Human α-Thrombin aptamer sequences in less than 40 seconds, whereas the current script-based method takes 2 hours and 18 minutes. Show less

Gene regulatory networks (GRNs) describe epistatic relationship of genes and how the expression of some genes influence the expression of other genes. This information is critical for understanding molecular mechanisms regulating various biological processes and molecular basis of several diseases. Current research work mostly attempts to infer such regulatory relationships (and GRN architecture topology) from gene expression data. This paper improves on this methodology by utilizing additional… Show more

Gene regulatory networks (GRNs) describe epistatic relationship of genes and how the expression of some genes influence the expression of other genes. This information is critical for understanding molecular mechanisms regulating various biological processes and molecular basis of several diseases. Current research work mostly attempts to infer such regulatory relationships (and GRN architecture topology) from gene expression data. This paper improves on this methodology by utilizing additional information available that describes which cis-elements are present in which genes, and at what locations. Using the underlying principle that target genes of a transcription factor should share the same binding site in their promoter regions, we propose a scoring method that facilitates the refinement of a candidate GRN. Improvements are demonstrated with three data sets, on which GRNs are first obtained from existing dataset (AtRegNet) or using an existing approach (ARACNe), and then modified using cis-element information. Show less