Browsing by Author "Hu, YJ"

Now showing 1 - 12 of 12

A combinatorial approach to reconstructing transcriptional regulatory networks
(2004)
Hsu, YZ
;
Hu, YJ
;
資訊工程學系
;
Department of Computer Science
One of the major challenges in molecular biology is to understand the precise mechanism by which gene expression is regulated. Reconstruction of transcription networks is essential to modeling this mechanism. In this paper, we describe a novel approach for building transcription networks from transcription modules by combining expression profile correlations with probabilistic element assessment. To demonstrate its performance, we systematically tested it on 27 transcription modules and reconstructed the transcription network for 6 transcription factors and 15 genes involved in the yeast cell cycle. The experimental results show that our combinatorial approach can better filter false positives to increase the selectivity in prediction of target genes. The regulatory control relationships described by the network reconstructed also mostly agree with those in earlier studies.
A site-based method for prediction of protein orthologous relations
(2004)
Chiu, HC
;
Hu, YJ
;
資訊工程學系
;
Department of Computer Science
Making accurate functional predictions for genes plays an important role in the era of proteomics. The most reliable functional information is extracted from orthologs in other species when annotating an unknown gene. Here a site-based approach is proposed to predict orthologous relations. The method first identifies important sites that confer specificity of paralogs in the multiple sequence alignment of homologous proteins. It then predicts orthologous relations for unannotated proteins based on the important sites found. When applied to the bacterial transcription factor PurR/LacI family and the protein kinase AGC family, our method was able to identify, with few false positives, the important sites that agree with those obtained from biological experiments. We also tested it on the AGC family, the a proteasome family, the glycoprotein hormone family and the growth hormone family to demonstrate its ability to predict orthologs. Compared with other prediction methods based on phylogenetic analysis or hidden Markov models, our method not only has competitive predictive accuracy, but also provides valuable biological information of important sites associated with orthologs which can be further studied in biological experiments.
An integrated approach for genome-wide gene expression analysis
(2001-06-01)
Hu, YJ
;
資訊工程學系
;
Department of Computer Science
Since efficient and relatively cheap methods were developed for determining biosequences, a lot of biosequence data has been generated. As the main problem in molecular biology is the analysis of the data instead of the data acquisition, part of the study of computational biology is to extract all kinds of meaningful information from the sequences. Computer-assisted methods have become very important in analyzing biosequence data. However, most of the current computer-assisted methods are limited to Ending motifs. Genes can be regulated in many ways, including combinations of regulatory elements. This research is aimed at developing a new integrated system for genome-wide gene expression analysis. This research begins with a new motif-finding method, using a new objective function combining multiple well defined components and an improved stochastic iterative sampling strategy. Combinatorial motif analysis is accomplished by constructive induction that analyzes potential motif combinations. We then apply standard inductive learning algorithms to generate hypotheses for different gene behaviors. A genome-wide gene expression analysis demonstrated the value of this novel integrated system. (C) 2001 Elsevier Science Ireland Ltd. All rights reserved.
Finding subtle motifs with variable gaps in unaligned DNA sequences
(2003-01-01)
Hu, YJ
;
資訊工程學系
;
Department of Computer Science
Biologists have determined that the control and regulation of gene expression is primarily determined by relatively short sequences in the region surrounding a gene. These sequences vary in length, position, redundancy, orientation, and bases. Finding these short sequences is a fundamental problem in molecular biology with important applications. Though there exist many different approaches to signal (i.e. short sequence) finding, some new study shows that this problem still leaves plenty of room for improvement. In 2000, Pevzner and Sze proposed the Challenge Problem of motif detection. They reported that most current motif finding algorithms are incapable of detecting the target motifs in their Challenge Problem. In this paper, we show that using an iterative-restart design, our new algorithm can correctly find the target motifs. Furthermore, taking into account the fact that some transcription factors form a dimer or even more complex structures, and transcription process can sometimes involve multiple factors with variable spacers in between, we extend the original problem to an even more challenging one by addressing the issue of combinatorial signals with gaps of variable lengths. To demonstrate the effectiveness of our algorithm, we tested it on a series of the new challenge problem as well as real regulons, and compared it with some current representative motif-finding algorithms. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.
GPRM: a genetic programming approach to finding common RNA secondary structure elements
(2003-07-01)
Hu, YJ
;
資訊工程學系
;
Department of Computer Science
RNA molecules play an important role in many biological activities. Knowing its secondary structure can help us better understand the molecule's ability to function. The methods for RNA structure determination have traditionally been implemented through biochemical, biophysical and phylogenetic analyses. As the advance of computer technology, an increasing number of computational approaches have recently been developed. They have different goals and apply various algorithms. For example, some focus on secondary structure prediction for a single sequence; some aim at finding a global alignment of multiple sequences. Some predict the structure based on free energy minimization; some make comparative sequence analyses to determine the structure. In this paper, we describe how to correctly use GPRM, a genetic programming approach to finding common secondary structure elements in a set of unaligned coregulated or homologous RNA sequences. GPRM can be accessed at http://bioinfo.cis.nctu.edu.tw/service/gprm/.
Mining a set of coregulated RNA sequences
(2002)
Hu, YJ
;
資訊工程學系
;
Department of Computer Science
Post-transcriptional regulation, though less studied, is an important research topic in bioinformatics. In a set of post-transcriptionally coregulated RNAs, the basepair interactions can organize the molecules into domains and provide a framework for functional interactions. Their consensus motifs may represent the binding sites of RNA regulatory proteins. Unlike DNA motifs, RNA motifs are more conserved in structures' than in sequences. Knowing the structural motifs can help us better understand the regulation activities. In this paper we propose a novel data mining approach to RNA secondary structure prediction. To demonstrate the performance of our new approach, we first tested it on the same data sets previously used and published in literature. Secondly, to show the flexibility of our new approach, we also tested it on a data set that contains pseudoknot motifs that most current systems cannot identify.
Prediction of consensus structural motifs in a family of coregulated RNA sequences
(2002-09-01)
Hu, YJ
;
資訊工程學系
;
Department of Computer Science
Given a set of homologous or functionally related RNA sequences, the consensus motifs may represent the binding sites of RNA regulatory proteins. Unlike DNA motifs, RNA motifs are more conserved in structures than in sequences. Knowing the structural motifs can help us gain a deeper insight of the regulation activities. There have been various studies of RNA secondary structure prediction, but most of them are not focused on finding motifs from sets of functionally related sequences. Although recent research shows some new approaches to RNA motif finding, they are limited to finding relatively simple structures, e.g. stem-loops. In this paper, we propose a novel genetic programming approach to RNA secondary structure prediction. It is capable of finding more complex structures than stem-loops. To demonstrate the performance of our new approach as well as to keep the consistency of our comparative study, we first tested it on the same data sets previously used to verify the current prediction systems. To show the flexibility of our new approach, we also tested it on a data set that contains pseudoknot motifs which most current systems cannot identify. A web-based user interface of the prediction system is set up at http://bioinfo. cis.nctu.edu.tw/service/gprm/.
Prediction of orthologous relationship by functionally important sites
(2005-06-01)
Chiu, HC
;
Chang, CA
;
Hu, YJ
;
生物科技學系
;
資訊工程學系
Making accurate functional predictions plays an important role in the era of proteomics. Reliable functional information can be extracted from orthologs in other species when annotating an unknown gene. Here a site-based approach called PORFIS is proposed to predict orthologous relationship. When applied to the bacterial. transcription factor PurR/Lacl family and the protein kinase AGC family, our method was able to identify, with few false positives, the important sites that agree with those verified by biological experiments. We also tested it on the alpha-proteasome family, the glycoprotein hormone family and the growth hormone family to demonstrate its ability to predict orthologous relationship. Compared with other prediction methods based on phylogenetic analysis or hidden Markov models, PORFIS not only has competitive prediction accuracy, but also provides valuable biological information of functionally important sites associated with orthologs which can be further studied in biological experiments. (c) 2005 Elsevier Ireland Ltd. All rights reserved.
Reconstruct transcription networks by combining gene expression correlations with TF binding sites
(2003)
Hsu, YZ
;
Hu, YJ
;
資訊工程學系
;
Department of Computer Science
One of the major challenges in molecular biology is to understand the precise mechanism by which gene expression is regulated. Reconstruction of transcription networks is essential to modeling this mechanism. In this paper we describe a novel approach for building transcription networks from transcription modules by combining expression profile correlations with probabilistic element assessment. To demonstrate its performance, we systematically tested it on 27 transcription modules and reconstructed the transcription network for 6 transcription factors and 15 genes involved in the yeast cell cycle. The experimental results show that our combinatorial approach can better filter false positives to increase the selectivity in prediction of target genes. The regulatory control relationships described by the network reconstructed also mostly, agree with those in earlier studies.
RNA clustering and secondary structure prediction
(2005)
Hu, YJ
;
資訊工程學系
;
Department of Computer Science
RNA plays a crucial role in post-transcriptional regulation. Similar to transcriptional regulation, post-transcriptional regulation is often accomplished by the binding of proteins to specific motifs in mRNA molecules. Unlike DNA binding proteins, which recognize motifs composed of conserved sequences, RNA protein binding sites are more conserved in structures than in sequences. A lot of works have been done for RNA structure prediction; however, most of them focus on single RNA structure prediction instead of finding characteristic structure motifs within a RNA family. Though some current approaches can now identify common structure motifs from a set of RNAs, they typically assume the given set forms a single family, which is not necessarily correct. We propose a new adaptive method that conducts structure prediction and clustering simultaneously. Its performance is demonstrated on several real RNA families.
Using functionally important sites to identify orthologous relations
(2003)
Chiu, HC
;
Hu, YJ
;
資訊工程學系
;
Department of Computer Science
Making accurate functional predictions for genes plays an important role in the era of proteomics. The most reliable functional information is extracted from orthologs in other species when annotating an unknown gene. Here a site-based approach is proposed to predict orthologous relations. The method first identifies important sites that confer specificity of paralogs in the multiple sequence alignment of homologous proteins. It then predicts orthologous relations for unannotated proteins based on the important sites found. When applied to the bacterial transcription factor PurR/LacI family and the protein kinase AGC group family, our method was able to identify with few false positives; the important sites that agree with those obtained from biological experiments. We also tested it on the AGC group family, the alpha-proteasome family, the glycoprotein hormone family and the growth hormone family to demonstrate its ability to predict orthologs. Compared with other prediction methods based on phylogenetic analysis or hidden Markov models, our method not only has competitive prediction accuracy, but also provides valuable biological information of important sites associated with orthologs which can be further studied in biological experiments.
Using iterative restart in finding subtle regulatory signals
(2001)
Hu, YJ
;
資訊工程學系
;
Department of Computer Science
Biologists have determined that the control or regulation of gene expression is primarily determined by relatively short sequences in the region surrounding a gene. These sequences vary in length, position, redundancy, orientation, and bases. Finding these short sequences is a fundamental problem in molecular biology with important applications. Though there exist many different approaches to signal (i.e. short sequence) finding, some new study suggests that this problem still leaves plenty of room for improvement. In this paper, we report an improvement to our previously developed algorithm by adding an iterative restart strategy. We demonstrate the effectiveness of the synergy by comparing this new system with other algorithms on the Challenge Problem proposed by Peuzner and Sze.