Computing exact P-values for DNA motifs
Many heuristic algorithms have been designed to approximate P-values of DNA motifs described by position weight matrices, for evaluating their statistical significance. They often significantly deviate from the true P-value by orders of magnitude. Exact P-value computation is needed for ranking the motifs. Furthermore, surprisingly, the complexity of the problem is unknown.



We show the problem to be NP-hard, and present MotifRank, software based on dynamic programming, to calculate exact P-values of motifs. We define the exact P-value on a general and more precise model. Asymptotically, MotifRank is faster than the best exact P-value computing algorithm, and is in fact practical. Our experiments clearly demonstrate that MotifRank significantly improves the accuracy of existing approximation algorithms.


AVAILABILITY
MotifRank is available from http://bio.dlg.cn.



Supplementary data are available at Bioinformatics online.

title: Computing Exact p-Value for Structured Motif
abstract: Extracting motifs from a set of DNA sequences is important in computational biology. Occurrence probability is a common used statistics to evaluate the statistical significance of a motif. A main problem is how to calculate the occurrence probability of the motif on the random model of DNA sequence efficiently and accurately. In this paper, we are interested in a particular motif model which is useful in transcription process. This motif, which is called structured motif, is composed two motif words on single nucleotide alphabet and with fixed spacers between them. We present an efficient algorithm to calculate the exact occurrence probability of a structured motif on a given sequence. It is the first nontrivial algorithm to calculate the exact p-value for such kind of motifs.

title: MicroRNA identification based on sequence and structure alignment
abstract: MicroRNAs (miRNA) are approximately 22 nt long non-coding RNAs that are derived from larger hairpin RNA precursors and play important regulatory roles in both animals and plants. The short length of the miRNA sequences and relatively low conservation of pre-miRNA sequences restrict the conventional sequence-alignment-based methods to finding only relatively close homologs. On the other hand, it has been reported that miRNA genes are more conserved in the secondary structure rather than in primary sequences. Therefore, secondary structural features should be more fully exploited in the homologue search for new miRNA genes.



In this paper, we present a novel genome-wide computational approach to detect miRNAs in animals based on both sequence and structure alignment. Experiments show this approach has higher sensitivity and comparable specificity than other reported homologue searching methods. We applied this method on Anopheles gambiae and detected 59 new miRNA genes.



This program is available at http://bioinfo.au.tsinghua.edu.cn/miralign.



Supplementary information is available at http://bioinfo.au.tsinghua.edu.cn/miralign/supplementary.htm.

title: ArnetMiner: extraction and mining of academic social networks
abstract: This paper addresses several key issues in the ArnetMiner system, which aims at extracting and mining academic social networks. Specifically, the system focuses on: 1) Extracting researcher profiles automatically from the Web; 2) Integrating the publication data into the network from existing digital libraries; 3) Modeling the entire academic network; and 4) Providing search services for the academic network. So far, 448,470 researcher profiles have been extracted using a unified tagging approach. We integrate publications from online Web databases and propose a probabilistic framework to deal with the name ambiguity problem. Furthermore, we propose a unified modeling approach to simultaneously model topical aspects of papers, authors, and publication venues. Search services such as expertise search and people association search have been provided based on the modeling results. In this paper, we describe the architecture and main features of the system. We also present the empirical evaluation of the proposed methods.

title: ArnetMiner: An Expertise Oriented Search System for Web Community
abstract: Expertise Oriented Search aims at providing comprehensive analysis and mining for people from distributed sources. In this paper, we give an overview of the expertise oriented search system (ArnetMiner). The system addresses several key research issues in extraction and mining of a researcher social network. The system is in operation on the internet for more than one year and receives accesses from about 1,500 users per month. Feedbacks from users and system logs indicate that users consider the system can really help people to find and share information in the web community.

title: htSNPer1.0: software for haplotype block partition and htSNPs selection
abstract: BackgroundThere is recently great interest in haplotype block structure and haplotype tagging SNPs (htSNPs) in the human genome for its implication on htSNPs-based association mapping strategy for complex disease. Different definitions have been used to characterize the haplotype block structure in the human genome, and several different performance criteria and algorithms have been suggested on htSNPs selection. heuristic algorithm, generalized branch-and-bound algorithm, is applied to the searching of minimal set of haplotype tagging SNPs (htSNPs) according to different htSNPs performance criteria. We develop a software htSNPer1.0 to implement the algorithm, and integrate three htSNPs performance criteria and four haplotype block definitions for haplotype block partitioning. It is a software with powerful Graphical User Interface (GUI), which can be used to characterize the haplotype block structure and select htSNPs in the candidate gene or interested genomic regions. It can find the global optimization with only a fraction of the computing time consumed by exhaustive searching algorithm.ConclusionhtSNPer1.0 allows molecular geneticists to perform haplotype block analysis and htSNPs selection using different definitions and performance criteria. The software is a powerful tool for those focusing on association mapping based on strategy of haplotype block and htSNPs.

title: Spectral decomposition approach for transient analysis of multi-server discrete-time queues
abstract: Previous work by the authors  where the spectral decomposition method was used for the transient analysis of a single-server queue is generalized. For the arrival process a general discrete-time Markovian batch arrival process is assumed, where the batch size distribution of the arrivals in successive slots is governed by a N-state discrete-time Markov chain. It is shown that once the N eigenvalues of the probability generating matrix of the arrival process are obtained, the complete solution in the transform domain may be given. Using the complex analysis technique and Cauchy's integral formula, an efficient numerical method is presented for the calculation of a few performance measures. The numerical method is generalized to the situations where the superpositions of a number of independent arrival sources are fed to the queue. It is shown that the numerical complexity are fed to the queue. It is shown that the numerical complexity of obtaining the transient solution in this case can be substantially reduced by using an approach based on the Kronecker product.<<ETX>>

title: A Novel Dimensionality Reduction Technique Based on Independent Component Analysis for Modeling Microarray Gene Expression Data
abstract: DNA microarray experiments generating thousands of gene 
expression measurements, are being used to gather information from tissue and cell samples regarding gene expression differences that will be useful in diagnosing disease. But one challenge of microarray studies is the fact that the number n of samples collected is relatively small compared to the number p of genes per sample which are usually in thousands. In statistical terms this very large number of predictors compared to a small number of samples or observations makes the classification problem difficult. This is known as the ”curse of dimensionality problem”. An efficient way to solve this problem is by using dimensionality reduction techniques. Principle Component Analysis(PCA) is a leading method for dimensionality reduction of gene expression data which is optimal in the sense of least square error. In this paper we propose a new dimensionality reduction technique for specific bioinformatics applications based on Independent component Analysis(ICA). Being able to exploit higher order statistics to identify a linear model result, this ICA based dimensionality reduction technique 
outperforms PCA from both statistical and biological 
significance aspects. We present experiments on NCI 60 
dataset to show this result.

title: Gait Planning Of Quadruped Robot Based On Third-Order Spline Interpolation
abstract: This paper presented a brief description for the gait planning of quadruped robot named Aibo ERS-7 which is a standard platform in the RoboCup 4-legged league. We approach a spline shaped locus to reduce the dimension of the parameter optimizing space and solve the problem of the significant bias between the planned locus and the real one. The result shows that the spline shaped locus is effective in finding the optimized locus shape in a short time. Finally, the robot achieves a gait faster than any previously known learned gait for Aibo

title: The Application of Cultural Algorithm in Load Balancing of Hybrid P2P Network
abstract: This paper analyses the defect of the designing model of load balancing in hybrid P2P network, and proposes a new strategy of scheduling for load balancing, which based on culture algorithm of multilayer belief spaces. According to the current load status of each super-node in the system, it can calculate its load weight of the next time section by using the cultural algorithm, and assign the current requests of login with weighted round-robin. The extracted knowledge which is got by selecting the best belief space from the multilayer belief space is used to optimize the constrains of the evolutionary, in order to accelerate the optimization process of the calculation of load weight. Simulation experiments show that the allocation of login request to each node is to be balance much faster through using of cultural algorithm based on multilayer belief spaces.

title: Limn Matrix: A Tool for Visualizing Large, Distributed, High-Dimension Data Sets