Zhipeng Cai

A stable gene selection in microarray data analysis.

BackgroundMicroarray data analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Gene selection is to detect the most significantly differentially expressed genes under different conditions, and it has been a central research focus. In general, a better gene selection method can improve the performance of classification significantly. One of the difficulties in gene selection is that the numbers of samples under different conditions vary a lot.ResultsTwo novel gene selection methods are proposed in this paper, which are not affected by the unbalanced sample class sizes and do not assume any explicit statistical model on the gene expression values. They were evaluated on eight publicly available microarray datasets, using leave-one-out cross-validation and 5-fold cross-validation. The performance is measured by the classification accuracies using the top ranked genes based on the training datasets.ConclusionThe experimental results showed that the proposed gene selection methods are efficient, effective, and robust in identifying differentially expressed genes. Adopting the existing SVM-based and KNN-based classifiers, the selected genes by our proposed methods in general give more accurate classification results, typically when the sample class sizes in the training dataset are unbalanced.

Indications that Live Poultry Markets are a Major Source of Human H5N1 Influenza Virus Infection in China

ABSTRACT Human infections of H5N1 highly pathogenic avian influenza virus have continued to occur in China without corresponding outbreaks in poultry, and there is little conclusive evidence of the source of these infections. Seeking to identify the source of the human infections, we sequenced 31 H5N1 viruses isolated from humans in China (2005 to 2010). We found a number of viral genotypes, not all of which have similar known avian virus counterparts. Guided by patient questionnaire data, we also obtained environmental samples from live poultry markets and dwellings frequented by six individuals prior to disease onset (2008 and 2009). H5N1 viruses were isolated from 4 of the 6 live poultry markets sampled. In each case, the genetic sequences of the environmental and corresponding human isolates were highly similar, demonstrating a link between human infection and live poultry markets. Therefore, infection control measures in live poultry markets are likely to reduce human H5N1 infection in China.

Avian-origin H3N2 canine influenza A viruses in Southern China

This study reports four sporadic cases of H3N2 canine influenza in Southern China, which were identified from sick dogs from May 2006 to October 2007. The evolutionary analysis showed that all eight segments of these four viruses are avian-origin and phylogenetically close to the H3N2 canine influenza viruses reported earlier in South Korea. Systematic surveillance is required to monitor the disease and evolutionary behavior of this virus in canine populations in China.

Bioinformatics Research and Applications

PNImodeler: Web Server for Inferring Protein Binding Nucleotides from Sequence Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Jinyong Im, Narankhuu Tuvshinjargal, Byungkyu Park, Wook Lee, and Kyungsook Han A MCI Decision Support System Based on Ontology . . . . . . . . . . . . . . . . . 368 Xiaowei Zhang, Yang Zhou, Bin Hu, Jing Chen, and Xu Ma Context Similarity Based Feature Selection Methods for Protein Interaction Article Classification . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 369 Yifei Chen, Yuxing Sun, and Ping Hou XVI Table ofThis book constitutes the refereed proceedings of the 9th International Symposium on Bioinformatics Research and Applications, ISBRA 2013, held in Charlotte, NC, USA, in May 2013. The 25 revised full papers presented together with 4 invited talks were carefully reviewed and selected from 46 submissions. The papers cover a wide range of biomedical databases and data integration, high-performance bio-computing, biomolecular imaging, high-throughput sequencing data analysis, bio-ontologies, molecular evolution, comparative genomics and phylogenomics, molecular modeling and simulation, pattern discovery and classification, computational proteomics, population genetics, data mining and visualization, software tools and applications.

A computational framework for influenza antigenic cartography.

Influenza viruses have been responsible for large losses of lives around the world and continue to present a great public health challenge. Antigenic characterization based on hemagglutination inhibition (HI) assay is one of the routine procedures for influenza vaccine strain selection. However, HI assay is only a crude experiment reflecting the antigenic correlations among testing antigens (viruses) and reference antisera (antibodies). Moreover, antigenic characterization is usually based on more than one HI dataset. The combination of multiple datasets results in an incomplete HI matrix with many unobserved entries. This paper proposes a new computational framework for constructing an influenza antigenic cartography from this incomplete matrix, which we refer to as Matrix Completion-Multidimensional Scaling (MC-MDS). In this approach, we first reconstruct the HI matrices with viruses and antibodies using low-rank matrix completion, and then generate the two-dimensional antigenic cartography using multidimensional scaling. Moreover, for influenza HI tables with herd immunity effect (such as those from Human influenza viruses), we propose a temporal model to reduce the inherent temporal bias of HI tables caused by herd immunity. By applying our method in HI datasets containing H3N2 influenza A viruses isolated from 1968 to 2003, we identified eleven clusters of antigenic variants, representing all major antigenic drift events in these 36 years. Our results showed that both the completed HI matrix and the antigenic cartography obtained via MC-MDS are useful in identifying influenza antigenic variants and thus can be used to facilitate influenza vaccine strain selection. The webserver is available at http://sysbio.cvm.msstate.edu/AntigenMap.

Improved approximation algorithms for the capacitated multicast routing problem

Two models for the Capacitated Multicast Routing Problem are considered, which are the Multicast k-Path Routing and the Multicast k-Tree Routing. Under these models, two improved approximation algorithms are presented, which have worst case performance ratios of 3 and (2 + ρ), respectively. Here ρ denotes the best approximation ratio for the Steiner Minimum Tree problem, and it is about 1.55 at the writing of the paper. The two approximation algorithms improve upon the previous best ones having performance ratios of 4 and (2.4 + ρ), respectively. The designing techniques developed in the paper could be applicable to other similar networking problems.

Curve Query Processing in Wireless Sensor Networks

Most existing query processing algorithms for wireless sensor networks (WSNs) can only deal with discrete values. However, since the monitored environment always changes continuously with time, discrete values cannot describe the environment accurately and, hence, may not satisfy a variety of query requirements, such as the queries of the maximal, minimal, and inflection points. It is, therefore, of great interest to introduce new queries capable of processing time-continuous data. This paper investigates curve query processing for WSNs as curve is an effective way to represent continuous sensed data. Specifically, a sensed curve derivation algorithm to support curve query processing in WSNs is first proposed. Then, the aggregation operation is employed as an example to illustrate curve query processing. The corresponding accurate and approximate aggregation algorithms are devised accordingly. We demonstrate that the energy cost of the approximate aggregation algorithm is optimal, provided that the required precision is satisfied. The theoretical analysis and experimental results indicate that the proposed algorithms can achieve high performance in terms of accuracy and energy efficiency.

Approximate aggregation for tracking quantiles and range countings in wireless sensor networks

We consider the problem of tracking quantiles and range countings in wireless sensor networks. The quantiles and range countings are two important aggregations to characterize a data distribution. Let S ( t ) = ( d 1 , ? , d n ) denote the multi-set of sensory data that have arrived until time t, which is a sequence of data orderly collected by nodes s 1 , s 2 , ? , s k . One of our goals is to continuously track ?-approximate ?-quantiles ( 0 ? ? ? 1 ) of S ( t ) for all ?s with efficient total communication cost and balanced individual communication cost. The other goal is to track ( ? , ? ) -approximate range countings satisfying the requirement of arbitrary precision specified by different users. In this paper, a deterministic tracking algorithm based on a dynamic binary tree is proposed to track ?-approximate ?-quantiles, whose total communication cost is O ( k / ? ? log ? n ? log 2 ? ( 1 / ? ) ) , where k is the number of the nodes in a network, n is the total number of the data, and ? is the user-specified approximation error. For range countings, a Bernoulli sampling based algorithm is proposed to track ( ? , ? ) -approximate range countings, whose total communication cost is O ( 2 ? 2 ln ? 2 1 - 1 - ? + n c ) , where ? is the user-specified error probability, n c is the number of clusters.

A first generation whole genome RH map of the river buffalo with comparison to domestic cattle

BackgroundThe recently constructed river buffalo whole-genome radiation hybrid panel (BBURH5000) has already been used to generate preliminary radiation hybrid (RH) maps for several chromosomes, and buffalo-bovine comparative chromosome maps have been constructed. Here, we present the first-generation whole genome RH map (WG-RH) of the river buffalo generated from cattle-derived markers. The RH maps aligned to bovine genome sequence assembly Btau_4.0, providing valuable comparative mapping information for both species.ResultsA total of 3990 markers were typed on the BBURH5000 panel, of which 3072 were cattle derived SNPs. The remaining 918 were classified as cattle sequence tagged site (STS), including coding genes, ESTs, and microsatellites. Average retention frequency per chromosome was 27.3% calculated with 3093 scorable markers distributed in 43 linkage groups covering all autosomes (24) and the X chromosomes at a LOD ≥ 8. The estimated total length of the WG-RH map is 36,933 cR5000. Fewer than 15% of the markers (472) could not be placed within any linkage group at a LOD score ≥ 8. Linkage group order for each chromosome was determined by incorporation of markers previously assigned by FISH and by alignment with the bovine genome sequence assembly (Btau_4.0).ConclusionWe obtained radiation hybrid chromosome maps for the entire river buffalo genome based on cattle-derived markers. The alignments of our RH maps to the current bovine genome sequence assembly (Btau_4.0) indicate regions of possible rearrangements between the chromosomes of both species. The river buffalo represents an important agricultural species whose genetic improvement has lagged behind other species due to limited prior genomic characterization. We present the first-generation RH map which provides a more extensive resource for positional candidate cloning of genes associated with complex traits and also for large-scale physical mapping of the river buffalo genome.

Approximate Physical World Reconstruction Algorithms in Sensor Networks

To observe the complicated physical world, the sensors in a network sense and sample the data from the physical world. Currently, most existing works use the Equi-Frequency Sampling (EFS) methods or EFS based methods for data acquisition. However, the accuracy of EFS and EFS based methods cannot be guaranteed in practice since the physical world keeps changing continuously, and these methods do not effectively support reconstruction of the monitored physical world. To overcome the shortages of EFS and EFS based methods, this paper focuses on designing physical-world-aware data acquisition algorithms to support O(ε)-approximation to the physical world for any ε ≥ 0. Two physical-world-aware data acquisition algorithms are proposed. Both algorithms can adjust the sensing frequency automatically based on the changing trend of the physical world and the given ε. The thorough analysis on the performances of the algorithms are also provided. It is proven that the error bounds of the algorithms are O(ε) and the complexities of the algorithms are O(1/(ε1/4)). Based on the new data acquisition algorithms, an algorithm for reconstructing the physical world is proposed and analyzed. The theoretical analysis and experimental results show that the proposed algorithms have high performances on the aspects of accuracy and energy consumption.