Chuan Yi Tang

A polynomial time approximation scheme for minimum routing cost spanning trees

Given an undirected graph with nonnegative costs on the edges, the routing cost of any of its spanning trees is the sum over all pairs of vertices of the cost of the path between the pair in the tree. Finding a spanning tree of minimum routing cost is NP-hard, even when the costs obey the triangle inequality. We show that the general case is in fact reducible to the metric case and present a polynomial-time approximation scheme valid for both versions of the problem. In particular, we show how to build a spanning tree of an n-vertex weighted graph with routing cost at most

An efficient algorithm for finding a maximum weight 2-independent set on interval graphs

(1+\epsilon)

A Novel Molecular Signature Identified by Systems Genetics Approach Predicts Prognosis in Oral Squamous Cell Carcinoma

of the minimum in time

Feature Selection and Combination Criteria for Improving Accuracy in Protein Structure Prediction

O(n^{O({\frac{1}{\epsilon}}% )})

Whole-genome sequencing and identification of Morganella morganii KT pathogenicity-related genes

. Besides the obvious connection to network design, trees with small routing cost also find application in the construction of good multiple sequence alignments in computational biology. The communication cost spanning tree problem is a generalization of the minimum routing cost tree problem where the routing costs of different pairs are weighted by different requirement amounts. We observe that a randomized O(log n log log n)-approximation for this problem follows directly from a recent result of Bartal, where n is the number of nodes in a metric graph. This also yields the same approximation for the generalized sum-of-pairs alignment problem in computational biology.

Approximation algorithms for some optimum communication spanning tree problems

Abstract In this paper, we introduce an O ( n ) time algorithm to solve the maximum weight independent set problem on an interval graph with n vertices given its interval representation with sorted endpoints list. Based on this linear algorithm, we design an O ( n 2 ) time algorithm using O ( n 2 ) space to solve the maximum weight 2-independent set problem on an interval graph with n vertices. With a slight extension and modification of our algorithm, the maximum weight k -independent set problem on an interval graph with n vertices can be solved in O ( n k ) time using O ( n k ) space.

A linear-time algorithm for the weighted feedback vertex problem on interval graphs

Molecular methods for predicting prognosis in patients with oral cavity squamous cell carcinoma (OSCC) are urgently needed, considering its high recurrence rate and tendency for metastasis. The present study investigated the genetic basis of variations in gene expression associated with poor prognosis in OSCC using Affymetrix SNP 6.0 and Affymetrix GeneChip Human Gene 1.0 ST arrays. We identified recurrent DNA amplifications scattered from 8q22.2 to 8q24.3 in 112 OSCC specimens. These amplicons demonstrated significant associations with increased incidence of extracapsular spread, development of second primary malignancies, and poor survival. Fluorescence in situ hybridization, in a validation panel consisting of 295 cases, confirmed these associations. Assessment of the effects of copy number variations (CNVs) on genome-wide variations in gene expression identified a total of 85 CNV-associated transcripts enriched in the MYC-centered regulatory network. Twenty-four transcripts associated with increased risk of second primary malignancies, tumor relapse, and poor survival. Besides MYC itself, a novel dysregulated MYC module plays a key role in OSCC carcinogenesis. This study identified a candidate molecular signature associated with poor prognosis in OSCC patients, which may ultimately facilitate patient-tailored selection of therapeutic strategies.

The full Steiner tree problem

The classification of protein structures is essential for their function determination in bioinformatics. At present, a reasonably high rate of prediction accuracy has been achieved in classifying proteins into four classes in the SCOP database according to their primary amino acid sequences. However, for further classification into fine-grained folding categories, especially when the number of possible folding patterns as those defined in the SCOP database is large, it is still quite a challenge. In our previous work, we have proposed a two-level classification strategy called hierarchical learning architecture (HLA) using neural networks and two indirect coding features to differentiate proteins according to their classes and folding patterns, which achieved an accuracy rate of 65.5%. In this paper, we use a combinatorial fusion technique to facilitate feature selection and combination for improving predictive accuracy in protein structure classification. When applying various criteria in combinatorial fusion to the protein fold prediction approach using neural networks with HLA and the radial basis function network (RBFN), the resulting classification has an overall prediction accuracy rate of 87% for four classes and 69.6% for 27 folding categories. These rates are significantly higher than the accuracy rate of 56.5% previously obtained by Ding and Dubchak. Our results demonstrate that data fusion is a viable method for feature selection and combination in the prediction and classification of protein structure.

Perfect edge domination and efficient edge domination in graphs

BackgroundThe opportunistic enterobacterium, Morganella morganii, which can cause bacteraemia, is the ninth most prevalent cause of clinical infections in patients at Changhua Christian Hospital, Taiwan. The KT strain of M. morganii was isolated during postoperative care of a cancer patient with a gallbladder stone who developed sepsis caused by bacteraemia. M. morganii is sometimes encountered in nosocomial settings and has been causally linked to catheter-associated bacteriuria, complex infections of the urinary and/or hepatobiliary tracts, wound infection, and septicaemia. M. morganii infection is associated with a high mortality rate, although most patients respond well to appropriate antibiotic therapy. To obtain insights into the genome biology of M. morganii and the mechanisms underlying its pathogenicity, we used Illumina technology to sequence the genome of the KT strain and compared its sequence with the genome sequences of related bacteria.ResultsThe 3,826,919-bp sequence contained in 58 contigs has a GC content of 51.15% and includes 3,565 protein-coding sequences, 72 tRNA genes, and 10 rRNA genes. The pathogenicity-related genes encode determinants of drug resistance, fimbrial adhesins, an IgA protease, haemolysins, ureases, and insecticidal and apoptotic toxins as well as proteins found in flagellae, the iron acquisition system, a type-3 secretion system (T3SS), and several two-component systems. Comparison with 14 genome sequences from other members of Enterobacteriaceae revealed different degrees of similarity to several systems found in M. morganii. The most striking similarities were found in the IS4 family of transposases, insecticidal toxins, T3SS components, and proteins required for ethanolamine use (eut operon) and cobalamin (vitamin B12) biosynthesis. The eut operon and the gene cluster for cobalamin biosynthesis are not present in the other Proteeae genomes analysed. Moreover, organisation of the 19 genes of the eut operon differs from that found in the other non-Proteeae enterobacterial genomes.ConclusionsThis is the first genome sequence of M. morganii, which is a clinically relevant pathogen. Comparative genome analysis revealed several pathogenicity-related genes and novel genes not found in the genomes of other members of Proteeae. Thus, the genome sequence of M. morganii provides important information concerning virulence and determinants of fitness in this pathogen.

Sequence of Leptospira santarosai serovar Shermani genome and prediction of virulence-associated genes.

Abstract Let G =( V , E , w ) be an undirected graph with nonnegative edge length function w and nonnegative vertex weight function r . The optimal product-requirement communication spanning tree (PROCT) problem is to find a spanning tree T minimizing ∑ u , v ∈ V r ( u ) r ( v ) d T ( u , v ), where d T ( u , v ) is the length of the path between u and v on T . The optimal sum-requirement communication spanning tree (SROCT) problem is to find a spanning tree T such that ∑ u , v ∈ V ( r ( u )+ r ( v )) d T ( u , v ) is minimized. Both problems are special cases of the optimum communication spanning tree problem, and are reduced to the minimum routing cost spanning tree (MRCT) problem when all the vertex weights are equal to each other. In this paper, we present an O( n 5 )-time 1.577-approximation algorithm for the PROCT problem, and an O( n 3 ) time 2-approximation algorithm for the SROCT problem, where n is the number of vertices. We also show that a 1.577-approximation solution for the MRCT problem can be obtained in O( n 3 )-time, which improves the time complexity of the previous result.