Phase diagram of a Bose-Fermi mixture in a one-dimensional optical lattice in terms of fidelity and entanglement

We study the ground-state phase diagram of a Bose-Fermi mixture loaded in a one-dimensional optical lattice by computing the ground-state fidelity and quantum entanglement. We find that the fidelity is able to signal quantum phase transitions between the Luttinger liquid phase, the density-wave phase, and the phase separation state of the system; and the concurrence can be used to signal the transition between the density-wave phase and the Ising phase.Comment: 4 pages 3 figure

The ground state of a mixture of two species of fermionic atoms in 1D optical lattice

In this paper, we investigate the ground state properties of a mixture of two species of fermionic atoms in one-dimensional optical lattice, as described by the asymmetric Hubbard model. The quantum phase transition from density wave to phase separation is investigated by studying both the corresponding charge order parameter and quantum entanglement. A rigorous proof that even for the single hole doping case, the density wave is unstable to the phase separation in the infinite U limit, is given. Therefore, our results are quite instructive for both on-going experiments on strongly correlated cold-atomic systems and traditional heavy fermion systems.Comment: 9 pages, 10 figures, extended versio

Motif-All: discovering all phosphorylation motifs

Background: Phosphorylation motifs represent common patterns around the phosphorylation site. The discovery of such kinds of motifs reveals the underlying regulation mechanism and facilitates the prediction of unknown phosphorylation event. To date, people have gathered large amounts of phosphorylation data, making it possible to perform substrate-driven motif discovery using data mining techniques. Results: We describe an algorithm called Motif-All that is able to efficiently identify all statistically significant motifs. The proposed method explores a support constraint to reduce search space and avoid generating random artifacts. As the number of phosphorylated peptides are far less than that of unphosphorylated ones, we divide the mining process into two stages: The first step generates candidates from the set of phosphorylated sequences using only support constraint and the second step tests the statistical significance of each candidate using the odds ratio derived from the whole data set. Experimental results on real data show that Motif-All outperforms current algorithms in terms of both effectiveness and efficiency. Conclusions: Motif-All is a useful tool for discovering statistically significant phosphorylation motifs. Source codes and data sets are available at: http://bioinformatics.ust.hk/MotifAll.rar

Finding optimal threshold for correction error reads in DNA assembling

Background: DNA assembling is the problem of determining the nucleotide sequence of a genome from its substrings, called reads. In the experiments, there may be some errors on the reads which affect the performance of the DNA assembly algorithms. Existing algorithms, e.g. ECINDEL and SRCorr, correct the error reads by considering the number of times each length-k substring of the reads appear in the input. They treat those length-k substrings appear at least M times as correct substring and correct the error reads based on these substrings. However, since the threshold M is chosen without any solid theoretical analysis, these algorithms cannot guarantee their performances on error correction. Results: In this paper, we propose a method to calculate the probabilities of false positive and false negative when determining whether a length-k substring is correct using threshold M. Based on this optimal threshold M that minimizes the total errors (false positives and false negatives). Experimental results on both real data and simulated data showed that our calculation is correct and we can reduce the total error substrings by 77.6% and 65.1% when compared to ECINDEL and SRCorr respectively. Conclusion: We introduced a method to calculate the probability of false positives and false negatives of the length-k substring using different thresholds. Based on this calculation, we found the optimal threshold to minimize the total error of false positive plus false negative. © 2009 Chin et al; licensee BioMed Central Ltd.published_or_final_versio

Semi-supervised protein subcellular localization

<p>Abstract</p> <p>Background</p> <p>Protein subcellular localization is concerned with predicting the location of a protein within a cell using computational method. The location information can indicate key functionalities of proteins. Accurate predictions of subcellular localizations of protein can aid the prediction of protein function and genome annotation, as well as the identification of drug targets. Computational methods based on machine learning, such as support vector machine approaches, have already been widely used in the prediction of protein subcellular localization. However, a major drawback of these machine learning-based approaches is that a large amount of data should be labeled in order to let the prediction system learn a classifier of good generalization ability. However, in real world cases, it is laborious, expensive and time-consuming to experimentally determine the subcellular localization of a protein and prepare instances of labeled data.</p> <p>Results</p> <p>In this paper, we present an approach based on a new learning framework, semi-supervised learning, which can use much fewer labeled instances to construct a high quality prediction model. We construct an initial classifier using a small set of labeled examples first, and then use unlabeled instances to refine the classifier for future predictions.</p> <p>Conclusion</p> <p>Experimental results show that our methods can effectively reduce the workload for labeling data using the unlabeled data. Our method is shown to enhance the state-of-the-art prediction results of SVM classifiers by more than 10%.</p

Score regularization for peptide identification

<p>Abstract</p> <p>Background</p> <p>Peptide identification from tandem mass spectrometry (MS/MS) data is one of the most important problems in computational proteomics. This technique relies heavily on the accurate assessment of the quality of peptide-spectrum matches (PSMs). However, current MS technology and PSM scoring algorithm are far from perfect, leading to the generation of incorrect peptide-spectrum pairs. Thus, it is critical to develop new post-processing techniques that can distinguish true identifications from false identifications effectively.</p> <p>Results</p> <p>In this paper, we present a consistency-based PSM re-ranking method to improve the initial identification results. This method uses one additional assumption that two peptides belonging to the same protein should be correlated to each other. We formulate an optimization problem that embraces two objectives through regularization: the smoothing consistency among scores of correlated peptides and the fitting consistency between new scores and initial scores. This optimization problem can be solved analytically. The experimental study on several real MS/MS data sets shows that this re-ranking method improves the identification performance.</p> <p>Conclusions</p> <p>The score regularization method can be used as a general post-processing step for improving peptide identifications. Source codes and data sets are available at: <url>http://bioinformatics.ust.hk/SRPI.rar</url>.</p

Bioinformatics research in the Asia Pacific: a 2007 update

We provide a 2007 update on the bioinformatics research in the Asia-Pacific from the Asia Pacific Bioinformatics Network (APBioNet), Asia's oldest bioinformatics organisation set up in 1998. From 2002, APBioNet has organized the first International Conference on Bioinformatics (InCoB) bringing together scientists working in the field of bioinformatics in the region. This year, the InCoB2007 Conference was organized as the 6th annual conference of the Asia-Pacific Bioinformatics Network, on Aug. 27–30, 2007 at Hong Kong, following a series of successful events in Bangkok (Thailand), Penang (Malaysia), Auckland (New Zealand), Busan (South Korea) and New Delhi (India). Besides a scientific meeting at Hong Kong, satellite events organized are a pre-conference training workshop at Hanoi, Vietnam and a post-conference workshop at Nansha, China. This Introduction provides a brief overview of the peer-reviewed manuscripts accepted for publication in this Supplement. We have organized the papers into thematic areas, highlighting the growing contribution of research excellence from this region, to global bioinformatics endeavours