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TOC Alert for Publication# 8857 2016August 22<![CDATA[A New Efficient Algorithm for the All Sorting Reversals Problem with No Bad Components]]>O(n^{3})-time algorithm. Most complications of his algorithm stem from some peculiar structures called bad components. Since bad components are very rare in both real and simulated data, it is practical to study the ASR problem with no bad components. For the ASR problem with no bad components, Swenson et al. gave an O (n^{2})-time algorithm. Very recently, Swenson found that their algorithm does not always work. In this paper, a new algorithm is presented for the ASR problem with no bad components. The time complexity is O(n^{2}) in the worst case and is linear in the size of input and output in practice.]]>134599609467<![CDATA[Detecting Functional Modules Based on a Multiple-Grain Model in Large-Scale Protein-Protein Interaction Networks]]>134610622860<![CDATA[AMAS: Optimizing the Partition and Filtration of Adaptive Seeds to Speed up Read Mapping]]>https://sourceforge.net/projects/ngsamas/.]]>134623633342<![CDATA[An Unsupervised Graph Based Continuous Word Representation Method for Biomedical Text Mining]]>1346346421135<![CDATA[Classifying the Progression of Ductal Carcinoma from Single-Cell Sampled Data via Integer Linear Programming: A Case Study]]>134643655939<![CDATA[Discovering Recurrent Copy Number Aberrations in Complex Patterns via Non-Negative Sparse Singular Value Decomposition]]>1346566681955<![CDATA[Extracting Biomedical Event with Dual Decomposition Integrating Word Embeddings]]>134669677673<![CDATA[Extraction of Blebs in Human Embryonic Stem Cell Videos]]>134678688846<![CDATA[Global Alignment of Protein-Protein Interaction Networks: A Survey]]>134689705964<![CDATA[Mem-mEN: Predicting Multi-Functional Types of Membrane Proteins by Interpretable Elastic Nets]]> http://bioinfo.eie.polyu.edu.hk/MemmENServer/.]]>1347067181510<![CDATA[Optimal ROC-Based Classification and Performance Analysis under Bayesian Uncertainty Models]]>134719729948<![CDATA[Performance Analysis of Chemotaxis Controllers: Which has Better Chemotaxis Controller, Escherichia coli or Paramecium caudatum?]]>environmental response of organisms, it is a fundamental topic in biology and related fields. We discuss the performance of the internal controllers that generate chemotaxis. We first propose performance indices to evaluate the controllers. Based on these indices, we evaluate the performance of two controller models of Escherichia coli and Paramecium caudatum. As a result, it is disclosed that the E. coli-type controller achieves chemotaxis quickly but roughly, whereas the P. caudatum-type controller achieves it slowly but precisely. This result will be a biological contribution from a control theoretic point of view.]]>1347307411617<![CDATA[Prediction of Protein Coding Regions Using a Wide-Range Wavelet Window Method]]>134742753804<![CDATA[Probabilistic Boolean Network Modelling and Analysis Framework for mRNA Translation]]>134754766723<![CDATA[Reconstruction of Gene Regulatory Networks Based on Repairing Sparse Low-Rank Matrices]]>1347677771626<![CDATA[Robust Multiobjective Controllability of Complex Neuronal Networks]]>1347787911510<![CDATA[The Max-Min High-Order Dynamic Bayesian Network for Learning Gene Regulatory Networks with Time-Delayed Regulations]]>gene regulatory network (GRN) from gene expression data is a challenging task in systems biology. Although some progresses have been made, the performance of GRN reconstruction still has much room for improvement. Because many regulatory events are asynchronous, learning gene interactions with multiple time delays is an effective way to improve the accuracy of GRN reconstruction. Here, we propose a new approach, called Max-Min high-order dynamic Bayesian network (MMHO-DBN) by extending the Max-Min hill-climbing Bayesian network technique originally devised for learning a Bayesian network's structure from static data. Our MMHO-DBN can explicitly model the time lags between regulators and targets in an efficient manner. It first uses constraint-based ideas to limit the space of potential structures, and then applies search-and-score ideas to search for an optimal HO-DBN structure. The performance of MMHO-DBN to GRN reconstruction was evaluated using both synthetic and real gene expression time-series data. Results show that MMHO-DBN is more accurate than current time-delayed GRN learning methods, and has an intermediate computing performance. Furthermore, it is able to learn long time-delayed relationships between genes. We applied sensitivity analysis on our model to study the performance variation along different parameter settings. The result provides hints on the setting of parameters of MMHO-DBN.]]>134792803726<![CDATA[<inline-formula> <img src="/images/tex/41722.gif" alt="boldsymbol{\ell _2}"> <alternatives> <inline-graphic xlink:type="simple" xlink:href="jian-ieq1-2480084.gif"/></alternatives></inline-formula> Multiple Kernel Fuzzy SVM-Based Data Fusion for Improving Peptide Identification]]>[1] . In this paper, we propose a fast algorithm for validating peptide identification by incorporating heterogeneous information from SEQUEST scores and peptide digested knowledge. To automate the peptide identification process and incorporate additional information, we employ multiple kernel learning (MKL) to implement the current peptide identification task. Results on experimental datasets indicate that compared with state-of-the-art methods, i.e., PeptideProphet and Percolator, our data fusing strategy has comparable performance but reduces the running time significantly.]]>1348048091181