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IEEE/ACM Transactions on Computational Biology and Bioinformatics

Yi-Nan Guo, Jian Cheng, Sha Luo, Dun-Wei Gong
For dynamic multi-objective vehicle routing problems, the waiting time of vehicle, the number of serving vehicles, the total distance of routes were normally considered as the optimization objectives. Except for above objectives, fuel consumption that leads to the environmental pollution and energy consumption was focused on in this paper. Considering the vehicles' load and the driving distance, corresponding carbon emission model was built and set as an optimization objective. Dynamic multi-objective vehicle routing problems with hard time windows and randomly appeared dynamic customers, subsequently, were modeled...
March 21, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Xiangtao Li, Ka-Chun Wong
In the past years, the high-throughput sequencing technologies have enabled massive insights into genomic annotations. In contrast, the full-scale three-dimensional arrangements of genomic regions are relatively unknown. Thanks to the recent breakthroughs in High-throughput Chromosome Conformation Capture (Hi-C) techniques, non-negative matrix factorization (NMF) has been adopted to identify local spatial clusters of genomic regions from Hi-C data. However, such non-negative matrix factorization entails a high-dimensional non-convex objective function to be optimized with non-negative constraints...
March 20, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Marco Frasca, Nicolo Cesa-Bianchi
Automated protein function prediction is a challenging problem with distinctive features, such as the hierarchical organization of protein functions and the scarcity of annotated proteins for most biological functions. We propose a multitask learning algorithm addressing both issues. Unlike standard multitask algorithms, which use task (protein functions) similarity information as a bias to speed up learning, we show that dissimilarity information enforces separation of rare class labels from frequent class labels, and for this reason is better suited for solving unbalanced protein function prediction problems...
March 17, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Qirong Tang, Lu Ding, Fangchao Yu, Yuan Zhang, Yinghao Li, Haibo Tu
Swarm robots search for multiple targets in collaboration in unknown environments has been addressed in this paper. An improved grouping strategy based on constriction factors Particle Swarm Optimization is proposed. Robots are grouped under this strategy after several iterations of stochastic movements, which considers the influence range of targets and environmental information they have sensed. The group structure may change dynamically and each group focuses on searching one target. All targets are supposed to be found finally...
March 14, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Chengyu Liu, Rainer Lehtonen, Sampsa Hautaniemi
Identification of intracellular pathways that play key roles in cancer progression and drug resistance is a prerequisite for developing targeted cancer treatments. The era of personalized medicine calls for computational methods that can function with one sample or very small set of samples. Developing such methods is challenging because standard statistical approaches pose several limiting assumptions, such as number of samples, that prevent their application when n approaches to one. We have developed a novel pathway analysis method called Per- PAS to estimate pathway activity at a single sample level by integrating pathway topology and transcriptomics data...
March 8, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Bin Waang, Xuedong Zheng, Shihua Zhou, Changjun Zhou, Xiaopeng Wei, Qiang Zhang, Ziqi Wei
Following the completion of the human genome project, a large amount of high-throughput bio-data was generated. To analyze these data, massively parallel sequencing, namely next-generation sequencing, was rapidly developed. DNA barcodes are used to identify the ownership between sequences and samples when they are attached at the beginning or end of sequencing reads. Constructing DNA barcode sets provides the candidate DNA barcodes for this application. To increase the accuracy of DNA barcode sets, a particle swarm optimization (PSO) algorithm has been modified and used to construct the DNA barcode sets in this paper...
March 7, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Wei Shao, Mingxia Liu, Ying-Ying Xu, Hong-Bin Shen, Daoqiang Zhang
Nowadays, with the advances in microscopic imaging, accurate classification of bioimage-based protein subcellular location pattern has attracted as much attention as ever. One of the basic challenging problems is how to select the useful feature components among thousands of potential features to describe the images. This is not an easy task especially considering there is a high ratio of multi-location proteins. Existing feature selection methods seldom take the correlation among different cellular compartments into consideration, and thus may miss some features that will be co-important for several subcellular locations...
March 3, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Leyi Wei, Pengwei Xing, Gaotao Shi, Zhi-Liang Ji, Quan Zou
Protein methylation, an important post-translational modification, plays crucial roles in many cellular processes. The accurate prediction of protein methylation sites is fundamentally important for revealing the molecular mechanisms undergoing methylation. In recent years, computational prediction based on machine learning algorithms has emerged as a powerful and robust approach for identifying methylation sites, and much progress has been made in predictive performance improvement. However, the predictive performance of existing methods is not satisfactory in terms of overall accuracy...
February 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Yan Shi, Bolun Zhang, Maolin Cai, Weiqing Xu
For patients with mechanical ventilation, secretions in airway are harmful and sometimes even mortal, it's of great significance to clear secretion timely and efficiently. In this paper, a new secretion clearance method for VCV (volume-controlled ventilation) ventilator is put forward, and a secretion clearance system with a VCV ventilator and double lungs is designed. Furthermore, the mathematical model of the secretion clearance system is built and verified via experimental study. Finally, to illustrate the influence of key parameters of respiratory system and secretion clearance system on the secretion clearance characteristics, coupling effects of two lungs on VCV secretion clearance system are studied by an orthogonal experiment, it can be obtained that rise of tidal volume adds to efficiency of secretion clearance while effect of area, compliance and suction pressure on efficiency of secretion clearance needs further study...
February 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Yan Lin, Fa-Zhan Zhang, Kai Xue, Yi-Zhou Gao, Feng-Biao Guo
Essential genes are those genes of an organism that are considered to be crucial for its survival. Identification of essential genes is therefore of great significance to advance our understanding of the principles of cellular life. We have developed a novel computational method, which can effectively predict bacterial essential genes by extracting and integrating homologous features, protein domain feature, gene intrinsic features and network topological features. By performing the principal component regression (PCR) analysis for Escherichia coli MG1655, we established a classification model with the average area under curve (AUC) value of 0...
February 15, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jun-Ping Zhang, Yi Liu, Wei Sun, Xiaoyang Zhao, Ta La, Wei-Sheng Guo
Myosin V is a processive doubled-headed biomolecular motor involved in many intracellular organelle and vesicle transport. The unidirectional movement is coupled with the adenosine triphosphate (ATP) hydrolysis and product release cycle. With the progress of experimental techniques and the enhancement of measuring directness, detailed knowledge of the motility of myosin V has been obtained.Following the ATPase cycle, the 4-state mechanochemical model of the myosin V's processive movement is used. The transitions between various states take place in a stochastic manner...
February 14, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Pengmian Feng, Zhenyi Wang, Xiaoyu Yu
Antimicrobial peptides are crucial components of the innate host defense system of most living organisms and promising candidates for antimicrobial agents. Accurate classification of antimicrobial peptides will be helpful to the discovery of new therapeutic targets. In this work, the Increment of Diversity with Quadratic Discriminant analysis (IDQD) was presented to classify antifungal and antibacterial peptides based on primary sequence information. In the jackknife test, the proposed IDQD model yields an accuracy of 86...
February 14, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Hao Lin, Zhi-Yong Liang, Hua Tang, Wei Chen
Promoters are DNA regulatory elements located directly upstream or at the 5' end of the transcription initiation site (TSS), which are in charge of gene transcription initiation. With the completion of a large number of microorganism genomics, it is urgent to predict promoters accurately in bacteria by using computational method. In this work, a sequence-based predictor named "iPro70-PseZNC" was designed for identifying sigma70 promoters in prokaryote. In the predictor, the samples of DNA sequences are formulated by a novel pseudo nucleotide composition, called PseZNC, into which the multi-window Z-curve composition and six local DNA structural properties are incorporated...
February 8, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jin-Xing Liu, Dong Wang, Ying-Lian Gao, Chun-Hou Zheng, Yong Xu, Jiguo Yu
Non-negative Matrix Factorization (NMF), a classical method for dimensionality reduction, has been applied in many fields. It is based on the idea that negative numbers are physically meaningless in various data-processing tasks. Apart from its contribution to conventional data analysis, the recent overwhelming interest in NMF is due to its newly discovered ability to solve challenging data mining and machine learning problems, especially in relation to gene expression data. This survey paper mainly focuses on research examining the application of NMF to identify differentially expressed genes and to cluster samples, and the main NMF models, properties, principles, and algorithms with its various generalizations, extensions, and modifications are summarized...
February 7, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Guoshi Chai, Min Yu, Lixu Jiang, Yaocong Duan, Jian Huang
The CRISPR-Cas (clustered regularly interspaced short palindromic repeats-CRISPR-associated proteins) adaptive immune systems are discovered in many bacteria and most archaea. These systems are encoded by cas (CRISPR-associated) operons that have an extremely diverse architecture. The most crucial step in the depiction of cas operons composition is the identification of cas genes or Cas proteins. With the continuous increase of the newly sequenced archaeal and bacterial genomes, the recognition of new Cas proteins is becoming possible, which not only provides candidates for novel genome editing tools but also helps to understand the prokaryotic immune system better...
February 7, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Maiju Pesonen, Jaakko Nevalainen, Steven Potter, Somnath Datta, Susmita Datta
A major challenge of genomics data is to detect interactions displaying functional associations from large-scale observations. In this study, a new cPLS-algorithm combining partial least squares approach with negative binomial regression is suggested to reconstruct a genomic association network for high-dimensional next-generation sequencing count data. The suggested approach is applicable to the raw counts data, without requiring any further pre-processing steps. In the settings investigated, the cPLS-algorithm outperformed the two widely used comparative methods, graphical lasso and weighted correlation network analysis...
February 7, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Min Li, Peng Ni, Xiaopei Chen, Jianxin Wang, Fangxiang Wu, Yi Pan
Identification of essential proteins based on protein interaction network (PIN) is a very important and hot topic in the post genome era. Up to now, a number of network-based essential protein discovery methods have been proposed. Generally, a static protein interaction network was constructed by using the protein-protein interactions obtained from different experiments or databases. Unfortunately, most of the network-based essential protein discovery methods are sensitive to the reliability of the constructed PIN...
February 7, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Nina Luhmann, Manuel Lafond, Annelyse Thevenin, Aida Ouangraoua, Roland Wittler, Cedric Chauve
Reconstructing ancestral gene orders in a given phylogeny is a classical problem in comparative genomics. Most existing methods compare conserved features in extant genomes in the phylogeny to define potential ancestral gene adjacencies, and either try to reconstruct all ancestral genomes under a global evolutionary parsimony criterion, or, focusing on a single ancestral genome, use a scaffolding approach to select a subset of ancestral gene adjacencies, generally aiming at reducing the fragmentation of the reconstructed ancestral genome...
January 31, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Xin Peng, Yang Tang, Wangli He, Wenli Du, Feng Qian
This study focuses on the classification and pathological status monitoring of hyper/hypo-calcemia in the calcium regulatory system. By utilizing the Independent Component Analysis (ICA) mixture model, samples from healthy patients are collected, diagnosed, and subsequently classified according to their underlying behaviors, characteristics, and mechanisms. Then, a Just-in-Time Learning (JITL) has been employed in order to estimate the diseased status dynamically. In terms of JITL, for the purpose of the construction of an appropriate similarity index to identify relevant datasets, a novel similarity index based on the ICA mixture model is proposed in this paper to improve online model quality...
January 20, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Aseel Awdeh, Hilary Phenix, Mads Kaern, Theodore Perkins
Finding regulatory relationships between genes, including the direction and nature of influence between them, is a fundamental challenge in the field of molecular genetics. One classical approach to this problem is epistasis analysis. Broadly speaking, epistasis analysis infers the regulatory relationships between a pair of genes in a genetic pathway by considering the patterns of change in an observable trait resulting from single and double deletion of genes. While classical epistasis analysis has yielded deep insights on numerous genetic pathways, it is not without limitations...
January 16, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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