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IEEE/ACM Transactions on Computational Biology and Bioinformatics

Lijun Zhang, Ming Wang, Nicholas W Sterling, Eun-Young Lee, Paul J Eslinger, Daymond Wagner, Guangwei Du, Mechelle M Lewis, Young Truong, F DuBois Bowman, Xuemei Huang
Parkinson's disease (PD) is a progressive neurodegenerative disorder characterized clinically by motor dysfunction (bradykinesia, rigidity, tremor, and postural instability), and pathologically by the loss of dopaminergic neurons in the substantia nigra of the basal ganglia. Growing literature supports that cognitive deficits may also be present in PD, even in non-demented patients. Gray matter (GM) atrophy has been reported in PD and may be related to cognitive decline. This study investigated cortical thickness in non-demented PD subjects and elucidated its relationship to cognitive impairment using high-resolution T1-weighted brain MRI and comprehensive cognitive function scores from 71 non-demented PD and 48 control subjects matched for age, gender, and education...
March 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Meng Hu, Wu Li, Hualou Liang
In systems neuroscience, it is becoming increasingly common to record the activity of hundreds of neurons simultaneously via electrode arrays. The ability to accurately measure the causal interactions among multiple neurons in the brain is crucial to understanding how neurons work in concert to generate specific brain functions. The development of new statistical methods for assessing causal influence between spike trains is still an active field of neuroscience research. Here, we suggest a copula-based Granger causality measure for the analysis of neural spike train data...
March 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Lin Yuan, Fanglin Chen, Ling-Li Zeng, Lubin Wang, Dewen Hu
Determining gender by examining the human brain is not a simple task because the spatial structure of the human brain is complex, and no obvious differences can be seen by the naked eyes. In this paper, we propose a novel three-dimensional feature descriptor, the three-dimensional weighted histogram of gradient orientation (3D WHGO) to describe this complex spatial structure. The descriptor combines local information for signal intensity and global three-dimensional spatial information for the whole brain. We also improve a framework to address the classification of three-dimensional images based on MRI...
March 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Yize Zhao, Jian Kang, Qi Long
Ultra-high dimensional variable selection has become increasingly important in analysis of neuroimaging data. For example, in the Autism Brain Imaging Data Exchange (ABIDE) study, neuroscientists are interested in identifying important biomarkers for early detection of the autism spectrum disorder (ASD) using high resolution brain images that include hundreds of thousands voxels. However, most existing methods are not feasible for solving this problem due to their extensive computational costs. In this work, we propose a novel multiresolution variable selection procedure under a Bayesian probit regression framework...
March 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Xiaodong Cui, Lin Zhang, Jia Meng, Manjeet K Rao, Yidong Chen, Yufei Huang
N6-Methyladenosine (m6 A) transcriptome methylation is an exciting new research area that just captures the attention of research community. We present in this paper, MeTDiff, a novel computational tool for predicting differential m6 A methylation sites from Methylated RNA immunoprecipitation sequencing (MeRIP-Seq) data. Compared with the existing algorithm exomePeak, the advantages of MeTDiff are that it explicitly models the reads variation in data and also devices a more power likelihood ratio test for differential methylation site prediction...
March 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Arghavan Bahadorinejad, Ulisses M Braga-Neto
We propose a methodology for model-based fault detection and diagnosis for stochastic Boolean dynamical systems indirectly observed through a single time series of transcriptomic measurements using Next Generation Sequencing (NGS) data. The fault detection consists of an innovations filter followed by a fault certification step, and requires no knowledge about the possible system faults. The innovations filter uses the optimal Boolean state estimator, called the Boolean Kalman Filter (BKF). In the presence of knowledge about the possible system faults, we propose an additional step of fault diagnosis based on a multiple model adaptive estimation (MMAE) method consisting of a bank of BKFs running in parallel...
March 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Mansuck Kim, Huan Zhang, Charles Woloshuk, Won-Bo Shim, Byung-Jun Yoon
Fusarium verticillioides is a fungal pathogen that triggers stalk rots and ear rots in maize. In this study, we performed a comparative analysis of wild type and loss-of-virulence mutant F. verticillioides co-expression networks to identify subnetwork modules that are associated with its pathogenicity. We constructed the F. verticillioides co-expression networks from RNA-Seq data and searched through these networks to identify subnetwork modules that are differentially activated between the wild type and mutant F...
March 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Noushin Ghaffari, Osama A Arshad, Hyundoo Jeong, John Thiltges, Michael F Criscitiello, Byung-Jun Yoon, Aniruddha Datta, Charles D Johnson
New de novo transcriptome assembly and annotation methods provide an incredible opportunity to study the transcriptome of organisms that lack an assembled and annotated genome. There are currently a number of de novo transcriptome assembly methods, but it has been difficult to evaluate the quality of these assemblies. In order to assess the quality of the transcriptome assemblies, we composed a workflow of multiple quality check measurements that in combination provide a clear evaluation of the assembly performance...
March 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Congping Lin, Laurent Lemarchand, Reinhardt Euler, Imogen Sparkes
The endoplasmic reticulum (ER) is an intricate network that pervades the entire cortex of plant cells and its geometric shape undergoes drastic changes. This paper proposes a mathematical model to reconstruct geometric network dynamics by combining the node movements within the network and topological changes engendered by these nodes. The network topology in the model is determined by a modified optimization procedure from the work (Lemarchand, et al. 2014) which minimizes the total length taking into account both degree and angle constraints, beyond the conditions of connectedness and planarity...
March 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Anton V Ushakov, Xenia Klimentova, Igor Vasilyev
Recent advances in high-throughput technologies have given rise to collecting large amounts of multidimensional heterogeneous data that provide diverse information on the same biological samples. Integrative analysis of such multisource datasets may reveal new biological insights into complex biological mechanisms and therefore remains an important research field in systems biology. Most of the modern integrative clustering approaches rely on independent analysis of each dataset and consensus clustering, probabilistic or statistical modeling, while flexible distance-based integrative clustering techniques are sparsely covered...
January 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Narayanan C Viswanath
In this study, the expected time required to eradicate HIV-1 completely was found as the conditional absorbing time in a finite state space continuous-time Markov chain model. The Markov chain has two absorbing states: one corresponds to HIV eradication and another representing the possible disaster. This method allowed us to calculate the expected eradication time by solving systems of linear equations. To overcome the challenge of huge dimension of the problem, we applied a novel stop and resume technique...
January 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Wei Zhang, Jia Xu, Yuanyuan Li, Xiufen Zou
The identification of essential proteins in protein-protein interaction (PPI) networks is of great significance for understanding cellular processes. With the increasing availability of large-scale PPI data, numerous centrality measures based on network topology have been proposed to detect essential proteins from PPI networks. However, most of the current approaches focus mainly on the topological structure of PPI networks, and largely ignore the gene ontology annotation information. In this paper, we propose a novel centrality measure, called TEO, for identifying essential proteins by combining network topology, gene expression profiles, and GO information...
January 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Tom Hartmann, An-Chiang Chu, Martin Middendorf, Matthias Bernt
The tandem duplication random loss operation (TDRL) is an important genome rearrangement operation in metazoan mitochondrial genomes. A TDRL consists of a duplication of a contiguous set of genes in tandem followed by a random loss of one copy of each duplicated gene. This paper presents an analysis of the combinatorics of TDRLs on circular genomes, e.g., the mitochondrial genome. In particular, results on TDRLs for circular genomes and their linear representatives are established. Moreover, the distance between gene orders with respect to linear TDRLs and circular TDRLs is studied...
January 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Ahed Elmsallati, Abdulghani Msalati, Jugal Kalita
Network Alignment over graph-structured data has received considerable attention in many recent applications. Global network alignment tries to uniquely find the best mapping for a node in one network to only one node in another network. The mapping is performed according to some matching criteria that depend on the nature of data. In molecular biology, functional orthologs, protein complexes, and evolutionary conserved pathways are some examples of information uncovered by global network alignment. Current techniques for global network alignment suffer from several drawbacks, e...
January 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Cinzia Pizzi, Mattia Ornamenti, Simone Spangaro, Simona E Rombo, Laxmi Parida
Entropy, being closely related to repetitiveness and compressibility, is a widely used information-related measure to assess the degree of predictability of a sequence. Entropic profiles are based on information theory principles, and can be used to study the under-/over-representation of subwords, by also providing information about the scale of conserved DNA regions. Here, we focus on the algorithmic aspects related to entropic profiles. In particular, we propose linear time algorithms for their computation that rely on suffix-based data structures, more specifically on the truncated suffix tree (TST) and on the enhanced suffix array (ESA)...
January 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Feng Bao, Yue Deng, Qionghai Dai
Genome-wide association study (GWAS) has been widely witnessed as a powerful tool for revealing suspicious loci from various diseases. However, real world GWAS tasks always suffer from the data imbalance problem of sufficient control samples and limited case samples. This imbalance issue can cause serious biases to the result and thus leads to losses of significance for true causal markers. To tackle this problem, we proposed a computational framework to perform association correction for imbalanced data (ACID) that could potentially improve the performance of GWAS under the imbalance condition...
January 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Elizabeth S Allman, James H Degnan, John A Rhodes
The method was proposed by Liu and Yu to infer a species tree topology from unrooted topological gene trees. While its statistical consistency under the multispecies coalescent model was established only for a four-taxon tree, simulations demonstrated its good performance on gene trees inferred from sequences for many taxa. Here, we prove the statistical consistency of the method for an arbitrarily large species tree. Our approach connects to a generalization of the STAR method of Liu, Pearl, and Edwards, and a previous theoretical analysis of it...
January 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Chendra Hadi Suryanto, Hiroto Saigo, Kazuhiro Fukui
Computing similarity or dissimilarity between protein structures is an important task in structural biology. A conventional method to compute protein structure dissimilarity requires structural alignment of the proteins. However, defining one best alignment is difficult, especially when the structures are very different. In this paper, we propose a new similarity measure for protein structure comparisons using a set of multi-view 2D images of 3D protein structures. In this approach, each protein structure is represented by a subspace from the image set...
January 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Gerd Anders, Ulrich Hassiepen, Stephan Theisgen, Stephan Heymann, Lionel Muller, Tania Panigada, Daniel Huster, Sergey A Samsonov
Interleukin-8 (IL-8, CXCL8) is a neutrophil chemotactic factor belonging to the family of chemokines. IL-8 was shown to resist pepsin cleavage displaying its high resistance to this protease. However, the molecular mechanisms underlying this resistance are not fully understood. Using our in-house database containing the data on three-dimensional arrangements of secondary structure elements from the whole Protein Data Bank, we found a striking structural similarity between IL-8 and pepsin inhibitor-3. Such similarity could play a key role in understanding IL-8 resistance to the protease pepsin...
January 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Frederick A Matsen, Sara C Billey, Arnold Kas, Matjaz Konvalinka
Many discrete mathematics problems in phylogenetics are defined in terms of the relative labeling of pairs of leaf-labeled trees. These relative labelings are naturally formalized as tanglegrams, which have previously been an object of study in coevolutionary analysis. Although there has been considerable work on planar drawings of tanglegrams, they have not been fully explored as combinatorial objects until recently. In this paper, we describe how many discrete mathematical questions on trees "factor" through a problem on tanglegrams, and how understanding that factoring can simplify analysis...
January 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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