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IEEE/ACM Transactions on Computational Biology and Bioinformatics

Wei Shao, Sheng-Jun Huang, MingXia Liu, Daoqiang Zhang
Segmenting bioimage based filaments is a critical step in a wide range of applications, including neuron reconstruction and blood vessel tracing. To achieve an acceptable segmentation performance, most of the existing methods need to annotate filamentary images in the training stage. Hence, these methods have to face the common challenge that the annotation cost is usually high. To address this problem,we propose an interactive segmentation method to actively select a few super-pixels for annotation,which can alleviate the burden of annotators...
January 14, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Abhinandan Khan, Goutam Saha, Rajat Kumar Pal
The accurate reconstruction of gene regulatory networks for proper understanding of the intricacies of complex biological mechanisms still provides motivation for researchers. Due to accessibility of various gene expression data, we can now attempt to computationally infer genetic interactions. Among the established network inference techniques, S-system is preferred because of its efficiency in replicating biological systems though it is computationally more expensive. This provides motivation for us to develop a similar system with lesser computational load...
January 14, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Norman John Mapes, Christopher Rodriguez, Pradeep Chowriappa, Sumeet Dua
Accurately predicting three dimensional protein structures from sequences would present us with targets for drugs via molecular dynamics that would treat cancer, viral infections and neurological diseases. These treatments would have a far reaching impact to our economy, quality of life and society. The goal of this research was to build a data mining framework to predict cysteine connectivity in proteins from the sequence and oxidation state of cysteines. Accurately predicting the cysteine bonding configuration improves the TM-Score, a quantitative measurement of protein structure prediction accuracy...
January 14, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jiyun Zhou, Qin Lu, Ruifeng Xu, Lin Gui, Hongpeng Wang
Most proposed methods for TF-binding site (TFBS) predictions only use low order dependencies for predictions due to the lack of efficient methods to extract higher order dependencies. In this work, We first propose a novel method to extract higher order dependencies by applying CNN on histone modification features. We then propose a novel TFBS prediction method, referred to as CNN_TF, by incorporating low order and higher order dependencies. CNN_TF is first evaluated on 13 TFs in the mES cell. Results show that using higher order dependencies outperforms low order dependencies significantly on 11 TFs...
January 10, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Alexandre Victor Fassio, Lucianna H Santos, Sabrina A Silveira, Rafaela S Ferreira, Raquel Cardoso de Melo-Minardi
Essential roles in biological systems depend on protein-ligand recognition, which is mostly driven by specific non-covalent interactions. Consequently, investigating these interactions contributes to understanding how molecular recognition occurs. Nowadays, a large-scale data set of protein-ligand complexes is available in the Protein Data Bank, what led several tools to be proposed as an effort to elucidate protein-ligand interactions. Nonetheless, there is not an all-in-one tool that couples large-scale statistical, visual, and interactive analysis of conserved protein-ligand interactions...
January 10, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Syed Ali Ahmed, Saad Mneimneh
Multiple RNA interaction can be modeled as a problem in combinatorial optimization, where the "optimal" structure is driven by an energy-minimization-like algorithm. However, the actual structure may not be optimal in this computational sense. Moreover, it is not necessarily unique. Therefore, alternative sub-optimal solutions are needed to cover the biological ground. We present a combinatorial formulation for the Multiple RNA Interaction problem with approximation algorithms to handle various interaction patterns, which when combined with Gibbs sampling and MCMC (Markov Chain Monte Carlo), can efficiently generate a reasonable number of optimal and sub-optimal solutions...
January 10, 2019: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Abdollah Amirkhani, Mojtaba Kolahdoozi, Chen Wang, Lukasz Kurgan
While protein-DNA interactions are crucial for a wide range of cellular functions, only a small fraction of these interactions was annotated to date. One solution to close this annotation gap is to employ computational methods that accurately predict protein-DNA interactions from widely available protein sequences. We present and empirically test first-of-its-kind predictor of DNA-binding residues in local segments of protein sequences that relies on the Fuzzy Cognitive Map (FCM) model. The FCM model uses information about putative solvent accessibility, evolutionary conservation and relative propensities of amino acid to interact with DNA to generate putative DNA-binding residues...
December 28, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Gaoshi Li, Min Li, Jianxin Wang, Yaohang Li, Yi Pan
Identifying essential proteins plays an important role in disease study, drug design, and understanding the minimal requirement for cellular life. Computational methods for essential proteins discovery overcome the disadvantages of biological experimental methods that are often time-consuming, expensive, and inefficient. The topological features of protein-protein interaction (PPI) networks are often used to design computational prediction methods, such as Degree Centrality (DC), Betweenness Centrality (BC), Closeness Centrality (CC), Subgraph Centrality (SC), Eigenvector Centrality (EC), Information Centrality (IC), and Neighborhood Centrality (NC)...
December 27, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Celia Biane, Franck Delaplace
Complex diseases such as Cancer or Alzheimer's are caused by multiple molecular perturbations leading to pathological cellular behavior. However, the identification of the disease-induced molecular perturbations and the subsequent development of efficient therapies are challenged by the complexity of the genotype-phenotype relationship. Accordingly, a key issue is to develop frameworks relating the molecular perturbations and drug effects to their consequences on cellular phenotypes. Such framework aims at identifying the sets of causal molecular factors leading to phenotypic reprogramming...
December 21, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Michael Barros, Subhrakanti Dey
Synaptic plasticity depends on the gliotransmitters' concentration in the synaptic channel. And, an abnormal concentration of gliotransmitters is linked to neurodegenerative diseases, including Alzheimer's, Parkinson's, and epilepsy. In this paper, a theoretical investigation of the cause of the abnormal concentration of gliotransmitters and how to achieve its control is presented through a Ca2+-signalling-based molecular communications framework. A feed-forward and feedback control technique is used to manipulate IP3 values to stabilise the concentration of Ca2+ inside the astrocytes...
December 18, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Xiaohui Yang, Li Tian, Yunmei Chen, Lijun Yang, Shuang Xu, Weming Wu
Sparse representation based classification (SRC) methods have achieved remarkable results. SRC, however, still suffer from requiring enough training samples, insufficient use of test samples and instability of representation. In this paper, a stable inverse projection representation based classification (IPRC) is presented to tackle these problems by effectively using test samples. An IPR is firstly proposed and its feasibility and stability are analyzed. A classification criterion named category contribution rate is constructed to match the IPR and complete classification...
December 18, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Hannes Klarner, Heike Siebert, Sarah Nee, Frederike Heinitz
The attractors of Boolean networks and their basins have been shown to be highly relevant for model validation and predictive modelling, e.g., in systems biology. Yet there are currently very few tools available that are able to compute and visualise not only attractors but also their basins. In the realm of asynchronous, non-deterministic modeling not only is the repertoire of software even more limited, but also the formal notions for basins of attraction are often lacking. In this setting, the difficulty both for theory and computation arises from the fact that states may be elements of several distinct basins...
December 18, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Xin Mou, Hasan M Jamil
Programming or querying usually presupposes some degree of technical familiarity with the syntax of a language and the peculiarity of the objects it manipulates to produce useful information. The degree of abstractions supported in a language helps lessen the depth of such familiarity needed, and aids in improving access to and usability of these resources. To help biologists concentrate more on their science questions and not on how to compute it, several successful workflow orchestration languages and systems have been proposed...
December 12, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Haluk Damgacioglu, Emrah Celik, Nurcin Celik
Recent advances in DNA methylation profiling have paved the way for understanding the underlying epigenetic mechanisms of various diseases such as cancer. While conventional distance-based clustering algorithms (e.g., hierarchical and k-means clustering) have been heavily used in such profiling owing to their speed in conduct of high-throughput analysis, these methods commonly converge to suboptimal solutions and/or trivial clusters due to their greedy search nature. Hence, methodologies are needed to improve the quality of clusters formed by these algorithms without sacrificing from their speed...
December 10, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Liana Amaya Moreno, Maryam Omidi, Marcus Wurlitzer, Berengere Luthringer, Heike Helmholz, Hartmut Schluter, Regine Willumeit-Romer, Armin Fugenschuh
Magnesium-based biomaterials belong to the third generation of biomaterials that are also bioactive. These smart materials combine bioactivity and biodegradability, and elicit specific cellular responses at the molecular level. In fact, osteoinductive properties have been observed in mesenchymal stem cells in the presence of Magnesium. The mechanistic understanding of the physiological effects however, remains a difficult task as Mg is involved in a multitude of biological reactions. The study of protein interactions may shed light on the molecular processes in Mg-stimulated cells, therefore, suitable data mining tools are required to analyze the large amount data generated via proteomics...
December 10, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Venkateshwarlu Yellaswamy Gudur, Amit Acharyya
Research for new technologies and methods in computational bioinformatics has resulted in many folds biological data generation. To cope up with the ever increasing growth of biological data, there is a need for accelerated solutions in various domains of computational bioinformatics. In these domains, string matching is a most versatile operation performed at various stages of the computational pipeline. For search patterns that are updated with time, there is a need for accelerated and reconfigurable string matching to perform faster searching in the ever-growing biological databases...
December 10, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Houssam Abbas, Alena Rodionova, Konstantinos Mamouras, Ezio Bartocci, Scott Smolka, Radu Grosu
Implantable medical devices are safety-critical systems whose incorrect operation can jeopardize a patient's health, and whose algorithms must meet tight platform constraints like memory consumption and runtime. In particular, we consider here the case of implantable cardioverter defibrillators, where peak detection algorithms and various others discrimination algorithms serve to distinguish fatal from non-fatal arrhythmias in a cardiac signal. Motivated by the need for powerful formal methods to reason about the performance of arrhythmia detection algorithms, we show how to specify all these algorithms using Quantitative Regular Expressions (QREs)...
December 10, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jose Manuel Herruzo, Sonia Gonzalez Navarro, Pablo Ibanez, Victor Vinals Yufera, Jesus Alastruey, Oscar Plata
FM-index is a compact data structure suitable for fast matches of short reads to large reference genomes. The matching algorithm using this index exhibits irregular memory access patterns that cause frequent cache misses, resulting in a memory bound problem. This paper analyzes different FM-index versions presented in the literature, focusing on those computing aspects related to the data access. As a result of the analysis, we propose a new organization of FM-index that minimizes the demand for memory bandwidth, allowing a great improvement of performance on processors with high-bandwidth memory, such as the second-generation Intel Xeon Phi (Knights Landing, or KNL), integrating ultra high-bandwidth stacked memory technology...
December 6, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Niko Yasui, Chrysafis Vogiatzis, Ruriko Yoshida, Kenji Fukumizu
Given a set of organisms, the available corresponding genetic information is often incomplete and most gene trees fail to contain all individuals. This incompleteness causes difficulties in data collection, information extraction, and gene tree inference. Outlying gene trees may represent horizontal gene transfers, gene duplications, hybridizations, but they are difficult to detect in the presence of missing data. One typical approach is to discard all individuals with missing data and focus the analysis on individuals whose information is fully available...
November 30, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Xiguo Yuan, Jun Bai, Junying Zhang, Liying Yang, Junbo Duan, Yaoyao Li, Meihong Gao
Characterizing copy number variations (CNVs) from sequenced genomes is a both feasible and cost-effective way to search for driver genes in cancer diagnosis. A number of existing algorithms for CNV detection only explored part of the features underlying sequence data and copy number structures, resulting in limited performance. Here, we describe CONDEL, a method for detecting CNVs from single tumor samples using high-throughput sequence data. CONDEL utilizes a novel statistic in combination with a peel-off scheme to assess the statistical significance of genome bins, and adopts a Bayesian approach to infer copy number gains, losses, and deletion zygosity based on statistical mixture models...
November 26, 2018: IEEE/ACM Transactions on Computational Biology and Bioinformatics
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