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Ghazaleh Taherzadeh, Yaoqi Zhou, Alan Wee-Chung Liew, Yuedong Yang
Motivation: Protein-peptide interactions are one of the most important biological interactions and play crucial role in many diseases including cancer. Therefore, knowledge of these interactions provides invaluable insights into all cellular processes, functional mechanisms, and drug discovery. Protein-peptide interactions can be analyzed by studying the structures of protein-peptide complexes. However, only a small portion has known complex structures and experimental determination of protein-peptide interaction is costly and inefficient...
September 26, 2017: Bioinformatics
C Yones, G Stegmayer, D H Milone
Motivation: Although many machine learning techniques have been proposed for distinguishing miRNA hairpins from other stem-loop sequences, most of the current methods use supervised learning, which requires a very good set of positive and negative examples. Those methods have important practical limitations when they have to be applied to a real prediction task. First, there is the challenge of dealing with a scarce number of positive (well-known) pre-miRNA examples. Secondly, it is very difficult to build a good set of negative examples for representing the full spectrum of non-miRNA sequences...
September 25, 2017: Bioinformatics
Mahito Sugiyama, M Elisabetta Ghisu, Felipe Llinares-López, Karsten Borgwardt
Summary: Measuring the similarity of graphs is a fundamental step in the analysis of graph-structured data, which is omnipresent in computational biology. Graph kernels have been proposed as a powerful and efficient approach to this problem of graph comparison. Here we provide graphkernels, the first R and Python graph kernel libraries including baseline kernels such as label histogram based kernels, classic graph kernels such as random walk based kernels, and the state-of-the-art Weisfeiler-Lehman graph kernel...
September 22, 2017: Bioinformatics
Jun Ding, Xiaoman Li, Haiyan Hu
Motivation: The identification of microRNA (miRNA) target sites is important. In the past decade, dozens of computational methods have been developed to predict miRNA target sites. Despite their existence, rarely does a method consider the well-known competition and cooperation among miRNAs when attempts to discover target sites. To fill this gap, we developed a new approach called CCmiR, which takes the cooperation and competition of multiple miRNAs into account in a statistical model to predict their target sites...
September 25, 2017: Bioinformatics
Tom Paridaens, Glenn Van Wallendael, Wesley De Neve, Peter Lambert
Motivation: The past decade has seen the introduction of new technologies that significantly lowered the cost of genome sequencing. As a result, the amount of genomic data that must be stored and transmitted is increasing exponentially. To mitigate storage and transmission issues, we introduce a framework for lossless compression of quality scores. Results: This paper proposes AQUa, an adaptive framework for lossless compression of quality scores. To compress these quality scores, AQUa makes use of a configurable set of coding tools, extended with a Context-Adaptive Binary Arithmetic Coding scheme (CABAC)...
September 25, 2017: Bioinformatics
Anup Kumar Halder, Pritha Dutta, Mahantapas Kundu, Subhadip Basu, Mita Nasipuri
Identification of potential virus-host interactions is useful and vital to control the highly infectious virus-caused diseases. This may contribute toward development of new drugs to treat the viral infections. Recently, database records of clinically and experimentally validated interactions between a small set of human proteins and Ebola virus (EBOV) have been published. Using the information of the known human interaction partners of EBOV, our main objective is to identify a set of proteins that may interact with EBOV proteins...
September 26, 2017: Briefings in Functional Genomics
Saira Afzal, Irene Gil-Farina, Richard Gabriel, Shahzad Ahmad, Christof von Kalle, Manfred Schmidt, Raffaele Fronza
High-throughput sequencing technologies have exposed the possibilities for the in-depth evaluation of T-cell receptor (TCR) repertoires. These studies are highly relevant to gain insights into human adaptive immunity and to decipher the composition and diversity of antigen receptors in physiological and disease conditions. The major objective of TCR sequencing data analysis is the identification of V, D and J gene segments, complementarity-determining region 3 (CDR3) sequence extraction and clonality analysis...
September 23, 2017: Briefings in Bioinformatics
Şerife Seda Kucur, Raphael Sznitman
Perimetry testing is an automated method to measure visual function and is heavily used for diagnosing ophthalmic and neurological conditions. Its working principle is to sequentially query a subject about perceived light using different brightness levels at different visual field locations. At a given location, this query-patient-feedback process is expected to converge at a perceived sensitivity, such that a shown stimulus intensity is observed and reported 50% of the time. Given this inherently time-intensive and noisy process, fast testing strategies are necessary in order to measure existing regions more effectively and reliably...
2017: PloS One
Yulia R Shaltaeva, Boris I Podlepetsky, Vyacheslav S Pershenkov
This article deals with the state-of-the-art instrumentation and application in the field of solid state gas sensorics, ion mobility spectrometry and mass-spectrometry-related research for the detection and measurements of low gas and vapor concentrations. The advantages and disadvantages of gas-analytical devices and systems are discussed, as well as the possibilities of its complex and/or complementary applications. Ion mobility spectrometry-mass spectrometry and subsequent techniques based on solid-state gas sensors are proposed for planned medical study...
August 2017: European Journal of Mass Spectrometry
Fang Chen, Zhe Zhao, Jia Liu, Cong Gao, Xiuyun Su, Jingxin Zhao, Peifu Tang, Hongen Liao
Intramedullary (IM) nail implantation is currently the standard treatment for femoral intertrochanteric fractures. However, individual differences in femur cavity bring a challenge in designing well-matched IM nails and cause difficulties in IM nail implantation. Therefore, there is an intense need to analyze femur cavities to predict difficulties in IM nail implantation to assist the design of IM nails. This study proposed a method to automatically identify subtypes of femur cavities that exhibit differences in potential difficulties in nail implantation by clustering the morphological features of femur models...
October 11, 2017: IEEE Journal of Biomedical and Health Informatics
Gregory Ditzler, Joseph LaBarck, James Ritchie, Gail Rosen, Robi Polikar
Feature subset selection can be used to sieve through large volumes of data and discover the most informative subset of variables for a particular learning problem. Yet, due to memory and other resource constraints (e.g., CPU availability), many of the state-of-the-art feature subset selection methods cannot be extended to high dimensional data, or data sets with an extremely large volume of instances. In this brief, we extend online feature selection (OFS), a recently introduced approach that uses partial feature information, by developing an ensemble of online linear models to make predictions...
October 11, 2017: IEEE Transactions on Neural Networks and Learning Systems
Kin On Cheng, Ngai Fong Law, W-C Siu
Due to the advancement of DNA sequencing techniques, the number of sequenced individual genomes has experienced an exponential growth. Thus, effective compression of this kind of sequences is highly desired. In this work, we present a novel compression algorithm called Reference-based Compression algorithm using the concept of Clustering (RCC). The rationale behind RCC is based on the observation about the existence of substructures within the population sequences. To utilize these substructures, k-means clustering is employed to partition sequences into clusters for better compression...
October 12, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Jorge Gonzalez-Dominguez, Maria J Martin
In this work we present MPIGeneNet, a parallel tool that applies Pearson's correlation and Random Matrix Theory to construct gene co-expression networks. It is based on the state-of-the-art sequential tool RMTGeneNet, which provides networks with high robustness and sensitivity at the expenses of relatively long runtimes for large scale input datasets. MPIGeneNet returns the same results as RMTGeneNet but improves the memory management, reduces the I/O cost, and accelerates the two most computationally demanding steps of co-expression network construction by exploiting the compute capabilities of common multicore CPU clusters...
October 10, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Xiaoke Ma, Penggang Sun, Guimin Qin
Condition-specific modules in multiple networks must be determined to reveal the underlying molecular mechanisms of diseases. Current algorithms exhibit limitations such as low accuracy and high sensitivity to the number of networks because these algorithms discover condition-specific modules in multiple networks by separating specificity and modularity of modules. To overcome these limitations, we characterize condition-specific module as a group of genes whose connectivity is strong in the corresponding network and weak in other networks; this strategy can accurately depict the topological structure of condition-specific modules...
October 10, 2017: IEEE/ACM Transactions on Computational Biology and Bioinformatics
Pan Zhou, Canyi Lu, Zhouchen Lin, Chao Zhang
Recently, a tensor nuclear norm (TNN) based method [1] was proposed to solve the tensor completion problem, which has achieved state-of-the-art performance on image and video inpainting tasks. However, it requires computing tensor singular value decomposition (t-SVD), which costs much computation and thus cannot efficiently handle tensor data, due to its natural large scale. Motivated by TNN, we propose a novel low-rank tensor factorization method for efficiently solving the 3-way tensor completion problem...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Jia Li, Changqun Xia, Xiaowu Chen
Image-based salient object detection (SOD) has been extensively studied in past decades. However, video-based SOD is much less explored due to the lack of large-scale video datasets within which salient objects are unambiguously defined and annotated. Toward this end, this paper proposes a video-based SOD dataset that consists of 200 videos. In constructing the dataset, we manually annotate all objects and regions over 7,650 uniformly sampled keyframes and collect the eye-tracking data of 23 subjects who free-view all videos...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Deepak Mishra, Santanu Chaudhury, Mukul Sarkar, Arvinder Singh Soin, Vivek Sharma
Anisotropic diffusion filters are one of the best choices for speckle reduction in the ultrasound images. These filters control the diffusion flux flow using local image statistics and provide the desired speckle suppression. However, inefficient use of edge characteristics results in either oversmooth image or an image containing misinterpreted spurious edges. As a result, the diagnostic quality of the images becomes a concern. To alleviate such problems, a novel anisotropic diffusion based speckle reducing filter is proposed in this paper...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Yuchen Yuan, Changyang Li, Jinman Kim, Weidong Cai, David Dagan Feng
In recent saliency detection research, many graph-based algorithms have applied boundary priors as background queries, which may generate completely "reversed" saliency maps if the salient objects are on the image boundaries. Moreover, these algorithms usually depend heavily on pre-processed superpixel segmentation, which may lead to notable degradation in image detail features. In this paper, a novel saliency detection method is proposed to overcome the above issues. First, we propose a saliency reversion correction (RC) process, which locates and removes the boundary-adjacent foreground superpixels, and thereby increases the accuracy and robustness of the boundary prior based saliency estimations...
October 12, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Sebastian Bosse, Dominique Maniry, Klaus-Robert Muller, Thomas Wiegand, Wojciech Samek
We present a deep neural network-based approach to image quality assessment (IQA). The network is trained endto- end and comprises 10 convolutional layers and 5 pooling layers for feature extraction, and 2 fully connected layers for regression, which makes it significantly deeper than related IQA models. Unique features of the proposed architecture are that (1) with slight adaptations it can be used in a no-reference (NR) as well as in a full-reference (FR) IQA setting and (2) it allows for joint learning of local quality and local weights, i...
October 10, 2017: IEEE Transactions on Image Processing: a Publication of the IEEE Signal Processing Society
Tae Hyun Kim, Seungjun Nah, Kyoung Mu Lee
State-of-the-art video deblurring methods cannot handle blurry videos recorded in dynamic scenes since they are built under a strong assumption that the captured scenes are static. Contrary to the existing methods, we propose a new video deblurring algorithm that can deal with general blurs inherent in dynamic scenes. To handle general and locally varying blurs caused by various sources, such as moving objects, camera shake, depth variation, and defocus, we estimate pixel-wise varying non-uniform blur kernels...
October 10, 2017: IEEE Transactions on Pattern Analysis and Machine Intelligence
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