Computers in Biology and Medicine RSS feed: Current Issue. Computers in Biology and Medicine is a medium of international communication of the revolutionary advances being made in the application of the computer to the fields of bioscience and medicine. The Journal encourages the exchange of important research, instruction, ideas and information on all aspects of the rapidly expanding area of computer usage in these fields. The Journal will focus on such areas as (1) Analysis of Biomedical Systems: Solutions of Equations; (2) Synthesis of Biomedical Systems: Simulations; (3) Special Medical Data Processing Methods; (4) Special Purpose Computers and Clinical Data Processing for Real Time, Clinical and Experimental Use; and (5) Medical Diagnosis and Medical Record Processing. Also included are the fields of (6) Biomedical Engineering; and (7) Medical Informatics as well as Bioinformatics. The journal is expanding to include (8) Medical Applications of the Internet and World Wide Web; (9) Human Genomics; (10) Proteomics; and (11) Functional Brain Studies. The publication policy is to publish (1) new, original articles that have been appropriately reviewed by competent scientific people, (2) surveys of developments in the fields, (3) pedagogical papers covering specific areas of interest, and (4) book reviews pertinent to the field. Articles which examine the following topics of special interest are being featured in Computers in Biology and Medicine: computer aids to the analysis of biochemical systems, computer aids to biocontrol-systems engineering, neuronal simulation by digital-computer gating components, automatic computer analysis of pictures of biological and medical importance, use of computers by commercial pharmaceutical and chemical organizations, radiation-dosage computers, and accumulating and recalling individual medical records, real-time languages, interfaces to patient monitors, clinical chemistry equipment, data handling and display in nuclear medicine and therapy. http://www.computersinbiologyandmedicine.com/?rss=yesElsevier Inc.en © 2012 Published by Elsevier Inc. All rights reserved. Computers in Biology and Medicine0010-4825425May 2012 © 2012 Published by Elsevier Inc. All rights reserved. healthpermissions@elsevier.comhttp://www.computersinbiologyandmedicine.com/article/PIIS0010482512000601/abstract?rss=yesEditorial Board & Publication information10.1016/S0010-4825(12)00060-1Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2IFCIFChttp://www.computersinbiologyandmedicine.com/article/PIIS0010482512000157/abstract?rss=yesAbstract: In this paper we address the “skull-stripping” problem in 3D MR images. We propose a new method that employs an efficient and unique histogram analysis. A fundamental component of this analysis is an algorithm for partitioning a histogram based on the position of the maximum deviation from a Gaussian fit. In our experiments we use a comprehensive image database, including both synthetic and real MRI, and compare our method with other two well-known methods, namely BSE and BET. For all datasets we achieved superior results. Our method is also highly independent of parameter tuning and very robust across considerable variations of noise ratio.Smart histogram analysis applied to the skull-stripping problem in T1-weighted MRIAndré G.R. A.G.R. Balan, Agma J.M. A.J.M. Traina, Marcela X. M.X. Ribeiro, Paulo M.A. P.M.A. Marques, Caetano Traina Jr.10.1016/j.compbiomed.2012.01.004Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2509522http://www.computersinbiologyandmedicine.com/article/PIIS0010482512000169/abstract?rss=yesAbstract: In this paper, we present a new segmentation method using the level set framework for medical volume images. The method was implemented using the surface evolution principle based on the geometric deformable model and the level set theory. And, the speed function in the level set approach consists of a hybrid combination of three integral measures derived from the calculus of variation principle. The terms are defined as robust alignment, active region, and smoothing. These terms can help to obtain the precise surface of the target object and prevent the boundary leakage problem. The proposed method has been tested on synthetic and various medical volume images with normal tissue and tumor regions in order to evaluate its performance on visual and quantitative data. The quantitative validation of the proposed segmentation is shown with higher Jaccard's measure score (72.52%–94.17%) and lower Hausdorff distance (1.2654mm–3.1527mm) than the other methods such as mean speed (67.67%–93.36% and 1.3361mm–3.4463mm), mean-variance speed (63.44%–94.72% and 1.3361mm–3.4616mm), and edge-based speed (0.76%–42.44% and 3.8010mm–6.5389mm). The experimental results confirm that the effectiveness and performance of our method is excellent compared with traditional approaches.Segmentation of interest region in medical volume images using geometric deformable modelMyungeun Lee, Wanhyun Cho, Sunworl Kim, Soonyoung Park, Jong Hyo Kim10.1016/j.compbiomed.2012.01.005Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2523537http://www.computersinbiologyandmedicine.com/article/PIIS0010482512000170/abstract?rss=yesAbstract: Metalloendopeptidases are zinc-dependent hydrolases enzymes with many different roles in biological systems, ranging from remodeling conjunctive tissue to removing signaling sequences from nascent proteins. Here, we describe the three-dimensional structure of the metalloendopeptidase from Corynebacterium pseudotuberculosis generated by homology modeling and molecular dynamics. Analysis of key distances shows that His-132, Asp-136, His-211, Leu-212 and one molecule of water play an important role in the protein–Zn2+ ion interaction. The model obtained may provide structural insights into this enzyme and can be useful for the design of new caseous lymphadenitis vaccines based on genetic attenuation from key point mutation.Structure modeling of a metalloendopeptidase from Corynebacterium pseudotuberculosisLuis C. Guimarães, Natália F. Silva, Anderson Miyoshi, Maria P.C. Schneider, Artur Silva, Vasco Azevedo, Davi S.B. Brasil, Jerônimo Lameira, Cláudio N. Alves10.1016/j.compbiomed.2012.01.006Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2538541http://www.computersinbiologyandmedicine.com/article/PIIS0010482512000182/abstract?rss=yesAbstract: The incidence of fungal disease has increased dramatically over the past decades, mainly due to the emergence and transmission of antifungal resistance within the fungal pathogens. The present study investigates the use of novel antifungal compound 4-Phenyl-1-Napthyl Phenyl Acetamide (4P1NPA), isolated from marine Streptomyces sp. DPTB16 as a potent antifungal drug. The preclinical studies and molecular docking for 4P1NPA against Cytochrome P450 51 (CYP 51) were performed using in silico pharmacology and docking tools. The finding reveals the drug likeliness of 4P1NPA and satisfactory interaction of 4P1NPA with CYP 51. These results collectively evidence the use of 4P1NPA as a drug to treat fungal infections. On the whole, we highlight the findings of this research will be helpful to design 4P1NPA as novel antifungal drug to defend the emerging antifungal resistance.Preclinical evaluation and molecular docking of 4-phenyl-1-Napthyl phenyl acetamide (4P1NPA) from Streptomyces sp. DPTB16 as a potent antifungal compoundSubhasish Saha, Aravindh Priyadharshini, Dharumadurai Dhanasekaran, Nooruddin Thajuddin, Saravanan Chandraleka, Govindasamy Chandramohan, Annamalai Panneerselvam10.1016/j.compbiomed.2012.01.007Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2542547http://www.computersinbiologyandmedicine.com/article/PIIS0010482512000200/abstract?rss=yesAbstract: An innovative benchmark representing a dental arch with classic features corresponding to different kinds of prepared teeth is proposed. Dental anatomy and general rules for tooth preparation are taken into account. This benchmark includes tooth orientation and provides oblique surfaces similar to those of real prepared teeth. The benchmark is produced by additive manufacturing (AM) and subjected to digitization by a dental three-dimensional scanner. The evaluation procedure proves that the scan data can be used as reference model for crown restorations design. Therefore this benchmark is at the basis for comparative studies about different CAD/CAM and AM techniques for dental crowns.Proposal of an innovative benchmark for accuracy evaluation of dental crown manufacturingEleonora Atzeni, Luca Iuliano, Paolo Minetola, Alessandro Salmi10.1016/j.compbiomed.2012.01.009Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2548555http://www.computersinbiologyandmedicine.com/article/PIIS0010482512000224/abstract?rss=yesAbstract: Based on Huffman tree method, we propose a new 2D graphic representation of protein sequence. This representation can completely avoid loss of information in the transfer of data from a protein sequence to its graphic representation. The method consists of two parts. One is about the 0–1 codes of 20 amino acids by Huffman tree with amino acid frequency. The amino acid frequency is defined as the statistical number of an amino acid in the analyzed protein sequences. The other is about the 2D graphic representation of protein sequence based on the 0–1 codes. Then the applications of the method on ten ND5 genes and seven Escherichia coli strains are presented in detail. The results show that the proposed model may provide us with some new sights to understand the evolution patterns determined from protein sequences and complete genomes.Application of 2D graphic representation of protein sequence based on Huffman tree methodZhao-Hui Qi, Jun Feng, Xiao-Qin Qi, Ling Li10.1016/j.compbiomed.2012.01.011Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2556563http://www.computersinbiologyandmedicine.com/article/PIIS0010482512000236/abstract?rss=yesAbstract: Knowing the type of an uncharacterized membrane protein often provides a useful clue in both basic research and drug discovery. With the explosion of protein sequences generated in the post genomic era, determination of membrane protein types by experimental methods is expensive and time consuming. It therefore becomes important to develop an automated method to find the possible types of membrane proteins. In view of this, various computational membrane protein prediction methods have been proposed. They extract protein feature vectors, such as PseAAC (pseudo amino acid composition) and PsePSSM (pseudo position-specific scoring matrix) for representation of protein sequence, and then learn a distance metric for the KNN (K nearest neighbor) or NN (nearest neighbor) classifier to predicate the final type. Most of the metrics are learned using linear dimensionality reduction algorithms like Principle Components Analysis (PCA) and Linear Discriminant Analysis (LDA). Such metrics are common to all the proteins in the dataset. In fact, they assume that the proteins lie on a uniform distribution, which can be captured by the linear dimensionality reduction algorithm. We doubt this assumption, and learn local metrics which are optimized for local subset of the whole proteins. The learning procedure is iterated with the protein clustering. Then a novel ensemble distance metric is given by combining the local metrics through Tikhonov regularization. The experimental results on a benchmark dataset demonstrate the feasibility and effectiveness of the proposed algorithm named ProClusEnsem.ProClusEnsem: Predicting membrane protein types by fusing different modes of pseudo amino acid compositionJingyan Wang, Yongping Li, Quanquan Wang, Xinge You, Jiaju Man, Chao Wang, Xin Gao10.1016/j.compbiomed.2012.01.012Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2564574http://www.computersinbiologyandmedicine.com/article/PIIS0010482512000248/abstract?rss=yesAbstract: Antiepileptic drugs are commonly used for many therapeutic indications, including epilepsy, neuropathic pain, bipolar disorder and anxiety. Accumulating data suggests that many of them may lower the seizure threshold in men. In the present paper we deal with the possibility of using Support Vector Regression (SVR) to forecast the proconvulsant activity of compounds exerting anticonvulsant activity in the electroconvulsive threshold test in mice. A new approach to forecast this drug-related toxic effect by means of the support vector machine (SVM) in the regression mode is discussed below. The efficacy of this mathematical method is compared to the results obtained in vivo. Since SVR investigates the anticonvulsant activity of the compounds more thoroughly than it is possible using animal models, this method seems to be a very helpful tool for predicting additional dose ranges at which maximum anticonvulsant activity without toxic effects is observed. Good generalizing properties of SVR allow to assess the therapeutic dose range and toxicity threshold. Noteworthy, this method is very interesting for ethical reasons as this mathematical model enables to limit the use of living animals during the anticonvulsant screening process.New approach to predicting proconvulsant activity with the use of Support Vector RegressionRobert Salat, Kinga Salat10.1016/j.compbiomed.2012.02.001Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2575581http://www.computersinbiologyandmedicine.com/article/PIIS0010482512000376/abstract?rss=yesAbstract: Sparse methods have a significant advantage to reduce the complexity of genes expression data and to make them more comprehensible and interpretable. In this paper, based on penalized matrix decomposition (PMD), a novel approach is proposed to extract plants core genes, i.e., the characteristic gene set, responding to abiotic stresses. Core genes can capture the changes of the samples. In other words, the features of samples can be caught by the core genes. The experimental results show that the proposed PMD-based method is efficient to extract the core genes closely related to the abiotic stresses.Extracting plants core genes responding to abiotic stresses by penalized matrix decompositionJin-Xing Liu, Chun-Hou Zheng, Yong Xu10.1016/j.compbiomed.2012.02.002Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2582589http://www.computersinbiologyandmedicine.com/article/PIIS0010482512000418/abstract?rss=yesAbstract: Hypnosis has long been known to be associated with heightened control over physical processes and researchers put it under consideration because of its usage as a therapeutic tool in many medical and psychological problems. Determination of hypnosis susceptibility level is important before prescribing any hypnotic treatment. In this study different features are introduced to classify hypnotizability levels. These features were extracted from electroencephalogram (EEG) signals which were recorded from 32 subjects during hypnosis suggestion. Based on the obtained result, a method was suggested to estimate the hypnosis susceptibility level from hypnosis EEG signals instead of using traditional clinical subjective tests.Comparison of different EEG features in estimation of hypnosis susceptibility levelGolnaz Baghdadi, Ali Motie Nasrabadi10.1016/j.compbiomed.2012.02.003Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2590597http://www.computersinbiologyandmedicine.com/article/PIIS001048251200042X/abstract?rss=yesAbstract: This paper presents a novel user interface suitable for adaptive Brain Computer Interface (BCI) system. A customized self-paced BCI architecture is introduced where the system combines onset detection system along with an adaptive classifier working in parallel. An unsupervised adaptive method based on sequential expectation maximization for Gaussian mixture model is employed with new timing scheme and an additional averaging step to avoid over-fitting. Sigmoid function based post-processing approach is proposed to enhance the classifiers' output. The adaptive system is compared to a non-adaptive one and tested on five subjects who used the BCI to play the hangman game. The results show significant improvement of the True–False difference for all the classes and a reduction in the number of steps required to solve the problem.Hangman BCI: An unsupervised adaptive self-paced Brain–Computer Interface for playing gamesBashar Awwad Shiekh Hasan, John Q. J.Q. Gan10.1016/j.compbiomed.2012.02.004Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2598606http://www.computersinbiologyandmedicine.com/article/PIIS0010482512000443/abstract?rss=yesAbstract: The objective of this study was to make an interactive method for development of a tissue model, based on anatomical information in computed tomography (CT) images, for use in an ultrasound simulator for training or surgical pre-planning. The method consisted of (1) comparison of true ultrasound B-mode images with corresponding ultrasound-like images, and (2) modification of tissue properties to decrease the difference between these images. Ultrasound-like images that reproduced many, but not all the properties of corresponding true ultrasound images were generated. The tissue model could be used for real-time simulation of ultrasound-like B-mode images on a moderately priced computer.Interactive development of a CT-based tissue model for ultrasound simulationSjur Urdson Gjerald, Reidar Brekken, Lars Eirik Bø, Torbjørn Hergum, Toril A. T.A. Nagelhus Hernes10.1016/j.compbiomed.2012.02.006Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2607613http://www.computersinbiologyandmedicine.com/article/PIIS0010482512000455/abstract?rss=yesAbstract: In this paper we present a real-time tracking system of surgical instruments in laparoscopic operations. We combine Condensation tracking, with the Hough Transform in order to obtain an efficient and accurate tracking. The Condensation algorithm performs well in heavy clutter, and the Hough Transform is robust under illumination changes, occlusion and distractions.The Hough array is computed using the gradient direction image obtained by means of a Principal Component Analysis. This improves accuracy in the determination of edge orientation and speeds up computation of the Hough Transform.The experiments on image sequences of actual laparoscopic surgical operations show that the instrument tip is located even in the presence of smoke, occlusions or motion blurring.Particle filtering in the Hough space for instrument trackingJoan Climent, Roberto A. Hexsel10.1016/j.compbiomed.2012.02.007Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2614623http://www.computersinbiologyandmedicine.com/article/PIIS0010482512000467/abstract?rss=yesAbstract: MicroRNAs (miRNAs) though present themselves as a group of non-coding small RNAs play critical roles in many biological and pathological processes. Among which the regulation of human cancer is one of the most excited potentiality. The goal of this study is to obtain miRNAs robustly associated with cancer by screening all of the possible miRNAs/cancer pairs in three consecutive steps. First, in co-gene analysis, gene set enrichment analysis is carried out for all miRNA/cancer pairs. Second, in co-function analysis, information theoretic similarity on GO is calculated for miRNA/cancer pairs screened from the former step. Third, in co-pathway analysis, pathway enrichment analysis is performed for miRNA/cancer pairs screened from the second step. In this study, we totally included 776 miRNAs and 25 cancer types. As a result, 94 miRNAs were identified with robust association with 17 types of cancer. Meanwhile, 83 pathways with relevance to both miRNAs and cancer were also singled out. This framework provides an effective way to narrow down miRNAs for cancer and to pinpoint corresponding pathways.Screening for cancer associated MiRNAs through co-gene, co-function and co-pathway analysisXue Xiao, Dongguo Li, Lei Gao, Xia Li, Qianghu Wang, Shaojun Zhang, Zhicheng Liu10.1016/j.compbiomed.2012.02.008Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2624630http://www.computersinbiologyandmedicine.com/article/PIIS001048251200056X/abstract?rss=yesAbstract: The velocity field and deposition fraction of the particles in the trachea and the first third generations of tracheobronchial tree are investigated with CFD simulation. The air flow rate of trachea is considered to be in the range of 15–60l/min in accordance to four different activity levels of male adults. A physiologically realistic dichotomic airway bifurcation geometry with structured hexahedral meshes are constructed. The simulations with different hexahedral mesh densities have shown that the grid independent results will be reached with the average dimensionless distance of the first cell to the walls of y+≈0.5. The deposition fraction graph for particles in the range of 0.1–10μm diameter has a minimum in the range of 0.1–1μm particle diameter and after that it increases for larger particles. The results of the simulations under different breathing pattern have shown that deposition fraction significantly increases at higher Reynolds and Stokes numbers.Flow patterns and deposition fraction of particles in the range of 0.1–10μm at trachea and the first third generations under different breathing conditionsE.M. Saber, G. Heydari10.1016/j.compbiomed.2012.03.002Computers in Biology and Medicine 42, 5 (2012)2012-05-01Computers in Biology and Medicine2012-05-01425S0010-4825(12)X0005-2631638