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ieee International conference on Computer Science and Automation Engineering (CSAE)

作者： Wen-Lin Huang Chyn Liaw Shinn-Ying Ho Department of Management Information System Asia-Pacific Institute of Creativity Miaoli Taiwan Institute of Bioinformatics and Systems Biology National Chiao Tung University Hsinchu Taiwan

Eukaryotic secretory proteins that traverse classical ER-Golgi pathway are usually characterized by short N-terminal signal peptides. However, several secretory proteins lacking the signal peptides are found to be exported by a non-classical secretion pathway. Therefore, predicting non-classical secretory proteins regardless of the N-terminal signal peptides is necessary for developing a critical computational approach. Several prediction methods have been proposed by using various types of features to predict secretory proteins. However, prediction performance seems not acceptable. This study proposes an SVM-based prediction method, namely ProSec-iGOX, which uses a major set of informative Gene Ontology (GO) terms and a minor set of assistance features. Physicochemical properties as the assistance features are useful when a query protein sequence without homologous protein with annotated GO terms. Two data sets, S25 and S40, having the identity 25% and 40%, respectively, are adopted for performance comparisons. The ProSec-iGOX yields test accuracies of 95.1% and 96.8% when adopting on the data sets S25 and S40 respectively. The latter accuracy (96.8%) is significantly higher than that of SPRED (82.2%), which uses frequency of tri-peptides and short peptides, secondary structure, physicochemical properties as input features to a random forest classifier. The experimental results show that GO terms are effective features for predicting non-classical secretory proteins.

关键词： Proteins Accuracy Peptides Ontologies Databases Training Feature extraction

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Robust Catheter Identification and Tracking in X-Ray Angiographic Sequences

Robust Catheter Identification and Tracking in X-Ray Angiogr...

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Annual International conference of the ieee Engineering in Medicine and biology Society

ISBN: (纸本)9781424492695

Coronary artery disease (CAD) is one of the major causes of death worldwide. Today X-ray angiography is a standard method for CAD diagnosis. Usually, the quality of these images is not good enough. Noise, camera and heart motions, non-uniform illumination and even the presence of catheter are sources of quality degradation. The existence of catheter can produce difficulties in vessel extraction methods because catheter is structurally similar to arteries. In this paper we propose a fully automatic method for catheter detection and tracking during the whole angiography sequence. In this method with a vesselness map, we smooth each frame using guided filter. The catheter is detected in the first frame using Hough transform. We then fit a second order polynomial on the catheter and accurately track it throughout the sequence. Our method is tested on 25 X-ray angiography sequences w here a precision of 0.9597 is achieved.

关键词： Robust Catheter Identification Tracking X-Ray Angiographic Sequences

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Comparison of Multi-Sample Variant Calling Methods for Whole Genome Sequencing

Comparison of Multi-Sample Variant Calling Methods for Whole...

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ieee International conference on Systems biology.

作者： Kwangsik Nho John D. West Apoorva Bharthur Robert C. Green Andrew J. Saykin Huian Li Robert Henschel Michel C. Tavares Michael W. Weiner Arthur W. Toga Center for Neuroimaging Department of Radiology and Imaging Sciences Indiana University School of

ISBN: (纸本)9781479972951

Rapid advancement of next-generation sequencing (NGS) technologies has facilitated the search for genetic susceptibility factors that influence disease risk in the field of human genetics. In particular whole genome sequencing (WGS) has been used to obtain the most comprehensive genetic variation of an individual and perform detailed evaluation of all genetic variation. To this end, sophisticated methods to accurately call high-quality variants and genotypes simultaneously on a cohort of individuals from raw' sequence data are required. On chromosome 22 of 818 WGS data from the Alzheimer's Disease Neuroimaging Initiative (ADNI), which is the largest WGS related to a single disease, we compared two multi-sample variant calling methods for the detection of single nucleotide variants (SNVs) and short insertions and deletions (indels) in WGS: (1) reduce the analysis-ready reads (BAM) file to a manageable size by keeping only essential information for variant calling ("REDUCE") and (2) call variants individually on each sample and then perform a joint genotyping analysis of the variant files produced for all samples in a cohort ("JOINT"). JOINT identified 515,210 SNVs and 60,042 indels, while REDUCE identified 358303 SNVs and 52,855 indels. JOINT identified many more SNVs and indels compared to REDUCE. Both methods had concordance rate of 99.60% for SNVs and 99.06% for indels. For SNVs, evaluation with HumanOmni 2.5M genotyping arrays revealed a concordance rate of 99.68% for JOINT and 99.50% for REDUCE. REDUCE needed more computational time and memory compared to JOINT. Our findings indicate that the multi-sample variant calling method using the JOINT process is a promising strategy for the variant detection, which should facilitate our understanding of the underlying pathogenesis of human diseases.

关键词： Joints Sequential analysis Genomics bioinformatics World Wide Web Magnetic analysis

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Deep Multi-Instance Learning with Adaptive Recurrent Pooling for Medical Image Classification

Deep Multi-Instance Learning with Adaptive Recurrent Pooling...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yi Ding Lu Zhao Liming Yuan Xianbin Wen School of Computer Science and Engineering Tianjin University of Technology Tianjin China Key Laboratory of Computer Vision and System Ministry of Education Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin China School of Computer and Information Engineering Tianjin Chengjian University Tianjin China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Recently, deep multi-instance neural networks have been successfully applied for medical image classification, where only image-level labels rather than fine-grained patch-level labels are available for use. One key issue for these multi-instance neural networks is how to aggregate all patch (instance) features into an entire image (bag) representation, referred to as multi-instance pooling, e.g., max, mean, and attention based pooling. Nevertheless, these multi-instance pooling operations do not take the structural information within an image into account. This is obviously inappropriate for medical image classification since there often exist certain dependencies among regional patches/lesions. We propose an adaptive recurrent pooling based deep multi-instance neural network in this paper. In this network, we first extract multi-view global structural features from every bag using the self-attention mechanism, and then aggregate these multi-view features into a whole bag representation based on the adaptive recurrent pooling operation in order to further capture the contextual information within the bag. Moreover, we introduce the cross-normalization operation used in the Unit Force Operated Vision Transformer into the self-attention module to reduce its computational complexity. We have experimentally evaluated the performance of the proposed network on three medical image datasets, namely UCSB breast, Messidor, and Colon cancer. The results demonstrate the advantage of our network over current state-of-the-art deep multi-instance networks in terms of classification accuracy and interpretability.

关键词： Technological innovation Adaptive systems Aggregates Neural networks Force Feature extraction Transformers

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MedDet: Generative Adversarial Distillation for Efficient Cervical Disc Herniation Detection

MedDet: Generative Adversarial Distillation for Efficient Ce...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Zeyu Zhang Nengmin Yi Shengbo Tan Ying Cai Yi Yang Lei Xu Qingtai Li Zhang Yi Daji Ergu Yang Zhao Key Laboratory of Electronic Information Engineering Southwest Minzu University The Australian National University West China Hospital Orthopedic Research Institute Machine Intelligence Laboratory Sichuan University La Trobe University

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Cervical disc herniation (CDH) is a prevalent musculoskeletal disorder that significantly impacts health and requires labor-intensive analysis from experts. Despite advancements in automated detection of medical imaging, two significant challenges hinder the real-world application of these methods. First, the computational complexity and resource demands present a significant gap for real-time application. Second, noise in MRI reduces the effectiveness of existing methods by distorting feature extraction. To address these challenges, we propose three key contributions: Firstly, we introduced MedDet, which leverages the multi-teacher single-student knowledge distillation for model compression and efficiency, meanwhile integrating generative adversarial training to enhance performance. Additionally, we customize the second-order nmODE to improve the model’s resistance to noise in MRI. Lastly, we conducted comprehensive experiments on the CDH-1848 dataset, achieving up to a 5% improvement in mAP compared to previous methods. Our approach also delivers over 5 times faster inference speed, with approximately 67.8% reduction in parameters and 36.9% reduction in FLOPs compared to the teacher model. These advancements significantly enhance the performance and efficiency of automated CDH detection, demonstrating promising potential for future application in clinical practice.

关键词： Training Knowledge engineering Musculoskeletal system Magnetic resonance imaging Noise Noise reduction Feature extraction Real-time systems Resilience Immune system

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Region of Interest Extraction for Lossless Compression of Bone X-Ray Images

Region of Interest Extraction for Lossless Compression of Bo...

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Annual International conference of the ieee Engineering in Medicine and biology Society

作者： S. Kazeminia N. Karimi S.M.R. Soroushmehr S. Samavi H. Derksen K. Najarian the ECE department Isfahan University of Technology Isfahan Iran the University of Michigan Center for Integrative Research in Critical Care (MCIRCC) and the department of Emergency Medicine University of Michigan Ann Arbor MI USA the ECE department of Isfahan University of Technology Iran the University of Michigan Center for Integrative Research in Critical Care (MCIRCC) and the department of Mathematics University of Michigan Ann Arbor MI USA the department of Computational Medicine and Bioinformatics as well as University of Michigan Center for Integrative Research in Critical Care (MCIRCC) and the department of Emergency Medicine University of Michigan Ann Arbor MI USA

ISBN: (纸本)9781424492695

For few decades digital X-ray imaging has been one of the most important tools for medical diagnosis. With the advent of distance medicine and the use of big data in this respect, the need for efficient storage and online transmission of these images is becoming an essential feature. Limited storage space and limited transmission bandwidth are the main challenges. Efficient image compression methods are lossy while the information of medical images should be preserved with no change. Hence, lossless compression methods are necessary for this purpose. In this paper, a novel method has been proposed to eliminate the non-ROI data from bone X-ray images. Background pixels do not contain any valuable medical information. The proposed method is based on the histogram dispersion method. ROI is separated from the background and it is compressed with a lossless compression method to preserve medical information of the image. Compression ratios of the implemented results show that the proposed algorithm is capable of effective reduction of the statistical and spatial redundancies.

关键词： Region Lossless Compression X-Ray Images lossless compression bone x-ray region of interest Region images transmission bandwidth big data

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An Estimation of Distribution Algorithm based on Clayton Copula and Empirical Margins

An Estimation of Distribution Algorithm based on Clayton Cop...

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2010 International conference on Life System Modeling and Simulation & 2010 International conference on Intelligent Computing for Sustainable Energy and Environment

作者： L.F. Wang, Y.C. Wang, J.C. Zeng, and Y. Hong Colloge of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, 730050, China Complex System and computational intelligence Laboratory, Taiyuan University of Science and Technology, Taiyuan, 030024, China

Estimation of Distribution Algorithms (EDAs) are new evolutionary algorithms which based on the estimation and sampling the distribution model of the selected population in each generation. The way of copula used in EDAs is introduced in this paper. The joint distribution of the selected population is separated into the univariate marginal distribution and a function called copula to represent the dependence structure. And the new individuals are obtained by sampling from copula and then calculating the inverse of the univariate marginal distribution function. The empirical distribution and Clayton copula are used to implement the proposed copula Estimation of Distribution Algorithm (copula EDA). The experimental results show that the proposed algorithm is equivalent to some conventional continuous EDAs in performance.

关键词： Estimation of distribution algorithms (EDAs), copula theory, the joint distribution, the marginal distribution, Clayton copula

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Learning in Glaucoma Genetic Risk Assessment

Learning in Glaucoma Genetic Risk Assessment

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： Zhuo Zhang Jiang Liu Chee Keong Kwoh Xueling Sim Wan Ting Tay Yonghua Tan Fengshou Yin Tien Yin Wong Institute of Infocomm Research Agency for Science Technology and Research Singapore Singapore Nanyang Technological University Singapore Singapore Nanyang Technology Singapore Singapore Singapore Eye Research Institute Singapore Singapore Eye Research Nanyang Technology Singapore Singapore

Genome Wide Association (GWA) studies are powerful tools to identify genes involved in common human diseases, and are becoming increasingly important in genetic epidemiology research. However, the statistical approaches behind GWA studies lack capability in taking into account the possible interactions among genetic markers; and true disease variants may be lost in statistical noise due to high threshold. A typical GWA study reports a few highly suspected signals, e.g. Single-nucleotide polymorphisms (SNPs), which usually account for a tiny portion of overall genetic risks for the disease of interest. This study proposes a computational learning approach in addition to parametric statistical methods along with a filtering mechanism, to build glaucoma genetic risk assessment model. Our data set was obtained from Singapore Malay Eye Study (SiMES), genotyped on Illumina 610quad arrays. We constructed case-control data set with 233 glaucoma and 458 healthy samples. A standard case-control association test was conducted on post-QC dataset with more than 500k SNPs. Genetic profile is constructed using genotype information from a list of 412 SNPs filtered by a relaxed p-value threshold of 1×10 -3 , and forms the feature space for learning. Among the five learning algorithms we performed, Support Vector Machines with radial kernel (SVM-radial) achieved the best result, with area under curve (ROC) of 99.4% and accuracy of 95.9%. The result illustrates that, learning approach in post GWAS data analysis is able to accurately assess genetic risk for glaucoma. The approach is more robust and comprehensive than individual SNPs matching method. We will further validate our results in several other data sets obtained in consequential population studies conducted in Singapore.

关键词： Diseases Genomics bioinformatics Machine learning Risk management Accuracy

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Towards a piRNA prediction using multiple kernel fusion and support vector machine

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bioinformatics 2014年第17期30卷 I364-I370页

作者： Brayet, Jocelyn Zehraoui, Farida Jeanson-Leh, Laurence Israeli, David Tahi, Fariza IBGBI UEVE Genopole IBISC EA 4526 F-91000 Evry France Genethon F-91002 Evry France

Motivation: Piwi-interacting RNA (piRNA) is the most recently discovered and the least investigated class of Argonaute/Piwi protein-interacting small non-coding RNAs. The piRNAs are mostly known to be involved in protecting the genome from invasive transposable elements. But recent discoveries suggest their involvement in the pathophysiology of diseases, such as cancer. Their identification is therefore an important task, and computational methods are needed. However, the lack of conserved piRNA sequences and structural elements makes this identification challenging and difficult. Results: In the present study, we propose a new modular and extensible machine learning method based on multiple kernels and a support vector machine (SVM) classifier for piRNA identification. Very few piRNA features are known to date. The use of a multiple kernels approach allows editing, adding or removing piRNA features that can be heterogeneous in a modular manner according to their relevance in a given species. Our algorithm is based on a combination of the previously identified features [sequence features (k-mer motifs and a uridine at the first position) and piRNAs cluster feature] and a new telomere/centromere vicinity feature. These features are heterogeneous, and the kernels allow to unify their representation. The proposed algorithm, named piRPred, gives promising results on Drosophila and Human data and outscores previously published piRNA identification algorithms.

关键词： Algorithms Animals Artificial intelligence Drosophila/genetics Humans RNA, Small Interfering/chemistry RNA, Small Interfering/chemistry Sequence Analysis, RNA/methods Software Support Vector Machine

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Call for Papers for Special Issue on computational intelligence Approaches in computational biology and bioinformatics

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ieee/ACM Transactions on computational biology and bioinformatics 2005年第3期2卷 271-271页

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.

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