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Annual International Conference of the IEEE Engineering in Medicine and Biology Society

作者： Hiroto Sugano Shinsuke Hara Tetsuo Tsujioka Tadayuki Inoue Shigeyoshi Nakajima Takaaki Kozaki Hajime Namkamura Kazuhide Takeuchi Graduate school of engineering Osaka City University Graduate school of medicine Osaka City University

ISBN: (纸本)9781424441211

For ubiquitous health care systems which continuously monitor a person's vital signs such as electrocardiogram (ECG), body surface temperature and three-dimensional (3D) acceleration by wireless, it is important to accurately detect the occurrence of an abnormal event in the data and immediately inform a medical doctor of its detail. In this paper, we introduce a remote health care system, which is composed of a wireless vital sensor, multiple receivers and a triage engine installed in a desktop personal computer (PC). The middleware installed in the receiver, which was developed in C++, supports reliable data handling of vital data to the ethernet port. On the other hand, the human interface of the triage engine, which was developed in JAVA, shows graphics on his/her ECG data, 3D acceleration data, body surface temperature data and behavior status in the display of the desktop PC and sends an urgent e-mail containing the display data to a pre-registered medical doctor when it detects the occurrence of an abnormal event. In the triage engine, the lethal arrhythmia detection algorithm based on short time Fourier transform (STFT) analysis can achieve 100% sensitivity and 99.99% specificity, and the behavior recognition algorithm based on the combination of the nearest neighbor method and the Naive Bayes method can achieve more than 71% classification accuracy.

关键词： Health Care Systems abnormal event Triage nearest neighbor algorithm Otolaryngologists Data Display STFT Engines Java Java programming language User Interface Device Component Bayes methods Ethernet networks Cardiac Arrhythmias wireless

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Historical document enhancement using LUT classification

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INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION 2010年第1期13卷 1-17页

作者： Obafemi-Ajayi, Tayo Agam, Gady Frieder, Ophir IIT Dept Comp Sci Chicago IL 60616 USA

The fast evolution of scanning and computing technologies in recent years has led to the creation of large collections of scanned historical documents. It is almost always the case that these scanned documents suffer from some form of degradation. Large degradations make documents hard to read and substantially deteriorate the performance of automated document processing systems. Enhancement of degraded document images is normally performed assuming global degradation models. When the degradation is large, global degradation models do not perform well. In contrast, we propose to learn local degradation models and use them in enhancing degraded document images. Using a semi-automated enhancement system, we have labeled a subset of the Frieder diaries collection (The diaries of Rabbi Dr. Avraham Abba Frieder. http://***/collections/). This labeled subset was then used to train classifiers based on lookup tables in conjunction with the approximated nearest neighbor algorithm. The resulting algorithm is highly efficient and effective. Experimental evaluation results are provided using the Frieder diaries collection (The diaries of Rabbi Dr. Avraham Abba Frieder. http://***/collections/).

关键词： Rabbis Subset document image deterioration degradation model nearest neighbor algorithm Refractive surgery enhancement Classifiers

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Prediction of protein structural class using a complexity-based distance measure

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AMINO ACIDS 2010年第3期38卷 721-728页

作者： Liu, Taigang Zheng, Xiaoqi Wang, Jun Shanghai Normal Univ Dept Math Sci Comp Key Lab Shanghai Univ Shanghai 200234 Peoples R China Dalian Univ Technol Dept Appl Math Dalian 116024 Peoples R China Dalian Univ Technol Coll Adv Sci & Technol Dalian 116024 Peoples R China

Knowledge of structural class plays an important role in understanding protein folding patterns. So it is necessary to develop effective and reliable computational methods for prediction of protein structural class. To this end, we present a new method called NN-CDM, a nearest neighbor classifier with a complexity-based distance measure. Instead of extracting features from protein sequences as done previously, distance between each pair of protein sequences is directly evaluated by a complexity measure of symbol sequences. Then the nearest neighbor classifier is adopted as the predictive engine. To verify the performance of this method, jackknife cross-validation tests are performed on several benchmark datasets. Results show that our approach achieves a high prediction accuracy over some classical methods.

关键词： Symbol sequence complexity nearest neighbor algorithm Jackknife cross-validation test Performance measure

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Protein sumoylation sites prediction based on two-stage feature selection

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MOLECULAR DIVERSITY 2010年第1期14卷 81-86页

作者： Lu, Lin Shi, Xiao-He Li, Su-Jun Xie, Zhi-Qun Feng, Yong-Li Lu, Wen-Cong Li, Yi-Xue Li, Haipeng Cai, Yu-Dong Chinese Acad Sci Shanghai Inst Biol Sci MPG Partner Inst Computat Biol Shanghai 200031 Peoples R China Shanghai Univ Inst Syst Biol Shanghai 200244 Peoples R China Shanghai Jiao Tong Univ Dept Biomed Engn Shanghai 200240 Peoples R China Sch Shanghai Jiao Tong Univ Shanghai 200240 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Inst Hlth Sci Shanghai 200025 Peoples R China Coll Sci Dept Chem Shanghai 200444 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Key Lab Syst Biol Shanghai 200031 Peoples R China

Protein sumoylation is one of the most important post-translational modifications. Accurate prediction of sumoylation sites is very useful for the analysis of proteome. Though the putative motif IK XE can be used, optimization of prediction models still remains a challenge. In this study, we developed a prediction system based on feature selection strategy. A total of 1,272 peptides with 14 residues from SUMOsp (Xue et al. [8] Nucleic Acids Res 34:W254-W257, 2006) were investigated in this study, including 212 substrates and 1,060 non-substrates. Among the substrates, only 162 substrates comply to the motif IK XE. First, 1,272 substrates were divided into training set and test set. All the substrates were encoded into feature vectors by hundreds of amino acid properties collected by Amino Acid Index Database (AAIndex, http://***/aaindex ). Then, mRMR (minimum redundancy-maximum relevance) method was applied to extract the most informative features. Finally, nearest neighbor algorithm (NNA) was used to produce the prediction models. Tested by Leave-one-out (LOO) cross-validation, the optimal prediction model reaches the accuracy of 84.4% for the training set and 76.4% for the test set. Especially, 180 substrates were correctly predicted, which was 18 more than using the motif IK XE. The final selected features indicate that amino acid residues with two-residue downstream and one-residue upstream of the sumoylation sites play the most important role in determining the occurrence of sumoylation. Based on the feature selection strategy, our prediction system can not only be used for high throughput prediction of sumoylation sites but also as a tool to investigate the mechanism of sumoylation.

关键词： Prediction Protein sumoylation mRMR AAIndex nearest neighbor algorithm Leave-one-out cross-validation Bioinformatics

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Prediction of Tyrosine Sulfation with mRMR Feature Selection and Analysis

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JOURNAL OF PROTEOME RESEARCH 2010年第12期9卷 6490-6497页

作者： Niu, Shen Huang, Tao Feng, Kaiyan Cai, Yudong Li, Yixue Chinese Acad Sci Shanghai Inst Biol Sci Key Lab Syst Biol Shanghai 200031 Peoples R China Shanghai Univ Inst Syst Biol Shanghai 200444 Peoples R China Shanghai Ctr Bioinformat Technol Shanghai 200235 Peoples R China Fudan Univ Ctr Computat Syst Biol Shanghai 200433 Peoples R China

Protein tyrosine sulfation is a ubiquitous post-translational modification (PTM) of secreted and transmembrane proteins that pass through the Golgi apparatus In this study, we developed a new method for protein tyrosine sulfation prediction based on a nearest neighbor algorithm with the maximum relevance minimum redundancy (mRMR) method followed by incremental feature selection (IFS) We incorporated features of sequence conservation, residual disorder, and amino acid factor 229 features in total, to predict tyrosine sulfation sites From these 229 features, 145 features were selected and deemed as the optimized features for the prediction The prediction model achieved a prediction accuracy of 90 01% using the optimal 145-feature set Feature analysis showed that conservation disorder, and physicochemical/biochemical properties of amino acids all contributed to the sulfation process Site-specific feature analysis showed that the features derived from its surrounding sites contributed profoundly to sulfation site determination in addition to features derived from the sulfation site itself The detailed feature analysis in this paper might help understand more of the sulfation mechanism and guide the related experimental validation

关键词： Sulfation maximum relevance minimum redundancy incremental feature selection nearest neighbor algorithm

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An Intent-Based Control Approach for an Intelligent Mobility Aid

An Intent-Based Control Approach for an Intelligent Mobility...

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2nd International Asia Conference on Informatics in Control, Automation and Robotics (CAR)

作者： Zhou, Wuxiao Xu, Li Yang, Jie Zhejiang Univ Coll Elect Engn Hangzhou 310027 Zhejiang Peoples R China Zhejiang Univ Sci & Technol Sch Civil Engn & Architectures Hangzhou 310023 Zhejiang Peoples R China

ISBN: (纸本)9781424451944

A prototype of an intelligent mobility aid, ZJU Walker, is designed to help the elderly walk easily, safely and independently. This paper proposes a control approach based on walking intent to control the motion of ZJU Walker. Walking intent is described in a 3D intent space which consists of directional intent, pace intent and rotary intent. Extraction of walking intent from a user's operations on the handles is implemented by the nearest neighbor algorithm. An intent-based user-oriented dynamic model is employed to activate a smooth control and to meet a user's requirements in the different stages of motion. Experimental results are presented to show the validity of the proposed control approach.

关键词： assistive robots mobility aid walking intent nearest neighbor algorithm

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Estimating cavity tree and snag abundance using negative binomial regression models and nearest neighbor imputation methods

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CANADIAN JOURNAL OF FOREST RESEARCH 2009年第9期39卷 1749-1765页

作者： Eskelson, Bianca N. I. Temesgen, Hailemariam Barrett, Tara M. Oregon State Univ Dept Forest Engn Resources & Management Corvallis OR 97331 USA US Forest Serv Pacific NW Res Stn Forestry Sci Lab Anchorage AK 99503 USA

Cavity tree and snag abundance data are highly variable and contain many zero observations. We predict cavity tree and snag abundance from variables that are readily available from forest cover maps or remotely sensed data using negative binomial (NB), zero-inflated NB, and zero-altered NB (ZANB) regression models as well as nearest neighbor (NN) imputation methods. The models were developed and fit to data collected by the Forest Inventory and Analysis program of the US Forest Service in Washington, Oregon, and California. For predicting cavity tree and snag abundance per stand, all three NB regression models performed better in terms of mean square prediction error than the NN imputation methods. The most similar neighbor imputation, however, outperformed the NB regression models in predicting overall cavity tree and snag abundance.

关键词： US Forest Service cavity tree abundance snag abundance forest inventory and analysis program Binomial nearest neighbor USDA Forest Service Negative nearest neighbor algorithm

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Computational Analysis of Protein Tyrosine Nitration

Computational Analysis of Protein Tyrosine Nitration

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第四届最优化与系统生物学国际研讨会

作者： ZhiSong He Tao Huang XiaoHe Shi LeLe Hu Lei Chen Fang Liu Kai Wang TieQiao Wen XiangYin Kong Yudong Cai CAS-MPG Partner Institute for Computational Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Centre for Computational Systems Biology Fudan University Key Laboratory of Systems Biology Shanghai Institutes for Biological Sciences Chinese Academy of Sciences Shanghai Center for Bioinformation Technology Institute of Health Sciences Shanghai Institutes for Biological Sciences (SIBS) Chinese Academy of Sciences (CAS) and Shanghai Jiao Tong University School of Medicine (SJTUSM) Institute of System Biology Shanghai University Shanghai Key Laboratory of Trustworthy Computing East China Normal University School of Life Sciences Shanghai University State Key Laboratory of Medical Genomics Ruijin Hospital Shanghai Jiaotong University

As an important covalent post-translational modification, Tyrosine nitration is closely related to the causes of various diseases. However, its mechanism is still largely unknown. Based on experimentally verified tyrosine nitration sites, we introduced a novel computational approach to analyze tyrosine nitration. nearest neighbor algorithm armed by maximum relevancy minimum redundancy feature selection approach was used to construct the tyrosine nitration predictor. The problem of the imbalance of dataset sizes was dealt with by dividing large dataset into several smaller ones. Analysis of the selected features shows some interesting phenomena of the tyrosine nitration, which could be helpful for further computational and experimental investigations.

关键词： Tyrosine nitration feature selection nearest neighbor algorithm

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Shannon Entropy-Based Fingerprint Similarity Search Strategy

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JOURNAL OF CHEMICAL INFORMATION AND MODELING 2009年第7期49卷 1687-1691页

作者： Wang, Yuan Geppert, Hanna Bajorath, Juergen Rhein Freidrich Wilhelms Univ Bonn Dept Life Sci Informat B IT LIMES Program Unit Chem Biol & Med Chem D-53113 Bonn Germany

For fingerprint searching using multiple active reference compounds. an information entropy-based similarity method is introduced as an alternative to conventional similarity coefficients and search strategies. The approach involves the determination of the fingerprint bit pattern entropy of a compound reference set and recalculation of the entropy following,the addition of individual test compounds. If a database compound shares similar, bit patterns with reference set molecules, adding this compound to the reference set only produces a small change in system entropy. By contrast, inclusion of a compound having a dissimilar fingerprint leads to a notable increase in entropy. Thus, database compounds can be screened for candidate molecules that do not cause significant changes in reference set fingerprint entropy. Compared to nearest neighbor methods, this approach has the computational advantage that it extracts reference set information only once prior to similarity searching. Test calculations on different compound data sets, fingerprints, and screening databases reveal that the ability of our entropy-based method to detect active compounds is often superior to data fusion techniques and Tanimoto similarity calculations.

关键词： bit pattern similarity method Fingerprints entropy Data fusion search strategies compounds recalculation nearest neighbor algorithm

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GalNAc-transferase specificity prediction based on feature selection method

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PEPTIDES 2009年第2期30卷 359-364页

作者： Lu, Lin Niu, Bing Zhao, Jun Liu, Liang Lu, Wen-Cong Liu, Xiao-Jun Li, Yi-Xue Cai, Yu-Dong Chinese Acad Sci Dept Combinator & Geometry CAS MPG Partner Inst Computat Biol Shanghai Inst Biol Sci Shanghai 200031 Peoples R China Shanghai Jiao Tong Univ Dept Biomed Engn Shanghai 200240 Peoples R China Shanghai Univ Sch Mat Sci & Engn Shanghai 200072 Peoples R China Shanghai Univ Dept Chem Coll Sci Shanghai 200444 Peoples R China Queens Med Res Inst Ctr Cardiovasc Sci Mol Physiol Lab Edinburgh EH16 4TJ Midlothian Scotland Chinese Acad Sci Shanghai Inst Biol Sci Key Lab Syst Biol Shanghai 200031 Peoples R China

GalNAc-transferase can catalyze the biosynthesis of O-linked oligosaccharides. The specificity of GalNAc-transferase is composed of nine amino acid residues denoted by R4, R3, R2, R1, R0, R1', R2', R3', R4'. To predict whether the reducing monosaccharide will be covalently linked to the central residue R0(Ser or Thr), a new method based on feature selection has been proposed in our work. 277 nonapeptides from reference [Chou KC. A sequence-coupled vector-projection model for predicting the specificity of GalNAc-transferase. Protein Sci 1995;4:1365-83] are chosen for training set. Each nonapeptide is represented by hundreds of amino acid properties collected by Amino Acid index database (http://***/aaindex) and transformed into a numeric vector with 4554 features. The Maximum Relevance Minimum Redundancy (mRMR) method combining with incremental Feature Selection (IFS) and Feature Forward Selection (FFS) are then applied for feature selection. nearest neighbor algorithm (NNA) is used to build prediction models. The optimal model contains 54 features and its correct rate tested by jackknife cross-validation test reaches 91.34%. Final feature analysis indicates that amino acid residues at position RT play the most important role in the recognition of GalNAc-transferase specificity, which were confirmed by the experiments [Elhammer AP, Poorman RA, Brown E, Maggiora LL, Hoogerheide JG, Kezdy FJ. The specificity of UDP-GalNAc:polypeptide N-acetylgalactosaminyltransferase as inferred from a database of in vivo substrates and from the in vitro glycosylation of proteins and peptides. J Biol Chem 1993;269:10029-38;O'Connell BC, Hagen FK, Tabak LA. The influence of flanking sequence on the O-glycosylation of threonine in vitro. J Biol Chem 1992;267:25010-8;Yoshida A, Suzuki M, Ikenaga H, Takeuchi M. Discovery of the shortest sequence motif for high level mucin-type O-glycosylation. J Biol Chem 1997;272:16884-8]. Our method can be used as a tool for predicting O-glycos

关键词： GalNAc-transferase Maximum Relevance Minimum Redundancy method nearest neighbor algorithm Amino Acid Index database O-glycosylation

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