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Association of different prediction methods for determination of the efficiency and selectivity on neuron-based sensors

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BIOSENSORS & BIOELECTRONICS 2006年第7期21卷 1045-1058页

作者： Prasad, S Tuncel, E Ozkan, M Univ Calif Riverside Dept Elect Engn Riverside CA 92521 USA Univ Calif Riverside Ctr Nanoscale Sci & Engn Riverside CA 92521 USA

A technique has been developed to determine the efficiency and the selectivity of a single neuron-based sensor in identifying the nature of the chemical agents in an unknown sample. This has been achieved by exploiting the unique electrical identifiers, also known as "signature patterns", generated by the neuronal cell membrane. These were generated based on the variations to the extracellular electrical activity, due to the effect of a broad range of chemical agents. We demonstrate the prediction capability of the sensor in identifying the nature of an unknown test sample from a combination of three chemical agents, namely, ethanol, pyrethroid, and hydrogen peroxide. This was achieved through a two-step process. The first step was experimentally achieved by in situ recording of the changes to the extracellular electrical activity from the sensing sites or the array of microelectrodes that form the platform for patterning neurons. Simultaneous optical characterization of the cell array during the sensing process was performed to identify the associated physiological changes. The second step was mathematical and was based on developing a library of signature patterns for a set of concentrations of the various combinations of the three chemical agents. Two variants of the nearest neighbor algorithm scheme - (a) partial distance search method, and (b) search tree method, were implemented for the accurate detection of all the components with varying concentrations in the test samples of unknown nature. This technique exhibits reliability in identification up to parts-per-billion (ppb) sensitivity. The capability of standardization of this technique for potential commercial applications is also discussed. (c) 2005 Elsevier B.V. All rights reserved.

关键词： extracellular sensing nearest neighbor algorithm chemical agent identification

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A novel computational method to predict transcription factor DNA binding preference

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BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 2006年第3期348卷 1034-1037页

作者： Qian, Ziliang Cai, Yu-Dong Li, Yixue Chinese Acad Sci Shanghai Inst Biol Sci MPG Partner Int Computat Biol Beijing 100864 Peoples R China Chinese Acad Sci Shanghai Inst Biol Sci Bioinformat Ctr Shanghai 200031 Peoples R China Chinese Acad Sci Grad Sch Beijing 100039 Peoples R China Univ Manchester Inst Sci & Technol Dept Math Manchester M60 1QD Lancs England Shanghai Ctr Bioinformat Technol Shanghai 200235 Peoples R China

Transcription factor binds to sequence specific sites in regulatory region to control nearby gene's expression. It is termed as the major regulator of transcription. However, identifying DNA binding preference of transcription factors systematically is still a challenge. By using the nearest neighbor algorithm, a novel computational approach was developed to predict transcription factor DNA binding preference based on the gene ontology [M. Ashburner, C.A. Ball, J.A. Blake, D. Botstein, H. Butler, J.M. Cherry, A.P. Davis, K. Dolinski, S.S. Dwight, J.T. Eppig, M.A. Harris, D.P. Hill, L. Issel-Tarver, A. Kasarskis, S. Lewis, J.C. Matese, J.E. Richardson, M. Ringwald, G.M. Rubin, G. Sherlock, Gene Ontology: tool for the unification of biology, Nat. Genet. 25 (2000) 25-29.] and 0/1 encoding system of nucleotide. The overall success rate of Jackknife cross-validation test for our predictor reaches 76.6%, which indicates the DNA binding preference is closely correlated with its biological functions and computational method developed in this contribution could be a powerful tool to investigate transcription factor DNA binding preference, especially for those novel transcription factors with little prior knowledge on its DNA binding preference. (c) 2006 Elsevier Inc. All rights reserved.

关键词： transcription factor transcription factor DNA binding preference nearest neighbor algorithm 0/1 system jackknife cross-validation test gene ontology

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The multirelation method for link prediction in social networks

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Computational Mathematics and Modeling 2006年第1期17卷 88-96页

作者： Levchenkov, V.S. Levchenkova, L.G.

The multirelation method originally developed for group choice problems is applied to study the formation of new links in a social network graph. A natural generalization of the "nearest neighbor" method is proposed for predicting the appearance of links between points that have no common neighbors in the original graph. © 2006 Springer Science+Business Media, Inc.

关键词： Social Media Line graph Social Networks nearest neighbor algorithm Social Capital

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Collaborative Filtering Recommendation algorithm Based on Sample Reduction

Collaborative Filtering Recommendation Algorithm Based on Sa...

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2006年国际电子、工程及科学领域的分布式计算和应用学术研讨会

作者： Linqi Gao School of Management,Tianjin University & Tianjin Normal University Tianjin 300387,China Congdong Li School of Management,Tianjin University Tianjin 300072,China

Personalized Recommender System has become an important research field in Electronic Commerce,and the clustering of customers is the basis to produce the *** customers clustering in recommender system has some unique characters,such as the extreme sparsity of user rating data and huge sample *** Collaborative Filtering(CF) algorithm works poorly in this *** improve the quantity of recommending,sample reduction method is proposed in CF schema to lessen the sample space in both row and column aspects before carrying on the *** has the following features:(1) a Restraint Function is introduced into the basic CF model to solve the problem of sparsity of user rating data;(2) selective sampling and look-ahead framework are combined,based on the nearest neighbors algorithm,to reduce the number of samples while maintaining the quality of *** last,experiments are designed on the basis of MoveLens data set;recall and precision are applied as evaluating *** with general CF, the proposed algorithm has higher quality of recommendation.

关键词： nearest neighbor algorithm Selective Sampling Collaborative Filtering Recommendation System and Electronic Commerce

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Feasibility of air target identification using laser radar vibrometry

Feasibility of air target identification using laser radar v...

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15th Conference on Automatic Target Recognition

作者： Jolivet, V Fournier, K Normandin, X Cariou, JP Off Natl Etud & Rech Aerosp F-91761 Palaiseau France

ISBN: (纸本)0819457922

Vibration measurement using coherent laser radar (LADAR) is a promising way to identify air targets at long range. Laser vibrometers can remotely measure the velocity of micrometric displacements and thus exhibit the target surface vibration frequencies. Some of these frequencies are modal frequencies, which result from the target structure. They define a unique signature and allow target identification to be performed. As vibration amplitudes are not reliable, we choose to consider only frequency positions. In this article, we explain an "extended identification" method which takes into account cumulative signatures in space and time to improve global system identification performance. Using a nearest neighbor classifier and a suitable metric taking into account a simple off-line processing of measured data, the recognition algorithm leads to good identification rates and very low rejection rates for a nine class problem. We show a strong improvement of the identification rate thanks to the "extended identification" method.

关键词： ATR laser radar vibrometry Wagner-Fisher algorithm nearest neighbor algorithm remote sensing target vibration

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Electronic structure of high density carrier states in PbS, PbSe and PbTe

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SEMICONDUCTOR SCIENCE AND TECHNOLOGY 2004年第3期19卷 433-441页

作者： Das, RK Sahoo, S Tripathi, GS Gopalpur Coll Dept Phys Gopalpur On Sea 761002 Orissa India Berhampur Univ Dept Phys Berhampur 760007 Orissa India Univ Duisburg Essen Dept Phys D-47048 Duisburg Germany

Carrier concentration dependent electronic structures are usually studied through the (k) over right arrow (.)(p) over right arrow method. In the traditional method the band edge hamiltonians are diagonalized exactly and the far bands are treated using perturbation theory. However, this approach does not work well beyond carrier densities of about 10(18) cm(-3). In this paper, we present the results of an improved (k) over right arrow (.) (p) over right arrow method for lead salts in which the band edge states are rediagonalized considering the wavefunctions and the energies of the first diagonalization as the basis. The far bands are treated, as usual, using perturbation theory. We calculate the Fermi energy and the density of states (DOS) as functions of carrier densities. These are the first ever analyses for PbS and PbSe. We compare the present results with those obtained in the limiting case of single diagonalization. At high carrier densities, in both n-and p-type materials, the difference is substantial. A comparison with the results obtained for PbTe is also made. The data are expected to be useful for calculations of carrier dependent properties in these semiconductors and their alloys, and also their experimental analyses. Good agreement with experiment for the EPR shift in PbMnTe calculated using our new theory is an encouraging feature.

关键词： LEAD SALT Fermi level K-HARMONICS METHOD band edge perturbation theory Density of states Electronic structure diagonalization K-ES process WAVE FUNCTIONS lead selenides disease carrier state Carrier density nearest neighbor algorithm

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Fast nearest neighbour testing algorithm for small feature sizes

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ELECTRONICS LETTERS 2004年第3期40卷 171-172页

作者： Yildiz, OT Akarun, L Akin, HL Bogazici Univ Dept Comp Engn TR-80815 Bebek Turkey

A fast nearest neighbour algorithm with a logarithmic expected testing time for small feature sizes is presented. It has been tested on a Robot Vision application for YUV space. The algorithm time requirement is bette... 详细信息

关键词： Multilayer perceptrons feature size nearest neighbor algorithms TESTING TIME Fasting robot vision YUV nearest neighbor algorithm Small

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Predicting 22 protein localizations in budding yeast

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BIOCHEMICAL AND BIOPHYSICAL RESEARCH COMMUNICATIONS 2004年第2期323卷 425-428页

作者： Cai, YD Chou, KC Univ Manchester Dept Biomol Sci Manchester M60 1QD Lancs England Gordon Life Sci Inst San Diego CA 92130 USA TIBDD Tianjin Peoples R China

According to the recent experiments, proteins in budding yeast can be distinctly classified into 22 subcellular locations. Of these proteins, some bear the multi-locational feature, i.e., occur in more than one location. However, so far all the existing methods in predicting protein subcellular location were developed to deal with only the mono-locational case where a query protein is assumed to belong to one, and only one, subcellular location. To stimulate the development of subcellular location prediction, an augmentation procedure is formulated that will enable the existing methods to tackle the multi-locational problem as well. It has been observed thru a jackknife cross-validation test that the success rate obtained by the augmented GO-FnD-PseAA algorithm [BBRC 320 (2004) 1236] is overwhelmingly higher than those by the other augmented methods. It is anticipated that the augmented GO-FunD-PseAA predictor will become a very useful tool in predicting protein subcellular localization for both basic research and practical application. (C) 2004 Elsevier Inc. All rights reserved.

关键词： gene ontology functional domain composition pseudo-amino acid composition GO-FunD-PseAA predictor InterPro database hybrid space nearest neighbor algorithm

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A simple algorithm for training fuzzy systems using input-output data

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ADVANCES IN ENGINEERING SOFTWARE 2003年第5期34卷 247-259页

作者： Tsekouras, G Sarimveis, H Bafas, G Natl Tech Univ Athens Sch Chem Engn Athens 15780 Greece Univ Aegean Dept Cultural Technol & Commun Lab Multimedia Applicat GR-81100 Mitilini Greece

This paper proposes a simple algorithm for training fuzzy systems from numerical data: The main advantage of the method is the lack of complicated iterative mechanisms and therefore, its implementation is carried out easily. The suggested algorithm employs a fuzzy model with simplified rules, assuming a fuzzy partition of the input space into fuzzy subspaces. The output is inferred by expanding the model into fuzzy basis functions (FBFs), where each FBF corresponds to a certain fuzzy subspace. The number of rules and the respective premise parts (fuzzy subspaces) are determined using the nearest neighbor approach. Then, the optimal consequent parameters are obtained by the least-squares method. Finally, simulations show the validity of the method. (C) 2003 Elsevier Science Ltd. All rights reserved.

关键词： training fuzzy systems fuzzy basis function nearest neighbor algorithm

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Functional classification of proteins using a nearest neighbour algorithm

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In Silico Biology 2003年第3期3卷 265-275页

作者： Keck, Hans-Peter Wetter, Thomas LION Bioscience AG 69121 Heidelberg Waldhofer Str. 98 Germany Universitätsklinikum Inst. fur Med. Biometr. und Info. 69120 Heidelberg Im Neuenheimer Feld 400 Germany

With the large volume of genomic data being analysed nowadays it becomes extremely important to provide automated ways of protein classification to give the scientist a good overview of the analysed data. The system described here is very flexible and can be used with any given protein classification scheme. Before using the system, it has to be trained with a set of already classified proteins. Afterwards, other proteins can be classified by the system automatically. Several tests have been performed to assess the quality of this classification;they have shown the usefulness of the system. The system will be available as part of a commercial software package.

关键词： Classification Genome analysis Machine learning nearest neighbor algorithm Protein function

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