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sequence-based PROTEIN-PROTEIN INTERACTION PREDICTION VIA SUPPORT VECTOR MACHINE

Journal of Systems Science & Complexity 2010年第5期23卷 1012-1023页

作者： Yongcui WANG Jiguang WANG Zhixia YANG Naiyang DENG College of Science China Agricultural University Beijing 100083 China Key Laboratory of Adaptation and Evolution of Plateau Biota Northwest Institute of Plateau Biology Chinese Academy of Sciences Xining 810008 China Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing 100190 China College of Mathematics and Systems Science Xinjiang University Urumuchi 830046 China College of Science China Agricultural University Beijing 100083 China

This paper develops sequence-based methods for identifying novel protein-protein interactions （PPIs） by means of support vector machines （SVMs）. The authors encode proteins ont only in the gene level but also in the amino acid level, and design a procedure to select negative training set for dealing with the training dataset imbalance problem, i.e., the number of interacting protein pairs is scarce relative to large scale non-interacting protein pairs. The proposed methods are validated on PPIs data of Plasmodium falciparum and Escherichia coli, and yields the predictive accuracy of 93.8% and 95.3%, respectively. The functional annotation analysis and database search indicate that our novel predictions are worthy of future experimental validation. The new methods will be useful supplementary tools for the future proteomics studies.

关键词： Imbalance problem protein-protein interactions sequence-based support vector machine.

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sequence-based context-aware music recommendation

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INFORMATION RETRIEVAL JOURNAL 2018年第2-3期21卷 230-252页

作者： Wang, Dongjing Deng, Shuiguang Xu, Guandong Zhejiang Univ Coll Comp Sci & Technol Hangzhou Zhejiang Peoples R China Univ Technol Sydney Adv Analyt Inst Sydney NSW Australia

Contextual factors greatly affect users' preferences for music, so they can benefit music recommendation and music retrieval. However, how to acquire and utilize the contextual information is still facing challenges. This paper proposes a novel approach for context-aware music recommendation, which infers users' preferences for music, and then recommends music pieces that fit their real-time requirements. Specifically, the proposed approach first learns the low dimensional representations of music pieces from users' music listening sequences using neural network models. based on the learned representations, it then infers and models users' general and contextual preferences for music from users' historical listening records. Finally, music pieces in accordance with user's preferences are recommended to the target user. Extensive experiments are conducted on real world datasets to compare the proposed method with other state-of-the-art recommendation methods. The results demonstrate that the proposed method significantly outperforms those baselines, especially on sparse data.

关键词： Recommender systems Context-aware sequence-based Embedding Neural network

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sequence-based Bidirectional Merge Map-Matching Algorithm for Simplified Road Network 1

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30th Canadian Conference on Artificial Intelligence (AI)

作者： Cui, Ge Ma, Chunlin Wang, Xin Univ Calgary Dept Geomat Engn 2500 Univ Dr NW Calgary AB T2N 1N4 Canada Geomat Ctr Xinjiang Uygur Autonomous Reg Urumqi 830002 Xinjiang Peoples R China

ISBN: (数字)9783319573519

ISBN: (纸本)9783319573519;9783319573502

Current map matching algorithms do not perform well for simplified road networks. In this paper, we propose a sequence-based bidirectional merge algorithm called SBBM for map matching on the simplified road network. SBBM splits a GPS trajectory into a set of sequences first, and then merges the sequences from the one with the highest confidence. During the merging procedure, the algorithm would address the problems of outliers. Last, an experiment is conducted based on GeoLife dataset in Beijing, China, and the result shows that the proposed algorithm in this paper performs better than Passby algorithm and incremental algorithm.

关键词： Map matching Simplified road network sequence-based

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sequence-based Motion Recognition Assisted Pedestrian Dead Reckoning Using a Smartphone 6th

Sequence-Based Motion Recognition Assisted Pedestrian Dead R...

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6th China Satellite Navigation Conference (CSNC)

作者： Liu, Chengxuan Pei, Ling Qian, Jiuchao Wang, Lin Liu, Peilin Yu, Wenxian Shanghai Jiao Tong Univ Sch Elect Informat & Elect Engn Shanghai Key Lab Nav & Locat Based Serv Shanghai 200240 Peoples R China

ISBN: (纸本)9783662466322;9783662466315

PDR (Pedestrian Dead Reckoning) is a relative positioning method using step length and heading. However, the step length is varying while a pedestrian performs different motions. With estimated motion state, the adaptive step length model can be applied. Considering that human motion is a continuous procedure, in this paper, we propose a sequence-based motion recognition method which estimates the motion states from a sequence of data. In contrast with traditional classifiers, this paper deploys a HMM (Hidden Markov Model) to infer the state labels of sequence motion. And with the assist of motion recognition, a 3D indoor pedestrian localization is presented. Experimental results show that the classification accuracy of sequence-based motion recognition is improved comparing to that of using Naive Bayes classifier on standalone motion states. Furthermore, the positioning accuracy of a pedestrian in indoor environments is promoted using proposed method in this paper. The mean positioning error is reduced from 0.78 to 0.30 m. The 50th and 95th percentile errors are also cut down in the test within a typical office building.

关键词： Motion recognition sequence-based PDR 3D localization

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SPiCE: a web-based tool for sequence-based protein classification and exploration

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BMC BIOINFORMATICS 2014年第1期15卷 1-10页

作者： van den Berg, Bastiaan A. Reinders, Marcel J. T. Roubos, Johannes A. de Ridder, Dick Delft Univ Technol Fac Elect Engn Math & Comp Sci Delft Bioinformat Lab Dept Intelligent Syst NL-2628 CD Delft Netherlands DSM Biotechnol Ctr Delft Netherlands Netherlands Bioinformat Ctr Nijmegen Netherlands Kluyver Ctr Genom Ind Fermentat Delft Netherlands

Background: Amino acid sequences and features extracted from such sequences have been used to predict many protein properties, such as subcellular localization or solubility, using classifier algorithms. Although software tools are available for both feature extraction and classifier construction, their application is not straightforward, requiring users to install various packages and to convert data into different formats. This lack of easily accessible software hampers quick, explorative use of sequence-based classification techniques by biologists. Results: We have developed the web-based software tool SPiCE for exploring sequence-based features of proteins in predefined classes. It offers data upload/download, sequence-based feature calculation, data visualization and protein classifier construction and testing in a single integrated, interactive environment. To illustrate its use, two example datasets are included showing the identification of differences in amino acid composition between proteins yielding low and high production levels in fungi and low and high expression levels in yeast, respectively. Conclusions: SPiCE is an easy-to-use online tool for extracting and exploring sequence-based features of sets of proteins, allowing non-experts to apply advanced classification techniques. The tool is available at http://***/spice.

关键词： sequence-based Data visualization and exploration Protein feature extraction Protein classification

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SPPPred: sequence-based Protein-Peptide Binding Residue Prediction Using Genetic Programming and Ensemble Learning

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第3期20卷 2029-2040页

作者： Shafiee, Shima Fathi, Abdolhossein Taherzadeh, Ghazaleh Razi Univ Dept Comp Engn & Informat Technol Kermanshah *** Iran Wilkes Univ Dept Math & Comp Sci Wilkes Barre PA 18766 USA

Peptide-binding proteins play significant roles in various applications such as gene expression, metabolism, signal transmission, DNA (Deoxyribose Nucleic Acid) repair, and replication. Investigating the binding residues in protein-peptide complexes, especially from their sequence only, is challenging experimentally and computationally. Although several computational approaches have been introduced to determine and predict these binding residues, there is still ample room to improve the prediction performance. In this work, we introduce a novel ensemble machine learning-based approach called SPPPred (sequence-based Protein-Peptide binding residue Prediction) to predict protein-peptide binding residues. First, we extract relevant sequential information and employ genetic programming algorithm for feature construction to find more distinctive features. We then, in the next step, build an ensemble-based machine learning classifier to predict binding residues. The proposed method shows consistent and comparable performance on both ten-fold cross-validation and independent test set. Furthermore, SPPPred yields F-Measure (F-M), Accuracy(ACC), and Matthews' Correlation Coefficient (MCC) of 0.310, 0.949, and 0.230 on the independent test set, respectively, which outperforms other competing methods by approximately up to 9% on the independent test set. SPPPred is publicly available https://***/GTaherzadeh/***.

关键词： Proteins Feature extraction Prediction algorithms Classification algorithms Support vector machines Amino acids Peptides Binding residue prediction ensemble learning genetic programming protein-peptide interaction sequence-based

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TargetDBP: Accurate DNA-Binding Protein Prediction Via sequence-based Multi-View Feature Learning

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2020年第4期17卷 1419-1429页

作者： Hu, Jun Zhou, Xiao-Gen Zhu, Yi-Heng Yu, Dong-Jun Zhang, Gui-Jun Zhejiang Univ Technol Coll Informat Engn Hangzhou 310023 Peoples R China Nanjing Univ Sci & Technol Sch Comp Sci & Engn Nanjing 210094 Peoples R China

Accurately identifying DNA-binding proteins (DBPs) from protein sequence information is an important but challenging task for protein function annotations. In this paper, we establish a novel computational method, named TargetDBP, for accurately targeting DBPs from primary sequences. In TargetDBP, four single-view features, i.e., AAC (Amino Acid Composition), PsePSSM (Pseudo Position-Specific Scoring Matrix), PsePRSA (Pseudo Predicted Relative Solvent Accessibility), and PsePPDBS (Pseudo Predicted Probabilities of DNA-Binding Sites), are first extracted to represent different base features, respectively. Second, differential evolution algorithm is employed to learn the weights of four base features. Using the learned weights, we weightedly combine these base features to form the original super feature. An excellent subset of the super feature is then selected by using a suitable feature selection algorithm SVM-REF+CBR (Support Vector Machine Recursive Feature Elimination with Correlation Bias Reduction). Finally, the prediction model is learned via using support vector machine on the selected feature subset. We also construct a new gold-standard and non-redundant benchmark dataset from PDB database to evaluate and compare the proposed TargetDBP with other existing predictors. On this new dataset, TargetDBP can achieve higher performance than other state-of-the-art predictors. The TargetDBP web server and datasets are freely available at >http://***/bioinf/targetdbp/ for academic use.

关键词： DNA-binding protein prediction sequence-based differential evolution feature selection support vector machine

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Experimental and Computational Challenges from Array-based to sequence-based ChIP Techniques

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CURRENT BIOINFORMATICS 2012年第4期7卷 447-453页

作者： Lan, Xun Jin, Victor X. Ohio State Univ Dept Biomed Informat Columbus OH 43210 USA

Transcriptional regulation is a key step to control the level of mRNA formed. Recent view of transcriptional regulation has evolved from a one-dimensional mode, i.e. RNA Polymerase II assembles with general transcription factors, and cis-regulatory elements (CREs) interact with transcription factors, to a much complex multiple-dimensional mode, involving combinatorial interactions between transcription factors and regulatory sequences, chromatin structure, histone modifications, DNA methylation. High throughput experimental technologies, such as array-based ChIP-chip and sequencing-based ChIP-seq, have been developed to survey in vivo transcription factor binding sites and histone modifications. Despite many efforts have been made to analyze and interpret the data, challenges remain in many aspects of both experimental protocols and computational analyses. For example, how to determine the optimized number of PCR cycles? How to normalize multiple datasets from multiple experiments? How to utilize the large number of unmapped and multiple mapped tags in ChIP-seq experiment? This review focuses on issues emerged in high throughput data processing and discusses advantages and disadvantages of various strategies.

关键词： Array-based ChiP ChiP-seq cis-regulatory elements (CREs) omics sequence-based

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Cost-effective sequence-based nonhuman primate MHC class I genotyping from RNA

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METHODS 2009年第1期49卷 11-17页

作者： Karl, Julie A. Wiseman, Roger W. O'Connor, David H. Univ Wisconsin Wisconsin Natl Primate Res Ctr Madison WI 53706 USA Univ Wisconsin Dept Pathol & Lab Med Madison WI 53706 USA

The major histocompatibility complex (MHC) class I region of nonhuman primates (NHPs) is highly polymorphic and has undergone complex segmental duplications such that the number of expressed genes differs between individuals. In addition the relative abundance of transcripts varies more than 100-fold between NHP class I genes. This unparalleled complexity makes rapid, efficient class I genotyping difficult for NHPs. The 'gold standard' of cDNA library construction, screening and sequencing is both costly and labor-intensive. Several rapid genotyping methods have been utilized, but all require some degree of prior sequence knowledge. Here, we describe a method for sequence-based MHC class I genotyping which reduces cost by (1) pooling molecularly barcoded class I cDNA-PCR amplicons for cloning and (2) targeting sequencing of a region of concentrated polymorphism spanning the two exons encoding the peptide binding domain. This method can efficiently genotype both known and novel MHC class I alleles. In addition, full-length cDNA amplicons with novel sequences can be resequenced in their entireties to expand the repertoire of characterized MHC class I sequences for NHPs. (C) 2009 Elsevier Inc. All rights reserved.

关键词： Nonhuman primates Major histocompatibility complex Class I sequence-based Genotyping

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Simplified sequence-based method for ATP-binding prediction using contextual local evolutionary conservation

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ALGORITHMS FOR MOLECULAR BIOLOGY 2014年第1期9卷 1-13页

作者： Fang, Chun Noguchi, Tamotsu Yamana, Hayato Engn Waseda Univ Dept Comp Sci Tokyo Japan CBRC Tokyo Japan Meiji Pharmaceut Univ Tokyo Japan

Background: Identifying ligand-binding sites is a key step to annotate the protein functions and to find applications in drug design. Now, many sequence-based methods adopted various predicted results from other classifiers, such as predicted secondary structure, predicted solvent accessibility and predicted disorder probabilities, to combine with position-specific scoring matrix (PSSM) as input for binding sites prediction. These predicted features not only easily result in high-dimensional feature space, but also greatly increased the complexity of algorithms. Moreover, the performances of these predictors are also largely influenced by the other classifiers. Results: In order to verify that conservation is the most powerful attribute in identifying ligand-binding sites, and to show the importance of revising PSSM to match the detailed conservation pattern of functional site in prediction, we have analyzed the Adenosine-5'-triphosphate (ATP) ligand as an example, and proposed a simple method for ATP-binding sites prediction, named as CLCLpred (Contextual Local evolutionary Conservation-based method for Ligand-binding prediction). Our method employed no predicted results from other classifiers as input;all used features were extracted from PSSM only. We tested our method on 2 separate data sets. Experimental results showed that, comparing with other 9 existing methods on the same data sets, our method achieved the best performance. Conclusions: This study demonstrates that: 1) exploiting the signal from the detailed conservation pattern of residues will largely facilitate the prediction of protein functional sites;and 2) the local evolutionary conservation enables accurate prediction of ATP-binding sites directly from protein sequence.

关键词： ATP-binding site sequence-based Local evolutionary conservation

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