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Inferring a protein interaction map of Mycobacterium tuberculosis based on sequences and interologs

BMC BIOINFORMATICS 2012年第s7期13卷 S6-S6页

作者： Liu, Zhi-Ping Wang, Jiguang Qiu, Yu-Qing Leung, Ross K. K. Zhang, Xiang-Sun Tsui, Stephen K. W. Chen, Luonan Chinese Acad Sci Shanghai Inst Biol Sci SIBS Novo Nordisk Translat Res Ctr PreDiabet Key Lab Syst Biol Shanghai 200031 Peoples R China Chinese Acad Sci Natl Ctr Math & Interdisciplinary Sci Beijing 100190 Peoples R China Chinese Acad Sci Beijing Inst Genom Beijing 100029 Peoples R China Chinese Acad Sci Acad Math & Syst Sci Beijing 100190 Peoples R China Chinese Univ Hong Kong Sch Biomed Sci Hong Kong Bioinformat Ctr Shatin Hong Kong Peoples R China Univ Tokyo Inst Ind Sci Collaborat Res Ctr Innovat Math Modelling Tokyo 1538505 Japan

Background: Mycobacterium tuberculosis is an infectious bacterium posing serious threats to human health. Due to the difficulty in performing molecular biology experiments to detect protein interactions, reconstruction of a protein interaction map of M. tuberculosis by computational methods will provide crucial information to understand the biological processes in the pathogenic microorganism, as well as provide the framework upon which new therapeutic approaches can be developed. Results: In this paper, we constructed an integrated M. tuberculosis protein interaction network by machine learning and ortholog-based methods. Firstly, we built a support vector machine (SVM) method to infer the protein interactions of M. tuberculosis H37Rv by gene sequence information. We tested our predictors in Escherichia coli and mapped the genetic codon features underlying its protein interactions to M. tuberculosis. Moreover, the documented interactions of 14 other species were mapped to the interactome of M. tuberculosis by the interolog method. The ensemble protein interactions were validated by various functional relationships, i.e., gene coexpression, evolutionary relationship and functional similarity, extracted from heterogeneous data sources. The accuracy and validation demonstrate the effectiveness and efficiency of our framework. Conclusions: A protein interaction map of M. tuberculosis is inferred from genetic codons and interologs. The prediction accuracy and numerically experimental validation demonstrate the effectiveness and efficiency of our method. Furthermore, our methods can be straightforwardly extended to infer the protein interactions of other bacterial species.

关键词： Tuberculosis support vector machine Gene Ontology support vector machine classifier Area Under Curve

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Antepartum fetal heart rate feature extraction and classification using empirical mode decomposition and support vector machine

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BIOMEDICAL ENGINEERING ONLINE 2011年第1期10卷 1-15页

作者： Krupa, Niranjana Ali, Mohd M. A. Zahedi, Edmond Ahmed, Shuhaila Hassan, Fauziah M. Univ Kebangsaan Malaysia Dept Elect Elect & Syst Engn Bangi 43600 Malaysia Sharif Univ Technol Sch Elect Engn Tehran Iran Univ Kebangsaan Malaysia Med Ctr Dept Obstet & Gynecol Cheras Malaysia Cyberjaya Univ Coll Med Sci Dept Obstet & Gynecol Cyberjaya Malaysia

Background: Cardiotocography (CTG) is the most widely used tool for fetal surveillance. The visual analysis of fetal heart rate (FHR) traces largely depends on the expertise and experience of the clinician involved. Several approaches have been proposed for the effective interpretation of FHR. In this paper, a new approach for FHR feature extraction based on empirical mode decomposition (EMD) is proposed, which was used along with support vector machine (SVM) for the classification of FHR recordings as 'normal' or 'at risk'. Methods: The FHR were recorded from 15 subjects at a sampling rate of 4 Hz and a dataset consisting of 90 randomly selected records of 20 minutes duration was formed from these. All records were labelled as 'normal' or 'at risk' by two experienced obstetricians. A training set was formed by 60 records, the remaining 30 left as the testing set. The standard deviations of the EMD components are input as features to a support vector machine (SVM) to classify FHR samples. Results: For the training set, a five-fold cross validation test resulted in an accuracy of 86% whereas the overall geometric mean of sensitivity and specificity was 94.8%. The Kappa value for the training set was .923. Application of the proposed method to the testing set (30 records) resulted in a geometric mean of 81.5%. The Kappa value for the testing set was .684. Conclusions: Based on the overall performance of the system it can be stated that the proposed methodology is a promising new approach for the feature extraction and classification of FHR signals.

关键词： support vector machine support vector machine classifier Empirical Mode Decomposition Fetal Heart Rate Intrinsic Mode Function

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Novel climate classification based on the information of solar radiation intensity: An application to the climatic zoning of Morocco

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ENERGY CONVERSION AND MANAGEMENT 2021年 247卷 114770-114770页

作者： Anas, Hajou Youness, El Mghouchi Halima, Yakoubi Nawal, Abdou Mohamed, Chaoui Moulay Ismail Univ Fac Sci Meknes Meknes Morocco Moulay Ismail Univ ENSAM Meknes Morocco

Prior knowledge of solar radiation potential is a necessity for the decision making and the establishment of solar projects. Current prediction methods are site-dependent and their performance outside the area of application is questionable. Therefore, a solar radiation zoning method using a combination of supervised and unsupervised machine learning techniques and satellite gridded data is proposed. The method uses Agglomerative Hierarchical Clustering (AHC) following the ward's method based on the monthly mean solar insolation on a horizontal surface (H), and support vector machine classifier (SVM-C) to assist AHC to effectively classify all the areas based on solar radiation data plus temperature, humidity, wind speed and precipitation. Four solar radiation zones have been established. The zones are homogeneous in space and have distinctive solar and meteorological characteristics. The t-distributed Stochastic Neighbor Embedding (t-SNE) is applied to the obtained zones to distinguish trends within solar radiation zones based on temperature, humidity and wind speed, and a total of 8 sub-zones with distinctive meteorological characteristics have been identified. The resulting solar radiation zones and sub-zones cover all Morocco can help for preliminary assessment and decision making, especially for areas with no solar radiation records.

关键词： Solar radiation forecasting Solar radiation zoning Agglomerative hierarchical clustering support vector machine classifier Morocco

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Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes

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BMC BIOINFORMATICS 2009年第1期10卷 1-16页

作者： Maulik, Ujjwal Mukhopadhyay, Anirban Bandyopadhyay, Sanghamitra Jadavpur Univ Dept Comp Sci & Engn Kolkata 700032 India Univ Kalyani Dept Comp Sci & Engn Kalyani 741235 W Bengal India Indian Stat Inst Inst Machine Intelligence Kolkata 700108 India

Background: The landscape of biological and biomedical research is being changed rapidly with the invention of microarrays which enables simultaneous view on the transcription levels of a huge number of genes across different experimental conditions or time points. Using microarray data sets, clustering algorithms have been actively utilized in order to identify groups of co-expressed genes. This article poses the problem of fuzzy clustering in microarray data as a multiobjective optimization problem which simultaneously optimizes two internal fuzzy cluster validity indices to yield a set of Pareto-optimal clustering solutions. Each of these clustering solutions possesses some amount of information regarding the clustering structure of the input data. Motivated by this fact, a novel fuzzy majority voting approach is proposed to combine the clustering information from all the solutions in the resultant Pareto-optimal set. This approach first identifies the genes which are assigned to some particular cluster with high membership degree by most of the Pareto-optimal solutions. Using this set of genes as the training set, the remaining genes are classified by a supervised learning algorithm. In this work, we have used a support vector machine (SVM) classifier for this purpose. Results: The performance of the proposed clustering technique has been demonstrated on five publicly available benchmark microarray data sets, viz., Yeast Sporulation, Yeast Cell Cycle, Arabidopsis Thaliana, Human Fibroblasts Serum and Rat Central Nervous System. Comparative studies of the use of different SVM kernels and several widely used microarray clustering techniques are reported. Moreover, statistical significance tests have been carried out to establish the statistical superiority of the proposed clustering approach. Finally, biological significance tests have been carried out using a web based gene annotation tool to show that the proposed method is able to produce biologically relevant

关键词： support vector machine Radial Basis Function Multiobjective Optimization support vector machine classifier Radial Basis Function Kernel

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Self-training in significance space of support vectors for imbalanced biomedical event data

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BMC BIOINFORMATICS 2015年第7-Sup期16卷 S6-S6页

作者： Munkhdalai, Tsendsuren Namsrai, Oyun-Erdene Ryu, Keun Ho Chungbuk Natl Univ Database Bioinformat Lab Cheongju South Korea Natl Univ Mongolia Sch Informat Technol Ulaanbaatar Mongolia

Background: Pairwise relationships extracted from biomedical literature are insufficient in formulating biomolecular interactions. Extraction of complex relations (namely, biomedical events) has become the main focus of the text-mining community. However, there are two critical issues that are seldom dealt with by existing systems. First, an annotated corpus for training a prediction model is highly imbalanced. Second, supervised models trained on only a single annotated corpus can limit system performance. Fortunately, there is a large pool of unlabeled data containing much of the domain background that one can exploit. Results: In this study, we develop a new semi-supervised learning method to address the issues outlined above. The proposed algorithm efficiently exploits the unlabeled data to leverage system performance. We furthermore extend our algorithm to a two-phase learning framework. The first phase balances the training data for initial model induction. The second phase incorporates domain knowledge into the event extraction model. The effectiveness of our method is evaluated on the Genia event extraction corpus and a PubMed document pool. Our method can identify a small subset of the majority class, which is sufficient for building a well-generalized prediction model. It outperforms the traditional self-training algorithm in terms of f measure. Our model, based on the training data and the unlabeled data pool, achieves comparable performance to the state-of-the-art systems that are trained on a larger annotated set consisting of training and evaluation data.

关键词： support vector machine support vector machine classifier support vector machine Model Unlabeled Data Event Extraction

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Network motif-based identification of transcription factor-target gene relationships by integrating multi-source biological data

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BMC BIOINFORMATICS 2008年第1期9卷 203-203页

作者： Zhang, Yuji Xuan, Jianhua de los Reyes, Benilo G. Clarke, Robert Ressom, Habtom W. Virginia Polytech Inst & State Univ Dept Elect & Comp Engn Arlington VA USA Univ Maine Sch Biol & Ecol Orono ME 04469 USA Georgetown Univ Lombardi Comprehens Canc Ctr Washington DC USA

Background: Integrating data from multiple global assays and curated databases is essential to understand the spatiotemporal interactions within cells. Different experiments measure cellular processes at various widths and depths, while databases contain biological information based on established facts or published data. Integrating these complementary datasets helps infer a mutually consistent transcriptional regulatory network (TRN) with strong similarity to the structure of the underlying genetic regulatory modules. Decomposing the TRN into a small set of recurring regulatory patterns, called network motifs (NM), facilitates the inference. Identifying NMs defined by specific transcription factors (TF) establishes the framework structure of a TRN and allows the inference of TF-target gene relationship. This paper introduces a computational framework for utilizing data from multiple sources to infer TF-target gene relationships on the basis of NMs. The data include time course gene expression profiles, genome-wide location analysis data, binding sequence data, and gene ontology (GO) information. Results: The proposed computational framework was tested using gene expression data associated with cell cycle progression in yeast. Among 800 cell cycle related genes, 85 were identified as candidate TFs and classified into four previously defined NMs. The NMs for a subset of TFs are obtained from literature. support vector machine (SVM) classifiers were used to estimate NMs for the remaining TFs. The potential downstream target genes for the TFs were clustered into 34 biologically significant groups. The relationships between TFs and potential target gene clusters were examined by training recurrent neural networks whose topologies mimic the NMs to which the TFs are classified. The identified relationships between TFs and gene clusters were evaluated using the following biological validation and statistical analyses: (1) Gene set enrichment analysis (GSEA) to evaluate th

关键词： Gene Ontology Root Mean Square Error support vector machine classifier Recurrent Neural Network Network Motif

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Computational identification of ubiquitylation sites from protein sequences

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BMC BIOINFORMATICS 2008年第1期9卷 310-310页

作者： Tung, Chun-Wei Ho, Shinn-Ying Natl Chiao Tung Univ Inst Bioinformat Hsinchu 300 Taiwan Natl Chiao Tung Univ Dept Biol Sci & Technol Hsinchu 300 Taiwan

Background: Ubiquitylation plays an important role in regulating protein functions. Recently, experimental methods were developed toward effective identification of ubiquitylation sites. To efficiently explore more undiscovered ubiquitylation sites, this study aims to develop an accurate sequence-based prediction method to identify promising ubiquitylation sites. Results: We established an ubiquitylation dataset consisting of 157 ubiquitylation sites and 3676 putative non-ubiquitylation sites extracted from 105 proteins in the UbiProt database. This study first evaluates promising sequence-based features and classifiers for the prediction of ubiquitylation sites by assessing three kinds of features (amino acid identity, evolutionary information, and physicochemical property) and three classifiers (support vector machine, k-nearest neighbor, and NaiveBayes). Results show that the set of used 531 physicochemical properties and support vector machine (SVM) are the best kind of features and classifier respectively that their combination has a prediction accuracy of 72.19% using leave-one-out cross-validation. Consequently, an informative physicochemical property mining algorithm (IPMA) is proposed to select an informative subset of 531 physicochemical properties. A prediction system UbiPred was implemented by using an SVM with the feature set of 31 informative physicochemical properties selected by IPMA, which can improve the accuracy from 72.19% to 84.44%. To further analyze the informative physicochemical properties, a decision tree method C5.0 was used to acquire if-then rule-based knowledge of predicting ubiquitylation sites. UbiPred can screen promising ubiquitylation sites from putative non-ubiquitylation sites using prediction scores. By applying UbiPred, 23 promising ubiquitylation sites were identified from an independent dataset of 3424 putative non-ubiquitylation sites, which were also validated by using the obtained prediction rules. Conclusion: We have prop

关键词： support vector machine Prediction Accuracy Training Dataset Amino Acid Identity support vector machine classifier

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Automatic detection of exonic splicing enhancers (ESEs) using SVMs

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BMC BIOINFORMATICS 2008年第1期9卷 1-15页

作者： Mersch, Britta Gepperth, Alexander Suhai, Sandor Hotz-Wagenblatt, Agnes German Canc Res Ctr Dept Mol Biophys D-69120 Heidelberg Germany Honda Res Inst Europe GmbH D-63073 Offenbach Germany

Background: Exonic splicing enhancers (ESEs) activate nearby splice sites and promote the inclusion (vs. exclusion) of exons in which they reside, while being a binding site for SR proteins. To study the impact of ESEs on alternative splicing it would be useful to have a possibility to detect them in exons. Identifying SR protein-binding sites in human DNA sequences by machine learning techniques is a formidable task, since the exon sequences are also constrained by their functional role in coding for proteins. Results: The choice of training examples needed for machine learning approaches is difficult since there are only few exact locations of human ESEs described in the literature which could be considered as positive examples. Additionally, it is unclear which sequences are suitable as negative examples. Therefore, we developed a motif-oriented data-extraction method that extracts exon sequences around experimentally or theoretically determined ESE patterns. Positive examples are restricted by heuristics based on known properties of ESEs, e.g. location in the vicinity of a splice site, whereas negative examples are taken in the same way from the middle of long exons. We show that a suitably chosen SVM using optimized sequence kernels (e.g., combined oligo kernel) can extract meaningful properties from these training examples. Once the classifier is trained, every potential ESE sequence can be passed to the SVM for verification. Using SVMs with the combined oligo kernel yields a high accuracy of about 90 percent and well interpretable parameters. Conclusion: The motif-oriented data-extraction method seems to produce consistent training and test data leading to good classification rates and thus allows verification of potential ESE motifs. The best results were obtained using an SVM with the combined oligo kernel, while oligo kernels with oligomers of a certain length could be used to extract relevant features.

关键词： support vector machine Splice Site support vector machine classifier Markov Chain Model Exonic Splice Enhancer

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NOXclass: prediction of protein-protein interaction types

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BMC BIOINFORMATICS 2006年第1期7卷 27-27页

作者： Zhu, HB Domingues, FS Sommer, I Lengauer, T Max Planck Inst Informat D-66123 Saarbrucken Germany

Background: Structural models determined by X-ray crystallography play a central role in understanding protein-protein interactions at the molecular level. Interpretation of these models requires the distinction between non-specific crystal packing contacts and biologically relevant interactions. This has been investigated previously and classification approaches have been proposed. However, less attention has been devoted to distinguishing different types of biological interactions. These interactions are classified as obligate and non-obligate according to the effect of the complex formation on the stability of the protomers. So far no automatic classification methods for distinguishing obligate, non-obligate and crystal packing interactions have been made available. Results: Six interface properties have been investigated on a dataset of 243 protein interactions. The six properties have been combined using a support vector machine algorithm, resulting in NOXclass, a classifier for distinguishing obligate, non-obligate and crystal packing interactions. We achieve an accuracy of 91.8% for the classification of these three types of interactions using a leave-one-out cross-validation procedure. Conclusion: NOXclass allows the interpretation and analysis of protein quaternary structures. In particular, it generates testable hypotheses regarding the nature of protein-protein interactions, when experimental results are not available. We expect this server will benefit the users of protein structural models, as well as protein crystallographers and NMR spectroscopists. A web server based on the method and the datasets used in this study are available at http://***/.

关键词： Amino Acid Composition Protein Data Bank support vector machine classifier Interface Property Conservation Score

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LBP-based information assisted intelligent system for COVID-19 identification

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COMPUTERS IN BIOLOGY AND MEDICINE 2021年 134卷 104453-104453页

作者： Maheshwari, Shishir Sharma, Rishi Raj Kumar, Mohit Birla Inst Technol & Sci Discipline Elect & Elect Engn Pilani 333031 Rajasthan India Def Inst Adv Technol Dept Elect Engn Pune 411025 Maharashtra India Indian Inst Technol Kanpur NAF Dept Kanpur Uttar Pradesh India

A real-time COVID-19 detection system is an utmost requirement of the present situation. This article presents a chest X-ray image-based automated COVID-19 detection system which can be employed with the RT-PCR test to improve the diagnosis rate. In the proposed approach, the textural features are extracted from the chest X-ray images and local binary pattern (LBP) based images. Further, the image-based and LBP image-based features are jointly investigated. Thereafter, highly discriminatory features are provided to the classifier for developing an automated model for COVID-19 identification. The performance of the proposed approach is investigated over 2905 chest X-ray images of normal, pneumonia, and COVID-19 infected persons on various class combinations to analyze the robustness. The developed method achieves 97.97% accuracy (acc) and 99.88% sensitivity (sen) for classifying COVID-19 X-ray images against pneumonia infected and normal person's X-ray images. It attains 98.91% acc and 99.33% sen for COVID-19 X-ray against the normal X-ray classification. This method can be employed to assist the radiologists during mass screening for fast, accurate, and contact-free COVID-19 diagnosis.

关键词： COVID-19 Local binary pattern Entropy Gray level co-occurrence matrix support vector machine classifier Chest X-ray

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