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A comparative study of cell classifiers for image-based high-throughput screening

BMC BIOINFORMATICS 2014年第1期15卷 1-10页

作者： Abbas, Syed Saiden Dijkstra, Tjeerd M. H. Heskes, Tom Radboud Univ Nijmegen Inst Comp & Informat Sci NL-6525 ED Nijmegen Netherlands Eindhoven Univ Technol Dept Elect Engn NL-5600 MB Eindhoven Netherlands

Background: Millions of cells are present in thousands of images created in high-throughput screening (HTS). Biologists could classify each of these cells into a phenotype by visual inspection. But in the presence of millions of cells this visual classification task becomes infeasible. Biologists train classification models on a few thousand visually classified example cells and iteratively improve the training data by visual inspection of the important misclassified phenotypes. Classification methods differ in performance and performance evaluation time. We present a comparative study of computational performance of gentle boosting, joint boosting CellProfiler Analyst (CPA), support vector machines (linear and radial basis function) and linear discriminant analysis (LDA) on two data sets of HT29 and HeLa cancer cells. Results: For the HT29 data set we find that gentle boosting, SVM (linear) and SVM (RBF) are close in performance but SVM (linear) is faster than gentle boosting and SVM (RBF). For the HT29 data set the average performance difference between SVM (RBF) and SVM (linear) is 0.42%. For the HeLa data set we find that SVM (RBF) outperforms other classification methods and is on average 1.41% better in performance than SVM (linear). Conclusions: Our study proposes SVM (linear) for iterative improvement of the training data and SVM (RBF) for the final classifier to classify all unlabeled cells in the whole data set.

关键词： support vector machine Radial Basis Function Linear Discriminant Analysis HT29 Cell support vector machine classifier

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Using machine learning to speed up manual image annotation: application to a 3D imaging protocol for measuring single cell gene expression in the developing C. elegans embryo

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BMC BIOINFORMATICS 2010年第1期11卷 1-13页

作者： Aydin, Zafer Murray, John I. Waterston, Robert H. Noble, William S. Univ Washington Dept Genome Sci Seattle WA 98195 USA Univ Washington Dept Comp Sci & Engn Seattle WA 98195 USA

Background: Image analysis is an essential component in many biological experiments that study gene expression, cell cycle progression, and protein localization. A protocol for tracking the expression of individual C. elegans genes was developed that collects image samples of a developing embryo by 3-D time lapse microscopy. In this protocol, a program called StarryNite performs the automatic recognition of fluorescently labeled cells and traces their lineage. However, due to the amount of noise present in the data and due to the challenges introduced by increasing number of cells in later stages of development, this program is not error free. In the current version, the error correction (i.e., editing) is performed manually using a graphical interface tool named AceTree, which is specifically developed for this task. For a single experiment, this manual annotation task takes several hours. Results: In this paper, we reduce the time required to correct errors made by StarryNite. We target one of the most frequent error types (movements annotated as divisions) and train a support vector machine (SVM) classifier to decide whether a division call made by StarryNite is correct or not. We show, via cross-validation experiments on several benchmark data sets, that the SVM successfully identifies this type of error significantly. A new version of StarryNite that includes the trained SVM classifier is available at http://***. Conclusions: We demonstrate the utility of a machine learning approach to error annotation for StarryNite. In the process, we also provide some general methodologies for developing and validating a classifier with respect to a given pattern recognition task.

关键词： support vector machine Feature Selection support vector machine classifier Indexing Error Parent Nucleus

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On the statistical assessment of classifiers using DNA microarray data

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BMC BIOINFORMATICS 2006年第1期7卷 387-387页

作者： Ancona, N. Maglietta, R. Piepoli, A. D'Addabbo, A. Cotugno, R. Savino, M. Liuni, S. Carella, M. Pesole, G. Perri, F. CNR Ist Studi Sistemi Intelligenti Automaz I-70126 Bari Italy Casa Sollievo Sofferenza Osped IRCCS Gen Med Serv Unita Operat Gastroenterol I-71013 San Giovanni Rotondo FG Italy Univ Bari Dipartmento Biochim & Biol Mol I-70126 Bari Italy CNR Sede Bari Ist Tecnol Biomed I-70126 Bari Italy

Background: In this paper we present a method for the statistical assessment of cancer predictors which make use of gene expression profiles. The methodology is applied to a new data set of microarray gene expression data collected in Casa Sollievo della Sofferenza Hospital, Foggia - Italy. The data set is made up of normal (22) and tumor (25) specimens extracted from 25 patients affected by colon cancer. We propose to give answers to some questions which are relevant for the automatic diagnosis of cancer such as: Is the size of the available data set sufficient to build accurate classifiers? What is the statistical significance of the associated error rates? In what ways can accuracy be considered dependant on the adopted classification scheme? How many genes are correlated with the pathology and how many are sufficient for an accurate colon cancer classification? The method we propose answers these questions whilst avoiding the potential pitfalls hidden in the analysis and interpretation of microarray data. Results: We estimate the generalization error, evaluated through the Leave-K-Out Cross Validation error, for three different classification schemes by varying the number of training examples and the number of the genes used. The statistical significance of the error rate is measured by using a permutation test. We provide a statistical analysis in terms of the frequencies of the genes involved in the classification. Using the whole set of genes, we found that the Weighted Voting Algorithm (WVA) classifier learns the distinction between normal and tumor specimens with 25 training examples, providing e = 21% (p = 0.045) as an error rate. This remains constant even when the number of examples increases. Moreover, Regularized Least Squares (RLS) and support vector machines (SVM) classifiers can learn with only 15 training examples, with an error rate of e = 19% (p = 0.035) and e = 18% (p = 0.037) respectively. Moreover, the error rate decreases as the training set

关键词： support vector machine support vector machine classifier Generalization Error Mitotic Checkpoint Random classifier

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A graphical model approach to automated classification of protein subcellular location patterns in multi-cell images

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BMC BIOINFORMATICS 2006年第1期7卷 90-90页

作者： Chen, Shann-Ching Murphy, Robert F. Carnegie Mellon Univ Dept Biomed Engn Pittsburgh PA 15213 USA Carnegie Mellon Univ Ctr Bioimage Informat Pittsburgh PA 15213 USA Carnegie Mellon Univ Dept Biol Sci Pittsburgh PA 15213 USA Carnegie Mellon Univ Ctr Automated Learning & Discovery Pittsburgh PA 15213 USA

Background: Knowledge of the subcellular location of a protein is critical to understanding how that protein works in a cell. This location is frequently determined by the interpretation of fluorescence microscope images. In recent years, automated systems have been developed for consistent and objective interpretation of such images so that the protein pattern in a single cell can be assigned to a known location category. While these systems perform with nearly perfect accuracy for single cell images of all major subcellular structures, their ability to distinguish subpatterns of an organelle ( such as two Golgi proteins) is not perfect. Our goal in the work described here was to improve the ability of an automated system to decide which of two similar patterns is present in a field of cells by considering more than one cell at a time. Since cells displaying the same location pattern are often clustered together, considering multiple cells may be expected to improve discrimination between similar patterns. Results: We describe how to take advantage of information on experimental conditions to construct a graphical representation for multiple cells in a field. Assuming that a field is composed of a small number of classes, the classification accuracy can be improved by allowing the computed probability of each pattern for each cell to be influenced by the probabilities of its neighboring cells in the model. We describe a novel way to allow this influence to occur, in which we adjust the prior probabilities of each class to reflect the patterns that are present. When this graphical model approach is used on synthetic multi-cell images in which the true class of each cell is known, we observe that the ability to distinguish similar classes is improved without suffering any degradation in ability to distinguish dissimilar classes. The computational complexity of the method is sufficiently low that improved assignments of classes can be obtained for fields of twelve cel

关键词： Classification Accuracy Graphical Model support vector machine classifier Base classifier Loopy Belief Propagation

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Multichannel optimization for electromyogram signals with complex features in a decomposition-based multi-objective evolution framework with adaptive angle selection

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INTERNATIONAL JOURNAL OF ADVANCED ROBOTIC SYSTEMS 2020年第2期17卷

作者： Wang, Zheng Chen, Guoqi Li, Weikun Liu, Honghai Wang, Wanliang Zhejiang Univ Technol Coll Comp Sci & Technol Hangzhou 310014 Zhejiang Peoples R China Univ Portsmouth Sch Comp Intelligent Syst & Biomed Robot Grp Portsmouth PO1 3HE Hants England

Intelligent manufacturing is a focus of current manufacturing research, and, in combination with the Internet, it enables accurate real-time control of intelligent equipment. Highly accurate real-time prosthesis control has very important applications in therapeutics, intelligent prosthesis, and other fields. However, the applicability of the current electromyogram signal recognition method is not strong because of multiple factors. These include considering one objective (correctness) only and the inability to consider differences of recognition accuracy between actions, to recognize the number of channels, or to recognize computational complexity. In this article, we propose a multi-objective evolutionary algorithm based on a decomposition-based multi-objective differential evolution framework to construct a multi-objective model for electromyogram signals with multiple features and channels. Such channels and features are balanced and selected by using a support vector machine as an electromyogram signal classifier. Results of substantial experiment analyses indicate that the multi-objective electromyogram signal recognition method is superior to the single-objective ant colony algorithm and that the decomposition-based multiobjective evolutionary algorithms with Angle-based updating and global margin ranking is better than the decomposition-based multi-objective evolutionary algorithm and decomposition-based multiobjective evolutionary algorithms with angle-based updating strategy in handling multi-objective models for electromyogram signals.

关键词： Multichannel optimization multi-objective evolution surface electromyogram signal support vector machine classifier adaptive angle selection

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IsoSVM - Distinguishing isoforms and paralogs on the protein level

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BMC BIOINFORMATICS 2006年第1期7卷 110-110页

作者： Spitzer, M Lorkowski, S Cullen, P Sczyrba, A Fuellen, G Dept Biol Div Bioinformat D-48149 Munster Germany Leibniz Inst Arteriosclerosis Res D-48149 Munster Germany Inst Biochem D-48149 Munster Germany Univ Bielefeld Res Grp Pract Comp Sci D-33501 Bielefeld Germany AG Bioinformat Dept Med D-48149 Munster Germany

Background: Recent progress in cDNA and EST sequencing is yielding a deluge of sequence data. Like database search results and proteome databases, this data gives rise to inferred protein sequences without ready access to the underlying genomic data. Analysis of this information ( e. g. for EST clustering or phylogenetic reconstruction from proteome data) is hampered because it is not known if two protein sequences are isoforms ( splice variants) or not (i.e. paralogs/orthologs). However, even without knowing the intron/exon structure, visual analysis of the pattern of similarity across the alignment of the two protein sequences is usually helpful since paralogs and orthologs feature substitutions with respect to each other, as opposed to isoforms, which do not. Results: The IsoSVM tool introduces an automated approach to identifying isoforms on the protein level using a support vector machine (SVM) classifier. Based on three specific features used as input of the SVM classifier, it is possible to automatically identify isoforms with little effort and with an accuracy of more than 97%. We show that the SVM is superior to a radial basis function network and to a linear classifier. As an example application we use IsoSVM to estimate that a set of Xenopus laevis EST clusters consists of approximately 81% cases where sequences are each other's paralogs and 19% cases where sequences are each other's isoforms. The number of isoforms and paralogs in this allotetraploid species is of interest in the study of evolution. Conclusion: We developed an SVM classifier that can be used to distinguish isoforms from paralogs with high accuracy and without access to the genomic data. It can be used to analyze, for example, EST data and database search results. Our software is freely available on the Web, under the name IsoSVM.

关键词： support vector machine Alternative Splice Radial Basis Function Testing Dataset support vector machine classifier

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Modular prediction of protein structural classes from sequences of twilight-zone identity with predicting sequences

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BMC BIOINFORMATICS 2009年第1期10卷 414-414页

作者： Mizianty, Marcin J. Kurgan, Lukasz Univ Alberta Dept Elect & Comp Engn Edmonton AB Canada

Background: Knowledge of structural class is used by numerous methods for identification of structural/functional characteristics of proteins and could be used for the detection of remote homologues, particularly for chains that share twilight-zone similarity. In contrast to existing sequence-based structural class predictors, which target four major classes and which are designed for high identity sequences, we predict seven classes from sequences that share twilight-zone identity with the training sequences. Results: The proposed MODular Approach to Structural class prediction (MODAS) method is unique as it allows for selection of any subset of the classes. MODAS is also the first to utilize a novel, custom-built feature-based sequence representation that combines evolutionary profiles and predicted secondary structure. The features quantify information relevant to the definition of the classes including conservation of residues and arrangement and number of helix/strand segments. Our comprehensive design considers 8 feature selection methods and 4 classifiers to develop support vector machine-based classifiers that are tailored for each of the seven classes. Tests on 5 twilight-zone and 1 high-similarity benchmark datasets and comparison with over two dozens of modern competing predictors show that MODAS provides the best overall accuracy that ranges between 80% and 96.7% (83.5% for the twilight-zone datasets), depending on the dataset. This translates into 19% and 8% error rate reduction when compared against the best performing competing method on two largest datasets. The proposed predictor provides accurate predictions at 58% accuracy for membrane proteins class, which is not considered by majority of existing methods, in spite that this class accounts for only 2% of the data. Our predictive model is analyzed to demonstrate how and why the input features are associated with the corresponding classes. Conclusions: The improved predictions stem from the novel f

关键词： Secondary Structure support vector machine classifier Feature Selection Method Amino Acid Type Position Specific Score Matrix

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Predicting binding sites of hydrolase-inhibitor complexes by combining several methods

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BMC BIOINFORMATICS 2004年第1期5卷 205-205页

作者： Sen, TZ Kloczkowski, A Jernigan, RL Yan, CH Honavar, V Ho, KM Wang, CZ Ihm, Y Cao, HB Gu, X Dobbs, D Iowa State Univ LH Baker Ctr Bioinformat & Biol Sci Ames IA 50011 USA Iowa State Univ Dept Biochem Biophys & Mol Biol Ames IA 50011 USA Iowa State Univ Dept Comp Sci Ames IA 50011 USA Iowa State Univ Bioinformat & Computat Biol Program Ames IA 50011 USA Iowa State Univ Dept Phys & Astron Ames IA 50011 USA Iowa State Univ Dept Genet Dev & Cell Biol Ames IA 50011 USA

Background: Protein-protein interactions play a critical role in protein function. Completion of many genomes is being followed rapidly by major efforts to identify interacting protein pairs experimentally in order to decipher the networks of interacting, coordinated-in-action proteins. Identification of protein-protein interaction sites and detection of specific amino acids that contribute to the specificity and the strength of protein interactions is an important problem with broad applications ranging from rational drug design to the analysis of metabolic and signal transduction networks. Results: In order to increase the power of predictive methods for protein-protein interaction sites, we have developed a consensus methodology for combining four different methods. These approaches include: data mining using support vector machines, threading through protein structures, prediction of conserved residues on the protein surface by analysis of phylogenetic trees, and the Conservatism of Conservatism method of Mirny and Shakhnovich. Results obtained on a dataset of hydrolase-inhibitor complexes demonstrate that the combination of all four methods yield improved predictions over the individual methods. Conclusions: We developed a consensus method for predicting protein-protein interface residues by combining sequence and structure-based methods. The success of our consensus approach suggests that similar methodologies can be developed to improve prediction accuracies for other bioinformatic problems.

关键词： support vector machine support vector machine classifier Surface Residue Interface Residue Consensus Method

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Gene prediction in metagenomic fragments based on the SVM algorithm

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BMC BIOINFORMATICS 2013年第Sup5期14卷 S12-S12页

作者： Liu, Yongchu Guo, Jiangtao Hu, Gangqing Zhu, Huaiqiu Peking Univ State Key Lab Turbulence & Complex Syst Beijing 100871 Peoples R China Peking Univ Dept Biomed Engn Coll Engn Beijing 100871 Peoples R China Peking Univ Ctr Theoret Biol Beijing 100871 Peoples R China Peking Univ Ctr Prot Sci Beijing 100871 Peoples R China NHLBI Lab Mol Immunol NIH Bethesda MD 20892 USA

Background: Metagenomic sequencing is becoming a powerful technology for exploring micro-ogranisms from various environments, such as human body, without isolation and cultivation. Accurately identifying genes from metagenomic fragments is one of the most fundamental issues. Results: In this article, we present a novel gene prediction method named MetaGUN for metagenomic fragments based on a machine learning approach of SVM. It implements in a three-stage strategy to predict genes. Firstly, it classifies input fragments into phylogenetic groups by a k-mer based sequence binning method. Then, protein-coding sequences are identified for each group independently with SVM classifiers that integrate entropy density profiles (EDP) of codon usage, translation initiation site (TIS) scores and open reading frame (ORF) length as input patterns. Finally, the TISs are adjusted by employing a modified version of MetaTISA. To identify protein-coding sequences, MetaGun builds the universal module and the novel module. The former is based on a set of representative species, while the latter is designed to find potential functionary DNA sequences with conserved domains. Conclusions: Comparisons on artificial shotgun fragments with multiple current metagenomic gene finders show that MetaGUN predicts better results on both 3' and 5' ends of genes with fragments of various lengths. Especially, it makes the most reliable predictions among these methods. As an application, MetaGUN was used to predict genes for two samples of human gut microbiome. It identifies thousands of additional genes with significant evidences. Further analysis indicates that MetaGUN tends to predict more potential novel genes than other current metagenomic gene finders.

关键词： support vector machine support vector machine classifier Prediction Module Translation Initiation Site support vector machine Algorithm

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Burn characterization using object-oriented hyperspectral image classification

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JOURNAL OF BIOPHOTONICS 2022年第11期15卷 e202200106页

作者： Parasca, Sorin Viorel Calin, Mihaela Antonina Carol Davila Univ Med & Pharm Bucharest Bucharest Romania Emergency Clin Hosp Plast Reconstruct Surg & Burn Bucharest Romania Natl Inst Res & Dev Optoelect INOE 2000 409 Atomistilor StPOB MG5 Magurele 077125 Ilfov Romania

This paper presents a new approach based on hyperspectral imaging combined with an object-oriented classification method that allows the generation of burn depth classification maps facilitating easier characterization of burns. Hyperspectral images of 14 patients diagnosed with burns on the upper and lower limbs were acquired using a pushbroom hyperspectral imaging system. The images were analyzed using an object-oriented classification approach that uses objects with specific spectral, textural and spatial attributes as the minimum unit for classifying information. The method performance was evaluated in terms of overall accuracy, sensitivity, precision and specificity computed from the confusion matrix. The results revealed that the approach proposed in this study performed well in differentiating burn classes with a high level of overall accuracy (95.99% +/- 0.60%), precision (97.30% +/- 2.46%), sensitivity (97.23% +/- 3.02%) and specificity (98.02% +/- 1.98%). In conclusion, the object-based approach for burns hyperspectral images classification can provide maps that can help surgeons identify with better precision different depths of burn wounds.

关键词： burn depth precision segmentation sensitivity specificity support vector machine classifier

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