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Automated in-silico detection of cell populations in flow cytometry readouts and its application to leukemia disease monitoring

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BMC BIOINFORMATICS 2006年第1期7卷 282-282页

作者： Toedling, Joern Rhein, Peter Ratei, Richard Karawajew, Leonid Spang, Rainer Max Planck Inst Mol Genet D-14195 Berlin Germany Berlin Ctr Genome Based Bioinformat D-14195 Berlin Germany HELIOS Klin Charite Med Sch Robert Roessle Clin Dept Hematol Oncol & Tumor Immunol Berlin Germany EMBL European Bioinformat Inst Cambridge CB10 1SD England

Background: Identification of minor cell populations, e. g. leukemic blasts within blood samples, has become increasingly important in therapeutic disease monitoring. Modern flow cytometers enable researchers to reliably measure six and more variables, describing cellular size, granularity and expression of cell-surface and intracellular proteins, for thousands of cells per second. Currently, analysis of cytometry readouts relies on visual inspection and manual gating of one- or two-dimensional projections of the data. This procedure, however, is labor-intensive and misses potential characteristic patterns in higher dimensions. Results: Leukemic samples from patients with acute lymphoblastic leukemia at initial diagnosis and during induction therapy have been investigated by 4-color flow cytometry. We have utilized multivariate classification techniques, support vector machines (SVM), to automate leukemic cell detection in cytometry. classifiers were built on conventionally diagnosed training data. We assessed the detection accuracy on independent test data and analyzed marker expression of incongruently classified cells. SVM classification can recover manually gated leukemic cells with 99.78% sensitivity and 98.87% specificity. Conclusion: Multivariate classification techniques allow for automating cell population detection in cytometry readouts for diagnostic purposes. They potentially reduce time, costs and arbitrariness associated with these procedures. Due to their multivariate classification rules, they also allow for the reliable detection of small cell populations.

关键词： support vector machine Leukemic Cell support vector machine classifier support vector machine Classification Kernel Trick

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Data Mining Applications in Understanding Electricity Consumers' Behavior: A Case Study of Tulkarm District, Palestine

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ENERGIES 2019年第22期12卷 4287-4287页

作者： AbuBaker, Maher An Najah Natl Univ Dept Management Informat Syst Fac Engn & Informat Technol Nablus Palestine An Najah Natl Univ POB 7 Nablus Palestine

This paper presents a comprehensive data analysis and visualization of electricity consumers' prepaid bills of Tulkarm district. We analyzed 250,000 electricity consumers' prepaid bills covering the time period from June to December 2018. The application of data mining techniques for understanding electricity consumers' behavior in electricity consumption and their behavior in charging their electricity meter's smart cards in terms of quantities charged and charging frequencies in different time periods, areas and tariffs are used. Understanding consumers' behavior will support planning and decision making at strategic, tactical and operational levels. This analysis is useful for predicting and forecasting future demand with a certain degree of accuracy. Monthly, weekly, daily and hourly time periods are covered in the analysis. Outliers detection using visualization tools such as box plot is applied. K-means unsupervised machine learning clustering algorithm is implemented. The support vector machine classification method is applied. As a result of this study, electricity consumers' behavior in different areas, tariffs and timing periods is understood and presented by numbers and graphs and new electricity consumer segmentation is proposed.

关键词： data mining data visualization K-means clustering support vector machine classifier principal components analysis elbow method silhouette analysis

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machine learning and word sense disambiguation in the biomedical domain: design and evaluation issues

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BMC BIOINFORMATICS 2006年第1期7卷 334-334页

作者： Xu, Hua Markatou, Marianthi Dimova, Rositsa Liu, Hongfang Friedman, Carol Columbia Univ Dept Biomed Informat New York NY USA Columbia Univ Dept Biostat New York NY USA Georgetown Univ Med Ctr Dept Biostat Bioinformat & Biomath Washington DC 20007 USA

Background: Word sense disambiguation (WSD) is critical in the biomedical domain for improving the precision of natural language processing (NLP), text mining, and information retrieval systems because ambiguous words negatively impact accurate access to literature containing biomolecular entities, such as genes, proteins, cells, diseases, and other important entities. Automated techniques have been developed that address the WSD problem for a number of text processing situations, but the problem is still a challenging one. Supervised WSD machine learning (ML) methods have been applied in the biomedical domain and have shown promising results, but the results typically incorporate a number of confounding factors, and it is problematic to truly understand the effectiveness and generalizability of the methods because these factors interact with each other and affect the final results. Thus, there is a need to explicitly address the factors and to systematically quantify their effects on performance. Results: Experiments were designed to measure the effect of "sample size" (i.e. size of the datasets), "sense distribution" (i.e. the distribution of the different meanings of the ambiguous word) and "degree of difficulty" (i. e. the measure of the distances between the meanings of the senses of an ambiguous word) on the performance of WSD classifiers. support vector machine (SVM) classifiers were applied to an automatically generated data set containing four ambiguous biomedical abbreviations: BPD, BSA, PCA, and RSV, which were chosen because of varying degrees of differences in their respective senses. Results showed that: 1) increasing the sample size generally reduced the error rate, but this was limited mainly to well- separated senses (i. e. cases where the distances between the senses were large);in difficult cases an unusually large increase in sample size was needed to increase performance slightly, which was impractical, 2) the sense distribution did not have an

关键词： support vector machine support vector machine classifier Gene Symbol Ambiguous Word Sense Distribution

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Predicting RNA-Protein Interactions Using Only Sequence Information

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BMC BIOINFORMATICS 2011年第1期12卷 1-11页

作者： Muppirala, Usha K. Honavar, Vasant G. Dobbs, Drena Iowa State Univ Bioinformat & Computat Biol Program Ames IA 50011 USA Iowa State Univ Dept Genet Dev & Cell Biol Ames IA USA Iowa State Univ Dept Comp Sci Ames IA USA

Background: RNA-protein interactions (RPIs) play important roles in a wide variety of cellular processes, ranging from transcriptional and post-transcriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA-protein interactions are beginning to provide valuable information about the complexity of RNA-protein interaction networks, but are expensive and time consuming. Hence, there is a need for reliable computational methods for predicting RNA-protein interactions. Results: We propose RPISeq, a family of classifiers for predicting RNA-protein interactions using only sequence information. Given the sequences of an RNA and a protein as input, RPIseq predicts whether or not the RNA-protein pair interact. The RNA sequence is encoded as a normalized vector of its ribonucleotide 4-mer composition, and the protein sequence is encoded as a normalized vector of its 3-mer composition, based on a 7-letter reduced alphabet representation. Two variants of RPISeq are presented: RPISeq-SVM, which uses a support vector machine (SVM) classifier and RPISeq-RF, which uses a Random Forest classifier. On two non-redundant benchmark datasets extracted from the Protein-RNA Interface Database (PRIDB), RPISeq achieved an AUC (Area Under the Receiver Operating Characteristic (ROC) curve) of 0.96 and 0.92. On a third dataset containing only mRNA-protein interactions, the performance of RPISeq was competitive with that of a published method that requires information regarding many different features (e.g., mRNA half-life, GO annotations) of the putative RNA and protein partners. In addition, RPISeq classifiers trained using the PRIDB data correctly predicted the majority (57-99%) of non-coding RNA-protein interactions in NPInter-derived networks from E. coli, S. cerevisiae, D. melanogaster, M. musculus, and H. sapiens. Conclusions: Our experiments with RPISeq demonstrate that RNA-protein interactions can be reliably predicted using o

关键词： support vector machine Random Forest support vector machine classifier snRNA U4560 Random Forest classifier

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miTarget: microRNA target gene prediction using a support vector machine

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BMC BIOINFORMATICS 2006年第1期7卷 411-411页

作者： Kim, Sung-Kyu Nam, Jin-Wu Rhee, Je-Keun Lee, Wha-Jin Zhang, Byoung-Tak Seoul Natl Univ Grad Program Bioinformat Seoul South Korea Seoul Natl Univ Ctr Bioinformat Technol CBIT Seoul South Korea Seoul Natl Univ Sch Engn & Comp Sci Biointelligence Lab Seoul South Korea

Background: MicroRNAs ( miRNAs) are small noncoding RNAs, which play significant roles as posttranscriptional regulators. The functions of animal miRNAs are generally based on complementarity for their 5' components. Although several computational miRNA target-gene prediction methods have been proposed, they still have limitations in revealing actual target genes. Results: We implemented miTarget, a support vector machine (SVM) classifier for miRNA target gene prediction. It uses a radial basis function kernel as a similarity measure for SVM features, categorized by structural, thermodynamic, and position-based features. The latter features are introduced in this study for the first time and reflect the mechanism of miRNA binding. The SVM classifier produces high performance with a biologically relevant data set obtained from the literature, compared with previous tools. We predicted significant functions for human mIR-1, miR-124a, and miR-373 using Gene Ontology ( GO) analysis and revealed the importance of pairing at positions 4, 5, and 6 in the 5' region of a miRNA from a feature selection experiment. We also provide a web interface for the program. Conclusion: miTarget is a reliable miRNA target gene prediction tool and is a successful application of an SVM classifier. Compared with previous tools, its predictions are meaningful by GO analysis and its performance can be improved given more training examples.

关键词： support vector machine Gene Ontology miRNA Target support vector machine classifier miRNA Target Site

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Computer-aided skin cancer diagnosis based on a New meta-heuristic algorithm combined with support vector method

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2021年第0期68卷 102631-102631页

作者： Bi, Dezhong Zhu, Dongxia Sheykhahmad, Fatima Rashid Qiao, Mingqi Shandong Univ Tradit Chinese Med Inst Tradit Chinese Med 4655 Univ RdUniv Sci Pk Jinan 250355 Shandong Peoples R China Jinan City Peoples Hosp Jinan Peoples R China Islamic Azad Univ Ardabil Branch Young Researchers & Elite Club Ardebil Iran

Skin cancer has grown significantly over the past decades, and the importance of its initial treatment is increasing day by day. This study aims to use a computer-aided diagnosis automatic system for the exact diagnosis of cancerous cases. In the present study, after applying pre-processing operations including contrast enhancement, image thresholding, and mathematical morphology for highlighting the important areas, feature extraction based on 20 different features has been applied. Then, for decreasing the system complexity, a new improved version of the world cup optimization algorithm is utilized for the features pruning. After optimal selection of the features, the injected into a support vector machine as a classifier to determine the cancerous areas. Experimental results are compared by different new algorithms including the classic SVM method, hybrid optimized neural networks based on gray wolf optimizer and particle swarm optimization algorithm, and also convolutional neural network. Results obtained for skin lesions images from two different databases indicate that the proposed method has higher efficiency than other published methods, demonstrating even better performance than the well-known classification method, the convolutional neural networks. The proposed method presents the highest correct detection rate - 92.64 for ACS images and 87.5 for ISIC images with very low false acceptance rates (4.41 % and 9 % for ACS and ISIC images, respectively) and even less false rejection rates, this is, 2.94 % on ACS images and 3.5 % on ISIC images. These results prove that the proposed method may be reliably used to diagnose skin cancer through dermoscopy images.

关键词： Computer-aided diagnosis Skin cancer Kapur image thresholding Feature extraction Feature classification support vector machine classifier Chaotic world cup optimization algorithm

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An efficient image segmentation and classification of lung lesions in pet and CT image fusion using DTWT incorporated SVM

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MICROPROCESSORS AND MICROSYSTEMS 2021年 82卷 103958-103958页

作者： Priya, R. Mohana Venkatesan, P. Sri Chandrasekharendra Saraswathi Viswa Mahavidya Dept Elect & Commun Engn Kanchipuram Tamil Nadu India

Automatic segmentation and classification of fused lung Computed Tomography (CT) and Positron Emission Tomography (PET) images is presented. This system consists of four basic stages: 1). Lung image fusion process;2). Segmentation of fused lung CT/PET images;3). Post pre-processing;4). Classification of fused lung images. In the first step, the lung image fusion process is made by deep learning method. At first, the input CT/PET images are decomposed by Dual Tree m-band Wavelet Transform (DTWT). The coefficients of DTWT are fused by deep learning method. This fused image of CT/PET is the input for the following steps. In the next step, the fused CT/PET images are decomposed by DTWT. It produces lower and higher frequency sub-band coefficients. Then the lower frequency components are set to zero. Then higher frequency components are used for reconstruction. Then the clustering-based thresholding method is used for segmentation. In post pre-processing step, the unwanted small regions are removed by morphological operations. Then the lung region is detected. At last, in the classification step, the features are extracted by the intensity and texture-based features. These features are classified by hybrid classifiers like support vector machine (SVM) are used. The performance of the system has a higher classification accuracy of 99% using SVM classifier.

关键词： Computed tomography Positron emission tomography Fused lung image Dual tree m-band wavelet transform Thresholding support vector machine classifier

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Detection of non-coding RNAs on the basis of predicted secondary structure formation free energy change

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BMC BIOINFORMATICS 2006年第1期7卷 173-173页

作者： Uzilov, Andrew V. Keegan, Joshua M. Mathews, David H. Univ Rochester Med Ctr Dept Biochem & Biophys Rochester NY 14642 USA Univ Rochester Med Ctr Dept Biostat & Computat Biol Rochester NY 14642 USA Univ Rochester Med Ctr Ctr Pediat Biomed Res Rochester NY 14642 USA

Background: Non-coding RNAs ( ncRNAs) have a multitude of roles in the cell, many of which remain to be discovered. However, it is difficult to detect novel ncRNAs in biochemical screens. To advance biological knowledge, computational methods that can accurately detect ncRNAs in sequenced genomes are therefore desirable. The increasing number of genomic sequences provides a rich dataset for computational comparative sequence analysis and detection of novel ncRNAs. Results: Here, Dynalign, a program for predicting secondary structures common to two RNA sequences on the basis of minimizing folding free energy change, is utilized as a computational ncRNA detection tool. The Dynalign-computed optimal total free energy change, which scores the structural alignment and the free energy change of folding into a common structure for two RNA sequences, is shown to be an effective measure for distinguishing ncRNA from randomized sequences. To make the classification as a ncRNA, the total free energy change of an input sequence pair can either be compared with the total free energy changes of a set of control sequence pairs, or be used in combination with sequence length and nucleotide frequencies as input to a classification support vector machine. The latter method is much faster, but slightly less sensitive at a given specificity. Additionally, the classification support vector machine method is shown to be sensitive and specific on genomic ncRNA screens of two different Escherichia coli and Salmonella typhi genome alignments, in which many ncRNAs are known. The Dynalign computational experiments are also compared with two other ncRNA detection programs, RNAz and QRNA. Conclusion: The Dynalign-based support vector machine method is more sensitive for known ncRNAs in the test genomic screens than RNAz and QRNA. Additionally, both Dynalign-based methods are more sensitive than RNAz and QRNA at low sequence pair identities. Dynalign can be used as a comparable or more accurate

关键词： support vector machine Receiver Operating Characteristic Curve support vector machine classifier Sequence Pair Global Alignment

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Sequence-based identification of interface residues by an integrative profile combining hydrophobic and evolutionary information

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BMC BIOINFORMATICS 2010年第1期11卷 402-402页

作者： Chen, Peng Li, Jinyan Nanyang Technol Univ Sch Comp Engn Bioinformat Res Ctr Singapore 639798 Singapore

Background: Protein-protein interactions play essential roles in protein function determination and drug design. Numerous methods have been proposed to recognize their interaction sites, however, only a small proportion of protein complexes have been successfully resolved due to the high cost. Therefore, it is important to improve the performance for predicting protein interaction sites based on primary sequence alone. Results: We propose a new idea to construct an integrative profile for each residue in a protein by combining its hydrophobic and evolutionary information. A support vector machine (SVM) ensemble is then developed, where SVMs train on different pairs of positive (interface sites) and negative (non-interface sites) subsets. The subsets having roughly the same sizes are grouped in the order of accessible surface area change before and after complexation. A self-organizing map (SOM) technique is applied to group similar input vectors to make more accurate the identification of interface residues. An ensemble of ten-SVMs achieves an MCC improvement by around 8% and F1 improvement by around 9% over that of three-SVMs. As expected, SVM ensembles constantly perform better than individual SVMs. In addition, the model by the integrative profiles outperforms that based on the sequence profile or the hydropathy scale alone. As our method uses a small number of features to encode the input vectors, our model is simpler, faster and more accurate than the existing methods. Conclusions: The integrative profile by combining hydrophobic and evolutionary information contributes most to the protein-protein interaction prediction. Results show that evolutionary context of residue with respect to hydrophobicity makes better the identification of protein interface residues. In addition, the ensemble of SVM classifiers improves the prediction performance.

关键词： support vector machine support vector machine classifier Matthews Correlation Coefficient Interface Residue Integrative Profile

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SVM-based prediction of caspase substrate cleavage sites

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BMC BIOINFORMATICS 2006年第s5期7卷 S14-S14页

作者： Wee, Lawrence J. K. Tan, Tin Wee Ranganathan, Shoba Macquarie Univ Dept Chem Sydney NSW 2109 Australia Macquarie Univ Biomol Sci & Biotechnol Res Inst Sydney NSW 2109 Australia Natl Univ Singapore Yong Loo Lin Sch Med Dept Biochem Singapore 117548 Singapore

Background: Caspases belong to a class of cysteine proteases which function as critical effectors in apoptosis and inflammation by cleaving substrates immediately after unique sites. Prediction of such cleavage sites will complement structural and functional studies on substrates cleavage as well as discovery of new substrates. Recently, different computational methods have been developed to predict the cleavage sites of caspase substrates with varying degrees of success. As the support vector machines (SVM) algorithm has been shown to be useful in several biological classification problems, we have implemented an SVM-based method to investigate its applicability to this domain. Results: A set of unique caspase substrates cleavage sites were obtained from literature and used for evaluating the SVM method. Datasets containing (i) the tetrapeptide cleavage sites, (ii) the tetrapeptide cleavage sites, augmented by two adjacent residues, P-1' and P-2' amino acids and (iii) the tetrapeptide cleavage sites with ten additional upstream and downstream flanking sequences (where available) were tested. The SVM method achieved an accuracy ranging from 81.25% to 97.92% on independent test sets. The SVM method successfully predicted the cleavage of a novel caspase substrate and its mutants. Conclusion: This study presents an SVM approach for predicting caspase substrate cleavage sites based on the cleavage sites and the downstream and upstream flanking sequences. The method shows an improvement over existing methods and may be useful for predicting hitherto undiscovered cleavage sites.

关键词： support vector machine Cleavage Site Training Dataset support vector machine classifier Radial Basis Function Kernel

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