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Identification of germ cell-specific genes in mammalian meiotic prophase

BMC BIOINFORMATICS 2013年第1期14卷 1-13页

作者： Li, Yunfei Ray, Debjit Ye, Ping Washington State Univ Sch Mol Biosci Pullman WA 99164 USA Washington State Univ Pullman WA 99164 USA Washington State Univ Ctr Reprod Biol Pullman WA 99164 USA

Background: Mammalian germ cells undergo meiosis to produce sperm or eggs, haploid cells that are primed to meet and propagate life. Meiosis is initiated by retinoic acid and meiotic prophase is the first and most complex stage of meiosis when homologous chromosomes pair to exchange genetic information. Errors in meiosis can lead to infertility and birth defects. However, despite the importance of this process, germ cell-specific gene expression patterns during meiosis remain undefined due to difficulty in obtaining pure germ cell samples, especially in females, where prophase occurs in the embryonic ovary. Indeed, mixed signals from both germ cells and somatic cells complicate gonadal transcriptome studies. Results: We developed a machine-learning method for identifying germ cell-specific patterns of gene expression in microarray data from mammalian gonads, specifically during meiotic initiation and prophase. At 10% recall, the method detected spermatocyte genes and oocyte genes with 90% and 94% precision, respectively. Our method outperformed gonadal expression levels and gonadal expression correlations in predicting germ cell-specific expression. Top-predicted spermatocyte and oocyte genes were both preferentially localized to the X chromosome and significantly enriched for essential genes. Also identified were transcription factors and microRNAs that might regulate germ cell-specific expression. Finally, we experimentally validated Rps6ka3, a top-predicted X-linked spermatocyte gene. Protein localization studies in the mouse testis revealed germ cell-specific expression of RPS6KA3, mainly detected in the cytoplasm of spermatogonia and prophase spermatocytes. Conclusions: We have demonstrated that, through the use of machine-learning methods, it is possible to detect germ cell-specific expression from gonadal microarray data. Results from this study improve our understanding of the transition from germ cells to meiocytes in the mammalian gonad. Further, this appr

关键词： Germ Cell support vector machine classifier Essential Gene Meiotic Prophase Pachytene Spermatocyte

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Forensic detection of image manipulation using the Zernike moments and pixel-pair histogram

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IET IMAGE PROCESSING 2013年第9期7卷 817-828页

作者： Shabanifard, Mahmood Shayesteh, Mahrokh G. Akhaee, Mohammad Ali Urmia Univ Dept Elect Engn Orumiyeh Iran Univ Tehran Fac Engn Sch Elect & Comp Engn Tehran Iran Sharif Univ Technol Dept Elect Engn ACRI Wireless Res Lab Tehran Iran

Integrity verification or forgery detection of an image is a difficult procedure, since the forgeries use various transformations to create an altered image. Pixel mapping transforms, such as contrast enhancement, histogram equalisation, gamma correction and so on, are the most popular methods to improve the objective property of an altered image. In addition, fabricators add Gaussian noise to the altered image in order to remove the statistical traces produced because of pixel mapping transforms. A new method is introduced to detect and classify four various categories including original, contrast modified, histogram-equalised and noisy images. In the proposed method, the absolute value of the first 36 Zernike moments of the pixel-pair histogram and its binary form for each image in the polar coordinates are calculated, and then those features that yield the maximum between-class separation, are selected. Some other features obtained from Fourier transform are also utilised for more separation. Finally, support vector machine classifier is used to classify the input image into four categories. The experimental results show that the proposed method achieves high classification rate and considerably outperforms the previously presented methods.

关键词： Fourier transforms Gaussian noise image classification Zernike polynomials digital forensics forensic detection image manipulation Zernike moments pixel-pair histogram integrity verification image forgery detection image alteration pixel mapping transform contrast enhancement histogram equalisation gamma correction Gaussian noise statistical trace removal histogram equalised images noisy images polar coordinates between-class separation Fourier transform support vector machine classifier input image classification

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Protein localization prediction using random walks on graphs

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BMC BIOINFORMATICS 2013年第8-sup期14卷 1-9页

作者： Xu, Xiaohua Lu, Lin He, Ping Chen, Ling Yangzhou Univ Dept Comp Sci Yangzhou 225009 Peoples R China

Background: Understanding the localization of proteins in cells is vital to characterizing their functions and possible interactions. As a result, identifying the (sub) cellular compartment within which a protein is located becomes an important problem in protein classification. This classification issue thus involves predicting labels in a dataset with a limited number of labeled data points available. By utilizing a graph representation of protein data, random walk techniques have performed well in sequence classification and functional prediction;however, this method has not yet been applied to protein localization. Accordingly, we propose a novel classifier in the site prediction of proteins based on random walks on a graph. Results: We propose a graph theory model for predicting protein localization using data generated in yeast and gram-negative (Gneg) bacteria. We tested the performance of our classifier on the two datasets, optimizing the model training parameters by varying the laziness values and the number of steps taken during the random walk. Using 10-fold cross-validation, we achieved an accuracy of above 61% for yeast data and about 93% for gram-negative bacteria. Conclusions: This study presents a new classifier derived from the random walk technique and applies this classifier to investigate the cellular localization of proteins. The prediction accuracy and additional validation demonstrate an improvement over previous methods, such as support vector machine (SVM)-based classifiers.

关键词： support vector machine Random Walk support vector machine classifier Kernel Matrix Label Node

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Characterization of Primary and Secondary Malignant Liver Lesions from B-Mode Ultrasound

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JOURNAL OF DIGITAL IMAGING 2013年第6期26卷 1058-1070页

作者： Virmani, Jitendra Kumar, Vinod Kalra, Naveen Khandelwal, Niranjan Indian Inst Technol Roorkee Dept Elect Engn Biomed Instrumentat Lab Uttarakhand 247667 India Indian Inst Technol Roorkee Dept Elect Engn Uttarakhand 247667 India Post Grad Inst Med Educ & Res Dept Radiodiagnosis & Imaging Chandigarh 160012 India

Characterization of hepatocellular carcinomas (HCCs) and metastatic carcinomas (METs) from B-mode ultrasound presents a daunting challenge for radiologists due to their highly overlapping appearances. The differential diagnosis between HCCs and METs is often carried out by observing the texture of regions inside the lesion and the texture of background liver on which the lesion has evolved. The present study investigates the contribution made by texture patterns of regions inside and outside of the lesions for binary classification between HCC and MET lesions. The study is performed on 51 real ultrasound liver images with 54 malignant lesions, i.e., 27 images with 27 solitary HCCs (13 small HCCs and 14 large HCCs) and 24 images with 27 MET lesions (12 typical cases and 15 atypical cases). A total of 120 within-lesion regions of interest and 54 surrounding lesion regions of interest are cropped from 54 lesions. Subsequently, 112 texture features (56 texture features and 56 texture ratio features) are computed by statistical, spectral, and spatial filtering based texture features extraction methods. A two-step methodology is used for feature set optimization, i.e., feature pruning by removal of nondiscriminatory features followed by feature selection by genetic algorithm-support vector machine (SVM) approach. The SVM classifier is designed based on optimum features. The proposed computer-aided diagnostic system achieved the overall classification accuracy of 91.6 % with sensitivity of 90 % and 93.3 % for HCCs and METs, respectively. The promising results obtained by the proposed system indicate its usefulness to assist radiologists in diagnosing liver malignancies.

关键词： Texture analysis B-Mode liver ultrasound Hepatocellular carcinoma Metastasis Primary malignant liver lesion Secondary malignant liver lesion Genetic algorithm support vector machine classifier Small hepatocellular carcinoma Large hepatocellular carcinoma Typical metastasis Atypical metastasis Focal liver lesions

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SPECTRAL-SPATIAL CLASSIFICATION BASED ON INTEGRATED SEGMENTATION

SPECTRAL-SPATIAL CLASSIFICATION BASED ON INTEGRATED SEGMENTA...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Ghamisi, P. Benediktsson, J. A. Couceiro, M. S. Fauvel, M. Univ Iceland Fac Elect & Comp Engn IS-101 Reykjavik Iceland Robo Corp Inst Engn Coimbra P-3030199 Coimbra Portugal Univ Toulouse INRA DYNAFOR Lab F-31042 Toulouse France

ISBN: (纸本)9781479911141

A new spectral-spatial method for the classification of hyperspectral images is introduced. The proposed approach is based on two segmentation methods, Fractional-Order Darwinian Particle Swarm Optimization and Mean Shift Segmentation and one clustering method, K-means. In parallel, the input data set is classified by support vector machines (SVM). Furthermore, the result of the segmentation and clustering steps are combined with the result of SVM through majority voting within each object. The final classification map is made by using majority voting between three produced classification maps. Experimental results indicate that the proposed method can significantly improve SVM and other studied methods in terms of accuracies.

关键词： Multilevel Segmentation Swarm Optimization Mean Shift Segmentation support vector machine classifier Remote Sensing Hyperspectral Image Analysis

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Multi-temporal classification of TerraSAR-X data for wetland vegetation mapping

Multi-temporal classification of TerraSAR-X data for wetland...

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Conference on Remote Sensing for Agriculture, Ecosystems, and Hydrology XV part of the 20th International Symposium on Remote Sensing

作者： Betbeder, Julie Rapinel, Sebastien Corpetti, Thomas Pottier, Eric Corgne, Samuel Hubert-Moy, Laurence Univ Rennes 2 LETG Rennes COSTEL UMR LETG OSUR 6554 Pl Recteur Henri Le Moal F-35043 Rennes France Univ Rennes 1 IETR UMR CNRS 6164 F-35042 Rennes France

ISBN: (纸本)9780819497567

This paper is concerned with vegetation wetland mapping using multi-temporal SAR imagery. Whilst wetlands play a key role in controlling flooding and nonpoint source pollution, sequestering carbon and providing an abundance of ecological services, knowledge of the flora and fauna of these environments is patchy, and understanding of their ecological functioning is still insufficient for a reliable functional assessment on areas larger than a few ha. The aim of this paper is to evaluate multitemporal TerraSAR-X imagery to map precisely the distribution of vegetation formations within wetlands, in determining seasonally flooded areas of wetlands. A series of six dual-polarization TerraSAR-X images (HH/VV) were acquired in 2012 during dry and wet seasons. Polarimetric and intensity parameters, which present a temporal variation that depends on wetland flooding status and vegetation roughness, were firstly extracted. The parameters were then classified based on support vector machines (SVM) techniques using a specific kernel adapted to the comparison of time-series data. The results show that the Shannon entropy parameter allows discriminating vegetation formations within wetland with more accuracy than intensity parameters.

关键词： Wetlands synthetic aperture radar (SAR) TerraSAR-X imagery multi-temporal classifications support vector machine classifier

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Diagnosis of Diabetic Retinopathy Using Morphological Process and SVM classifier

Diagnosis of Diabetic Retinopathy Using Morphological Proces...

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2nd IEEE International Conference on Communications and Signal Processing (ICCSP)

作者： Gandhi, Mahendran Dhanasekaran, R. Syed Ammal Engn Coll Dept Elect & Commun Engn Ramanathapuram Tamil Nadu India

ISBN: (纸本)9781467348669;9781467348652

Diabetic Retinopathy (DR), the most common eye disease of the diabetic patients, occurs when small blood vessels gets damaged in the retina, due to high glucose level. It affects 80% of all patients who have had diabetes for 10 years or more, which can also lead to vision loss. Detection of diabetic retinopathy in advance, protects patients from vision loss. The major symptom of diabetic retinopathy is the exudates. Exudate is a fluid that filters from the circulatory system into lesions or area of inflammation. Detecting retinal fundus diseases in an early stage, helps the ophthalmologists apply proper treatments that might eliminate the disease or decrease the severity of it. This paper focuses on automatic detection of diabetic retinopathy through detecting exudates in colour fundus retinal images and also classifies the rigorousness of the lesions. Decision making of the severity level of the disease was performed by SVM classifier.

关键词： Diabetic Retinopathy Erosion Exudates support vector machine classifier

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An Efficient Method for Vision-Based Fire Detection Using SVM Classification

An Efficient Method for Vision-Based Fire Detection Using SV...

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International Conference of Soft Computing and Pattern Recognition (SoCPaR)

作者： Ha Dai Duong Dao Thanh Tinh LQDTU Fac Informat Technol Hanoi Vietnam

ISBN: (纸本)9781479934003

In this paper, we present a new vision-based algorithm for fire detection problem. The algorithm consists of three main tasks: pixel-based processing to identify potential fire blobs, blob-based statistical feature extraction, and a support vector machine classifier. In pixel-based processing phase, five feature vectors based on RGB color space are used to classify a pixel by using a Bayes classifier to build a potential fire mask (PFM) of image. Next step, a potential fire blob mask (PFBM) is computed by using the difference between two consecutive PFM and a recover technique. In blob-based phase, for each potential blob in a potential fire blobs image (PFBI) an 7-feature vector are evaluated;this vector includes three statistical features of colour, four texture parameters and one shape roundness parameter. Finally, a SVM classifier is designed and trained for distinguish a potential fire blob are fire or fire-like object. Experimental results demonstrate the effectiveness and robustness of the proposed method.

关键词： Vision-based fire detection support vector machine classifier pixel-based processing blob-based processing

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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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A negative selection heuristic to predict new transcriptional targets

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BMC BIOINFORMATICS 2013年第Sup1期14卷 S3-S3页

作者： Cerulo, Luigi Paduano, Vincenzo Zoppoli, Pietro Ceccarelli, Michele Univ Sannio Dept Sci Benevento Italy BioGeM Scarl Inst Genet Res Gaetano Salvatore Ariano Irpino AV Italy Columbia Univ Inst Canc Genet New York NY USA

Background: Supervised machine learning approaches have been recently adopted in the inference of transcriptional targets from high throughput trascriptomic and proteomic data showing major improvements from with respect to the state of the art of reverse gene regulatory network methods. Beside traditional unsupervised techniques, a supervised classifier learns, from known examples, a function that is able to recognize new relationships for new data. In the context of gene regulatory inference a supervised classifier is coerced to learn from positive and unlabeled examples, as the counter negative examples are unavailable or hard to collect. Such a condition could limit the performance of the classifier especially when the amount of training examples is low. Results: In this paper we improve the supervised identification of transcriptional targets by selecting reliable counter negative examples from the unlabeled set. We introduce an heuristic based on the known topology of transcriptional networks that in fact restores the conventional positive/negative training condition and shows a significant improvement of the classification performance. We empirically evaluate the proposed heuristic with the experimental datasets of Escherichia coli and show an example of application in the prediction of BCL6 direct core targets in normal germinal center human B cells obtaining a precision of 60%. Conclusions: The availability of only positive examples in learning transcriptional relationships negatively affects the performance of supervised classifiers. We show that the selection of reliable negative examples, a practice adopted in text mining approaches, improves the performance of such classifiers opening new perspectives in the identification of new transcriptional targets.

关键词： support vector machine Negative Selection Gene Regulatory Network support vector machine classifier Unlabeled Data

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