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Predicting success of oligomerized pool engineering (OPEN) for zinc finger target site sequences

BMC BIOINFORMATICS 2010年第1期11卷 543-543页

作者： Sander, Jeffry D. Reyon, Deepak Maeder, Morgan L. Foley, Jonathan E. Thibodeau-Beganny, Stacey Li, Xiaohong Regan, Maureen R. Dahlborg, Elizabeth J. Goodwin, Mathew J. Fu, Fengli Voytas, Daniel F. Joung, J. Keith Dobbs, Drena Massachusetts Gen Hosp Mol Pathol Unit Ctr Canc Res Charlestown MA 02129 USA Massachusetts Gen Hosp Ctr Computat & Integrat Biol Charlestown MA 02129 USA Harvard Univ Sch Med Dept Pathol Boston MA 02115 USA Iowa State Univ Dept Genet Dev & Cell Biol Interdept Grad Program Bioinformat & Computat Bio Ames IA 50011 USA Harvard Univ Sch Med Biol & Biomed Sci Program Boston MA 02115 USA Univ Minnesota Ctr Genome Engn Dept Genet Cell Biol & Dev Minneapolis MN 55455 USA

Background: Precise and efficient methods for gene targeting are critical for detailed functional analysis of genomes and regulatory networks and for potentially improving the efficacy and safety of gene therapies. Oligomerized Pool ENgineering (OPEN) is a recently developed method for engineering C2H2 zinc finger proteins (ZFPs) designed to bind specific DNA sequences with high affinity and specificity in vivo. Because generation of ZFPs using OPEN requires considerable effort, a computational method for identifying the sites in any given gene that are most likely to be successfully targeted by this method is desirable. Results: Analysis of the base composition of experimentally validated ZFP target sites identified important constraints on the DNA sequence space that can be effectively targeted using OPEN. Using alternate encodings to represent ZFP target sites, we implemented Naive Bayes and support vector machine classifiers capable of distinguishing "active" targets, i.e., ZFP binding sites that can be targeted with a high rate of success, from those that are "inactive" or poor targets for ZFPs generated using current OPEN technologies. When evaluated using leave-one-out cross-validation on a dataset of 135 experimentally validated ZFP target sites, the best Naive Bayes classifier, designated ZiFOpT, achieved overall accuracy of 87% and specificity(+) of 90%, with an ROC AUC of 0.89. When challenged with a completely independent test set of 140 newly validated ZFP target sites, ZiFOpT performance was comparable in terms of overall accuracy (88%) and specificity(+) (92%), but with reduced ROC AUC (0.77). Users can rank potentially active ZFP target sites using a confidence score derived from the posterior probability returned by ZiFOpT. Conclusion: ZiFOpT, a machine learning classifier trained to identify DNA sequences amenable for targeting by OPEN-generated zinc finger arrays, can guide users to target sites that are most likely to function successfully in viv

关键词： Target Site Zinc Finger support vector machine classifier Confidence Score machine Learning classifier

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Computer-aided diagnosis of cataract using deep transfer learning

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2019年第0期53卷 101533-000页

作者： Pratap, Turimerla Kokil, Priyanka Indian Inst Informat Technol Design & Mfg Dept Elect & Commun Engn Chennai 600127 Tamil Nadu India

Cataract is a leading eye disease across the world. If cataract is not diagnosed in earlier stage, then it may lead to blindness. Earlier detection is the best way to control the risk and to avoid painful surgery. Thus, this paper mainly focuses on cataract detection from fundus retinal images. A computer-aided automatic cataract detection method is proposed to detect various stages of the cataract such as normal, mild, moderate, and severe from the fundus images. The proposed method uses the pre-trained convolutional neural network (CNN) for the transfer learning to carry out automatic cataract classification. Then pre-trained CNN model is used for the feature extraction and the extracted features are then applied to a support vector machine (SVM) classifier. The fundus cataract images are collected from the various open access datasets and labelled into four stages with the help of ophthalmologic experts. The four stage classification accuracy obtained is 92.91%. Since, the image quality is important in CNN, an image quality selection module is incorporated to decide the quality of fundus image for diagnosis. The revaluation of results based on the quality of fundus images is also presented. Based on the results, the proposed method proved to be an efficient method that uses pre-trained CNN as transfer learning for the classification of the cataract. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Computer-aided diagnosis Pre-trained convolutional neural networks Feature extraction support vector machine classifier Transfer learning

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SCPRED: Accurate prediction of protein structural class for sequences of twilight-zone similarity with predicting sequences

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BMC BIOINFORMATICS 2008年第1期9卷 226-226页

作者： Kurgan, Lukasz Cios, Krzysztof Chen, Ke Univ Alberta Dept Elect & Comp Engn ECEFR Edmonton AB T6G 2V4 Canada Virginia Commonwealth Univ Dept Comp Sci Richmond VA 23284 USA

Background: Protein structure prediction methods provide accurate results when a homologous protein is predicted, while poorer predictions are obtained in the absence of homologous templates. However, some protein chains that share twilight-zone pairwise identity can form similar folds and thus determining structural similarity without the sequence similarity would be desirable for the structure prediction. The folding type of a protein or its domain is defined as the structural class. Current structural class prediction methods that predict the four structural classes defined in SCOP provide up to 63% accuracy for the datasets in which sequence identity of any pair of sequences belongs to the twilight- zone. We propose SCPRED method that improves prediction accuracy for sequences that share twilight- zone pairwise similarity with sequences used for the prediction. Results: SCPRED uses a support vector machine classifier that takes several custom-designed features as its input to predict the structural classes. Based on extensive design that considers over 2300 index-, composition- and physicochemical properties-based features along with features based on the predicted secondary structure and content, the classifier's input includes 8 features based on information extracted from the secondary structure predicted with PSI-PRED and one feature computed from the sequence. Tests performed with datasets of 1673 protein chains, in which any pair of sequences shares twilight- zone similarity, show that SCPRED obtains 80.3% accuracy when predicting the four SCOP-defined structural classes, which is superior when compared with over a dozen recent competing methods that are based on support vector machine, logistic regression, and ensemble of classifiers predictors. Conclusion: The SCPRED can accurately find similar structures for sequences that share low identity with sequence used for the prediction. The high predictive accuracy achieved by SCPRED is attributed to the desig

关键词： support vector machine Secondary Structure Feature vector Protein Data Bank support vector machine classifier

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Combining classifiers for improved classification of proteins from sequence or structure

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BMC BIOINFORMATICS 2008年第1期9卷 389-389页

作者： Melvin, Iain Weston, Jason Leslie, Christina S. Noble, William S. Univ Washington Dept Comp Sci & Engn Dept Genome Sci Seattle WA 98195 USA NEC Labs Amer Princeton NJ USA Mem Sloan Kettering Canc Ctr Sloan Kettering Inst Computat Biol Program New York NY 10021 USA

Background: Predicting a protein's structural or functional class from its amino acid sequence or structure is a fundamental problem in computational biology. Recently, there has been considerable interest in using discriminative learning algorithms, in particular support vector machines (SVMs), for classification of proteins. However, because sufficiently many positive examples are required to train such classifiers, all SVM-based methods are hampered by limited coverage. Results: In this study, we develop a hybrid machine learning approach for classifying proteins, and we apply the method to the problem of assigning proteins to structural categories based on their sequences or their 3D structures. The method combines a full-coverage but lower accuracy nearest neighbor method with higher accuracy but reduced coverage multiclass SVMs to produce a full coverage classifier with overall improved accuracy. The hybrid approach is based on the simple idea of "punting" from one method to another using a learned threshold. Conclusion: In cross-validated experiments on the SCOP hierarchy, the hybrid methods consistently outperform the individual component methods at all levels of coverage.

关键词： support vector machine support vector machine classifier Near Neighbor Hybrid classifier Multiclass support vector machine

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Protein-RNA interface residue prediction using machine learning: an assessment of the state of the art

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BMC BIOINFORMATICS 2012年第1期13卷 1-20页

作者： Walia, Rasna R. Caragea, Cornelia Lewis, Benjamin A. Towfic, Fadi Terribilini, Michael El-Manzalawy, Yasser Dobbs, Drena Honavar, Vasant Iowa State Univ Bioinformat & Computat Biol Program Ames IA 50011 USA Iowa State Univ Dept Comp Sci Ames IA USA Iowa State Univ Ctr Computat Intelligence Learning & Discovery Ames IA USA Penn State Univ Coll Informat Sci & Technol University Pk PA 16802 USA Iowa State Univ Dept Genet Dev & Cell Biol Ames IA USA Broad Inst Cambridge MA USA Elon Univ Dept Biol Elon NC USA Al Azhar Univ Dept Syst & Comp Engn Cairo Egypt

Background: RNA molecules play diverse functional and structural roles in cells. They function as messengers for transferring genetic information from DNA to proteins, as the primary genetic material in many viruses, as catalysts (ribozymes) important for protein synthesis and RNA processing, and as essential and ubiquitous regulators of gene expression in living organisms. Many of these functions depend on precisely orchestrated interactions between RNA molecules and specific proteins in cells. Understanding the molecular mechanisms by which proteins recognize and bind RNA is essential for comprehending the functional implications of these interactions, but the recognition 'code' that mediates interactions between proteins and RNA is not yet understood. Success in deciphering this code would dramatically impact the development of new therapeutic strategies for intervening in devastating diseases such as AIDS and cancer. Because of the high cost of experimental determination of protein-RNA interfaces, there is an increasing reliance on statistical machine learning methods for training predictors of RNA-binding residues in proteins. However, because of differences in the choice of datasets, performance measures, and data representations used, it has been difficult to obtain an accurate assessment of the current state of the art in protein-RNA interface prediction. Results: We provide a review of published approaches for predicting RNA-binding residues in proteins and a systematic comparison and critical assessment of protein-RNA interface residue predictors trained using these approaches on three carefully curated non-redundant datasets. We directly compare two widely used machine learning algorithms (Naive Bayes (NB) and support vector machine (SVM)) using three different data representations in which features are encoded using either sequence-or structure-based windows. Our results show that (i) Sequence-based classifiers that use a position-specific scoring matrix

关键词： support vector machine classifier Radial Basis Function Kernel Matthews Correlation Coefficient Interface Residue Target Residue

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Reducing the Number of support vectors of SVM classifiers Using the Smoothed Separable Case Approximation

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2012年第4期23卷 682-688页

作者： Geebelen, Dries Suykens, Johan A. K. Vandewalle, Joos Katholieke Univ Leuven Dept Elect Engn ESAT SCD SISTA B-3001 Heverlee Belgium

In this brief, we propose a new method to reduce the number of support vectors of support vector machine (SVM) classifiers. We formulate the approximation of an SVM solution as a classification problem that is separable in the feature space. Due to the separability, the hard-margin SVM can be used to solve it. This approach, which we call the separable case approximation (SCA), is very similar to the cross-training algorithm explained in [1], which is inspired by editing algorithms [2]. The norm of the weight vector achieved by SCA can, however, become arbitrarily large. For that reason, we propose an algorithm, called the smoothed SCA (SSCA), that additionally upper-bounds the weight vector of the pruned solution and, for the commonly used kernels, reduces the number of support vectors even more. The lower the chosen upper bound, the larger this extra reduction becomes. Upper-bounding the weight vector is important because it ensures numerical stability, reduces the time to find the pruned solution, and avoids overfitting during the approximation phase. On the examined datasets, SSCA drastically reduces the number of support vectors.

关键词： Run time complexity sparse approximation support vector machine classifier

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Identifying Seasonal Groundwater-Irrigated Cropland Using Multi-Source NDVI Time-Series Images

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REMOTE SENSING 2021年第10期13卷 1960-1960页

作者： Sharma, Amit Hubert-Moy, Laurance Buvaneshwari, Sriramulu Sekhar, Muddu Ruiz, Laurent Moger, Hemanth Bandyopadhyay, Soumya Corgne, Samuel Univ Rennes Ctr Natl Rech Sci Unite Mixte Rech Littoral EnvironmGeomatTeledetect F-35043 Rennes France Indian Inst Sci Indo French Cell Water Sci Bangalore 560012 Karnataka India Indian Inst Sci Dept Civil Engn Bangalore 560012 Karnataka India Univ Toulouse III Paul Sabatier Ctr Natl Rech Sci Inst Rech Dev Unite Mixte Rech LittoralGeosci Environm F-31401 Toulouse France Inst Natl Rech Agr Alimentat & Environm Unite Mixte Rech Littoral Sol Agro & Hydrosyst Sp AgroCampus Ouest F-35043 Rennes France Indian Space Res Org Earth Observat & Disaster Management Programme Of Bangalore 560094 Karnataka India

Groundwater has become a major source of irrigation in the past few decades in India, but as it comes from millions of individual borewells owned by smallholders irrigating small fields, it is difficult to quantify the actual irrigated area across seasons and years. This study's main goal was to monitor seasonal irrigated cropland using multiple optical satellite images. The proposed research was performed over the Berambadi watershed, an experimental site in southern peninsular India. While cloud cover during crop growth is the greatest obstacle to optical remote sensing in tropical regions, the cloud-free images from multiple optical satellite platforms (Landsat-8 (OLI), EO1 (ALI), IRS-P6 (LISS3 and LISS4), and Spot5Take5 (HRG2)) were used to fill data gaps during crop growth periods. The seasonal cumulative normalized difference vegetation index (NDVI) was calculated and resampled at 5 m spatial resolution for various cropping seasons. The support vector machine (SVM) classification was applied to seasonal cumulative NDVI images for irrigated cropland area classification. Validation of the classified irrigated cropland was performed by calculating kappa coefficients for three cropping seasons (summer, kharif, and rabi) from 2014-2016 using ground observations. Kappa coefficients ranged from 0.81-0.96 for 2014-2015 and 0.62-0.89 for 2015-2016, except for summer 2016, when it was 1.00. Groundwater irrigation in the watershed ranged from 4.6% to 16.5% of total cropland during these cropping seasons. These results showed that multi-source optical satellite data are relevant for quantifying areas under groundwater irrigation in tropical regions.

关键词： groundwater irrigation optical remote sensing NDVI support vector machine classifier Kabini critical zone observatory

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Predicting and analyzing DNA-binding domains using a systematic approach to identifying a set of informative physicochemical and biochemical properties

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BMC BIOINFORMATICS 2011年第s1期12卷 S47-S47页

作者： Huang, Hui-Lin Lin, I-Che Liou, Yi-Fan Tsai, Chia-Ta Hsu, Kai-Ti Huang, Wen-Lin Ho, Shinn-Jang Ho, Shinn-Ying Natl Chiao Tung Univ Dept Biol Sci & Engn Hsinchu Taiwan Natl Chiao Tung Univ Inst Bioinformat & Syst Biol Hsinchu Taiwan Asia Pacific Inst Creat Dept Multimedia Entertainment Sci Miaoli Taiwan Natl Formosa Univ Dept Automat Engn Yunlin 632 Taiwan

Background: Existing methods of predicting DNA-binding proteins used valuable features of physicochemical properties to design support vector machine (SVM) based classifiers. Generally, selection of physicochemical properties and determination of their corresponding feature vectors rely mainly on known properties of binding mechanism and experience of designers. However, there exists a troublesome problem for designers that some different physicochemical properties have similar vectors of representing 20 amino acids and some closely related physicochemical properties have dissimilar vectors. Results: This study proposes a systematic approach (named Auto-IDPCPs) to automatically identify a set of physicochemical and biochemical properties in the AAindex database to design SVM-based classifiers for predicting and analyzing DNA-binding domains/proteins. Auto-IDPCPs consists of 1) clustering 531 amino acid indices in AAindex into 20 clusters using a fuzzy c-means algorithm, 2) utilizing an efficient genetic algorithm based optimization method IBCGA to select an informative feature set of size m to represent sequences, and 3) analyzing the selected features to identify related physicochemical properties which may affect the binding mechanism of DNA-binding domains/proteins. The proposed Auto-IDPCPs identified m=22 features of properties belonging to five clusters for predicting DNA-binding domains with a five-fold cross-validation accuracy of 87.12%, which is promising compared with the accuracy of 86.62% of the existing method PSSM-400. For predicting DNA-binding sequences, the accuracy of 75.50% was obtained using m=28 features, where PSSM-400 has an accuracy of 74.22%. Auto-IDPCPs and PSSM-400 have accuracies of 80.73% and 82.81%, respectively, applied to an independent test data set of DNA-binding domains. Some typical physicochemical properties discovered are hydrophobicity, secondary structure, charge, solvent accessibility, polarity, flexibility, normalized Van De

关键词： support vector machine Feature vector support vector machine classifier Binding Mechanism Orthogonal Experimental Design

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An efficient and robust deep learning based network anomaly detection against distributed denial of service attacks

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COMPUTER NETWORKS 2020年 180卷 107390-107390页

作者： Kasim, Omer Kutahya Dumlupinar Univ Simav Technol Fac Dept Elect & Elect Engn TR-43500 Kutahya Turkey

The number of devices connected to the Internet is increasing day by day. This increase causes cyber-attacks to be larger and more complex. It is important to sdetect the anomalies rapidly when there is a cyber-attack. In detecting anomalies, high false positive rate is obtained by using feature extraction based on statistical calculations and machine learning algorithms. In proposed approach, the measured values obtained from the network are normalized between 0 and 1. These values applied to autoencoder model trained with optimum hyper parameters. This model contributes to feature learning and dimensional reduction. support vector machines effectively differentiate between normal and DDOS attack traffic by using these features. The CICIDS dataset and virtually generated DDOS traffic are used to validate the proposed approach and measure its performance. The results show that the proposed approach speeds up training and testing times and performs better classification performance metrics than most previous approaches. The novelty of the study is that AE-SVM trained with CICIDS successfully captures virtually generated DDOS traffic data. Despite the unbalanced data set, 99.1% test success was achieved in detection of DDOS traffic which is produced with Kali Linux. This success contributed to the solution of the high false-positive problem compared to other models.

关键词： DOS attack detection Normalization Autoencoder deep learning model support vector machine classifier

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Improving the performance of SVM-RFE to select genes in microarray data

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BMC BIOINFORMATICS 2006年第s2期7卷 S12-S12页

作者： Ding, Yuanyuan Wilkins, Dawn Univ Mississippi Dept Comp & Informat Sci University MS 38677 USA

Background: Recursive Feature Elimination is a common and well-studied method for reducing the number of attributes used for further analysis or development of prediction models. The effectiveness of the RFE algorithm is generally considered excellent, but the primary obstacle in using it is the amount of computational power required. Results: Here we introduce a variant of RFE which employs ideas from simulated annealing. The goal of the algorithm is to improve the computational performance of recursive feature elimination by eliminating chunks of features at a time with as little effect on the quality of the reduced feature set as possible. The algorithm has been tested on several large gene expression data sets. The RFE algorithm is implemented using a support vector machine to assist in identifying the least useful gene(s) to eliminate. Conclusion: The algorithm is simple and efficient and generates a set of attributes that is very similar to the set produced by RFE.

关键词： support vector machine Simulated Annealing Acute Lymphoblastic Leukemia support vector machine classifier Prediction Rate

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