support vector machine

Summary

Summary: SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.

Top Publications

  1. Kundu K, Backofen R. Cluster based prediction of PDZ-peptide interactions. BMC Genomics. 2014;15 Suppl 1:S5 pubmed publisher
    ..families, covering 226 PDZ domains with available interaction data, we built specialized models using a support vector machine approach...
  2. Kim D, Lee S, Ro Y. Mass type-specific sparse representation for mass classification in computer-aided detection on mammograms. Biomed Eng Online. 2013;12 Suppl 1:S3 pubmed publisher
    ..In addition, a support vector machine (SVM) was used for comparing our method with state-of-the-arts classifier extensively used for mass ..
  3. Dansereau C, Benhajali Y, Risterucci C, Pich E, Orban P, Arnold D, et al. Statistical power and prediction accuracy in multisite resting-state fMRI connectivity. Neuroimage. 2017;149:220-232 pubmed publisher
    ..Taken together, our results support the feasibility of multisite studies in rs-fMRI provided the sample size is large enough. ..
  4. Liu K, Abdullah A, Huang M, Nishioka T, Altaf Ul Amin M, Kanaya S. Novel Approach to Classify Plants Based on Metabolite-Content Similarity. Biomed Res Int. 2017;2017:5296729 pubmed publisher
    ..We also discuss the predictive power of metabolite content in exploring nutritional and medicinal properties in plants. As a byproduct of our analysis, we could predict some currently unknown species-metabolite relations. ..
  5. Kim B, Jo J, Han J, Park C, Lee H. In silico re-identification of properties of drug target proteins. BMC Bioinformatics. 2017;18:248 pubmed publisher
    ..In addition, to predict the drug targetability of proteins, we exploited two machine learning methods (Support Vector Machine and Random Forest)...
  6. Ahmed F, Kaundal R, Raghava G. PHDcleav: a SVM based method for predicting human Dicer cleavage sites using sequence and secondary structure of miRNA precursors. BMC Bioinformatics. 2013;14 Suppl 14:S9 pubmed publisher
    ..In this study, a novel method has been developed to predict Dicer cleavage sites on pre-miRNAs using Support Vector Machine. We used the dataset of experimentally validated human miRNA hairpins from miRBase, and extracted fourteen ..
  7. Sakar B, Isenkul M, Sakar C, Sertbas A, Gürgen F, Delil S, et al. Collection and analysis of a Parkinson speech dataset with multiple types of sound recordings. IEEE J Biomed Health Inform. 2013;17:828-34 pubmed publisher
  8. Best M, Sol N, In t Veld S, Vancura A, Muller M, Niemeijer A, et al. Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets. Cancer Cell. 2017;32:238-252.e9 pubmed publisher
    ..PSO enabled selection of gene panels to diagnose cancer from TEPs, suggesting that swarm intelligence may also benefit the optimization of diagnostics readout of other liquid biopsy biosources. ..
  9. Elmas A, Wang X, Dresch J. The folded k-spectrum kernel: A machine learning approach to detecting transcription factor binding sites with gapped nucleotide dependencies. PLoS ONE. 2017;12:e0185570 pubmed publisher
    ..We also demonstrate the effectiveness of our new approach by reporting on its improved performance on a set of 127 genomic regions known to regulate gene expression along the anterio-posterior axis in early Drosophila embryos...

More Information

Publications106 found, 100 shown here

  1. Charoenkwan P, Hwang E, Cutler R, Lee H, Ko L, Huang H, et al. HCS-Neurons: identifying phenotypic changes in multi-neuron images upon drug treatments of high-content screening. BMC Bioinformatics. 2013;14 Suppl 16:S12 pubmed publisher
    ..and 3) generate an accurate classifier to group neurons treated by different drug concentrations using support vector machine and an intelligent feature selection method...
  2. Al Timemy A, Bugmann G, Escudero J, Outram N. Classification of finger movements for the dexterous hand prosthesis control with surface electromyography. IEEE J Biomed Health Inform. 2013;17:608-18 pubmed
    ..These accuracy values are higher than previous studies, whereas we typically employed half the number of EMG channels per identified movement. ..
  3. Printy B, Verma N, Cowperthwaite M, Markey M. Effects of genetic variation on the dynamics of neurodegeneration in Alzheimer's disease. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:2464-7 pubmed publisher
  4. Zhang M, Yang L, Ren J, Ahlgren N, Fuhrman J, Sun F. Prediction of virus-host infectious association by supervised learning methods. BMC Bioinformatics. 2017;18:60 pubmed publisher
    ..We also study five machine learning methods including logistic regression, support vector machine, random forest, Gaussian naive Bayes and Bernoulli naive Bayes for separating infectious from non-..
  5. Wu C, Wang X. Preliminary research on the identification system for anthracnose and powdery mildew of sandalwood leaf based on image processing. PLoS ONE. 2017;12:e0181537 pubmed publisher
  6. Thomas E, Temko A, Marnane W, Boylan G, Lightbody G. Discriminative and generative classification techniques applied to automated neonatal seizure detection. IEEE J Biomed Health Inform. 2013;17:297-304 pubmed publisher
    ..A particular strength of the proposed algorithm is the performance of the algorithm when very low false detections are required, at 0.25 false detections per hour, the system is able to detect 75.4% of the seizure events. ..
  7. Yu X, Weber I, Harrison R. Prediction of HIV drug resistance from genotype with encoded three-dimensional protein structure. BMC Genomics. 2014;15 Suppl 5:S1 pubmed publisher
    ..approach gives high accuracy with a new sparse dictionary classification method, as well as support vector machine and artificial neural networks classifiers...
  8. Yin E, Zeyl T, Saab R, Chau T, Hu D, Zhou Z. A Hybrid Brain-Computer Interface Based on the Fusion of P300 and SSVEP Scores. IEEE Trans Neural Syst Rehabil Eng. 2015;23:693-701 pubmed publisher
    ..18%. These promising results suggest that the proposed hybrid BCI system could be used in the design of a high-performance BCI-based keyboard. ..
  9. Lee M, Roos P, Sharma N, Atalar M, Evans T, Pellicore M, et al. Systematic Computational Identification of Variants That Activate Exonic and Intronic Cryptic Splice Sites. Am J Hum Genet. 2017;100:751-765 pubmed publisher
    ..Our findings suggest that cryptic splice-site activation is more common than previously thought and should be routinely considered for all variants within the transcribed regions of genes. ..
  10. Deng C, Wu C, Lyu N, Huang Z. Driving style recognition method using braking characteristics based on hidden Markov model. PLoS ONE. 2017;12:e0182419 pubmed publisher
    ..The results showed that the driving style discrimination based on hidden Markov model algorithm could realize effective discriminant of driving style. ..
  11. Zieliński B, Plichta A, Misztal K, Spurek P, Brzychczy Włoch M, Ochońska D. Deep learning approach to bacterial colony classification. PLoS ONE. 2017;12:e0184554 pubmed publisher
    ..deep Convolutional Neural Networks to obtain image descriptors, which are then encoded and classified with Support Vector Machine or Random Forest...
  12. Chen Y, Huang P, Lin K, Lin H, Wang L, Cheng C, et al. Semi-automatic segmentation and classification of Pap smear cells. IEEE J Biomed Health Inform. 2014;18:94-108 pubmed publisher
    ..segmenting nuclear and cytoplasmic contours and for computing morphometric and textual features to train support vector machine (SVM) classifiers to classify four different types of cells and to discriminate dysplastic from normal ..
  13. Yu S, Wu S, Wang L, Jiang F, Xie Y, Li L. A shallow convolutional neural network for blind image sharpness assessment. PLoS ONE. 2017;12:e0176632 pubmed publisher
    ..Experiments on Gaussian blur images from LIVE-II, CSIQ, TID2008 and TID2013 demonstrate that CNN features with SVR achieves the best overall performance, indicating high correlation with human subjective judgment. ..
  14. González Castro V, Valdés Hernández M, Chappell F, Armitage P, Makin S, Wardlaw J. Reliability of an automatic classifier for brain enlarged perivascular spaces burden and comparison with human performance. Clin Sci (Lond). 2017;131:1465-1481 pubmed publisher
    ..We propose a fully automatic scheme that uses a support vector machine (SVM) to classify the burden of PVS in the basal ganglia (BG) region as low or high...
  15. Blanc Durand P, Van Der Gucht A, Guedj E, Abulizi M, Aoun Sebaiti M, Lerman L, et al. Cerebral 18F-FDG PET in macrophagic myofasciitis: An individual SVM-based approach. PLoS ONE. 2017;12:e0181152 pubmed publisher
    ..Aim was to generate and evaluate a support vector machine (SVM) procedure to classify patients between healthy or MMF 18F-FDG brain profiles...
  16. Obrzut B, Kusy M, Semczuk A, Obrzut M, Kluska J. Prediction of 5-year overall survival in cervical cancer patients treated with radical hysterectomy using computational intelligence methods. BMC Cancer. 2017;17:840 pubmed publisher
    ..818. The outcomes obtained by other classifiers were markedly worse. The PNN model is an effective tool for predicting 5-year overall survival in cervical cancer patients treated with radical hysterectomy. ..
  17. Kang T, Ding W, Zhang L, Ziemek D, Zarringhalam K. A biological network-based regularized artificial neural network model for robust phenotype prediction from gene expression data. BMC Bioinformatics. 2017;18:565 pubmed publisher
    ..The integrated group-wise regularization methods increases the interpretability of biological signatures and gives stable performance estimates across independent test sets. ..
  18. Folkman L, Stantic B, Sattar A. Feature-based multiple models improve classification of mutation-induced stability changes. BMC Genomics. 2014;15 Suppl 4:S6 pubmed publisher
    ..Therefore, our results support the presumption that different interactions govern stability changes in the exposed and buried residues or in residues with a different secondary structure. ..
  19. Salas Zárate M, Medina Moreira J, Lagos Ortiz K, Luna Aveiga H, Rodríguez García M, Valencia Garcia R. Sentiment Analysis on Tweets about Diabetes: An Aspect-Level Approach. Comput Math Methods Med. 2017;2017:5140631 pubmed publisher
    ..The experimental results show that the best result was obtained through the N-gram around method with a precision of 81.93%, a recall of 81.13%, and an F-measure of 81.24%. ..
  20. Kiselev V, Kirschner K, Schaub M, Andrews T, Yiu A, Chandra T, et al. SC3: consensus clustering of single-cell RNA-seq data. Nat Methods. 2017;14:483-486 pubmed publisher
    ..org/packages/SC3). We demonstrate that SC3 is capable of identifying subclones from the transcriptomes of neoplastic cells collected from patients. ..
  21. Kim E, Nam H. Prediction models for drug-induced hepatotoxicity by using weighted molecular fingerprints. BMC Bioinformatics. 2017;18:227 pubmed publisher
    ..fingerprint features, the prediction models were trained and evaluated with the Random Forest (RF) and Support Vector Machine (SVM) algorithms. The constructed models yielded accuracies of 73.8% and 72.6%, AUCs of 0.791 and 0...
  22. Carvalho S, Guerra Sa R, de C Merschmann L. The impact of sequence length and number of sequences on promoter prediction performance. BMC Bioinformatics. 2015;16 Suppl 19:S5 pubmed publisher
    ..Furthermore, increasing the number of sequences in a dataset proved to be beneficial to the predictive power of classifiers. ..
  23. Li Z, You Z, Chen X, Li L, Huang D, Yan G, et al. Accurate prediction of protein-protein interactions by integrating potential evolutionary information embedded in PSSM profile and discriminative vector machine classifier. Oncotarget. 2017;8:23638-23649 pubmed publisher
    ..To further validate the performance of the proposed method, we compared it with the state-of-the-art support vector machine (SVM) classifier on Human data set...
  24. Hsu K, Wang F. Model-based optimization approaches for precision medicine: A case study in presynaptic dopamine overactivity. PLoS ONE. 2017;12:e0179575 pubmed publisher
    ..The cluster analysis and support vector machine was used to detect optimal biomarkers in order to discriminate the accurate etiology from three classes of ..
  25. Marchese Robinson R, Palczewska A, Palczewski J, Kidley N. Comparison of the Predictive Performance and Interpretability of Random Forest and Linear Models on Benchmark Data Sets. J Chem Inf Model. 2017;57:1773-1792 pubmed publisher
    ..These programs are the rfFC package ( https://r-forge.r-project.org/R/?group_id=1725 ) for the R statistical programming language and the Python program HeatMapWrapper [ https://doi.org/10.5281/zenodo.495163 ] for heat map generation. ..
  26. Cabral C, Silveira M. Classification of Alzheimer's disease from FDG-PET images using favourite class ensembles. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:2477-80 pubmed publisher
    ..Although the large majority of the existing techniques rely on a single classifier such as the Support Vector Machine (SVM), several ensemble methods such as Adaboost or Random Forests (RF) have also been explored...
  27. Yang W, Yoshigoe K, Qin X, Liu J, Yang J, Niemierko A, et al. Identification of genes and pathways involved in kidney renal clear cell carcinoma. BMC Bioinformatics. 2014;15 Suppl 17:S2 pubmed publisher
    ..Based on these findings, we built a support vector machine based supervised-learning classifier to predict unknown samples, and the classifier achieved high accuracy ..
  28. Yuan F, Zhang Y, Kong X, Cai Y. Identification of Candidate Genes Related to Inflammatory Bowel Disease Using Minimum Redundancy Maximum Relevance, Incremental Feature Selection, and the Shortest-Path Approach. Biomed Res Int. 2017;2017:5741948 pubmed publisher
    ..Analyses of the 41 genes obtained indicate that they are closely associated with this disease. ..
  29. Wang W, Sun L, Zhang S, Zhang H, Shi J, Xu T, et al. Analysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences. BMC Bioinformatics. 2017;18:300 pubmed publisher
    ..position-specific scoring matrix profiles) and split amino acid composition (SAA), and then we adopt SVM (support vector machine) and RF (random forest) classification model to distinguish SSBs from DSBs...
  30. Kim S, Yoo T, Oh E, Kim D. Osteoporosis risk prediction using machine learning and conventional methods. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:188-91 pubmed publisher
    ..The machine learning methods may be effective tools for identifying postmenopausal women at high risk for osteoporosis. ..
  31. Hajiloo M, Rabiee H, Anooshahpour M. Fuzzy support vector machine: an efficient rule-based classification technique for microarrays. BMC Bioinformatics. 2013;14 Suppl 13:S4 pubmed publisher
    ..This paper introduces fuzzy support vector machine which is a learning algorithm based on combination of fuzzy classifiers and kernel machines for microarray ..
  32. Su M, Lee T. Incorporating substrate sequence motifs and spatial amino acid composition to identify kinase-specific phosphorylation sites on protein three-dimensional structures. BMC Bioinformatics. 2013;14 Suppl 16:S2 pubmed publisher
    ..cse.yzu.edu.tw/PhosK3D/. Due to the difficulty of identifying the kinase-specific phosphorylation sites with similar sequenced motifs, this work also integrates the 3D structural information to improve the cross classifying specificity. ..
  33. Wu H, Lu C, Kao H, Chen Y, Chen Y, Lee T. Characterization and identification of protein O-GlcNAcylation sites with substrate specificity. BMC Bioinformatics. 2014;15 Suppl 16:S1 pubmed publisher
    ..This method may help unravel their mechanisms and roles in signaling, transcription, chronic disease, and cancer. ..
  34. Xu R, Zhou J, Wang H, He Y, Wang X, Liu B. Identifying DNA-binding proteins by combining support vector machine and PSSM distance transformation. BMC Syst Biol. 2015;9 Suppl 1:S10 pubmed publisher
    ..a DNA-binding protein identification method (SVM-PSSM-DT) by combining PSSM Distance Transformation with support vector machine (SVM)...
  35. Moon M, Nakai K. Stable feature selection based on the ensemble L 1 -norm support vector machine for biomarker discovery. BMC Genomics. 2016;17:1026 pubmed publisher
    ..We apply an ensemble L 1 -norm support vector machine to efficiently reduce irrelevant features, considering the stability of features...
  36. Yavuz A, Sözer N, Sezerman O. Prediction of neddylation sites from protein sequences and sequence-derived properties. BMC Bioinformatics. 2015;16 Suppl 18:S9 pubmed publisher
    ..We have developed a neddylation site prediction method using a support vector machine based on various sequence properties, position-specific scoring matrices, and disorder...
  37. Liu H, Guo M, Xue T, Guan J, Luo L, Zhuang Z. Screening lifespan-extending drugs in Caenorhabditis elegans via label propagation on drug-protein networks. BMC Syst Biol. 2016;10:131 pubmed publisher
    ..elegans. In silico empirical evaluations and in vivo experiments in C. elegans have demonstrated that our method can effectively narrow down the scope of candidate drugs needed to be verified by wet lab experiments. ..
  38. Jin B, Liu R, Hao S, Li Z, Zhu C, Zhou X, et al. Defining and characterizing the critical transition state prior to the type 2 diabetes disease. PLoS ONE. 2017;12:e0180937 pubmed publisher
    ..This 6-month window before the disease state provides an early warning of the impending T2DM, warranting an opportunity to apply proactive interventions to prevent or delay the new onset of T2DM. ..
  39. Yin W, Zhang C, Zhu H, Zhao Y, He Y. Application of near-infrared hyperspectral imaging to discriminate different geographical origins of Chinese wolfberries. PLoS ONE. 2017;12:e0180534 pubmed publisher
    ..curves were pretreated with moving average smoothing (MA), and discriminant analysis models including support vector machine (SVM), neural network with radial basis function (NN-RBF) and extreme learning machine (ELM) were ..
  40. Zhang B, Kumar B, Zhang D. Detecting diabetes mellitus and nonproliferative diabetic retinopathy using tongue color, texture, and geometry features. IEEE Trans Biomed Eng. 2014;61:491-501 pubmed
    ..52% and 80.33%, respectively. This is on a database consisting of 130 Healthy and 296 DM samples, where 29 of those in DM are NPDR. ..
  41. Cecotti H, Eckstein M, Giesbrecht B. Single-trial classification of event-related potentials in rapid serial visual presentation tasks using supervised spatial filtering. IEEE Trans Neural Netw Learn Syst. 2014;25:2030-42 pubmed publisher
    ..The results support the conclusion that the choice of the systems architecture is critical and both spatial filtering and classification must be considered together. ..
  42. Yavuz A, Sezerman O. Predicting sumoylation sites using support vector machines based on various sequence features, conformational flexibility and disorder. BMC Genomics. 2014;15 Suppl 9:S18 pubmed publisher
  43. Bin Altaf M, Yoo J. A 1.83 ?J/Classification, 8-Channel, Patient-Specific Epileptic Seizure Classification SoC Using a Non-Linear Support Vector Machine. IEEE Trans Biomed Circuits Syst. 2016;10:49-60 pubmed publisher
    A non-linear support vector machine (NLSVM) seizure classification SoC with 8-channel EEG data acquisition and storage for epileptic patients is presented...
  44. Cheffena M. Fall Detection Using Smartphone Audio Features. IEEE J Biomed Health Inform. 2016;20:1073-80 pubmed publisher
    ..audio features, four different machine learning classifiers: k-nearest neighbor classifier (k-NN), support vector machine (SVM), least squares method (LSM), and artificial neural network (ANN) are investigated for distinguishing ..
  45. Rafii Tari H, Payne C, Bicknell C, Kwok K, Cheshire N, Riga C, et al. Objective Assessment of Endovascular Navigation Skills with Force Sensing. Ann Biomed Eng. 2017;45:1315-1327 pubmed publisher
    ..two experience groups in their force and motion patterns across different phases of the procedures, with support vector machine (SVM) classification showing cross-validation accuracies as high as 90% between the two skill levels...
  46. Du H, Cai Y, Yang H, Zhang H, Xue Y, Liu G, et al. In Silico Prediction of Chemicals Binding to Aromatase with Machine Learning Methods. Chem Res Toxicol. 2017;30:1209-1218 pubmed publisher
    ..Our study provided a systematic assessment of chemicals binding to aromatase. The built models can be helpful to rapidly identify potential EDCs targeting aromatase. ..
  47. Hardy J, Mooney S, Pearson A, McGuire D, Correa D, Simon R, et al. Assessing the accuracy of blood RNA profiles to identify patients with post-concussion syndrome: A pilot study in a military patient population. PLoS ONE. 2017;12:e0183113 pubmed publisher
    ..Patterns of differential exon expression were used for diagnostic modeling using support vector machine classification, and then validated in a second patient cohort...
  48. Chen L, Zhang Y, Wang S, Zhang Y, Huang T, Cai Y. Prediction and analysis of essential genes using the enrichments of gene ontology and KEGG pathways. PLoS ONE. 2017;12:e0184129 pubmed publisher
    ..Then, the incremental feature selection (IFS) and support vector machine (SVM) were employed to extract important GO terms and KEGG pathways...
  49. Dong Z, Wang R, Fan M, Fu X. Switching and optimizing control for coal flotation process based on a hybrid model. PLoS ONE. 2017;12:e0186553 pubmed publisher
    ..Finally, the least squares-support vector machine (LS-SVM) is used to identify the operating conditions of the flotation process and to select one of the ..
  50. Liu B, Xu J, Zou Q, Xu R, Wang X, Chen Q. Using distances between Top-n-gram and residue pairs for protein remote homology detection. BMC Bioinformatics. 2014;15 Suppl 2:S3 pubmed publisher
    ..The source code of SVM-DT and SVM-DR is available at http://bioinformatics.hitsz.edu.cn/DistanceSVM/index.jsp. ..
  51. Ruan P, Hayashida M, Maruyama O, Akutsu T. Prediction of heterotrimeric protein complexes by two-phase learning using neighboring kernels. BMC Bioinformatics. 2014;15 Suppl 2:S6 pubmed publisher
    ..The two-phase method with the extended features and the domain composition kernel using SVM as the second classifier is particularly useful for prediction of heterotrimeric protein complexes. ..
  52. Zhang F, Song Y, Cai W, Lee M, Zhou Y, Huang H, et al. Lung nodule classification with multilevel patch-based context analysis. IEEE Trans Biomed Eng. 2014;61:1155-66 pubmed publisher
    ..Our proposed method was evaluated on a publicly available dataset and clearly demonstrated promising classification performance. ..
  53. You Z, Zhu L, Zheng C, Yu H, Deng S, Ji Z. Prediction of protein-protein interactions from amino acid sequences using a novel multi-scale continuous and discontinuous feature set. BMC Bioinformatics. 2014;15 Suppl 15:S9 pubmed publisher
    ..developed by combining a novel Multi-scale Continuous and Discontinuous (MCD) feature representation and Support Vector Machine (SVM)...
  54. Huang G, Huang K, Lee T, Weng J. An interpretable rule-based diagnostic classification of diabetic nephropathy among type 2 diabetes patients. BMC Bioinformatics. 2015;16 Suppl 1:S5 pubmed publisher
    ..clinical or genetic features alone in various classifiers (decision tree, random forest, Naïve Bayes, and support vector machine) was compared with that of utilizing a combination of attributes...
  55. Chen K, Li R, Dou Y, Liang Z, Lv Q. Ranking Support Vector Machine with Kernel Approximation. Comput Intell Neurosci. 2017;2017:4629534 pubmed publisher
    ..Ranking support vector machine (RankSVM) is one of the state-of-art ranking models and has been favorably used...
  56. Hilbert K, Lueken U, Muehlhan M, Beesdo Baum K. Separating generalized anxiety disorder from major depression using clinical, hormonal, and structural MRI data: A multimodal machine learning study. Brain Behav. 2017;7:e00633 pubmed publisher
  57. Yan K, Xu Y, Fang X, Zheng C, Liu B. Protein fold recognition based on sparse representation based classification. Artif Intell Med. 2017;79:1-8 pubmed publisher
    ..It is anticipated that the proposed predictor may become a useful high throughput tool for large-scale fold recognition or at least, play a complementary role to the existing predictors in this regard. ..
  58. Begum T, Ghosh T, Basak S. Systematic Analyses and Prediction of Human Drug Side Effect Associated Proteins from the Perspective of Protein Evolution. Genome Biol Evol. 2017;9:337-350 pubmed publisher
    ..By employing all these entities, our Support Vector Machine model predicts human SET/NSET proteins to a high degree of accuracy (?86%).
  59. Leng X, Wang J, Ji H, Wang Q, Li H, Qian X, et al. Prediction of size-fractionated airborne particle-bound metals using MLR, BP-ANN and SVM analyses. Chemosphere. 2017;180:513-522 pubmed publisher
    ..based on multiple linear regression (MLR), back propagation artificial neural network (BP-ANN) and support vector machine (SVM) by using meteorological factors and PM concentrations as input parameters...
  60. Yokota S, Endo M, Ohe K. Establishing a Classification System for High Fall-Risk Among Inpatients Using Support Vector Machines. Comput Inform Nurs. 2017;35:408-416 pubmed publisher
    We constructed a model using a support vector machine to determine whether an inpatient will suffer a fall on a given day, depending on patient status on the previous day...
  61. Li C, Ai D. Automatic crack detection method for loaded coal in vibration failure process. PLoS ONE. 2017;12:e0185750 pubmed publisher
    ..by a vibration failure process is proposed based on the characteristics of the surface cracks of coal and support vector machine (SVM)...
  62. Petti M, Toppi J, Pichiorri F, Cincotti F, Salinari S, Babiloni F, et al. Aged-related changes in brain activity classification with respect to age by means of graph indexes. Conf Proc IEEE Eng Med Biol Soc. 2013;2013:4350-3 pubmed publisher
  63. Li F, Tran L, Thung K, Ji S, Shen D, Li J. A Robust Deep Model for Improved Classification of AD/MCI Patients. IEEE J Biomed Health Inform. 2015;19:1610-6 pubmed publisher
    ..Experimental results showed that the dropout technique is very effective in AD diagnosis, improving the classification accuracies by 5.9% on average as compared to the classical deep learning methods. ..
  64. Zhao B, Wang J, Wu F. Computational Methods to Predict Protein Functions from Protein-Protein Interaction Networks. Curr Protein Pept Sci. 2017;18:1120-1131 pubmed publisher
    ..We then focus on the various approaches available in categories of supervised and unsupervised methods for predicting protein functions. Finally, we discuss challenges and future works in this field. ..
  65. Xu Y, Jia Z, Wang L, Ai Y, Zhang F, Lai M, et al. Large scale tissue histopathology image classification, segmentation, and visualization via deep convolutional activation features. BMC Bioinformatics. 2017;18:281 pubmed publisher
    ..CNN features are significantly more powerful than expert-designed features. ..
  66. Araújo T, Aresta G, Castro E, Rouco J, Aguiar P, Eloy C, et al. Classification of breast cancer histology images using Convolutional Neural Networks. PLoS ONE. 2017;12:e0177544 pubmed publisher
    ..The features extracted by the CNN are also used for training a Support Vector Machine classifier. Accuracies of 77.8% for four class and 83.3% for carcinoma/non-carcinoma are achieved...
  67. Klein M, Stern D, Zhao H. GRAPE: a pathway template method to characterize tissue-specific functionality from gene expression profiles. BMC Bioinformatics. 2017;18:317 pubmed publisher
    ..GRAPE is available in R for download at https://CRAN.R-project.org/package=GRAPE . ..
  68. Tang Y, Liu D, Wang Z, Wen T, Deng L. A boosting approach for prediction of protein-RNA binding residues. BMC Bioinformatics. 2017;18:465 pubmed publisher
    ..92, which are significantly better than that of other widely used machine learning algorithms such as Support Vector Machine, Random Forest, and Adaboost...
  69. Wang X, Kuwahara H, Gao X. Modeling DNA affinity landscape through two-round support vector regression with weighted degree kernels. BMC Syst Biol. 2014;8 Suppl 5:S5 pubmed publisher
    ..Since the ability to modify genetic parts to fine-tune gene expression rates is crucial to the design of biological systems, such a tool may play an important role in the success of synthetic biology going forward. ..
  70. Liu X, Bordes A, Grandvalet Y. Extracting biomedical events from pairs of text entities. BMC Bioinformatics. 2015;16 Suppl 10:S8 pubmed publisher
    ..Our method focuses on the core event extraction of the Genia task of BioNLP challenges yielding the best result reported so far on the 2013 edition. ..
  71. Alam M, Garg A, Munia T, Fazel Rezai R, Tavakolian K. Vertical ground reaction force marker for Parkinson's disease. PLoS ONE. 2017;12:e0175951 pubmed publisher
    ..b>Support Vector Machine (SVM), K-nearest neighbor (KNN), random forest, and decision trees classifiers were used to build the ..
  72. Pani D, Usai I, Cosseddu P, Melis M, Sollai G, Crnjar R, et al. An automated system for the objective evaluation of human gustatory sensitivity using tongue biopotential recordings. PLoS ONE. 2017;12:e0177246 pubmed publisher
  73. Bajaj V, Pachori R. Classification of seizure and non-seizure EEG signals using empirical mode decomposition. IEEE Trans Inf Technol Biomed. 2012;16:1135-42 pubmed publisher
    ..modulation bandwidth (B(FM)), computed from the analytic IMFs, have been used as an input to least squares support vector machine (LS-SVM) for classifying seizure and non-seizure EEG signals...
  74. Lartizien C, Rogez M, Niaf E, Ricard F. Computer-aided staging of lymphoma patients with FDG PET/CT imaging based on textural information. IEEE J Biomed Health Inform. 2014;18:946-55 pubmed publisher
    ..Detection performance of the support vector machine (SVM) classifier was assessed based on feature sets including 105 positron emission tomography (PET) and ..
  75. Figueroa R, Soto D, Pino E. Identifying and extracting patient smoking status information from clinical narrative texts in Spanish. Conf Proc IEEE Eng Med Biol Soc. 2014;2014:2710-3 pubmed publisher
    ..For S vs NS classification level performance measures were: ACC=85%, Precision=85%, and Recall=90%. For CS vs PS classification level performance measures were: ACC=87%, Precision=91%, and Recall=94%. ..
  76. Srinivasulu Y, Wang J, Hsu K, Tsai M, Charoenkwan P, Huang W, et al. Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes. BMC Bioinformatics. 2015;16 Suppl 18:S14 pubmed publisher
    ..This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based ..
  77. Akhavan A, Moradi M, Vand S. Subject-based discriminative sparse representation model for detection of concealed information. Comput Methods Programs Biomed. 2017;143:25-33 pubmed publisher
    ..Indeed, this property eliminates the necessity of several subject EEG data in model learning and decision making for a specific subject. ..
  78. Lee Y, Hsieh Y, Shiah Y, Lin Y, Chen C, Tyan Y, et al. A cross-sectional evaluation of meditation experience on electroencephalography data by artificial neural network and support vector machine classifiers. Medicine (Baltimore). 2017;96:e6612 pubmed publisher
    ..the meditation experience with 2 artificial intelligence techniques: artificial neural network and support vector machine. Within this analysis system, 3 features of the electroencephalography alpha spectrum and variant ..
  79. Zhou Q, Goryawala M, Cabrerizo M, Wang J, Barker W, Loewenstein D, et al. An optimal decisional space for the classification of Alzheimer's disease and mild cognitive impairment. IEEE Trans Biomed Eng. 2014;61:2245-53 pubmed publisher
    ..Furthermore, hippocampal atrophy is seen to be the most significant for aMCI, while Accumbens area and ventricle are most significant for naMCI. ..
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    ..2% and weighted average recall 93.9% for all classes. Furthermore, the hybrid system can provide a tool for diagnosis of diabetes, and it supports a second opinion for lay users. ..
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    ..A web server is developed to facilitate the other researchers, which can be accessed at http://server.malab.cn/preTata/ . ..
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    ..Finally, the selected features are classified by support vector machine (SVM)...
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    ..Finally, the most discriminative connectivity features provided insights about the pathophysiology of ADHD and showed reduced and altered connectivity involving the left orbitofrontal cortex and various cerebellar regions in ADHD. ..
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    ..The experimental results denote that new features by support vector machine (SVM) have the best performance, with a recognition rate of 84.4% on NKI-CCRT corpus and 78...