Identification of a support vector machine-based biomarker panel with high sensitivity and specificity for nonalcoholic steatohepatitis

2012 ◽  
Vol 414 ◽  
pp. 154-157 ◽  
Author(s):  
Yusuf Yilmaz ◽  
Fatih Eren
Keyword(s):  
Support Vector Machine ◽  
Sensitivity And Specificity ◽  
Nonalcoholic Steatohepatitis ◽  
High Sensitivity ◽  
Support Vector ◽  
Biomarker Panel

scholarly journals A Novel Method for Evaluating the Cardiotoxicity of Traditional Chinese Medicine Compatibility by Using Support Vector Machine Model Combined with Metabonomics

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Yubo Li ◽  
Haonan Zhou ◽  
Jiabin Xie ◽  
Mayassa Salum Ally ◽  
Zhiguo Hou ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Chinese Medicine ◽  
Traditional Chinese Medicine ◽  
Sensitivity And Specificity ◽  
High Sensitivity ◽  
Support Vector ◽  
Machine Model ◽  
Svm Model ◽  
Long Time ◽  
Novel Method

Traditional biochemical and histopathological tests have been used to evaluate the safety of traditional Chinese medicine (TCM) compatibility for a long time. But these methods lack high sensitivity and specificity. In the previous study, we have found ten biomarkers related to cardiotoxicity and established a support vector machine (SVM) prediction model. Results showed a good sensitivity and specificity. Therefore, in this study, we used SVM model combined with metabonomics UPLC/Q-TOF-MS technology to build a rapid and sensitivity and specificity method to predict the cardiotoxicity of TCM compatibility. This study firstly applied SVM model to the prediction of cardiotoxicity in TCM compatibility containingAconiti Lateralis Radix Praeparataand further identified whether the cardiotoxicity increased afterAconiti Lateralis Radix Praeparatacombined with other TCM. This study provides a new idea for studying the evaluation of the cardiotoxicity caused by compatibility of TCM.

scholarly journals Proof-of-Concept Study on an Automatic Computational System in Detecting and Classifying Occlusal Caries Lesions from Smartphone Color Images of Unrestored Extracted Teeth

Diagnostics ◽  
2021 ◽  
Vol 11 (7) ◽  
pp. 1136
Author(s):  
Duc Long Duong ◽  
Quoc Duy Nam Nguyen ◽  
Minh Son Tong ◽  
Manh Tuan Vu ◽  
Joseph Dy Lim ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Dental Caries ◽  
Sensitivity And Specificity ◽  
Color Images ◽  
Caries Detection ◽  
Support Vector ◽  
Proof Of Concept ◽  
Computational System ◽  
Occlusal Caries ◽  
Concept Study

Dental caries has been considered the heaviest worldwide oral health burden affecting a significant proportion of the population. To prevent dental caries, an appropriate and accurate early detection method is demanded. This proof-of-concept study aims to develop a two-stage computational system that can detect early occlusal caries from smartphone color images of unrestored extracted teeth according to modified International Caries Detection and Assessment System (ICDAS) criteria (3 classes: Code 0; Code 1-2; Code 3-6): in the first stage, carious lesion areas were identified and extracted from sound tooth regions. Then, five characteristic features of these areas were intendedly selected and calculated to be inputted into the classification stage, where five classifiers (Support Vector Machine, Random Forests, K-Nearest Neighbors, Gradient Boosted Tree, Logistic Regression) were evaluated to determine the best one among them. On a set of 587 smartphone images of extracted teeth, our system achieved accuracy, sensitivity, and specificity that were 87.39%, 89.88%, and 68.86% in the detection stage when compared to modified visual and image-based ICDAS criteria. For the classification stage, the Support Vector Machine model was recorded as the best model with accuracy, sensitivity, and specificity at 88.76%, 92.31%, and 85.21%. As the first step in developing the technology, our present findings confirm the feasibility of using smartphone color images to employ Artificial Intelligence algorithms in caries detection. To improve the performance of the proposed system, there is a need for further development in both in vitro and in vivo modeling. Besides that, an applicable system for accurately taking intra-oral images that can capture entire dental arches including the occlusal surfaces of premolars and molars also needs to be developed.

scholarly journals Identification of an epigenetic biomarker panel with high sensitivity and specificity for colorectal cancer and adenomas

2011 ◽  
Vol 10 (1) ◽  
pp. 85 ◽  
Author(s):  
Guro E Lind ◽  
Stine A Danielsen ◽  
Terje Ahlquist ◽  
Marianne A Merok ◽  
Kim Andresen ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Sensitivity And Specificity ◽  
High Sensitivity ◽  
Biomarker Panel ◽  
Epigenetic Biomarker

scholarly journals Automated Cell Selection Using Support Vector Machine for Application to Spectral Nanocytology

2016 ◽  
Vol 2016 ◽  
pp. 1-10 ◽  
Author(s):  
Qin Miao ◽  
Justin Derbas ◽  
Aya Eid ◽  
Hariharan Subramanian ◽  
Vadim Backman
Keyword(s):  
Support Vector Machine ◽  
High Sensitivity ◽  
Diagnostic Value ◽  
Support Vector ◽  
Cell Selection ◽  
Selection Algorithm ◽  
Training Set ◽  
Cell Structures ◽  
Segmentation Algorithms ◽  
Partial Wave Spectroscopy

Partial wave spectroscopy (PWS) enables quantification of the statistical properties of cell structures at the nanoscale, which has been used to identify patients harboring premalignant tumors by interrogating easily accessible sites distant from location of the lesion. Due to its high sensitivity, cells that are well preserved need to be selected from the smear images for further analysis. To date, such cell selection has been done manually. This is time-consuming, is labor-intensive, is vulnerable to bias, and has considerable inter- and intraoperator variability. In this study, we developed a classification scheme to identify and remove the corrupted cells or debris that are of no diagnostic value from raw smear images. The slide of smear sample is digitized by acquiring and stitching low-magnification transmission. Objects are then extracted from these images through segmentation algorithms. A training-set is created by manually classifying objects as suitable or unsuitable. A feature-set is created by quantifying a large number of features for each object. The training-set and feature-set are used to train a selection algorithm using Support Vector Machine (SVM) classifiers. We show that the selection algorithm achieves an error rate of 93% with a sensitivity of 95%.

Diagnostic Classification of ADHD Versus Control: Support Vector Machine Classification Using Brief Neuropsychological Assessment

2016 ◽  
Vol 24 (11) ◽  
pp. 1547-1556 ◽  
Author(s):  
Jesse C. Bledsoe ◽  
Cao Xiao ◽  
Art Chaovalitwongse ◽  
Sonya Mehta ◽  
Thomas J. Grabowski ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Decision Tree ◽  
Sensitivity And Specificity ◽  
Neuropsychological Assessment ◽  
Feature Selection Method ◽  
Diagnostic Classification ◽  
Support Vector ◽  
Tree Model ◽  
Children With Adhd

Objective: Common methods for clinical diagnosis include clinical interview, behavioral questionnaires, and neuropsychological assessment. These methods rely on clinical interpretation and have variable reliability, sensitivity, and specificity. The goal of this study was to evaluate the utility of machine learning in the prediction and classification of children with ADHD–Combined presentation (ADHD-C) using brief neuropsychological measures (d2 Test of Attention, Children with ADHD-C and typically developing control children completed semi-structured clinical interviews and measures of attention/concentration and parents completed symptom severity questionnaires. Method: We used a forward feature selection method to identify the most informative neuropsychological features for support vector machine (SVM) classification and a decision tree model to derive a rule-based model. Results: The SVM model yielded excellent classification accuracy (100%) of individual children with and without ADHD (1.0). Decision tree algorithms identified individuals with and without ADHD-C with 100% sensitivity and specificity. Conclusion:This study observed highly accurate statistical diagnostic classification, at the individual level, in a sample of children with ADHD-C. The findings suggest data-driven behavioral algorithms based on brief neuropsychological data may present an efficient and accurate diagnostic tool for clinicians.

scholarly journals iHyd-LysSite (EPSV): Identifying Hydroxylysine Sites in Protein Using Statistical Formulation by Extracting Enhanced Position and Sequence Variant Feature Technique

2020 ◽  
Vol 21 (7) ◽  
pp. 536-545
Author(s):  
Muhammad Khalid Mahmood ◽  
Asma Ehsan ◽  
Yaser Daanial Khan ◽  
Kuo-Chen Chou
Keyword(s):  
Sensitivity And Specificity ◽  
High Sensitivity ◽  
Order Effect ◽  
Support Vector ◽  
Hydroxyl Groups ◽  
Sequence Variant ◽  
Cellular Functions ◽  
Post Translational Modifications ◽  
Jackknife Test ◽  
Prediction Rate

Introduction: Hydroxylation is one of the most important post-translational modifications (PTM) in cellular functions and is linked to various diseases. The addition of one of the hydroxyl groups (OH) to the lysine sites produces hydroxylysine when undergoes chemical modification. Methods: The method which is used in this study for identifying hydroxylysine sites based on powerful mathematical and statistical methodology incorporating the sequence-order effect and composition of each object within protein sequences. This predictor is called "iHyd-LysSite (EPSV)" (identifying hydroxylysine sites by extracting enhanced position and sequence variant technique). The prediction of hydroxylysine sites by experimental methods is difficult, laborious and highly expensive. In silico technique is an alternative approach to identify hydroxylysine sites in proteins. Results: The experimental results require that the predictive model should have high sensitivity and specificity values and must be more accurate. The self-consistency, independent, 10-fold crossvalidation and jackknife tests are performed for validation purposes. These tests are resulted by using three renowned classifiers, Neural Networks (NN), Random Forest (RF) and Support Vector Machine (SVM) with the demanding prediction rate. The overall predictive outcomes are extraordinarily superior to the results obtained by previous predictors. The proposed model contributed an excellent prediction rate in the system for NN, RF, and SVM classifiers. The sensitivity and specificity results using all these classifiers for jackknife test are 96.08%, 94.99%, 98.16% and 97.52%, 98.52%, 80.95%. Conclusion: The results obtained by the proposed tool show that this method may meet the future demand of hydroxylysine sites with a better prediction rate over the existing methods.

Detector-C: A Blood-based IVD with High Sensitivity and Specificity for Early Detection and Screening of Colorectal Cancer

2010 ◽  
Vol 48 (08) ◽  
Author(s):  
A Rosenthal ◽  
H Köppen ◽  
R Musikowski ◽  
R Schwanitz ◽  
J Behrendt ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Early Detection ◽  
Sensitivity And Specificity ◽  
High Sensitivity

Support vector machine? – not only for MRI-images

2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Support Vector

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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