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.

scholarly journals iHyd-PseAAC (EPSV): Identifying Hydroxylation Sites in Proteins by Extracting Enhanced Position and Sequence Variant Feature via Chou's 5- Step Rule and General Pseudo Amino Acid Composition

2019 ◽  
Vol 20 (2) ◽  
pp. 124-133 ◽  
Author(s):  
Asma Ehsan ◽  
Muhammad K. Mahmood ◽  
Yaser D. Khan ◽  
Omar M. Barukab ◽  
Sher A. Khan ◽  
...  
Keyword(s):  
Order Effect ◽  
Sequence Variant ◽  
Uncharacterized Protein ◽  
Post Translational Modifications ◽  
Jackknife Test ◽  
Cell Functions ◽  
Benchmark Datasets ◽  
Polypeptide Chains ◽  
Self Consistency

Background:In various biological processes and cell functions, Post Translational Modifications (PTMs) bear critical significance. Hydroxylation of proline residue is one kind of PTM, which occurs following protein synthesis. The experimental determination of hydroxyproline sites in an uncharacterized protein sequence requires extensive, time-consuming and expensive tests.Methods:With the torrential slide of protein sequences produced in the post-genomic age, certain remarkable computational strategies are desired to overwhelm the issue. Keeping in view the composition and sequence order effect within polypeptide chains, an innovative in-silico predictor via a mathematical model is proposed.Results:Later, it was stringently verified using self-consistency, cross-validation and jackknife tests on benchmark datasets. It was established after a rigorous jackknife test that the new predictor values are superior to the values predicted by previous methodologies.Conclusion:This new mathematical technique is the most appropriate and encouraging as compared with the existing models.

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 iHyd-ProSite: A novel Computational Approach for Identifying Hydroxylation Sites in Proline Via Mathematical Modeling

2020 ◽  
Author(s):  
Muhammad Khalid Mahmood ◽  
Asma Ehsan ◽  
Yaser Daanial Khan
Keyword(s):  
Mathematical Modeling ◽  
Covalent Bond ◽  
Vital Role ◽  
Health Issues ◽  
Cellular Functions ◽  
Consistency Test ◽  
Post Translational Modifications ◽  
Jackknife Test ◽  
Short Time ◽  
Self Consistency

AbstractIn various cellular functions, post translational modifications (PTM) of protein play a vital role. The addition of certain functional group through a covalent bond to the protein induces PTM. The number of PTMs are identified which are closely linked with diseases for example cancer and neurological disorder. Hydroxylation is one of the PTM, modified proline residue within a polypeptide sequence. The defective hydroxylation of proline causes absences of ascorbic acid in human which produce scurvy, and many other dominant health issues. Undoubtedly, the prediction of hydroxylation sites in proline residues is of challenging frontier. The experimental identification of hydroxyproline site is quite difficult, high-priced and time-consuming. The diversity in protein sequences instigates to develop a computational tool to identify hydroxylated site within short time with excellent prediction accuracy to handle such proteomics problems. In this work a novel in silico predictor is developed through rigorous mathematical modeling to identify which site of proline is hydroxylated and which site is not? Then performance of the predictor was verified using three validations tests, namely self-consistency test, cross-validation test and jackknife test over the benchmark dataset. A comparison was established for jackknife test with the previous methods. In comparison with previous predictors the proposed tool is more accurate than the existing techniques. Hence this scheme is highly useful and inspiring in contrast to all previous predictors.

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

Trends in Diagnosis for Active Tuberculosis Using Nanomaterials

2019 ◽  
Vol 26 (11) ◽  
pp. 1946-1959 ◽  
Author(s):  
Le Minh Tu Phan ◽  
Lemma Teshome Tufa ◽  
Hwa-Jung Kim ◽  
Jaebeom Lee ◽  
Tae Jung Park
Keyword(s):  
Sensitivity And Specificity ◽  
High Efficiency ◽  
Point Of Care ◽  
High Sensitivity ◽  
Signs And Symptoms ◽  
Diagnostic Methods ◽  
Advantages And Disadvantages ◽  
Treatment And Prevention ◽  
Clinical Tools ◽  
Diagnostic Applications

Background:Tuberculosis (TB), one of the leading causes of death worldwide, is difficult to diagnose based only on signs and symptoms. Methods for TB detection are continuously being researched to design novel effective clinical tools for the diagnosis of TB.Objective:This article reviews the methods to diagnose TB at the latent and active stages and to recognize prospective TB diagnostic methods based on nanomaterials.Methods:The current methods for TB diagnosis were reviewed by evaluating their advantages and disadvantages. Furthermore, the trends in TB detection using nanomaterials were discussed regarding their performance capacity for clinical diagnostic applications.Results:Current methods such as microscopy, culture, and tuberculin skin test are still being employed to diagnose TB, however, a highly sensitive point of care tool without false results is still needed. The utilization of nanomaterials to detect the specific TB biomarkers with high sensitivity and specificity can provide a possible strategy to rapidly diagnose TB. Although it is challenging for nanodiagnostic platforms to be assessed in clinical trials, active TB diagnosis using nanomaterials is highly expected to achieve clinical significance for regular application. In addition, aspects and future directions in developing the high-efficiency tools to diagnose active TB using advanced nanomaterials are expounded.Conclusion:This review suggests that nanomaterials have high potential as rapid, costeffective tools to enhance the diagnostic sensitivity and specificity for the accurate diagnosis, treatment, and prevention of TB. Hence, portable nanobiosensors can be alternative effective tests to be exploited globally after clinical trial execution.

Value of Coronary Calcium Scoring in Symptomatic and Asymptomatic Coronary Artery Disease Patients

2020 ◽  
Vol 16 ◽  
Author(s):  
Hala T. Salem ◽  
Eman A.S. Sabek
Keyword(s):  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Sensitivity And Specificity ◽  
High Sensitivity ◽  
Coronary Calcium ◽  
Risk Estimate ◽  
Calcium Scoring ◽  
Coronary Calcium Scoring ◽  
Framingham Risk ◽  
Artery Disease

Aim and Objective: To estimate the relationship between Coronary Calcium Scoring (CCS)and presence of different degrees of obstructive coronary artery disease (CAD) to avoid unnecessary examinations and hence unnecessary radiation exposure and contrast injection. Background: Coronary Calcium Scoring (CCS) is a test uses x-ray equipment to produce pictures of the coronary arteries to determine the degree of its narrowing by the build-up of calcified plaques. Despite the lack of definitive data linking ionizing radiation with cancer, the American Heart Association supports widely that practitioners of Computed tomography Coronary Angiography (CTCA) should keep “patient radiation doses as low as reasonably achievable but consistent with obtaining the desired medical information”. Methods: Data obtained from 275 CTCA examinations were reviewed. Radiation effective doses were estimated for both CCS and CTCA, measures to keep it as low as possible were presented, CCS and Framingham risk estimate were compared to the final results of CTCA to detect sensitivity and specificity of each one in detecting obstructive lesions. Results: CCS is a strong discriminator for obstructive CAD and can with high sensitivity and specificity and correlates well with the degree of obstruction even more than Framingham risk estimate which has high sensitivity and low specificity. Conclusion: CCS helps reducing the effective radiation dose if properly evaluated to skip unnecessary CTCA if obstructive lesions was unlikely, and as a test does not use contrast material, harmful effect on the kidney will be avoided as most of coronary atherosclerotic patients have renal problems.

scholarly journals Ultrasound Image Classification of Thyroid Nodules Using Machine Learning Techniques

Medicina ◽  
2021 ◽  
Vol 57 (6) ◽  
pp. 527
Author(s):  
Vijay Vyas Vadhiraj ◽  
Andrew Simpkin ◽  
James O’Connell ◽  
Naykky Singh Singh Ospina ◽  
Spyridoula Maraka ◽  
...  
Keyword(s):  
Decision Making ◽  
Sensitivity And Specificity ◽  
Thyroid Nodules ◽  
Median Filter ◽  
Ultrasound Image ◽  
Multiple Instance Learning ◽  
Image Features ◽  
Classification Model ◽  
Support Vector ◽  
Accuracy Score

Background and Objectives: Thyroid nodules are lumps of solid or liquid-filled tumors that form inside the thyroid gland, which can be malignant or benign. Our aim was to test whether the described features of the Thyroid Imaging Reporting and Data System (TI-RADS) could improve radiologists’ decision making when integrated into a computer system. In this study, we developed a computer-aided diagnosis system integrated into multiple-instance learning (MIL) that would focus on benign–malignant classification. Data were available from the Universidad Nacional de Colombia. Materials and Methods: There were 99 cases (33 Benign and 66 malignant). In this study, the median filter and image binarization were used for image pre-processing and segmentation. The grey level co-occurrence matrix (GLCM) was used to extract seven ultrasound image features. These data were divided into 87% training and 13% validation sets. We compared the support vector machine (SVM) and artificial neural network (ANN) classification algorithms based on their accuracy score, sensitivity, and specificity. The outcome measure was whether the thyroid nodule was benign or malignant. We also developed a graphic user interface (GUI) to display the image features that would help radiologists with decision making. Results: ANN and SVM achieved an accuracy of 75% and 96% respectively. SVM outperformed all the other models on all performance metrics, achieving higher accuracy, sensitivity, and specificity score. Conclusions: Our study suggests promising results from MIL in thyroid cancer detection. Further testing with external data is required before our classification model can be employed in practice.

scholarly journals Blood-Based Detection of Colorectal Cancer Using Cancer-Specific DNA Methylation Markers

Diagnostics ◽  
2020 ◽  
Vol 11 (1) ◽  
pp. 51
Author(s):  
Nam-Yun Cho ◽  
Ji-Won Park ◽  
Xianyu Wen ◽  
Yun-Joo Shin ◽  
Jun-Kyu Kang ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Dna Methylation ◽  
Sensitivity And Specificity ◽  
Stage Iv ◽  
High Sensitivity ◽  
Liquid Biopsies ◽  
Blood Leukocytes ◽  
Marker Panel ◽  
A Genome ◽  
Methylight Assay

Cancer tissues have characteristic DNA methylation profiles compared with their corresponding normal tissues that can be utilized for cancer diagnosis with liquid biopsy. Using a genome-scale DNA methylation approach, we sought to identify a panel of DNA methylation markers specific for cell-free DNA (cfDNA) from patients with colorectal cancer (CRC). By comparing DNA methylomes between CRC and normal mucosal tissues or blood leukocytes, we identified eight cancer-specific methylated loci (ADGRB1, ANKRD13, FAM123A, GLI3, PCDHG, PPP1R16B, SLIT3, and TMEM90B) and developed a five-marker panel (FAM123A, GLI3, PPP1R16B, SLIT3, and TMEM90B) that detected CRC in liquid biopsies with a high sensitivity and specificity with a droplet digital MethyLight assay. In a set of cfDNA samples from CRC patients (n = 117) and healthy volunteers (n = 60), a panel of five markers on the platform of the droplet digital MethyLight assay detected stages I–III and stage IV CRCs with sensitivities of 45.9% and 95.7%, respectively, and a specificity of 95.0%. The number of detected markers was correlated with the cancer stage, perineural invasion, lymphatic emboli, and venous invasion. Our five-marker panel with the droplet digital MethyLight assay showed a high sensitivity and specificity for the detection of CRC with cfDNA samples from patients with metastatic CRC.

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