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Balajee A, Murugan R, Venkatesh K. Security-enhanced machine learning model for diagnosis of knee joint disorders using vibroarthrographic signals. Soft comput 2023. [DOI: 10.1007/s00500-023-07934-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2023]
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2
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Karpiński R, Krakowski P, Jonak J, Machrowska A, Maciejewski M, Nogalski A. Diagnostics of Articular Cartilage Damage Based on Generated Acoustic Signals Using ANN-Part II: Patellofemoral Joint. SENSORS (BASEL, SWITZERLAND) 2022; 22:3765. [PMID: 35632174 PMCID: PMC9146478 DOI: 10.3390/s22103765] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/12/2022] [Revised: 05/10/2022] [Accepted: 05/15/2022] [Indexed: 12/04/2022]
Abstract
Cartilage loss due to osteoarthritis (OA) in the patellofemoral joint provokes pain, stiffness, and restriction of joint motion, which strongly reduces quality of life. Early diagnosis is essential for prolonging painless joint function. Vibroarthrography (VAG) has been proposed in the literature as a safe, noninvasive, and reproducible tool for cartilage evaluation. Until now, however, there have been no strict protocols for VAG acquisition especially in regard to differences between the patellofemoral and tibiofemoral joints. The purpose of this study was to evaluate the proposed examination and acquisition protocol for the patellofemoral joint, as well as to determine the optimal examination protocol to obtain the best diagnostic results. Thirty-four patients scheduled for knee surgery due to cartilage lesions were enrolled in the study and compared with 33 healthy individuals in the control group. VAG acquisition was performed prior to surgery, and cartilage status was evaluated during the surgery as a reference point. Both closed (CKC) and open (OKC) kinetic chains were assessed during VAG. The selection of the optimal signal measures was performed using a neighborhood component analysis (NCA) algorithm. The classification was performed using multilayer perceptron (MLP) and radial basis function (RBF) neural networks. The classification using artificial neural networks was performed for three variants: I. open kinetic chain, II. closed kinetic chain, and III. open and closed kinetic chain. The highest diagnostic accuracy was obtained for variants I and II for the RBF 9-35-2 and MLP 10-16-2 networks, respectively, achieving a classification accuracy of 98.53, a sensitivity of 0.958, and a specificity of 1. For variant III, a diagnostic accuracy of 97.79 was obtained with a sensitivity and specificity of 0.978 for MLP 8-3-2. This indicates a possible simplification of the examination protocol to single kinetic chain analyses.
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Affiliation(s)
- Robert Karpiński
- Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland; (J.J.); (A.M.)
| | - Przemysław Krakowski
- Department of Trauma Surgery and Emergency Medicine, Medical University of Lublin, Staszica 11, 20-081 Lublin, Poland;
- Orthopaedic Department, Łęczna Hospital, Krasnystawska 52, 21-010 Łęczna, Poland
| | - Józef Jonak
- Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland; (J.J.); (A.M.)
| | - Anna Machrowska
- Department of Machine Design and Mechatronics, Faculty of Mechanical Engineering, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland; (J.J.); (A.M.)
| | - Marcin Maciejewski
- Department of Electronics and Information Technology, Faculty of Electrical Engineering and Computer Science, Lublin University of Technology, Nadbystrzycka 36, 20-618 Lublin, Poland;
| | - Adam Nogalski
- Department of Trauma Surgery and Emergency Medicine, Medical University of Lublin, Staszica 11, 20-081 Lublin, Poland;
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Wang Y, Zheng T, Song J, Gao W. A novel automatic Knee Osteoarthritis detection method based on vibroarthrographic signals. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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4
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Screening of knee-joint vibroarthrographic signals using time and spectral domain features. Biomed Signal Process Control 2021. [DOI: 10.1016/j.bspc.2021.102808] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
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Ołowiana E, Selkow N, Laudner K, Puciato D, Bączkowicz D. Vibroarthrographic analysis of patellofemoral joint arthrokinematics during squats with increasing external loads. BMC Sports Sci Med Rehabil 2020; 12:51. [PMID: 32874592 PMCID: PMC7457288 DOI: 10.1186/s13102-020-00201-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Accepted: 08/24/2020] [Indexed: 12/20/2022]
Abstract
Background The patellofemoral joint (PFJ) provides extremely low kinetic friction, which results in optimal arthrokinematic motion quality. Previous research showed that these friction-reducing properties may be diminished due to the increase in articular contact forces. However, this phenomenon has not been analyzed in vivo during functional daily-living activities. The aim of this study was the vibroarthrographic assessment of changes in PFJ arthrokinematics during squats with variated loads. Methods 114 knees from 57 asymptomatic subjects (23 females and 34 males) whose ages ranged from 19 to 26 years were enrolled in this study. Participants were asked to perform 3 trials: 4 repetitions of bodyweight squats (L0), 4 repetitions of 10 kg barbell back loaded squats (L10), 4 repetitions of 20 kg barbell back loaded squats (L20). During the unloaded and loaded (L10, L20) squats, vibroarthrographic signals were collected using an accelerometer placed on the patella and were described by the following parameters: variation of mean square (VMS), mean range (R4), and power spectral density for frequency of 50–250 Hz (P1) and 250–450 Hz (P2). Results Obtained results showed that the lowest values were noted in the unloaded condition and that the increased applied loads had a significant concomitant increase in all the aforementioned parameters bilaterally (p < 0.05). Conclusion This phenomenon indicates that the application of increasing knee loads during squats corresponds to higher intensity of vibroacoustic emission, which might be related to higher contact stress and kinetic friction as well as diminished arthrokinematic motion quality.
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Affiliation(s)
- Ewelina Ołowiana
- Faculty of Physical Education and Physiotherapy, Opole University of Technology, Prószkowska 76, PL-45-578 Opole, Poland
| | - Noelle Selkow
- Illinois State University, School of Kinesiology and Recreation, Normal, IL USA
| | - Kevin Laudner
- Beth El College of Nursing and Health Sciences, University of Colorado, Colorado Springs, CO USA
| | - Daniel Puciato
- Faculty of Physical Education and Physiotherapy, Opole University of Technology, Prószkowska 76, PL-45-578 Opole, Poland
| | - Dawid Bączkowicz
- Faculty of Physical Education and Physiotherapy, Opole University of Technology, Prószkowska 76, PL-45-578 Opole, Poland
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Rexwinkle JT, Werner NC, Stoker AM, Salim M, Pfeiffer FM. Investigating the relationship between proteomic, compositional, and histologic biomarkers and cartilage biomechanics using artificial neural networks. J Biomech 2018; 80:136-143. [DOI: 10.1016/j.jbiomech.2018.08.032] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2017] [Revised: 08/09/2018] [Accepted: 08/29/2018] [Indexed: 10/28/2022]
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7
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Cheng YT, Tai CC, Chou W, Tang ST, Lin JH. Analyzing the audio signals of degenerative arthritis with an electronic stethoscope. THE REVIEW OF SCIENTIFIC INSTRUMENTS 2018; 89:085111. [PMID: 30184721 DOI: 10.1063/1.5018006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/02/2017] [Accepted: 07/15/2018] [Indexed: 06/08/2023]
Abstract
The advance of modern medical technology has extended people's life and increased the average age of the society. Some chronic diseases are due to the aging of the population and knee joint aging is a common disease in the elderly. Common joint pathology contains degenerative arthritis, arthroncus of knees, and gouty arthritis. Knee joints are the largest and the most complicated joints in a human body as well as the joint bearing huge pressure. Wrong posture, overuse, or vigorous exercise often cause injuries to knee joints, and such injuries could easily result in joint pathology and patients falling and breaking bones due to pain and powerlessness. An acoustic wave technology, aiming at knee joints, is designed to examine a patient's current condition of joints. An electronic stethoscope or high-resolution recording equipment is utilized for collecting necessary signals, through which the wide-frequency audio signals of knee joints could be measured for the analyses and statistics in a back-end computer. Besides, it could classify the groups with healthy and degenerative knee joints to assist physicians in proceeding non-invasive joint degeneration examination clinically and doing the most suitable rehabilitation therapy.
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Affiliation(s)
- Yung-Tsung Cheng
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Cheng-Chi Tai
- Department of Electrical Engineering, National Cheng Kung University, Tainan 70101, Taiwan
| | - Willy Chou
- Department of Physical Medicine and Rehabilitation, Chi-Mei Medical Center, Tainan 71004, Taiwan
| | - Shih-Tsang Tang
- Department of Biomedical Engineering, Ming Chuan University, Taoyuan, Taiwan
| | - Jiun-Hung Lin
- Department of Electronic Engineering, Kun Shan University, Tainan, Taiwan
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Wei L, Tang J, Zou Q. SkipCPP-Pred: an improved and promising sequence-based predictor for predicting cell-penetrating peptides. BMC Genomics 2017. [PMID: 29513192 PMCID: PMC5657092 DOI: 10.1186/s12864-017-4128-1] [Citation(s) in RCA: 76] [Impact Index Per Article: 10.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023] Open
Abstract
Background Cell-penetrating peptides (CPPs) are short peptides (5–30 amino acids) that can enter almost any cell without significant damage. On account of their high delivery efficiency, CPPs are promising candidates for gene therapy and cancer treatment. Accordingly, techniques that correctly predict CPPs are anticipated to accelerate CPP applications in future therapeutics. Recently, computational methods have been reportedly successful in predicting CPPs. Unfortunately, the predictive performance of existing methods is not satisfactory and reliable so as to accurately identify CPPs. Results In this study, we propose a novel computational predictor called SkipCPP-Pred to further improve the predictive performance. The novelty of the proposed predictor is that we present a sequence-based feature representation algorithm called adaptive k-skip-n-gram that sufficiently captures the intrinsic correlation information of residues. By fusing the proposed adaptive skip features with a random forest (RF) classifier, we successfully construct the prediction model of SkipCPP-Pred. The various jackknife results demonstrate that the proposed SkipCPP-Pred is 3.6% higher than state-of-the-art CPP predictors in terms of accuracy. Moreover, we construct a high-quality benchmark dataset by reducing the data redundancy and enhancing the similarity between the positive and negative classes. Using this dataset to build prediction models, we can successfully avoid the performance bias lying in existing methods and yield a promising predictive model. Conclusions The proposed SkipCPP-Pred is a simple and fast sequence-based predictor featured with the adaptive k-skip-n-gram model for the improved prediction of CPPs. Currently, SkipCPP-Pred is publicly available from an online webserver (http://server.malab.cn/SkipCPP-Pred/Index.html). Electronic supplementary material The online version of this article (10.1186/s12864-017-4128-1) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Leyi Wei
- School of Computer Science and Technology, Tianjin University, Tianjin, 30050, China.,State Key Laboratory of Medicinal Chemical Biology, Nankai University, Tianjin, 300074, China
| | - Jijun Tang
- School of Computer Science and Technology, Tianjin University, Tianjin, 30050, China
| | - Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin, 30050, China.
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Jac Fredo AR, Josena TR, Palaniappan R, Mythili A. CLASSIFICATION OF NORMAL AND KNEE JOINT DISORDER VIBROARTHROGRAPHIC SIGNALS USING MULTIFRACTALS AND SUPPORT VECTOR MACHINES. BIOMEDICAL ENGINEERING: APPLICATIONS, BASIS AND COMMUNICATIONS 2017. [DOI: 10.4015/s1016237217500168] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/07/2022]
Abstract
The development of reliable Computer Aided Diagnosis (CAD) systems would help in the early detection of Knee Joint Disorder (KJD). In this work, normal and KJD vibroarthrographic (VAG) signals are classified using multifractals and Support Vector Machines (SVM). Multifractal dimension [Formula: see text] is calculated from the VAG signals for various [Formula: see text]-values ([Formula: see text]). Geometrical features are calculated from the multifractal spectrum. The dimension of the feature set is reduced using Principal Component Analysis (PCA). The significant features obtained from the multifractal spectrum are fed as the input to the SVM classifier. The accuracy of the classifier is analyzed using kernels such as linear, quadratic, polynomial and Radial Basis Functions (RBF). The results suggest that VAG signals exhibits the multifractal property. The fluctuations in the normal and abnormal signals are well predicted in small scales of segments of time series. The features such as [Formula: see text] and Mean[Formula: see text] are high in abnormal VAG signals. These features give statistically significant values in differentiating the normal and abnormal subjects ([Formula: see text]). The area under the Receiver Operating Characteristic (ROC) curve is high for polynomial function (0.98). The SVM classifier with polynomial function gives 92.13% of accuracy in differentiating the normal and abnormal subjects. The calculation of multifractal spectrum and geometrical features from VAG signals requires optimization of few parameters, easy to compute, computationally inexpensive, and less time consuming. Hence, the CAD system seems to be clinically significant for the classification of normal and KJD subjects.
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Affiliation(s)
| | - Thomas Raj Josena
- Department of Computer Science Engineering, Easwari Engineering College, Chennai, India
| | - Rajkumar Palaniappan
- School of Electronics Engineering, Biomedical Technology Division, VIT University, Vellore, India
| | - Asaithambi Mythili
- School of Electronics Engineering, Biomedical Technology Division, VIT University, Vellore, India
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Abstract
Wearable sensors, in particular inertial measurement units (IMUs) allow the objective, valid, discriminative and responsive assessment of physical function during functional tests such as gait, stair climbing or sit-to-stand. Applied to various body segments, precise capture of time-to-task achievement, spatiotemporal gait and kinematic parameters of demanding tests or specific to an affected limb are the most used measures. In activity monitoring (AM), accelerometry has mainly been used to derive energy expenditure or general health related parameters such as total step counts. In orthopaedics and the elderly, counting specific events such as stairs or high intensity activities were clinimetrically most powerful; as were qualitative parameters at the ‘micro-level’ of activity such as step frequency or sit-stand duration. Low cost and ease of use allow routine clinical application but with many options for sensors, algorithms, test and parameter definitions, choice and comparability remain difficult, calling for consensus or standardisation.
Cite this article: Grimm B, Bolink S. Evaluating physical function and activity in the elderly patient using wearable motion sensors. EFORT Open Rev 2016;1:112–120. DOI: 10.1302/2058-5241.1.160022.
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Affiliation(s)
- Bernd Grimm
- AHORSE Research Foundation, Zuyderland Medical Center, Heerlen, The Netherlands
| | - Stijn Bolink
- AHORSE Research Foundation, Zuyderland Medical Center, Heerlen, The Netherlands
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11
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Wu Y, Chen P, Luo X, Wu M, Liao L, Yang S, Rangayyan RM. Measuring signal fluctuations in gait rhythm time series of patients with Parkinson's disease using entropy parameters. Biomed Signal Process Control 2017. [DOI: 10.1016/j.bspc.2016.08.022] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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Zou Q, Wan S, Ju Y, Tang J, Zeng X. Pretata: predicting TATA binding proteins with novel features and dimensionality reduction strategy. BMC SYSTEMS BIOLOGY 2016; 10:114. [PMID: 28155714 PMCID: PMC5259984 DOI: 10.1186/s12918-016-0353-5] [Citation(s) in RCA: 135] [Impact Index Per Article: 16.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Background It is necessary and essential to discovery protein function from the novel primary sequences. Wet lab experimental procedures are not only time-consuming, but also costly, so predicting protein structure and function reliably based only on amino acid sequence has significant value. TATA-binding protein (TBP) is a kind of DNA binding protein, which plays a key role in the transcription regulation. Our study proposed an automatic approach for identifying TATA-binding proteins efficiently, accurately, and conveniently. This method would guide for the special protein identification with computational intelligence strategies. Results Firstly, we proposed novel fingerprint features for TBP based on pseudo amino acid composition, physicochemical properties, and secondary structure. Secondly, hierarchical features dimensionality reduction strategies were employed to improve the performance furthermore. Currently, Pretata achieves 92.92% TATA-binding protein prediction accuracy, which is better than all other existing methods. Conclusions The experiments demonstrate that our method could greatly improve the prediction accuracy and speed, thus allowing large-scale NGS data prediction to be practical. A web server is developed to facilitate the other researchers, which can be accessed at http://server.malab.cn/preTata/.
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Affiliation(s)
- Quan Zou
- School of Computer Science and Technology, Tianjin University, Tianjin, China
| | - Shixiang Wan
- School of Computer Science and Technology, Tianjin University, Tianjin, China.,Key Laboratory of Network Oriented Intelligent Computation, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong, 518055, China
| | - Ying Ju
- School of Information Science and Engineering, Xiamen University, Xiamen, China
| | - Jijun Tang
- School of Computer Science and Technology, Tianjin University, Tianjin, China.,School of Computational Science and Engineering, University of South Carolina, Columbia, USA
| | - Xiangxiang Zeng
- School of Information Science and Engineering, Xiamen University, Xiamen, China.
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BP Neural Network Could Help Improve Pre-miRNA Identification in Various Species. BIOMED RESEARCH INTERNATIONAL 2016; 2016:9565689. [PMID: 27635401 PMCID: PMC5011242 DOI: 10.1155/2016/9565689] [Citation(s) in RCA: 26] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/17/2016] [Revised: 07/05/2016] [Accepted: 07/17/2016] [Indexed: 01/21/2023]
Abstract
MicroRNAs (miRNAs) are a set of short (21–24 nt) noncoding RNAs that play significant regulatory roles in cells. In the past few years, research on miRNA-related problems has become a hot field of bioinformatics because of miRNAs' essential biological function. miRNA-related bioinformatics analysis is beneficial in several aspects, including the functions of miRNAs and other genes, the regulatory network between miRNAs and their target mRNAs, and even biological evolution. Distinguishing miRNA precursors from other hairpin-like sequences is important and is an essential procedure in detecting novel microRNAs. In this study, we employed backpropagation (BP) neural network together with 98-dimensional novel features for microRNA precursor identification. Results show that the precision and recall of our method are 95.53% and 96.67%, respectively. Results further demonstrate that the total prediction accuracy of our method is nearly 13.17% greater than the state-of-the-art microRNA precursor prediction software tools.
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Shieh CS, Tseng CD, Chang LY, Lin WC, Wu LF, Wang HY, Chao PJ, Chiu CL, Lee TF. Synthesis of vibroarthrographic signals in knee osteoarthritis diagnosis training. BMC Res Notes 2016; 9:352. [PMID: 27435313 PMCID: PMC4950531 DOI: 10.1186/s13104-016-2156-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2016] [Accepted: 07/13/2016] [Indexed: 11/30/2022] Open
Abstract
Background Vibroarthrographic (VAG) signals are used as useful indicators of knee osteoarthritis (OA) status. The objective was to build a template database of knee crepitus sounds. Internships can practice in the template database to shorten the time of training for diagnosis of OA. Methods A knee sound signal was obtained using an innovative stethoscope device with a goniometer. Each knee sound signal was recorded with a Kellgren–Lawrence (KL) grade. The sound signal was segmented according to the goniometer data. The signal was Fourier transformed on the correlated frequency segment. An inverse Fourier transform was performed to obtain the time-domain signal. Haar wavelet transform was then done. The median and mean of the wavelet coefficients were chosen to inverse transform the synthesized signal in each KL category. The quality of the synthesized signal was assessed by a clinician. Results The sample signals were evaluated using different algorithms (median and mean). The accuracy rate of the median coefficient algorithm (93 %) was better than the mean coefficient algorithm (88 %) for cross-validation by a clinician using synthesis of VAG. Conclusions The artificial signal we synthesized has the potential to build a learning system for medical students, internships and para-medical personnel for the diagnosis of OA. Therefore, our method provides a feasible way to evaluate crepitus sounds that may assist in the diagnosis of knee OA.
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Affiliation(s)
- Chin-Shiuh Shieh
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Applied Sciences, 415, Chien Kung Road, San-Min District, Kaohsiung, 807, Taiwan, ROC.,Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan, ROC
| | - Chin-Dar Tseng
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Applied Sciences, 415, Chien Kung Road, San-Min District, Kaohsiung, 807, Taiwan, ROC
| | - Li-Yun Chang
- Department of Medical Imaging and Radiological Sciences, I-Shou University, Kaohsiung, 82445, Taiwan, ROC
| | - Wei-Chun Lin
- Institute of Photonics and Communications, National Kaohsiung University of Applied Sciences, Kaohsiung, 80778, Taiwan, ROC.,Department of Orthopedic, Kaohsiung Municipal Min-Sheng Hospital, Kaohsiung, 80276, Taiwan, ROC
| | - Li-Fu Wu
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Applied Sciences, 415, Chien Kung Road, San-Min District, Kaohsiung, 807, Taiwan, ROC
| | - Hung-Yu Wang
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Applied Sciences, 415, Chien Kung Road, San-Min District, Kaohsiung, 807, Taiwan, ROC.,Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan, ROC
| | - Pei-Ju Chao
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Applied Sciences, 415, Chien Kung Road, San-Min District, Kaohsiung, 807, Taiwan, ROC.,Department of Radiation Oncology, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Kaohsiung, 83342, Taiwan, ROC
| | - Chien-Liang Chiu
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Applied Sciences, 415, Chien Kung Road, San-Min District, Kaohsiung, 807, Taiwan, ROC
| | - Tsair-Fwu Lee
- Medical Physics and Informatics Laboratory of Electronics Engineering, National Kaohsiung University of Applied Sciences, 415, Chien Kung Road, San-Min District, Kaohsiung, 807, Taiwan, ROC. .,Graduate Institute of Clinical Medicine, Kaohsiung Medical University, Kaohsiung, 807, Taiwan, ROC.
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Wu Y, Chen P, Luo X, Huang H, Liao L, Yao Y, Wu M, Rangayyan RM. Quantification of knee vibroarthrographic signal irregularity associated with patellofemoral joint cartilage pathology based on entropy and envelope amplitude measures. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 130:1-12. [PMID: 27208516 DOI: 10.1016/j.cmpb.2016.03.021] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2015] [Revised: 03/12/2016] [Accepted: 03/16/2016] [Indexed: 06/05/2023]
Abstract
BACKGROUND AND OBJECTIVE Injury of knee joint cartilage may result in pathological vibrations between the articular surfaces during extension and flexion motions. The aim of this paper is to analyze and quantify vibroarthrographic (VAG) signal irregularity associated with articular cartilage degeneration and injury in the patellofemoral joint. METHODS The symbolic entropy (SyEn), approximate entropy (ApEn), fuzzy entropy (FuzzyEn), and the mean, standard deviation, and root-mean-squared (RMS) values of the envelope amplitude, were utilized to quantify the signal fluctuations associated with articular cartilage pathology of the patellofemoral joint. The quadratic discriminant analysis (QDA), generalized logistic regression analysis (GLRA), and support vector machine (SVM) methods were used to perform signal pattern classifications. RESULTS The experimental results showed that the patients with cartilage pathology (CP) possess larger SyEn and ApEn, but smaller FuzzyEn, over the statistical significance level of the Wilcoxon rank-sum test (p<0.01), than the healthy subjects (HS). The mean, standard deviation, and RMS values computed from the amplitude difference between the upper and lower signal envelopes are also consistently and significantly larger (p<0.01) for the group of CP patients than for the HS group. The SVM based on the entropy and envelope amplitude features can provide superior classification performance as compared with QDA and GLRA, with an overall accuracy of 0.8356, sensitivity of 0.9444, specificity of 0.8, Matthews correlation coefficient of 0.6599, and an area of 0.9212 under the receiver operating characteristic curve. CONCLUSIONS The SyEn, ApEn, and FuzzyEn features can provide useful information about pathological VAG signal irregularity based on different entropy metrics. The statistical parameters of signal envelope amplitude can be used to characterize the temporal fluctuations related to the cartilage pathology.
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Affiliation(s)
- Yunfeng Wu
- School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China; Fujian Key Laboratory of Sensing and Computing for Smart City, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China.
| | - Pinnan Chen
- School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China
| | - Xin Luo
- School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China
| | - Hui Huang
- Department of Rehabilitation, Xiamen University Affiliated Zhongshan Hospital, 201 Hubin South Road, Xiamen, Fujian 361004, China
| | - Lifang Liao
- School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China
| | - Yuchen Yao
- School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China
| | - Meihong Wu
- School of Information Science and Technology, Xiamen University, 422 Si Ming South Road, Xiamen, Fujian 361005, China
| | - Rangaraj M Rangayyan
- Department of Electrical and Computer Engineering, Schulich School of Engineering, University of Calgary, Calgary, AB T2N 1N4, Canada
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Wu T, Wang X, Zhang Z, Gong F, Song T, Chen Z, Zhang P, Zhao Y. NES-REBS: A novel nuclear export signal prediction method using regular expressions and biochemical properties. J Bioinform Comput Biol 2016; 14:1650013. [PMID: 27225342 DOI: 10.1142/s021972001650013x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
A nuclear export signal (NES) is a protein localization signal, which is involved in binding of cargo proteins to nuclear export receptor, thus contributes to regulate localization of cellular proteins. Consensus sequences of NES have been used to detect NES from protein sequences, but suffer from poor predictive power. Some recent peering works were proposed to use biochemical properties of experimental verified NES to refine NES candidates. Those methods can achieve high prediction rates, but their execution time will become unacceptable for large-scale NES searching if too much properties are involved. In this work, we developed a novel computational approach, named NES-REBS, to search NES from protein sequences, where biochemical properties of experimental verified NES, including secondary structure and surface accessibility, are utilized to refine NES candidates obtained by matching popular consensus sequences. We test our method by searching 262 experimental verified NES from 221 NES-containing protein sequences. It is obtained that NES-REBS runs in 2-3[Formula: see text]mins and performs well by achieving precision rate 47.2% and sensitivity 54.6%.
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Affiliation(s)
- Tingfang Wu
- * School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, P. R. China
| | - Xun Wang
- † College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, Shandong, P. R. China
| | - Zheng Zhang
- * School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, P. R. China
| | - Faming Gong
- † College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, Shandong, P. R. China
| | - Tao Song
- † College of Computer and Communication Engineering, China University of Petroleum, Qingdao 266580, Shandong, P. R. China.,‡ Faculty of Engineering, Computing and Science Swinburne University of Technology, Sarawak Campus Kuching 93350, Malaysia
| | - Zhihua Chen
- * School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, P. R. China
| | - Pan Zhang
- * School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, P. R. China
| | - Yang Zhao
- * School of Automation, Huazhong University of Science and Technology, Wuhan 430074, Hubei, P. R. China
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17
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Ding Z, Zhang X, Sun D, Luo B. Overlapping Community Detection based on Network Decomposition. Sci Rep 2016; 6:24115. [PMID: 27066904 PMCID: PMC4828636 DOI: 10.1038/srep24115] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2015] [Accepted: 03/21/2016] [Indexed: 11/09/2022] Open
Abstract
Community detection in complex network has become a vital step to understand the structure and dynamics of networks in various fields. However, traditional node clustering and relatively new proposed link clustering methods have inherent drawbacks to discover overlapping communities. Node clustering is inadequate to capture the pervasive overlaps, while link clustering is often criticized due to the high computational cost and ambiguous definition of communities. So, overlapping community detection is still a formidable challenge. In this work, we propose a new overlapping community detection algorithm based on network decomposition, called NDOCD. Specifically, NDOCD iteratively splits the network by removing all links in derived link communities, which are identified by utilizing node clustering technique. The network decomposition contributes to reducing the computation time and noise link elimination conduces to improving the quality of obtained communities. Besides, we employ node clustering technique rather than link similarity measure to discover link communities, thus NDOCD avoids an ambiguous definition of community and becomes less time-consuming. We test our approach on both synthetic and real-world networks. Results demonstrate the superior performance of our approach both in computation time and accuracy compared to state-of-the-art algorithms.
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Affiliation(s)
- Zhuanlian Ding
- School of Computer Science and Technology, Anhui University, Hefei 230601, China.,Key Lab of Industrial Image Processing &Analysis of Anhui Province, Anhui Province, Hefei 230039, China
| | - Xingyi Zhang
- School of Computer Science and Technology, Anhui University, Hefei 230601, China
| | - Dengdi Sun
- School of Computer Science and Technology, Anhui University, Hefei 230601, China
| | - Bin Luo
- School of Computer Science and Technology, Anhui University, Hefei 230601, China.,Key Lab of Industrial Image Processing &Analysis of Anhui Province, Anhui Province, Hefei 230039, China
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18
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Wang R, Xu Y, Liu B. Recombination spot identification Based on gapped k-mers. Sci Rep 2016; 6:23934. [PMID: 27030570 PMCID: PMC4814916 DOI: 10.1038/srep23934] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2015] [Accepted: 03/16/2016] [Indexed: 12/14/2022] Open
Abstract
Recombination is crucial for biological evolution, which provides many new combinations of genetic diversity. Accurate identification of recombination spots is useful for DNA function study. To improve the prediction accuracy, researchers have proposed several computational methods for recombination spot identification. The k-mer feature is one of the most useful features for modeling the properties and function of DNA sequences. However, it suffers from the inherent limitation. If the value of word length k is large, the occurrences of k-mers are closed to a binary variable, with a few k-mers present once and most k-mers are absent. This usually causes the sparse problem and reduces the classification accuracy. To solve this problem, we add gaps into k-mer and introduce a new feature called gapped k-mer (GKM) for identification of recombination spots. By using this feature, we present a new predictor called SVM-GKM, which combines the gapped k-mers and Support Vector Machine (SVM) for recombination spot identification. Experimental results on a widely used benchmark dataset show that SVM-GKM outperforms other highly related predictors. Therefore, SVM-GKM would be a powerful predictor for computational genomics.
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Affiliation(s)
- Rong Wang
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
| | - Yong Xu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
| | - Bin Liu
- School of Computer Science and Technology, Harbin Institute of Technology Shenzhen Graduate School, Shenzhen, Guangdong 518055, China
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19
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Zou Q, Guo J, Ju Y, Wu M, Zeng X, Hong Z. Improving tRNAscan-SE Annotation Results via Ensemble Classifiers. Mol Inform 2015; 34:761-70. [DOI: 10.1002/minf.201500031] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2015] [Accepted: 07/01/2015] [Indexed: 01/18/2023]
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20
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Mukaka MM. Statistics corner: A guide to appropriate use of correlation coefficient in medical research. MALAWI MEDICAL JOURNAL : THE JOURNAL OF MEDICAL ASSOCIATION OF MALAWI 2012; 127:94-104. [PMID: 23638278 DOI: 10.1016/j.cmpb.2016.01.020] [Citation(s) in RCA: 34] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/23/2015] [Revised: 01/06/2016] [Accepted: 01/21/2016] [Indexed: 05/12/2023]
Abstract
Correlation is a statistical method used to assess a possible linear association between two continuous variables. It is simple both to calculate and to interpret. However, misuse of correlation is so common among researchers that some statisticians have wished that the method had never been devised at all. The aim of this article is to provide a guide to appropriate use of correlation in medical research and to highlight some misuse. Examples of the applications of the correlation coefficient have been provided using data from statistical simulations as well as real data. Rule of thumb for interpreting size of a correlation coefficient has been provided.
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Affiliation(s)
- M M Mukaka
- Malawi-Liverpool Wellcome Trust Clinical Research Program ; Department of Community Health, College of Medicine, University of Malawi ; The Liverpool School of Tropical Medicine, Liverpool, L69 3GA, UK, University of Liverpool
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