Bio-Medical Materials and Engineering - Volume 26, issue s1

Authors: Feng, Yu | Cao, Hui | Zhang, Yanbin

Article Type: Research Article

Abstract: High order partial least squares (HOPLS) is a novel data processing method. It is highly suitable for building prediction model which has tensor input and output. The objective of this study is to build a prediction model of the relationship between sinoatrial node field potential and high glucose using HOPLS. The three sub-signals of the sinoatrial node field potential made up the model’s input. The concentration and the actuation duration of high glucose made up the model’s output. The results showed that on the premise of predicting two dimensional variables, HOPLS had the same predictive ability and a lower dispersion …degree compared with partial least squares (PLS). Show more

Keywords: High order partial least squares, sinoatrial node field potential, prediction model, signal decomposition

DOI: 10.3233/BME-151482

Citation: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1805-S1811, 2015

Authors: Ling, Ping | Gao, Dajin | Zhou, Xifeng | Huang, Zhining | Rong, Xiangsheng

Article Type: Research Article

Abstract: Computer-aided diagnosis (CAD) approaches succeed in detecting a number of diseases, however, they are not good at addressing atrial hypertrophy disease due to the lack of training data. Support Vector Machine (SVM) is very popular in few CAD solutions to atrial hypertrophy. Yet the performance of SVM is moderate in atrial hypertrophy detection compared to its success in other classification problems. In this paper we propose a novel CAD algorithm, Local Discriminative SVM (LDSVM), to overcome the above-mentioned difficulty. LDSVM consists of a global SVM that is trained on the training data, and a local kNN that is trained based …on the information of SVM and query. When a query arrives, SVM gives the initial decision. If the initial decision is less confident, local kNN begins to modify the initial decision. LDSVM improves the accuracy of SVM through some contributions: the risk tube, the discriminant information derived from SVM hyperplane, the new metric and the self-tuning size of neighborhood. Empirical evidence on real medical datasets show high performance of LDSVM over the peers in addressing atrial hypertrophy problems. Show more

Keywords: Computer-aided diagnosis, support vector machine, discriminative direction, derivative of hyperplane function

DOI: 10.3233/BME-151483

Citation: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1813-S1820, 2015

Authors: Garcia-Chimeno, Yolanda | Garcia-Zapirain, Begonya

Article Type: Research Article

Abstract: The classification of subjects’ pathologies enables a rigorousness to be applied to the treatment of certain pathologies, as doctors on occasions play with so many variables that they can end up confusing some illnesses with others. Thanks to Machine Learning techniques applied to a health-record database, it is possible to make using our algorithm. hClass contains a non-linear classification of either a supervised, non-supervised or semi-supervised type. The machine is configured using other techniques such as validation of the set to be classified (cross-validation), reduction in features (PCA) and committees for assessing the various classifiers. The tool is easy to …use, and the sample matrix and features that one wishes to classify, the number of iterations and the subjects who are going to be used to train the machine all need to be introduced as inputs. As a result, the success rate is shown either via a classifier or via a committee if one has been formed. A 90% success rate is obtained in the ADABoost classifier and 89.7% in the case of a committee (comprising three classifiers) when PCA is applied. This tool can be expanded to allow the user to totally characterise the classifiers by adjusting them to each classification use. Show more

Keywords: Classification, machine learning, PCA, cross-validation, committee

DOI: 10.3233/BME-151484

Citation: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1821-S1828, 2015

Authors: Nie, Guoping | Li, Yong | Wang, Feichi | Wang, Siwen | Hu, Xuehai

Article Type: Research Article

Abstract: G-protein-coupled receptors (GPCRs) are seven membrane-spanning proteins and regulate many important physiological processes, such as vision, neurotransmission, immune response and so on. GPCRs-related pathways are the targets of a large number of marketed drugs. Therefore, the design of a reliable computational model for predicting GPCRs from amino acid sequence has long been a significant biomedical problem. Chaos game representation (CGR) reveals the fractal patterns hidden in protein sequences, and then fractal dimension (FD) is an important feature of these highly irregular geometries with concise mathematical expression. Here, in order to extract important features from GPCR protein sequences, CGR algorithm, fractal …dimension and amino acid composition (AAC) are employed to formulate the numerical features of protein samples. Four groups of features are considered, and each group is evaluated by support vector machine (SVM) and 10-fold cross-validation test. To test the performance of the present method, a new non-redundant dataset was built based on latest GPCRDB database. Comparing the results of numerical experiments, the group of combined features with AAC and FD gets the best result, the accuracy is 99.22% and Matthew’s correlation coefficient (MCC) is 0.9845 for identifying GPCRs from non-GPCRs. Moreover, if it is classified as a GPCR, it will be further put into the second level, which will classify a GPCR into one of the five main subfamilies. At this level, the group of combined features with AAC and FD also gets best accuracy 85.73%. Finally, the proposed predictor is also compared with existing methods and shows better performances. Show more

Keywords: GPCR, chaos game representation, support vector machine, fractal dimension

DOI: 10.3233/BME-151485

Citation: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1829-S1836, 2015

Authors: Huang, Hai-Hui | Liu, Xiao-Ying | Liang, Yong | Chai, Hua | Xia, Liang-Yong

Article Type: Research Article

Abstract: Tuberculosis (TB), caused by infection with mycobacterium tuberculosis, is still a major threat to human health worldwide. Current diagnostic methods encounter some limitations, such as sample collection problem or unsatisfied sensitivity and specificity issue. Moreover, it is hard to identify TB from some of other lung diseases without invasive biopsy. In this paper, the logistic models with three representative regularization approaches including Lasso (the most popular regularization method), and L 1 / 2 (the method that inclines to achieve more sparse …solution than Lasso) and Elastic Net (the method that encourages a grouping effect of genes in the results) adopted together to select the common gene signatures in microarray data of peripheral blood cells. As the result, 13 common gene signatures were selected, and sequentially the classifier based on them is constructed by the SVM approach, which can accurately distinguish tuberculosis from other pulmonary diseases and healthy controls. In the test and validation datasets of the blood gene expression profiles, the generated classification model achieved 91.86% sensitivity and 93.48% specificity averagely. Its sensitivity is improved 6%, but only 26% gene signatures used compared to recent research results. These 13 gene signatures selected by our methods can be used as the basis of a blood-based test for the detection of TB from other pulmonary diseases and healthy controls. Show more

Keywords: Tuberculosis, feature selection, early diagnostic, regularization, biomarkers

DOI: 10.3233/BME-151486

Citation: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1837-S1843, 2015

Authors: Xia, Hong | Li, Ao | Yu, Zhenhua | Liu, Xiaocheng | Feng, Huanqing

Article Type: Research Article

Abstract: Application of the Next generation sequencing (NGS) technology has demonstrated that most tumor samples exhibit intra-tumor heterogeneity. Here we proposed SAPPH (Somatic Aberrations Prediction for Paired Heterogeneous tumor samples), as a new method for estimating tumor somatic copy number aberrations as well as inferring tumor subclone proportions from heterogeneous tumor sequencing data. This method is based on CBS and local proportion clustering strategy. When SAPPH is applied on simulated tumor samples, the agreement between the results analyzed by SAPPH and the sequencing signals suggests that SAPPH can find the solution to best fit the signal distributions. We benchmark the performance …of SAPPH and show that it outperforms existing method in estimating tumor copy number aberrations. Show more

Keywords: Intra-tumor heterogeneity, NGS, copy number aberration, tumor subclone, proportion

DOI: 10.3233/BME-151487

Citation: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1845-S1853, 2015

Authors: Cheng, Ke | Chen, Qingfang | Yang, Xibei | Gao, Shang | Yu, Hualong

Article Type: Research Article

Abstract: To address the imbalanced classification problem emerging in Bioinformatics, a boundary movement-based extreme learning machine (ELM) algorithm called BM-ELM was proposed. BM-ELM tries to firstly explore the prior information about data distribution by condensing all training instances into the one-dimensional feature space corresponding to the original output in ELM, and then on the transformed space, to find the optimal moving distance of the classification hyperplane by estimating the probability density distributions of the instances in different classes. Experimental results on four real imbalanced bioinformatics classification data sets indicated that the proposed BM-ELM algorithm outperforms some traditional bias correction algorithms due …to it can greatly improve the sensitivity of the classification results with small loss of specificity as possible. Also, BM-ELM algorithm has presented better performance than the widely used support vector machine (SVM) classifier. The algorithm can be widely popularized in various large-scale bioinformatics applications. Show more

Keywords: Bioinformatics, extreme learning machine, imbalanced classification, kernel density estimation

DOI: 10.3233/BME-151488

Citation: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1855-S1862, 2015

Authors: Sun, Lin | Xu, Jiucheng | Yin, Jiaojiao

Article Type: Research Article

Abstract: Fuzzy clustering is an important tool for analyzing microarray data. A major problem in applying fuzzy clustering method to microarray gene expression data is the choice of parameters with cluster number and centers. This paper proposes a new approach to fuzzy kernel clustering analysis (FKCA) that identifies desired cluster number and obtains more steady results for gene expression data. First of all, to optimize characteristic differences and estimate optimal cluster number, Gaussian kernel function is introduced to improve spectrum analysis method (SAM). By combining subtractive clustering with max-min distance mean, maximum distance method (MDM) is proposed to determine cluster centers. …Then, the corresponding steps of improved SAM (ISAM) and MDM are given respectively, whose superiority and stability are illustrated through performing experimental comparisons on gene expression data. Finally, by introducing ISAM and MDM into FKCA, an effective improved FKCA algorithm is proposed. Experimental results from public gene expression data and UCI database show that the proposed algorithms are feasible for cluster analysis, and the clustering accuracy is higher than the other related clustering algorithms. Show more

Keywords: Spectral analysis, maximum distance, fuzzy clustering, gene expression data

DOI: 10.3233/BME-151489

Citation: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1863-S1869, 2015

Authors: Zhang, Hui | Wang, Jing | Sun, Ling | Xu, Qiuqin | Hou, Miao | Ding, Yueyue | Huang, Jie | Chen, Ye | Cao, Lei | Zhang, Jianmin | Qian, Weiguo | Lv, Haitao

Article Type: Research Article

Abstract: Obesity has become an increasingly serious health problem and popular research topic. It is associated with many diseases, especially cardiovascular disease (CVD)-related endothelial dysfunction. This study analyzed genes related to endothelial dysfunction and obesity and then summarized their most significant signaling pathways. Genes related to vascular endothelial dysfunction and obesity were extracted from a PubMed database, and analyzed by STRING, DAVID, and Gene-Go Meta-Core software. 142 genes associated with obesity were found to play a role in endothelial dysfunction in PubMed. A significant pathway (Angiotensin system maturation in protein folding and maturation) associated with obesity and endothelial dysfunction was explored. …The genes and the pathway explored may play an important role in obesity. Further studies about preventing vascular endothelial dysfunction obesity should be conducted through targeting these loci and pathways. Show more

Keywords: Pathway, obesity, vascular endothelial function, system biology

DOI: 10.3233/BME-151490

Citation: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1871-S1881, 2015

Authors: Guan, Changge | Niu, Xiaohui | Shi, Feng | Yang, Kun | Li, Nana

Article Type: Research Article

Abstract: DNA-binding proteins are involved and play a crucial role in a lot of important biological processes. Hence, the identification of the DNA-binding proteins is a challenging and significant problem. In order to reveal the intrinsic information correlated to DNA-binding, nine classes of candidate features based on different mathematical fields are applied to construct the prediction model with random forest. They are fractal dimension, conjoint triad feature, Hilbert-Huang Transformation, amino acid composition, dipeptide composition, chaos game representation, and the corresponding information entropies. These mathematical expressions are evaluated with 5-fold cross validation test. The results of numerical simulations show that the mathematical …features consisted of amino acid composition, fractal dimension and information entropies of amino acid and chaos game representation achieve the best performance. Its accuracy is 0.8157, and Matthew’s correlation coefficient (MCC) achieves 0.5968 on the benchmark dataset from DNA-Prot. By analyzing the components of top combination of the nine candidate features, the concepts of fractal dimension and information entropy are the effective and vital features, which can provide complementary sequence-order information on the basis of amino acid composition. Show more

Keywords: DNA-binding proteins, random forest, information entropy, fractal dimension, Hilbert-Huang transformation

DOI: 10.3233/BME-151491

Citation: Bio-Medical Materials and Engineering, vol. 26, no. s1, pp. S1883-S1889, 2015