bluuebunny/crossref_metadata_2025_split huggingface dataset:
Improve the diagnosis of atrial hypertrophy with the local discriminative support vector machine
Authors: Ling Ping , Gao Dajin , Zhou Xifeng , Huang Zhining , Rong Xiangsheng 
Published: August 2015

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.
HClass: Automatic classification tool for health pathologies using artificial intelligence techniques
Authors: Garcia-Chimeno Yolanda , Garcia-Zapirain Begonya 
Published: August 2015

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.
A novel fractal approach for predicting G-protein–coupled receptors and their subfamilies with support vector machines
Authors: Nie Guoping , Li Yong , Wang Feichi , Wang Siwen , Hu Xuehai 
Published: August 2015

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.
Predicting a DNA-binding protein using random forest with multiple mathematical features
Authors: Guan Changge , Niu Xiaohui , Shi Feng , Yang Kun , Li Nana 
Published: August 2015

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.
The study of threshold determination of gene identification and its improvement algorithms
Authors: Yan Chun , Liu Wei , Qiu Yiwei , Sun Haitang 
Published: August 2015

Abstract The problem of gene recognition based on the ratio of power spectrum, SNR, and Gabor transform and its implementation of the calculation were discussed. The optimal threshold could guarantee to identify the DNA sequences with the signal-to-noise ratio. It summarized three kinds of traditional ways to determine the threshold, and advanced the optimum entitled method showing the disparate degrees of highlight and the discrimination rate method of the exons or introns as far as possible to improve the rate of their accuracy. To evaluate different determination methods of threshold by using the calculation results of four kinds of DNA sequence. In order to ensure the analysis of DNA sequence more accurate, it adopted and improved gene identification method of Fourier transformation in a short time which is based on Gabor transformation. By using of the ergodic theory, the fixed percentage of the sequence length of exons in DNA has been improved to be the dynamic percentages which focus on different gene types. The exons of the DNA sequence which have been already discovered were identified by using the improved algorithm. With comparison of the results and the actual endpoint of exons, it confirmed that the improved algorithm can figure out the endpoint of the exons more accurate.
Integration of multi-microarray datasets to identify chronic obstructive pulmonary disease-related miRNAs
Authors: Hua Lin , Zheng Weiying , Xia Hong , Zhou Ping , An Li 
Published: August 2015

Abstract Currently, the mechanisms underlying chronic obstructive pulmonary disease (COPD) remain unclear. As potential biomarkers, microRNAs (miRNAs), which modulate the levels of specific genes and proteins, are important for enhancing our understanding of the mechanisms behind COPD. Although there have been a number of miRNA expression profiling analyses strategies used to document miRNA expression changes during physiological and pathological processes or used to identify differentially expressed miRNAs in disease or control samples, the study results have been inconsistently replicated using different datasets. For this reason, many findings cannot be well synthesized and interpreted. To address this issue, we used a multiple co-inertia analysis (MCIA) method to extract potential COPD-related miRNAs using three COPD microarray datasets. The results showed that miR-223, miR-132, and miR-199a-5p are obviously associated with COPD, and these results are consistent with the highly significant differentially-expressed miRNAs that were observed across three microarray datasets. Moreover, when miR-223, miR-132, and miR-199a-5p are taken as predictors to classify the samples of three datasets, the pooled sensitivity and specificity is 0.96 and 0.75, respectively, thereby suggesting that these three miRNAs can effectively distinguish COPD patients and controls.
Interactive Naive Bayesian network: A new approach of constructing gene-gene interaction network for cancer classification
Authors: Tian Xue W. , Lim Joon S. 
Published: August 2015

Abstract Naive Bayesian (NB) network classifier is a simple and well-known type of classifier, which can be easily induced from a DNA microarray data set. However, a strong conditional independence assumption of NB network sometimes can lead to weak classification performance. In this paper, we propose a new approach of interactive naive Bayesian (INB) network to weaken the conditional independence of NB network and classify cancers using DNA microarray data set. We selected the differently expressed genes (DEGs) to reduce the dimension of the microarray data set. Then, an interactive parent which has the biggest influence among all DEGs is searched for each DEG. And then we calculate a weight to represent the interactive relationship between a DEG and its parent. Finally, the gene-gene interaction network is constructed. We experimentally test the INB network in terms of classification accuracy using leukemia and colon DNA microarray data sets, then we compare it with the NB network. The INB network can get higher classification accuracies than NB network. And INB network can show the gene-gene interactions visually.
Simulation of the charge migration in DNA under irradiation with heavy ions
Authors: Belov Oleg V. , Boyda Denis L. , Plante Ianik , Shirmovsky Sergey Eh. 
Published: August 2015

Abstract A computer model to simulate the processes of charge injection and migration through DNA after irradiation by a heavy charged particle was developed. The most probable sites of charge injection were obtained by merging spatial models of short DNA sequence and a single 1 GeV/u iron particle track simulated by the code RITRACKS (Relativistic Ion Tracks). Charge migration was simulated by using a quantum-classical nonlinear model of the DNA–charge system. It was found that charge migration depends on the environmental conditions. The oxidative damage in DNA occurring during hole migration was simulated concurrently, which allowed the determination of probable locations of radiation-induced DNA lesions.
Control of HIV/AIDS infection system with drug dosages design via robust H∞ fuzzy controller
Authors: Assawinchaichote Wudhichai 
Published: August 2015

Abstract The designing of [Formula: see text] fuzzy controller for HIV/AIDS infected dynamic system has been considered in this paper. With TS fuzzy model and LMIs approach, the proposed controller is obtained for such a system. A set of sufficient conditions of the [Formula: see text] controller is given to ensure the closed-loop system asymptotic stability and the prescribed [Formula: see text] performance level. Finally, the effectiveness of the fuzzy controller design approach is finally presented through the simulation results.
Feature gene selection method based on logistic and correlation information entropy
Authors: Xu Jiucheng , Li Tao , Sun Lin 
Published: August 2015

Abstract In view of the characteristics of high dimension, small samples, nonlinearity and numeric type in the gene expression profile data, the logistic and the correlation information entropy are introduced into the feature gene selection. At first, the gene variable is screened preliminarily by logistic regression to obtain the genes that have a greater impact on the classification; then, the candidate features set is generated by deleting the unrelated features using Relief algorithm. On the basis of this, delete redundant features by using the correlation information entropy; finally, the feature gene subset is classified by using the classifier of support vector machine (SVM). Experimental results show that the proposed method can obtain smaller subset of genes and achieve higher recognition rate.
A label distance maximum-based classifier for multi-label learning
Authors: Liu Xiaoli , Bao Hang , Zhao Dazhe , Cao Peng 
Published: August 2015

Abstract Multi-label classification is useful in many bioinformatics tasks such as gene function prediction and protein site localization. This paper presents an improved neural network algorithm, Max Label Distance Back Propagation Algorithm for Multi-Label Classification. The method was formulated by modifying the total error function of the standard BP by adding a penalty term, which was realized by maximizing the distance between the positive and negative labels. Extensive experiments were conducted to compare this method against state-of-the-art multi-label methods on three popular bioinformatic benchmark datasets. The results illustrated that this proposed method is more effective for bioinformatic multi-label classification compared to commonly used techniques.
Gene environment interaction of GALNT2 and APOE gene with hypertension in the Chinese Han Population
Authors: Zhang Xuejuan , Zhao Haiping , Zhang Jietao , Han Di , Zheng Yu , Guo Xiaozi , He Dian , Guo Jin , Wang Yingcui 
Published: August 2015

Abstract In some GWAs studies, GALNT2 and APOE polymorphisms have been identified to be related to alterations of plasma or serum HDL-C and TG concentrations. The purpose of our study is to assess the contribution of GALNT2 rs4846914, APOE rs429358, rs7412, rs405509 variants, and several environmental factors to the development of hypertension disease in the China Han population. A hospital-based case-control study was conducted. Cases were hypertension ( n=211) and controls were normal participants ( n=434). The AA, AG, and GG genotype frequencies of GALNT2 rs4846914 were 22.8%, 43.1%, and 34.1% in hypertension subjects, and 35.3%, 44.2%, and 20.5% in controls ( P<0.05), respectively. The OR of the AG genotype adjusted for all risk factors compared to the AA genotype was 1.61 (95%CI: 1.02 to 2.56) and to the GG genotype 2.67 (95%CI: 1.59 to 4.488). There was no significant difference between the APOE rs429358, rs7412, and rs405509 genotype frequencies in hypertension and control subjects. The present work indicates that SNP rs4846914 in GALNT2 gene is related to an increased risk of hypertension in China Han population, but the APOE gene is not.
Integrative network analysis of rifampin-regulated miRNAs and their functions in human hepatocytes
Authors: Li Jin , Wang Ying , Wang Lei , Liang Hong , Feng Weixing , Meng Xianglian , Cong Wang , Liu Yunlong 
Published: August 2015

Abstract Rifampin is an important drug used in the treatment of tuberculosis, and it increases the drug metabolism in human hepatocytes. Previous studies have shown that rifampin can indirectly influence drug deposition through the regulation of molecular interactions of miRNA, PXR and other genes. The potential functions of miRNAs associated with rifampin- induced drug disposition are poorly understood. In this study, significantly differentially expressed miRNAs (SDEM) were extracted and used to predict the miRNA-regulated co-expression target genes (MCeTG). Additionally, a miRNA-regulated co-expressed protein interaction network (MCePIN) was constructed for SDEM by extending from the protein interaction network (PIN). The functioning of the miRNAs were analyzed using GO analysis and KEGG pathway enrichment analysis. A total of 20 miRNAs belonging to SDEM were identified, and 632 miRNA-regulated genes were predicted. The MCePIN was constructed by extending from PIN, and 10 miRNAs and 33 genes that are relevant to 7 functions, including response to wounding, wound healing, response to drug, defense response, inflammatory response, liver development and drug metabolism, were discerned. The results provided by this study offer valuable insights into the effect of rifampin on miRNAs, genes and protein levels.
Principal component-based feature selection for tumor classification
Authors: Sun Lin , Xu Jiucheng , Yin Ying 
Published: August 2015

Abstract One of the important problems in microarray gene expression data is tumor classification. This paper proposes a new feature selection method for tumor classification using gene expression data. In this method, three dimensionality reduction methods, including principal component analysis (PCA), factor analysis (FA) and independent component analysis (ICA), are first introduced to extract and select features for tumor classification, and their corresponding specific steps are given respectively. Then, the superiority of three algorithms is demonstrated by performing experimental comparisons on acute leukemia data sets. It is concluded that PCA compared with FA and ICA is the best under feature load ratio. However, PCA cannot make full use of the category information. To overcome the weak point, Fisher linear discriminant (FLD) is employed as those components of PCA, and a new approach to principal component discriminant analysis (PCDA) is proposed to retain all assets and work better than both PCA and FLD for classification. The further experimental results show that the classification ability of selected feature subsets by means of PCDA is higher than that of the other related dimensionality reduction methods, and the proposed algorithm is efficient and feasible for tumor classification.
Three-dimensional (3D) structure prediction and function analysis of the chitin-binding domain 3 protein HD73_3189 from Bacillus thuringiensis HD73
Authors: Zhan Yiling , Guo Shuyuan 
Published: August 2015

Abstract Bacillus thuringiensis (Bt) is capable of producing a chitin-binding protein believed to be functionally important to bacteria during the stationary phase of its growth cycle. In this paper, the chitin-binding domain 3 protein HD73_3189 from B. thuringiensis has been analyzed by computer technology. Primary and secondary structural analyses demonstrated that HD73_3189 is negatively charged and contains several α-helices, aperiodical coils and β-strands. Domain and motif analyses revealed that HD73_3189 contains a signal peptide, an N-terminal chitin binding 3 domains, two copies of a fibronectin-like domain 3 and a C-terminal carbohydrate binding domain classified as CBM_5_12. Moreover, analysis predicted the protein’s associated localization site to be the cell wall. Ligand site prediction determined that amino acid residues GLU-312, TRP-334, ILE-341 and VAL-382 exposed on the surface of the target protein exhibit polar interactions with the substrate.
Effects of hydrogen sulfide on myocardial fibrosis in diabetic rats: Changes in matrix metalloproteinases parameters
Authors: Xiao Ting , Zeng Ou , Luo Jian , Wu Zhixiong , Li Fang , Yang Jun 
Published: August 2015

Abstract In order to explore the effects of hydrogen sulfide (H2S) on myocardial fibrosis in diabetic rats and its underlying mechanisms, intraperitoneal injections of streptozotocin were used to establish the diabetes models and sodium hydrosulfide (NaHS) was used as an exogenous donor of H2S. Eight weeks later, Van Gieson staining was used to observe pathological changes, and basic hydrolysis methods were adopted to measure hydroxyproline content, while Western Blot was used to determine the expression of MMP2, MMP7, MMP11, MMP13, MMP16, TIMP1 and TGF β1.The results showed that significant myocardial fibrosis, decreased TIMP1 and MMP2 expression and increased MMP7, MMP11, MMP13, MMP16 expressions occurred in diabetic group, but all these changes were significantly reversed in diabetic rats after NaHS treatment. This suggests that H2S could attenuate cardiac fibrosis induced by diabetes and its mechanisms may be related to its modulation of MMPs/TIMPs expression and regulation of TGF β1.
The effect of different molecular weight collagen peptides on MC3T3-E1 cells differentiation
Authors: Liu Junli , Si Shaoyan , Qin Yaya , Zhang Bin , Song Shujun , Guo Yanchuan 
Published: August 2015

Abstract Our study aimed at investigating the effect of different molecular weight bovine collagen peptides, namely CH878, CH1370, CH2900, and CH7747 on the differentiation of MC3T3-E1 cells. Osteogenic differentiation of MC3T3-E1 cells was assessed by a series of specific assays, after culturing of cells in the presence of collagen peptides. Alkaline phosphatase activity (ALP) was evaluated by NBT/BCIP staining. Osteocalcin expression was determined by a radioimmunology method. Mineralization was quantified by Alizarin Red staining. ALP staining results demonstrated that the ALP staining of cells after culture in the presence of collagen peptides were significantly higher than the control group (P<0.05), indicating the promotion of ALP activity in MC3T3-E1 cells by these peptides. Radioimmunology results demonstrated that collagen peptides groups were all significantly higher than the control group (P<0.01). Alizarin Red staining results demonstrated that CH1370, CH2900, and CH7747 significantly promoted the formation of mineralized bone matrix. We therefore conclude that CH1370, CH2900, and CH7747 play an active role in the differentiation of MC3T3-E1 cells. Based on the above results, we provide molecular basis for further development of collagens with different molecular weight for the prevention and treatment of osteoporosis.
Abnormal increased re-released Hb from RBCs of an intrahepatic bile duct carcinoma patient was detected by electrophoresis release test
Authors: Su Yan , Han Lisha , Gao Lijun , Guo Jun , Sun Xiaorong , Li Jiaxin , Qin Wenbin 
Published: August 2015

Abstract In this paper, the hemoglobin (Hb) re-released from red blood cells (RBCs) and whole blood of 7 carcinoma patients were studied by using electrophoresis release test (ERT), which was established by our lab. Among the 7 carcinoma patients, the re-released Hb was distinctively increased from an intrahepatic bile duct carcinoma patient during one-dimension isotonic ERT. Different from the others, the result of double-dimension Hb re-release of this intrahepatic bile duct carcinoma patient showed that not only HbA but also HbA2 could be re-released from both RBCs and whole blood. The result of isotonic & hypotonic ERT which was performed at room temperature showed that more Hb could be re-released from both RBCs and whole blood of the intrahepatic bile duct carcinoma patient than that of the normal control. After keeping the samples at 37°C for 1 hour, the re-released Hb from RBCs could still be found more than that of the normal control, but was disappeared completely from the whole blood sample. To our surprise, when the isotonic & hypotonic ERT was repeated 2 days later at 37°C, the re-released Hb from RBCs of the intrahepatic bile duct carcinoma patient was increased only in tube 4-6, and disappeared in the other tube. Further mechanism research work cannot be continued because of the patient’s leave, but ERT is speculated to be a useful and effective technology to observe the physiological or pathological change of RBCs, blood or body in the future.