Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization

Changsha, China

Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization

Changsha, China
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Wang L.-F.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | Tan X.-S.,Hunan Institute of Humanities, Science and Technology | Bai L.-Y.,Hunan Institute of Humanities, Science and Technology | Yuan Z.-M.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization
Asian Journal of Chemistry | Year: 2012

Aiming at the poor interpretability of support vector regression (SVR), a complete set of interpretability system for support vector regression was established based on F-test. The novel interpretability system includes the significance tests of model and single-factor importance, the single-factor effects and sensitivity analysis, the significance test of two factor interactions and so on. The analysis results of ternary dissymmetric organic phosphate insecticide preliminarily indicate that this new interpretability system is reasonable. Meanwhile, the quantitative structure-activity relationship (QSAR) model of insecticide based on support vector regression are superior to reference model in both fit and prediction, which further confirmed the outstanding regression performance of support vector regression.


Yan J.,Hunan Agricultural University | Yan J.,National Center for Citrus Improvement | Yan J.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | Yuan F.,Hunan Agricultural University | And 8 more authors.
Molecular Biology Reports | Year: 2012

Quantitative real-time reverse transcription polymerase chain reaction (qPCR) has become the preferred method for studying low-abundant mRNA expression. Appropriate application of qPCR in such studies requires the use of reference gene(s) as an internal control in order to normalize the mRNA levels between different samples for an exact comparison of gene expression levels. Expression of the reference gene should be independent from development stage, cell/tissue types, treatments and environmental conditions. Recognizing the importance of reference gene(s) in normalization of qPCR data, various reference genes have been evaluated for stable expression under specific conditions in various organisms. In plants, only a few of them have been investigated, and very few reports about such reference genes in citrus. In the present study, seven candidate reference genes (18SrRNA, ACTB, rpII, UBQI, UBQ10, GAPDH and TUB) were tested, and three of them (18SrRNA, ACTB and rpII) proved to be the most stable ones among six leaf samples of different citrus genotypes. The three candidate reference genes were further analyzed for their stability of expression in five different tissues, and the results indicated that they were not completely stable. It is commonly accepted that gene expression studies should be normalized using more than one reference gene. Based on our results, we propose the use of the mean result rendered by18SrRNA, ACTB and rpII as reference genes to normalize mRNA levels in qPCR analysis of diverse cultivars and tissues of citrus. These results may provide a guideline for future works on gene expression in citrus by using qPCR. © 2011 Springer Science+Business Media B.V.


Zhou W.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | Zhou W.,Hunan Agricultural University | Dai Z.,Hunan Agricultural University | Chen Y.,Hunan Agricultural University | And 2 more authors.
Medicinal Chemistry Research | Year: 2013

ARC-111 has potent topoisomerase I-targeting activity and pronounced antitumor activity. To design ARC-111 analogues with improved efficiency, we performed analyses on the quantitative structure-activity relationship of 22 ARC-111 analogues assessed in P388 tumor cells. First, the support vector regression (SVR) models were constructed and optimized based on literature descriptors (the low-dimensional descriptor space) and the worst descriptor elimination multi-round (WDEM) method. The optimized SVR model had greater generalization ability than multiple linear regression (MLR) and stepwise linear regression (SLR) in the independence test, which indicated that our nonlinear WDEM method could remove redundant descriptors more effectively, and our optimized SVR was a more powerful modeling technique. Second, to identify more accessible and effective descriptors, our modeling descriptors with clear meanings were selected from a large number of descriptors calculated by the software PCLIENT. Through the high-dimensional descriptor selection nonlinear method and the WDEM method, seven independent variable combinations with tens of descriptors were selected out of 2,923 descriptors. The seven corresponding SVR models performed better in the independent test, compared to MLR and SLR. The evaluation measures supported the excellent predictive power of the new models. According to the interpretability analysis of the SVR model, the regression significance of the model and the importance of single indicator were evaluated based on F tests. Our study offers some useful theories for understanding the function mechanism and finds parameters for designing ARC-111 analogues with enhanced antitumor activity. © 2012 Springer Science+Business Media, LLC.


Zhang H.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | Zhang H.,Hunan Agricultural University | Wang H.,Kansas State University | Dai Z.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | And 4 more authors.
BMC Bioinformatics | Year: 2012

Background: Even though the classification of cancer tissue samples based on gene expression data has advanced considerably in recent years, it faces great challenges to improve accuracy. One of the challenges is to establish an effective method that can select a parsimonious set of relevant genes. So far, most methods for gene selection in literature focus on screening individual or pairs of genes without considering the possible interactions among genes. Here we introduce a new computational method named the Binary Matrix Shuffling Filter (BMSF). It not only overcomes the difficulty associated with the search schemes of traditional wrapper methods and overfitting problem in large dimensional search space but also takes potential gene interactions into account during gene selection. This method, coupled with Support Vector Machine (SVM) for implementation, often selects very small number of genes for easy model interpretability.Results: We applied our method to 9 two-class gene expression datasets involving human cancers. During the gene selection process, the set of genes to be kept in the model was recursively refined and repeatedly updated according to the effect of a given gene on the contributions of other genes in reference to their usefulness in cancer classification. The small number of informative genes selected from each dataset leads to significantly improved leave-one-out (LOOCV) classification accuracy across all 9 datasets for multiple classifiers. Our method also exhibits broad generalization in the genes selected since multiple commonly used classifiers achieved either equivalent or much higher LOOCV accuracy than those reported in literature.Conclusions: Evaluation of a gene's contribution to binary cancer classification is better to be considered after adjusting for the joint effect of a large number of other genes. A computationally efficient search scheme was provided to perform effective search in the extensive feature space that includes possible interactions of many genes. Performance of the algorithm applied to 9 datasets suggests that it is possible to improve the accuracy of cancer classification by a big margin when joint effects of many genes are considered. © 2012 Zhang et al.; licensee BioMed Central Ltd.


Wang H.,Kansas State University | Zhang H.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | Zhang H.,Hunan Agricultural University | Dai Z.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | And 4 more authors.
BMC Medical Genomics | Year: 2013

Background: One of the challenges in classification of cancer tissue samples based on gene expression data is to establish an effective method that can select a parsimonious set of informative genes. The Top Scoring Pair (TSP), k-Top Scoring Pairs (k-TSP), Support Vector Machines (SVM), and prediction analysis of microarrays (PAM) are four popular classifiers that have comparable performance on multiple cancer datasets. SVM and PAM tend to use a large number of genes and TSP, k-TSP always use even number of genes. In addition, the selection of distinct gene pairs in k-TSP simply combined the pairs of top ranking genes without considering the fact that the gene set with best discrimination power may not be the combined pairs. The k-TSP algorithm also needs the user to specify an upper bound for the number of gene pairs. Here we introduce a computational algorithm to address the problems. The algorithm is named Chisquare-statistic-based Top Scoring Genes (Chi-TSG) classifier simplified as TSG. Results: The TSG classifier starts with the top two genes and sequentially adds additional gene into the candidate gene set to perform informative gene selection. The algorithm automatically reports the total number of informative genes selected with cross validation. We provide the algorithm for both binary and multi-class cancer classification. The algorithm was applied to 9 binary and 10 multi-class gene expression datasets involving human cancers. The TSG classifier outperforms TSP family classifiers by a big margin in most of the 19 datasets. In addition to improved accuracy, our classifier shares all the advantages of the TSP family classifiers including easy interpretation, invariant to monotone transformation, often selects a small number of informative genes allowing follow-up studies, resistant to sampling variations due to within sample operations. Conclusions: Redefining the scores for gene set and the classification rules in TSP family classifiers by incorporating the sample size information can lead to better selection of informative genes and classification accuracy. The resulting TSG classifier offers a useful tool for cancer classification based on numerical molecular data. © 2013 Yuan; licensee BioMed Central Ltd.


Zhou W.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | Zhou W.,Hunan Agricultural University | Dai Z.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | Chen Y.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | And 3 more authors.
International Journal of Molecular Sciences | Year: 2012

To design ARC-111 analogues with improved efficiency, we constructed the QSAR of 22 ARC-111 analogues with RPMI8402 tumor cells. First, the optimized support vector regression (SVR) model based on the literature descriptors and the worst descriptor elimination multi-roundly (WDEM) method had similar generalization as the artificial neural network (ANN) model for the test set. Secondly, seven and 11 more effective descriptors out of 2,923 features were selected by the high-dimensional descriptor selection nonlinearly (HDSN) and WDEM method, and the SVR models (SVR3 and SVR4) with these selected descriptors resulted in better evaluation measures and a more precise predictive power for the test set. The interpretability system of better SVR models was further established. Our analysis offers some useful parameters for designing ARC-111 analogues with enhanced antitumor activity. © 2012 by the authors; licensee MDPI, Basel, Switzerland.


Dai Z.-J.,Hunan Agricultural University | Dai Z.-J.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | Zhou W.,Hunan Agricultural University | Yuan Z.-M.,Hunan Agricultural University | Yuan Z.-M.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization
Wuli Huaxue Xuebao/ Acta Physico - Chimica Sinica | Year: 2011

Each amino acid residue of one peptide was characterized directly by 531 physicochemical property parameters. Based on support vector regression (SVR) we developed a new nonlinear rapid feature selection method for high dimensional data, which was applied to a quantitative sequenceactivity relationship (QSAR) study of two peptide systems (bitter tasting thresholds and angiotensin converting enzyme inhibitors). In both systems, 10 descriptors with clear meaning were reserved. We established a SVR model for both peptide systems using the reserved descriptors of the peptides. For both models the accuracies of fitting, the leave-one-out cross validation, and the external prediction improved significantly compared with the results reported in literature. To enhance the interpretability of the models, significance tests of the nonlinear regression model, single-factor relative importance, and a single-factor effect analysis were carried out. The new method has broad application prospects for regression forecasting of high dimensional data such as QSAR modeling of peptide or proteins. © Editorial office of Acta Physico-Chimica Sinica.


PubMed | Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization and Hunan Agricultural University
Type: | Journal: BioMed research international | Year: 2014

In efforts to discover disease mechanisms and improve clinical diagnosis of tumors, it is useful to mine profiles for informative genes with definite biological meanings and to build robust classifiers with high precision. In this study, we developed a new method for tumor-gene selection, the Chi-square test-based integrated rank gene and direct classifier ((2)-IRG-DC). First, we obtained the weighted integrated rank of gene importance from chi-square tests of single and pairwise gene interactions. Then, we sequentially introduced the ranked genes and removed redundant genes by using leave-one-out cross-validation of the chi-square test-based Direct Classifier ((2)-DC) within the training set to obtain informative genes. Finally, we determined the accuracy of independent test data by utilizing the genes obtained above with (2)-DC. Furthermore, we analyzed the robustness of (2)-IRG-DC by comparing the generalization performance of different models, the efficiency of different feature-selection methods, and the accuracy of different classifiers. An independent test of ten multiclass tumor gene-expression datasets showed that (2)-IRG-DC could efficiently control overfitting and had higher generalization performance. The informative genes selected by (2)-IRG-DC could dramatically improve the independent test precision of other classifiers; meanwhile, the informative genes selected by other feature selection methods also had good performance in (2)-DC.


PubMed | Central South University, Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization and Hunan Agricultural University
Type: | Journal: BioMed research international | Year: 2014

Many evidences have demonstrated that protein complexes are overlapping and hierarchically organized in PPI networks. Meanwhile, the large size of PPI network wants complex detection methods have low time complexity. Up to now, few methods can identify overlapping and hierarchical protein complexes in a PPI network quickly. In this paper, a novel method, called MCSE, is proposed based on -module and seed-expanding. First, it chooses seeds as essential PPIs or edges with high edge clustering values. Then, it identifies protein complexes by expanding each seed to a -module. MCSE is suitable for large PPI networks because of its low time complexity. MCSE can identify overlapping protein complexes naturally because a protein can be visited by different seeds. MCSE uses the parameter _th to control the range of seed expanding and can detect a hierarchical organization of protein complexes by tuning the value of _th. Experimental results of S. cerevisiae show that this hierarchical organization is similar to that of known complexes in MIPS database. The experimental results also show that MCSE outperforms other previous competing algorithms, such as CPM, CMC, Core-Attachment, Dpclus, HC-PIN, MCL, and NFC, in terms of the functional enrichment and matching with known protein complexes.


PubMed | Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization and Hunan Agricultural University
Type: | Journal: Journal of chromatography. B, Analytical technologies in the biomedical and life sciences | Year: 2015

A specific and reliable HPLC-MS/MS method was developed and validated for simultaneously determination of sanguinarine, chelerythrine and their metabolites (dihydrosanguinarine and dihydrochelerythrine) in chicken tissue for the first time. This is important because these compounds are related to the use of a naturally occurring and novel feed additive with many benefits, but the levels of these compounds must be strictly controlled. The compounds were extracted by acetonitrile and 1% HCl-methanol solution successively and then separated on a C18 column. A triple-quadrupole mass spectrometer equipped with an electrospray ionization (ESI) source was used for detection. Quantification was performed using multiple reaction monitoring with positive mode. The method was validated in terms of specificity, linearity, precision, accuracy and stability. The calibration curves were linear over the concentration range of 0.5-100.0ng/g for sanguinarine, 0.5-100.0ng/g for chelerythrine, 0.2-100.0ng/g for dihydrosanguinarine and 0.1-100ng/g for dihydrochelerythrine, respectively. All of the recovery rates of the four analytes were over 85%. The RSD of intra-day and inter-day precision was less than 5.0%, and the relative error was all within 12.0%. This validated method has been successfully applied to assess the drug residue and metabolite residue characteristics of sanguinarine and chelerythrine in chicken tissue after oral administration of the extracts of Macleaya cordata (Willd.) R. Br, and to investigate the pharmacokinetic parameters of sanguinarine and dihydrosanguinarine in chicken plasma.

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