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Ren J.,Hunan Agricultural University | Ren J.,Central South University | Zhou W.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | Wang J.,Central South University
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. © 2014 Jun Ren et al.


Wang Z.-M.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | Wang Z.-M.,Hunan Provincial Key Laboratory for Biology | Wang Z.-M.,Hunan Agricultural University | Han N.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | And 4 more authors.
Wuli Huaxue Xuebao/ Acta Physico - Chimica Sinica | Year: 2013

Absolute weight values estimated from test data by ridge regression (RR) can reflect the significance of corresponding features. Based on RR and support vector machine (SVM), a new feature selection algorithm for high-dimensional data is proposed. Examples from bitter tasting thresholds (BTT) and cytotoxic T lymphocyte (CTL) epitopes are presented. All 531 physicochemical property parameters were employed to express each residue of one peptide, thus 1062 and 4779 descriptors were obtained for BTT and CTL, respectively. Each sample was divided into training and test sets, and weight estimates of all training set descriptors were generated by RR. According to the descending order of the weights, corresponding features were gradually selected until the mean square error (MSE) of leave-one-out cross validation (LOOCV) increased significantly. Based on smaller training datasets obtained from the previous step, the reserved features were available from multiple elimination rounds. 7 and 18 descriptors were selected by the new method for BTT and CTL, respectively. A quantitative structure-activity relationship (QSAR) model based on support vector regression (SVR) was established on extracted data with the reserved descriptors, and was then used for test data prediction. The fitting, LOOCV, and external prediction accuracies were significantly improved with respect to reported literature values. Because of the calculation speed, clear physicochemical meaning, and ease of interpretation, the new method is widely applicable to regression forecasting of high-dimensional data such as QSAR modeling of peptide or proteins. © Editorial office of Acta Physico-Chimica Sinica.


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.


She W.,Hunan Agricultural University | Jie Y.-C.,Hunan Agricultural University | Jie Y.-C.,Hunan Provincial Key Laboratory of Crop Germplasm Innovation and Utilization | Xing H.-C.,Hunan Agricultural University | And 5 more authors.
Acta Agriculturae Scandinavica Section B: Soil and Plant Science | Year: 2011

Ramie is a fibre crop originating from China that has great ability to tolerate and accumulate heavy metals. This study has analysed the capacity of ramie cultivars to absorb and accumulate cadmium (Cd) through two years of micro-regional field study and shown that stress under high concentration of Cd significantly affects the biomass of ramie; 25 mg kg -1 Cd treatment promotes the growth and development of ramie cultivars B. nivea 1 and 9. The Cd contents retained in different parts of ramie are ranked as the following: bast>stems>leaves. Cd concentrations in shoot systems among different ramie cultivars are significantly different and increase dramatically in association with the increasing Cd concentration in the soil. Ramie has the ability to accumulate large amounts of Cd; under the treatment with 25 and 100 mg kg -1 of Cd, three times of ramie harvest annually remove 0.76 and 0.97 kg hm -2 a -1 of Cd respectively. The results from this study demonstrate the feasibility of phytoremediation of Cd-contaminated farmland by ramie cultivars that have obtained Cd accumulating capacity through screening and training. © 2011 Taylor & Francis.


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.

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