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Qin W.,Shanghai JiaoTong University | Qin W.,University of Illinois at Chicago | Zhao G.,Shanghai JiaoTong University | Zhao G.,Key Laboratory of Molecular Embryology | And 4 more authors.
IET Systems Biology | Year: 2016

A structure-based statistical potential is developed for transcription factor binding site (TFBS) prediction. Besides the direct contact between amino acids from TFs and DNA bases, the authors also considered the influence of the neighbouring base. This three-body potential showed better discriminate powers than the two-body potential. They validate the performance of the potential in TFBS identification, binding energy prediction and binding mutation prediction. © The Institution of Engineering and Technology. Source

Zheng B.,Shanghai JiaoTong University | Zheng B.,Key Laboratory of Molecular Embryology | Liu J.,Shanghai JiaoTong University | Gu J.,Shanghai JiaoTong University | And 8 more authors.
International Journal of Cancer | Year: 2015

Reliable preoperative diagnosis of malignant thyroid tumors remains challenging because of the inconclusive cytological examination of fine-needle aspiration biopsies. Although numerous studies have successfully demonstrated the use of highthroughput molecular diagnostics in cancer prediction, the application of microarrays in routine clinical use remains limited. Our aim was, therefore, to identify a small subset of genes to develop a practical and inexpensive diagnostic tool for clinical use. We developed a two-step feature selection method composed of a linear models for microarray data (LIMMA) linear model and an iterative Bayesian model averaging model to identify a suitable gene set signature. Using one public dataset for training, we discovered a three-gene signature dipeptidyl-peptidase 4 (DPP4), secretogranin V (SCG5) and carbonic anhydrase XII (CA12). We then evaluated the robustness of our gene set using three other independent public datasets. The gene signature accuracy was 85.7, 78.8 and 85.7%, respectively. For experimental validation, we collected 70 thyroid samples from surgery and our three-gene signature method achieved an accuracy of 94.3% by quantitative polymerase chain reaction (QPCR) experiment. Furthermore, immunohistochemistry in 29 samples showed proteins expressed by these three genes are also differentially expressed in thyroid samples. Our protocol discovered a robust three-gene signature that can distinguish benign from malignant thyroid tumors, which will have daily clinical application. © 2014 UICC. Source

Gu J.-L.,Shanghai JiaoTong University | Gu J.-L.,Key Laboratory of Molecular Embryology | Lu Y.,Shanghai JiaoTong University | Lu Y.,Key Laboratory of Molecular Embryology | And 4 more authors.
Journal of Theoretical Biology | Year: 2014

Feature selection is an important research topic in bioinformatics, to date a large number of methods have been developed. Recently several pathway based feature selection protocols, such as the condition-responsive genes method, have been proposed for better classification performance. However, these conventional pathway based methods may lead to the selection of relevant but redundant genes in a given pathway while missing the other useful genes. Also these methods were limited to binary classification, while in many clinical problems a multiclass protocol is preferred such as the classification of sarcomas. Here, we propose a new pathway based feature selection method named Redundancy Removable Pathway based feature selection method (RRP) for the binary and multiclass classification problems. Three classifiers were implemented to compare the performance and gene functions of gene-based, conventional pathway based, and our RRP method. The validation results suggest that the RRP method is a feasible and robust feature selection method for multi-class prediction problems. © 2014 Elsevier Ltd. Source

Jiang S.,Shanghai JiaoTong University | Ren Z.,Shanghai JiaoTong University | Ren Z.,Key Laboratory of Molecular Embryology | Xie F.,Shanghai JiaoTong University | And 6 more authors.
Biotechnology Letters | Year: 2012

Prolactin promotes the expression of exogenous human transferrin gene in the milk of transgenic mice. To elucidate this, a recombinant plasmid of bovine prolactin plus human transferrin vector was co-transfected into cultured murine mammary gland epithelial cells. Prolactin-receptor antagonist and shRNA corresponding to prolactin-receptor mRNA were added into the cell culture mixture to investigate the relations between prolactin-receptor and human transferrin expression after bovine prolactin inducement. Levels of human transferrin in the supernatants were increased under the presentation of bovine prolactin (from 1,076 ± 115 to 1,886 ± 114 pg/ml). With the treatment of prolactin-receptor antagonist or shRNA, human transferrin in cells was declined (1,886 ± 113 vs. 1,233 ± 85 pg/ml or 1,114 ± 75 pg/ml, respectively). An inverse correlation was found between the dosage of prolactin-receptor antagonist and expression level of human transferrin. Real-time qRT-PCR analysis showed that the relative level of signal transducer and activator of transcription 5a (STAT5a) transcript in transfected cells correlated with expression levels of human transferrin in the supernatant of the same cells. Bovine prolactin thus improved the expression of human transferrin through such a possible mechanism that bovine prolactin activated STAT5a transcription expression via combined with prolactin-receptor and suggest a potential utility of the bovine prolactin for efficient expression of valuable pharmaceutical proteins in mammary glands of transgenic animals. © 2012 Springer Science+Business Media B.V. Source

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