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2019年度单位署名论文

发布时间:2021-01-05浏览量:660

61. Xie, T., Wang, Z., Zhao, Q., Bai, Q., Zhou, X., Gu, Y., Peng, W., & Wang, H. (2019). Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer. Frontiers in Oncology, 9. Machine Learning-Based Analysis of MR Multiparametric Radiomics for the Subtype Classification of Breast Cancer.pdf


62. Xie, W.B., Yan, H., & Zhao, X.M. (2019). EmDL: Extracting miRNA-drug interactions from literature. Ieee-Acm Transactions on Computational Biology and Bioinformatics, 16(5), 1722-1728. EmDL Extracting miRNA-drug interactions from literature.pdf


63. Yang, K., Zhao, X., Waxman, D., & Zhao, X.M. (2019). Predicting drug-disease associations with heterogeneous network embedding. Chaos, 29(12). Predicting drug-disease associations with heterogeneous network embedding.pdf


64. Yang, M., Yan, Y., & Wang, H. (2019). IMAge/enGINE: a freely available software for rapid computation of high-dimensional quantification. Quantitative Imaging in Medicine and Surgery, 9(2), 210-218. IMAge_enGINE- a freely available software for rapid computation of high-dimensional quantification.pdf


65. Ye, Y., Kang, X., Bailey, J., Li, C., & Hong, T. (2019). An enriched network motif family regulates multistep cell fate transitions with restricted reversibility. Plos Computational Biology, 15(3).


66. You, R., Yao, S., Xiong, Y., Huang, X., Sun, F., Mamitsuka, H., & Zhu, S. (2019). NetGO: improving large-scale protein function prediction with massive network information. Nucleic Acids Research, 47(W1), W379-W387. NetGO improving large-scale protein function prediction with massive network information.pdf


67. Yu, B., Jin, Y., Shen, Y., Yang, Y., Wang, G., Zhu, H., Yu, Y., & Wang, J. (2019). Loss of homeoprotein Msx1 and Msx2 leading to athletic and kinematic impairment related to the increasing neural excitability of neurons in aberrant neocortex in mice. Biochemical and Biophysical Research Communications, 516(1), 229-235. Loss of homeoprotein Msx1 and Msx2 leading to athletic and kinematic impairment related to the increasing neural excitability of neurons in aberrant neocortex in mice.pdf


68. Yuan, H., Zhu, X., Luo, Q., Halim, A., Halims, M., Yao, H., Cai, Y., & Shi, S. (2019). Early symptom non-improvement and aggravation are associated with the treatment response to SSRIs in MDD: a real-world study. Neuropsychiatric Disease and Treatment, 15, 957-966.


69. Zhang, F., Sun, F., & Luan, Y. (2019). Statistical significance approximation for local similarity analysis of dependent time series data. Bmc Bioinformatics, 20.


70. Zhang, T., Lu, Y., Yang, B., Zhang, C., Li, J., Liu, H., Wang, H., & Wang, D. (2020). Diffusion Metrics for Staging Pancreatic Fibrosis and Correlating With Epithelial-Mesenchymal Transition Markers in a Chronic Pancreatitis Rat Model at 11.7T MRI. Journal of Magnetic Resonance Imaging, 52(1), 197-206. Diffusion Metrics for Staging Pancreatic Fibrosis and Correlating With Epithelial-Mesenchymal Transition Markers in a Chronic Pancreatitis Rat Model at 11.7T MRI.PDF


71. Zhang, W., Fan, B., Agarwal, D., Li, T., & Yu, Y. (2019). Axonal sodium and potassium conductance density determines spiking dynamical properties of regular- and fast-spiking neurons. Nonlinear Dynamics, 95(2), 1035-1052.


72. Zhang, X.F., Le, O.Y., Shuo, Y., Zhao, X.M., Hu, X., & Hong, Y. (2019). EnImpute: imputing dropout events in single-cell RNA-sequencing data via ensemble learning. Bioinformatics, 35(22), 4827-4829.


73. Zhang, Y., Liu, N., Lin, W., & Li, C. (2019). Quantifying the interplay between genetic and epigenetic regulations in stem cell development. New Journal of Physics, 21(10). Quantifying the interplay between genetic and epigenetic regulations in stem cell development.pdf


74. Zhou, N., Jiang, Y., Bergquist, T., Lee, A., Kacsoh, B., Crocker, A., Lewis, K., Georghiou, G., Nguyen, H., Hamid, M., Davis, L., Dogan, T., Atalay, V., Rifaioglu, A., Dalkiran, A., Atalay, R., Zhang, C., Hurto, R., Freddolino, P., Zhang, Y., Bhat, P., Supek, F., Fernandez, J., Gemovic, B., Perovic, V., Davidovic, R., Sumonja, N., Veljkovic, N., Asgari, E., Mofrad, M., Profiti, G., Savojardo, C., Martelli, P., Casadio, R., Boecker, F., Schoof, H., Kahanda, I., Thurlby, N., McHardy, A., Renaux, A., Saidi, R., Gough, J., Freitas, A., Antczak, M., Fabris, F., Wass, M., Hou, J., Cheng, J., Wang, Z., Romero, A., Paccanaro, A., Yang, H., Goldberg, T., Zhao, C., Holm, L., Toronen, P., Medlar, A., Zosa, E., Borukhov, I., Novikov, I., Wilkins, A., Lichtarge, O., Chi, P.H., Tseng, W.C., Linial, M., Rose, P., Dessimoz, C., Vidulin, V., Dzeroski, S., Sillitoe, I., Das, S., Lees, J., Jones, D., Wan, C., Cozzetto, D., Fa, R., Torres, M., Vesztrocy, A., Rodriguez, J., Tress, M., Frasca, M., Notaro, M., Grossi, G., Petrini, A., Re, M., Valentini, G., Mesiti, M., Roche, D., Reeb, J., Ritchie, D., Aridhi, S., Alborzi, S., Devignes, M.D., Koo, D., Bonneau, R., Gligorijevic, V., Barot, M., Fang, H., Toppo, S., Lavezzo, E., & others (2019). The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens. Genome Biology, 20(1).


75. Zhou, Q., Zhang, L., Feng, J., & Lo, C.Y. (2019). Tracking the Main States of Dynamic Functional Connectivity in Resting State. Frontiers in Neuroscience, 13. Tracking the Main States of Dynamic Functional Connectivity in Resting State.pdf


76. Zhou, S., Guo, Y., Liu, M., Lai, Y.C., & Lin, W. (2019). Random temporal connections promote network synchronization. Physical Review E, 100(3). Random temporal connections promote network synchronization.pdf


77. Zhu, G.Y., Geng, X.Y., Zhang, R.L., Chen, Y.C., Liu, Y.Y., Wang, S.Y., & Zhang, J.G. (2019). Deep brain stimulation modulates pallidal and subthalamic neural oscillations in Tourette's syndrome. Brain and Behavior, 9(12).


78. Zhu, G., Li, S., Wu, J., Li, F., & Zhao, X.M. (2019). Identification of Functional Gene Modules Associated With STAT-Mediated Antiviral Responses to White Spot Syndrome Virus in Shrimp. Frontiers in Physiology, 10.


79. Zhu, Q., Ma, H., & Lin, W. (2019). Detecting unstable periodic orbits based only on time series: When adaptive delayed feedback control meets reservoir computing. Chaos, 29(9).


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