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Runyu Jing

Showing results (21-30 of 30) with videos related to

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NAR Genomics and Bioinformatics|October 7, 2021
DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning frameworkRunyu Jing, Tingke Wen, Chengxiang Liao, et al.
Biomed Research International|September 10, 2020
Improving Model Performance on the Stratification of Breast Cancer Patients by Integrating Multiscale Genomic FeaturesYingyi Hao, Li He, Yifan Zhou, et al.
Biomarkers in Medicine|October 27, 2015
Comparative analysis of oncogenes identified by microarray and RNA-sequencing as biomarkers for clinical prognosisYuan Liu, Runyu Jing, Junmei Xu, et al.
BMC Biology|August 1, 2025
Multimodal deep learning for allergenic proteins predictionLezheng Yu, Yuxin Luo, Shiqi Wu, et al.
International Journal of Oral Science|January 26, 2025
GenAI synthesis of histopathological images from Raman imaging for intraoperative tongue squamous cell carcinoma assessmentBing Yan, Zhining Wen, Lili Xue, et al.
The Neuroradiology Journal|April 9, 2026
Assessment of middle cerebral artery atherosclerotic stenosis using 7T high-resolution MRI and time-of-flight MRA: A comparative analysis with DSADanyang Fan, Jiafei Chen, Fei Chen, et al.
Computational Biology and Chemistry|January 27, 2012
Predicting deleterious non-synonymous single nucleotide polymorphisms in signal peptides based on hybrid sequence attributesWenli Qin, Yizhou Li, Juan Li, et al.
Amino Acids|May 23, 2014
Combination use of protein-protein interaction network topological features improves the predictive scores of deleterious non-synonymous single-nucleotide polymorphismsYiming Wu, Runyu Jing, Lin Jiang, et al.
Scientific Reports|March 7, 2017
Functional annotation of sixty-five type-2 diabetes risk SNPs and its application in risk predictionYiming Wu, Runyu Jing, Yongcheng Dong, et al.
Frontiers in Endocrinology|April 2, 2024
Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approachXiang Lv, Jiesi Luo, Wei Huang, et al.
Pageof 3

Showing results (21-30 of 30) with videos related to

Sort By:
Pageof 3
You have reached the last page of results.This site can display upto 30 results.
NAR Genomics and Bioinformatics|October 7, 2021
DeepT3 2.0: improving type III secreted effector predictions by an integrative deep learning frameworkRunyu Jing, Tingke Wen, Chengxiang Liao, et al.
Biomed Research International|September 10, 2020
Improving Model Performance on the Stratification of Breast Cancer Patients by Integrating Multiscale Genomic FeaturesYingyi Hao, Li He, Yifan Zhou, et al.
Biomarkers in Medicine|October 27, 2015
Comparative analysis of oncogenes identified by microarray and RNA-sequencing as biomarkers for clinical prognosisYuan Liu, Runyu Jing, Junmei Xu, et al.
BMC Biology|August 1, 2025
Multimodal deep learning for allergenic proteins predictionLezheng Yu, Yuxin Luo, Shiqi Wu, et al.
International Journal of Oral Science|January 26, 2025
GenAI synthesis of histopathological images from Raman imaging for intraoperative tongue squamous cell carcinoma assessmentBing Yan, Zhining Wen, Lili Xue, et al.
The Neuroradiology Journal|April 9, 2026
Assessment of middle cerebral artery atherosclerotic stenosis using 7T high-resolution MRI and time-of-flight MRA: A comparative analysis with DSADanyang Fan, Jiafei Chen, Fei Chen, et al.
Computational Biology and Chemistry|January 27, 2012
Predicting deleterious non-synonymous single nucleotide polymorphisms in signal peptides based on hybrid sequence attributesWenli Qin, Yizhou Li, Juan Li, et al.
Amino Acids|May 23, 2014
Combination use of protein-protein interaction network topological features improves the predictive scores of deleterious non-synonymous single-nucleotide polymorphismsYiming Wu, Runyu Jing, Lin Jiang, et al.
Scientific Reports|March 7, 2017
Functional annotation of sixty-five type-2 diabetes risk SNPs and its application in risk predictionYiming Wu, Runyu Jing, Yongcheng Dong, et al.
Frontiers in Endocrinology|April 2, 2024
Identifying diagnostic indicators for type 2 diabetes mellitus from physical examination using interpretable machine learning approachXiang Lv, Jiesi Luo, Wei Huang, et al.
Pageof 3