Результаты поиска - Ran Su

  • Отображение 1 - 17 результаты of 17
Отмена результатов
  1. 1
    M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning

    M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning по Leyi Wei, Huangrong Chen, Ran Su

    Опубликовано 2018
    Полный текст Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  2. 2
    Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine

    Identifying N6-methyladenosine sites using multi-interval nucleotide pair position specificity and support vector machine по Pengwei Xing, Ran Su, Fei Guo, Leyi Wei

    Опубликовано 2017
    Полный текст Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  3. 3
    High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures

    High-throughput imaging-based nephrotoxicity prediction for xenobiotics with diverse chemical structures по Ran Su, Sijing Xiong, Daniele Zink, Lit‐Hsin Loo

    Опубликовано 2015
    Полный текст Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  4. 4
    ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides

    ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides по Leyi Wei, Chen Zhou, Huangrong Chen, Jiangning Song, Ran Su

    Опубликовано 2018
    Полный текст Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  5. 5
    Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE

    Decision Variants for the Automatic Determination of Optimal Feature Subset in RF-RFE по Qi Chen, Zhaopeng Meng, Xinyi Liu, Qianguo Jin, Ran Su

    Опубликовано 2018
    Полный текст Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  6. 6
    M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species

    M6AMRFS: Robust Prediction of N6-Methyladenosine Sites With Sequence-Based Features in Multiple Species по Xiaoli Qiang, Huangrong Chen, Xiucai Ye, Ran Su, Leyi Wei

    Опубликовано 2018
    Полный текст Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  7. 7
    RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans

    RA-UNet: A Hybrid Deep Attention-Aware Network to Extract Liver and Tumor in CT Scans по Qiangguo Jin, Zhaopeng Meng, Changming Sun, Hui Cui, Ran Su

    Опубликовано 2020
    Полный текст Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  8. 8
    iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning

    iRNA5hmC: The First Predictor to Identify RNA 5-Hydroxymethylcytosine Modifications Using Machine Learning по Yuan Liu, Dasheng Chen, Ran Su, Wei Chen, Leyi Wei

    Опубликовано 2020
    Полный текст Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  9. 9
    Free-form tumor synthesis in computed tomography images via richer generative adversarial network

    Free-form tumor synthesis in computed tomography images via richer generative adversarial network по Qiangguo Jin, Hui Cui, Changming Sun, Zhaopeng Meng, Ran Su

    Опубликовано 2021
    Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  10. 10
    Supervised Brain Tumor Segmentation Based on Gradient and Context-Sensitive Features

    Supervised Brain Tumor Segmentation Based on Gradient and Context-Sensitive Features по Junting Zhao, Zhaopeng Meng, Leyi Wei, Changming Sun, Quan Zou, Ran Su

    Опубликовано 2019
    Полный текст Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  11. 11
    Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images

    Domain adaptation based self-correction model for COVID-19 infection segmentation in CT images по Qiangguo Jin, Hui Cui, Changming Sun, Zhaopeng Meng, Leyi Wei, Ran Su

    Опубликовано 2021
    Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  12. 12
    DUNet: A deformable network for retinal vessel segmentation

    DUNet: A deformable network for retinal vessel segmentation по Qiangguo Jin, Zhaopeng Meng, Tuan D. Pham, Qi Chen, Leyi Wei, Ran Su

    Опубликовано 2019
    Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  13. 13
    scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods

    scIMC: a platform for benchmarking comparison and visualization analysis of scRNA-seq data imputation methods по Chichi Dai, Yi Jiang, Chenglin Yin, Ran Su, Xiangxiang Zeng, Quan Zou, Kenta Nakai, Leyi Wei

    Опубликовано 2022
    Полный текст Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  14. 14
    DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis

    DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis по Ruheng Wang, Yi Jiang, Junru Jin, Chenglin Yin, Haoqing Yu, Fengsheng Wang, Jiuxin Feng, Ran Su, Kenta Nakai, Quan Zou, Leyi Wei

    Опубликовано 2023
    Полный текст Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  15. 15
    iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations

    iDNA-ABF: multi-scale deep biological language learning model for the interpretable prediction of DNA methylations по Junru Jin, Yingying Yu, Ruheng Wang, Xin Zeng, Chao Pang, Yi Jiang, Zhongshen Li, Yutong Dai, Ran Su, Q. Zou, Kenta Nakai, Leyi Wei

    Опубликовано 2022
    Полный текст Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  16. 16
    Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction

    Explainable Deep Hypergraph Learning Modeling the Peptide Secondary Structure Prediction по Yi Jiang, Ruheng Wang, Jiuxin Feng, Junru Jin, Sirui Liang, Zhongshen Li, Yingying Yu, Anjun Ma, Ran Su, Quan Zou, Qin Ma, Leyi Wei

    Опубликовано 2023
    Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:
  17. 17
    Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods

    Prediction of drug-induced nephrotoxicity and injury mechanisms with human induced pluripotent stem cell-derived cells and machine learning methods по Karthikeyan Kandasamy, Jacqueline Kai Chin Chuah, Ran Su, Peng Huang, Kim Guan Eng, Sijing Xiong, Yao Li, Chun Siang Chia, Lit‐Hsin Loo, Daniele Zink

    Опубликовано 2015
    Полный текст Полный текст
    Artigo
    Добавить в Избранное
    Сохранить в:

Инструменты поиска: