Хайлтын үр дүнгүүд - Tzong-Yi Lee

Үр дүнг сайжруулах
  1. 1
    Revealing the Immune Heterogeneity between Systemic Lupus Erythematosus and Rheumatoid Arthritis Based on Multi-Omics Data Analysis

    Revealing the Immune Heterogeneity between Systemic Lupus Erythematosus and Rheumatoid Arthritis Based on Multi-Omics Data Analysis -н Yuntian Zhang, Tzong-Yi Lee

    Хэвлэсэн 2022
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    Artigo
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  2. 2
    PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity

    PlantPhos: using maximal dependence decomposition to identify plant phosphorylation sites with substrate site specificity -н Tzong-Yi Lee, Neil Arvin Bretaña, Cheng-Tsung Lu

    Хэвлэсэн 2011
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    Artigo
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  3. 3
    Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method

    Characterization and Identification of Lysine Succinylation Sites based on Deep Learning Method -н Kai‐Yao Huang, Justin Bo‐Kai Hsu, Tzong-Yi Lee

    Хэвлэсэн 2019
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    Artigo
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  4. 4
    Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies

    Identifying anti-coronavirus peptides by incorporating different negative datasets and imbalanced learning strategies -н Yuxuan Pang, Zhuo Wang, Jhih-Hua Jhong, Tzong-Yi Lee

    Хэвлэсэн 2020
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    Artigo
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  5. 5
    An interpretable rule-based diagnostic classification of diabetic nephropathy among type 2 diabetes patients

    An interpretable rule-based diagnostic classification of diabetic nephropathy among type 2 diabetes patients -н Guan-Mau Huang, Kai‐Yao Huang, Tzong-Yi Lee, Julia Weng

    Хэвлэсэн 2015
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    Artigo
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  6. 6
    Incorporating Distant Sequence Features and Radial Basis Function Networks to Identify Ubiquitin Conjugation Sites

    Incorporating Distant Sequence Features and Radial Basis Function Networks to Identify Ubiquitin Conjugation Sites -н Tzong-Yi Lee, Shu-An Chen, Hsin‐Yi Hung, Yu‐Yen Ou

    Хэвлэсэн 2011
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    Artigo
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  7. 7
    Prediction of transporter targets using efficient RBF networks with PSSM profiles and biochemical properties

    Prediction of transporter targets using efficient RBF networks with PSSM profiles and biochemical properties -н Shu-An Chen, Yu‐Yen Ou, Tzong-Yi Lee, M. Michael Gromiha

    Хэвлэсэн 2011
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    Artigo
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  8. 8
    Integrating transformer and imbalanced multi-label learning to identify antimicrobial peptides and their functional activities

    Integrating transformer and imbalanced multi-label learning to identify antimicrobial peptides and their functional activities -н Yuxuan Pang, Lantian Yao, Jingyi Xu, Zhuo Wang, Tzong-Yi Lee

    Хэвлэсэн 2022
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    Artigo
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  9. 9
    Poly-γ-glutamic Acid Synthesis, Gene Regulation, Phylogenetic Relationships, and Role in Fermentation

    Poly-γ-glutamic Acid Synthesis, Gene Regulation, Phylogenetic Relationships, and Role in Fermentation -н Yi‐Huang Hsueh, Kai‐Yao Huang, Sikhumbuzo Charles Kunene, Tzong-Yi Lee

    Хэвлэсэн 2017
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    Revisão
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  10. 10
    RegPhos: a system to explore the protein kinase–substrate phosphorylation network in humans

    RegPhos: a system to explore the protein kinase–substrate phosphorylation network in humans -н Tzong-Yi Lee, Justin Bo‐Kai Hsu, Wen‐Chi Chang, Hsien‐Da Huang

    Хэвлэсэн 2010
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    Artigo
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  11. 11
    StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture

    StackTHPred: Identifying Tumor-Homing Peptides through GBDT-Based Feature Selection with Stacking Ensemble Architecture -н Jiahui Guan, Lantian Yao, Chia‐Ru Chung, Ying‐Chih Chiang, Tzong-Yi Lee

    Хэвлэсэн 2023
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    Artigo
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  12. 12
    Exploiting maximal dependence decomposition to identify conserved motifs from a group of aligned signal sequences

    Exploiting maximal dependence decomposition to identify conserved motifs from a group of aligned signal sequences -н Tzong-Yi Lee, Zong-Qing Lin, S C Hsieh, Neil Arvin Bretaña, Cheng-Tsung Lu

    Хэвлэсэн 2011
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    Artigo
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  13. 13
    KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-Specific Phosphorylation Sites

    KinasePhos 3.0: Redesign and Expansion of the Prediction on Kinase-Specific Phosphorylation Sites -н Renfei Ma, Shangfu Li, Wenshuo Li, Lantian Yao, Hsien‐Da Huang, Tzong-Yi Lee

    Хэвлэсэн 2022
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    Artigo
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  14. 14
    SNOSite: Exploiting Maximal Dependence Decomposition to Identify Cysteine S-Nitrosylation with Substrate Site Specificity

    SNOSite: Exploiting Maximal Dependence Decomposition to Identify Cysteine S-Nitrosylation with Substrate Site Specificity -н Tzong-Yi Lee, Yi‐Ju Chen, Tsung-Cheng Lu, Hsien‐Da Huang, Yu‐Ju Chen

    Хэвлэсэн 2011
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    Artigo
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  15. 15
    GPMiner: an integrated system for mining combinatorial cis-regulatory elements in mammalian gene group

    GPMiner: an integrated system for mining combinatorial cis-regulatory elements in mammalian gene group -н Tzong-Yi Lee, Wen‐Chi Chang, Justin Bo‐Kai Hsu, Tzu‐Hao Chang, Dray-Ming Shien

    Хэвлэсэн 2012
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    Artigo
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  16. 16
    Identification of potential biomarkers related to glioma survival by gene expression profile analysis

    Identification of potential biomarkers related to glioma survival by gene expression profile analysis -н Justin Bo‐Kai Hsu, Tzu‐Hao Chang, Gilbert Aaron Lee, Tzong-Yi Lee, Cheng‐Yu Chen

    Хэвлэсэн 2019
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    Artigo
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  17. 17
    SOHSite: incorporating evolutionary information and physicochemical properties to identify protein S-sulfenylation sites

    SOHSite: incorporating evolutionary information and physicochemical properties to identify protein S-sulfenylation sites -н Van-Minh Bui, Shun-Long Weng, Cheng-Tsung Lu, Tzu‐Hao Chang, Julia Weng, Tzong-Yi Lee

    Хэвлэсэн 2016
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    Artigo
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  18. 18
    UbiNet 2.0: a verified, classified, annotated and updated database of E3 ubiquitin ligase–substrate interactions

    UbiNet 2.0: a verified, classified, annotated and updated database of E3 ubiquitin ligase–substrate interactions -н Zhongyan Li, Siyu Chen, Jhih-Hua Jhong, Yuxuan Pang, Kai‐Yao Huang, Shangfu Li, Tzong-Yi Lee

    Хэвлэсэн 2021
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    Artigo
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  19. 19
    A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy

    A two-stage computational framework for identifying antiviral peptides and their functional types based on contrastive learning and multi-feature fusion strategy -н Jiahui Guan, Lantian Yao, Peilin Xie, Chia‐Ru Chung, Yixian Huang, Ying‐Chih Chiang, Tzong-Yi Lee

    Хэвлэсэн 2024
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    Artigo
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  20. 20
    UbiSite: incorporating two-layered machine learning method with substrate motifs to predict ubiquitin-conjugation site on lysines

    UbiSite: incorporating two-layered machine learning method with substrate motifs to predict ubiquitin-conjugation site on lysines -н Chien‐Hsun Huang, Min-Gang Su, Hui‐Ju Kao, Jhih-Hua Jhong, Shun‐Long Weng, Tzong-Yi Lee

    Хэвлэсэн 2016
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    Artigo
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