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      Bridging AI and biological discovery: novel applications of language models in enzyme prediction and protein classification = AI와 생물학적 발견의 연결: 효소 예측 및 단백질 분류에서 언어 기반 딥러닝 모델의 적용

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      https://www.riss.kr/link?id=T17189980

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      목차 (Table of Contents)

      • Content
      • Abstract 3
      • Chapter 1. Introduction 6
      • A. Bioinformatics with Biological Big Data and AI Advances 6
      • B. An Overview of Artificial Intelligence 7
      • Content
      • Abstract 3
      • Chapter 1. Introduction 6
      • A. Bioinformatics with Biological Big Data and AI Advances 6
      • B. An Overview of Artificial Intelligence 7
      • C. Machine Learning and Deep Learning in Biology 11
      • D. Application of Deep Learning in this Research 15
      • Chapter 2. Prediction Model for Specific Enzyme Reaction: SPRINT
      • (Substrate-Product-Reaction Inference with deep Neural neT) 17
      • A. Background 17
      • 1) Overview of Enzyme Reaction Screening 17
      • 2) Prior Research 19
      • 3) Objective 20
      • B. Material and Methods 21
      • 1) Data Collection from Three Reaction Databases 21
      • 2) Data Curation and Preprocessing for SPRINT 22
      • 3) Model Architecture and Training Procedures 24
      • 4) Data for Case Study: Plastic DB 26
      • 5) Methodologies for Validation and Evaluation 28
      • C. Results 29
      • 1) Predictive Performance of SPRINT 29
      • 2) Model Performance by Enzyme EC Classification 30
      • 3) Evaluation Across Protein Sequence Identity 31
      • 4) External data validation: Plastic DB (PETase) 32
      • Chapter 3. Prediction Model for Enzyme Turnover Number: KITCAT
      • (Kinetic Inference Tool for Catalysis Activity Turnover rate) 35
      • A. Background 35
      • 1) Description of kcat Parameter in Enzyme Kinetics 35
      • 2) Prior Research 36
      • 3) Objective 37
      • B. Material and Methods 38
      • 1) Collection of kcat Dataset 38
      • 2) Data Preprocessing for KITCAT 41
      • 3) Model Structure and Training Methods 41
      • 4) Methodologies for Validation and Evaluation 44
      • C. Results 46
      • 1) Predictive Performance of KITCAT 46
      • 2) Model Performance by Enzyme EC Classification 47
      • 3) Evaluation Across Protein Sequence Identity 49
      • 4) Model Comparison with Another Research 49
      • Chapter 4. Concluding Remarks and Further Works 51
      • A. Discussion on the Application of Biological Data Using Deep
      • Learning 51
      • B. Conclusion 54
      • References 56
      • 국문초록 64
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