Revolutionary Gene Testing Era Unveiled by Breakthrough Ph.D. Research, Backed by AlphaFold 3 and Multi-Omics AI Integration

May 8, 2024, GoogleDeepMindandIsomorphic LabsIsomorphic Labs and DeepMind Jointly Release AlphaFold 3, the Latest AI Model in the Field of Proteins, on Nature! This model can accurately predict the structures of proteins, DNA, RNA, and ligands, as well as their interaction modes.

This is another major breakthrough after AlphaFold 2 in the prediction of drug-like interactions. AlphaFold 3 has achieved unprecedented accuracy, including the binding of proteins with ligands and antibodies with their target proteins. In the PoseBusters benchmark, AlphaFold 3's accuracy is 50% higher than the best existing traditional methods, and it requires no structural information input, making it the first artificial intelligence system to surpass traditional physics-based prediction tools. The ability to predict antibody-protein binding is crucial for understanding various aspects of human immune responses and designing new antibodies.

In recent years, innovations in biotechnology have led to the accumulation of omics data at an astonishing rate, ushering in the "big data" era. Extracting inherent valuable knowledge from various omics data remains a daunting challenge in bioinformatics. Better solutions often require more innovative approaches for efficient processing and effective outcomes. Recent advances in the integrative analysis of multi-omics data and computational modeling have helped address these needs in an increasingly harmonious manner. The development and application of machine learning have significantly enhanced our understanding of biology and biomedicine, greatly advancing the development of therapeutic strategies, especially in precision medicine. Here, we propose a comprehensive investigation and discussion of what has happened, what is happening, and what will happen when machine learning meets omics. Specifically, we describe how artificial intelligence is applied to omics research and review the latest advances at the interface of machine learning with the broadest range of omics, including genomics, transcriptomics, proteomics, metabolomics, radiomics, and single-cell omics.

WORK OVERVIEW

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Day 1

Theoretical Part

Introduction to Deep Learning Algorithms

1. Neural Network Algorithms for Supervised Learning

1.1 Examples of Fully Connected Deep Neural Networks (DNN) in Genomics

1.2 Examples of Convolutional Neural Networks (CNN) Applications in Genomics

1.3 Application Examples of Recurrent Neural Networks (RNN) in Genomics

1.4 Application Examples of Graph Convolutional Network (GCN) in Genomics

2. Unsupervised Neural Network Algorithms

2.1 Application Examples of Autoencoder AE in Genomics

2.2 Examples of the Application of Generative Adversarial Networks (GAN) in Genomics

Practical Content

1. Linux Operating System

1.1 Commonly Used Linux Commands

1.2 Vim Editor

1.3 Genome Data File Management, Modify File Permissions

1.4 Explore Genomic Regions

2. Basics of Python Language

2.1.Python Package Installation and Environment Setup

2.2. Common Data Structures and Data Types

The Next Day

Fundamentals of Genomics

1. Genomic Database

2. Epigenome

3. Transcriptome

4.Proteomics

5. Functional Genomics

Practical Content

Common Deep Learning Frameworks in Genomics

1. Install and introduce deep learning toolkits TensorFlow, Keras, PyTorch

2. Identify deep learning model elements in the toolkit

2.1. Data Representation

2.2. Tensor Operations

2.3. "Layers" in Neural Networks

2.4. Model Composed of Layers

2.5. Loss Function and Optimizer

2.6. Dataset Splitting

2.7. Overfitting and Underfitting

3. Genomic Data Processing

3.1 Install and use keras_dna to process various genomic sequence data such as BED,GFF, GTF, BIGWIG, BEDGRAPH, WIG, etc.

3.2 Designing Deep Learning Models Using keras_dna

3.3 Using keras_dna to split training and testing sets

3.4 Using keras_dna to select gene sequences of specific chromosomes, etc.

Day Three

Commonly Used Analysis Methods for Microbiome (Practical Operations)

1. Microbial Abundance Analysis

2. Transcriptome Abundance Analysis

3. Phylogenetic Tree Analysis

4. Dimensionality Reduction Analysis

Day 4

Theoretical Part

Deep Learning in Identifying Copy Number Variations DeepCNV

1. Prediction of Copy Number Variations (CNV) in SNP Microarrays, DeepCNV

2. Prediction of premiRNA in RNA-Seq, dnnMiRPre

Practical Content

1. Reproduce DeepCNV using SNP microarray combined with image analysis to identify copy number variations

Re-implementing the Recurrent Neural Network (RNN) tool dnnMiRPre for predicting pre-miRNA from RNA-Seq

Day 5

Theoretical Part

Application of Deep Learning in Identification and Disease Phenotypes and Biomarkers

1. DeepType: A Deep Learning Tool for Identifying Breast Cancer Subtypes from Gene Expression Data

Practical Content

1. Reproduce DeepType to distinguish breast cancer subtypes from METABRIC breast cancer data.

Day Six

Theoretical Part

Application of Deep Learning in Predicting Drug Response Mechanisms

1. SWnet: A Deep Learning Tool for Predicting Drug Response Mechanisms by Integrating Tumor Gene Markers and Drug Molecular Structures

Practical Content

1. Preprocess drug molecular structure information

2. Calculate Drug Similarity

3. Building Self-Attention SWnet on Different Datasets

4. Evaluation of self-attention SWnet

5. Construction of Multi-task SWnet

6. Constructing a Single-layer SWnet

7. Constructing SWnet with Weighted Layers

Partial Model Case Images

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Day One

A1 Metabolites and the Development and Application of Metabolomics

(1) Metabolism and Physiological Processes;

(2) Metabolism and Disease;

(3) Untargeted and Targeted Metabolomics;

(4) Spatial Metabolomics and Mass Spectrometry Imaging (MSI);

(5) Metabolomics and drugs and biomarkers;

(6) Metabolic Flux and Mechanism Research.

A2 Metabolic Pathway and Metabolism Database

(1) An Introduction to Several Classical Metabolic Pathways;                               

(2) Three major common metabolite databases: HMDB, METLIN, and KEGG;

(3) Metabolomics Raw Database: Metabolomics Workbench and Metabolights.

A3 Recommended Reference Materials

A4 Metabolomics Experimental Workflow Overview

A5 Chromatography, Mass Spectrometry Hardware and Principle Analysis

(1) Principle and Structure of Chromatographic Analysis;

(2) Selection of chromatograph and chromatographic column;

(3) Chromatographic mobile phase: Gradient elution method;

（4） Ion Source, Quality Analyzer and Quality Detector Analysis;

(5) Principles of Mass Spectrometry and Animation Demonstration;

(6) Liquid Chromatography-Mass Spectrometry (LC-MS);

The Next Day

B1 Metabolite Sample Processing and Extraction

(1) Extraction procedures and precautions for various tissues, blood, and body fluid samples;

(2) Metabolite Extraction Process and Precautions;

(3) Issues related to the transportation and storage of samples and metabolites;

B2 LC-MS Data Quality Control and Database Search

(1) The method for setting QC and Blank samples during the LC-MS experiment;

(2) Data quality control monitoring and analysis during the LC-MS operation process;

(3) Principles of Upstream Metabolomics Analysis — Based on Compound Discoverer and Xcms Software;

(4) Data conversion, peak picking, peak alignment, and library search using Xcms software;

B3 R Software Foundation

(1) Installation of R and RStudio;

(2) Configuration of Rstudio Interface;

(3) Basic Operations and Statistical Calculations in R;

(4) Packages in R: The use of packages, functions, and parameters;

(5) R language syntax, data types and data structures;

(6) R Basic Plotting;

B4 ggplot2 

(1) Introduction to ggplot2

(2) The Plotting Philosophy of ggplot2;

(3) The color system of ggplot2;

(4) Practical Data Mining and Plotting with ggplot2;

Day 3

Machine Learning

C1 Application of Supervised Machine Learning in Metabolomics Data Processing

(1) The relationship among artificial intelligence, machine learning, and deep learning;

(2) Regression Algorithms: Starting from Linear Regression, Logistic Regression, and Cox Regression;

(3) PLS-DA Algorithm: Is There Hope for Data Without Differences After PCA Dimensionality Reduction?

(4) The Meaning and Selection of VIP Score;

(5) Classification Algorithms: Decision Trees, Random Forests, and Bayesian Network Models;

C2 An R Walkthrough for Implementing a Classification Algorithm on a Set of Metabolomics Data

(1) Data Interpretation;

(2) Drills and Operations;

C3 Application of Unsupervised Machine Learning in Metabolomics Data Processing

(1) Dimensionality reduction in big data processing;

(2) PCA analysis plotting;

(3) Three Common Types of Cluster Analysis: K-means, Hierarchical Analysis, and SOM

(4) Implementation of heatmaps and hcluster plots in R;

C4 A R Walkthrough for Dimensionality Reduction and Clustering Analysis of a Set of Metabolomics Data

(1) Data parsing;

(2) Drills and Operations;

Day Four

D1 Online Metabolomics Analysis Webpage Metaboanalyst Operation

(1) Use R to clean the data into the format required by the webpage;

(2) Data format issues for independent groups, paired groups, and multiple groups;

(3) Upstream analysis in Metaboanalyst (raw data peak extraction, peak alignment, and library search) (4) Metaboanalyst pipeline, parameter settings, and considerations;

(5) Viewing and exporting results from MetaboAnalyst;

(6) Data editing in Metaboanalyst;

(7) Full-process Drills and Operations.

D2 Metabolomics Data Cleaning and Advanced R Programming

(1) t, fold-change, and response values in metabolomics;

(2) Data Cleaning Process;

(3) R language tidyverse;

(4) Data Preprocessing: Data Filtering and Data Standardization (Normalization of Samples and Scaling of Metabolites);

(5) Metabolomics Data Cleaning Exercise;

Day 5

E1 Reproduction of Literature Data Analysis Section (1 paper)

(1) In-depth interpretation of literature;

(2) Practical operation: From raw data download to image reproduction;

(3) Students' practical operation.

E2 Machine Learning and Metabolomics Top Journal Interpretation (3 Articles);

(1) Signal Transduction and Targeted Therapy: A Mouse Brain Tissue Metabolic Atlas Study on the Effects of Starvation on Metabolomics in Different Brain Regions.(Database Type)

(2) A metabolomics analysis of pregnant women's blood throughout pregnancy, published in Cell, identified metabolic biomarkers for predicting gestational age and due date. (Biomarker type)

(3) A Nature article on the metabolomics analysis of gut microbiota in pancreatic cancer patients identifies metabolites that can enhance chemotherapy efficacy. (Mechanism research type)

Partial Model Case Images

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Day One

1. Review of Basic Microbiology Knowledge

2. Introduction to Basic Concepts of Machine Learning

a. What is Machine Learning

b. Supervised learning, unsupervised learning

c. Introduction to Commonly Used Machine Learning Models

3. Confusion Matrix

4.ROC Curve

The Next Day

Introduction to R Language and Practical Operations

1. Overview of R Language

2. R Studio Software and R Package Installation

3. R Language Syntax and Data Types

4. Conditional Statements and Loops

Linux Practical Operations

1. Linux Operating System

2. Installation and Setup of the Linux Operating System

3. Network Configuration and Service Process Management

4. Remote Login Management of Linux

5. Commonly Used Linux Commands

6. Obtaining Genetic Data Under Linux

7. Shell Script and Vim Editor ·

Day Three

Commonly Used Analysis Methods for Microbiome (Practical Operations)

1. Microbial Abundance Analysis

2. Transcriptome Abundance Analysis

3. Phylogenetic Tree Analysis

4. Dimensionality Reduction Analysis

Day 4

Application Case Sharing of Machine Learning in Microbiomics

1. Disease Prediction Application: Using Machine Learning to Predict Disease States Based on Microbiome Data

2. Gut Microbiota Research: Machine Learning Studies the Impact of Diet on Gut Microbes

Day 5

Machine Learning Model Training and Analysis (Hands-on)

1. Load Data and Data Normalization

2. Build Training Models (GLM, RF, SVM)

3. Model Parameter Optimization

4. Model Error Rate Curve Plotting

5. Confusion Matrix Calculation

6. Key Feature Selection

7. Model validation, ROC curve plotting using the model for prediction

Predicting Host Phenotypes Using Machine Learning Based on Microbiome Data

1. Load Data

2. Data Normalization

3. OUT Feature Processing

4. Construction of Machine Learning Models (Various machine learning methods such as RF, KNN, SVM, Lasso)

5. Plot ROC Curve to Compare Performance Evaluation of Different Machine Learning Models

Predicting Disease Risk Using Machine Learning Based on Clinical Features and Gut Microbiota

1. Load Data

2. Construction of Machine Learning Models (RF, GBM, SVM, etc.)

3. Cross-validation

4. Model Performance Evaluation

Partial Model Case Images

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Day One

Introduction and Fundamentals

1. The Importance of Protein 3D Structure Prediction for Drug Discovery

1.1 Homology Modeling

1.2 De Novo Modeling

2. The Importance of Protein (Enzyme/Target) Active Sites in Drug Discovery

3. Key Structural Features in Drug Discovery (Especially Small Molecules)

4. Common Computational Methods for Drug Discovery

4.1 Molecular Docking

4.2 Virtual Screening

4.3 Molecular Dynamics Simulation

4.4 Others

Introduction to the PDB Database

1.1 Retrieval Protein

1.2 Page Functions and Interpretation

1.3 Data Download

1.4 Interpretation of the PDB File Format

2. PyMol

2.1 Software Introduction

2.2 Introduction to Basic Operations

2.3 Protein and Small Molecule Surface Diagrams, Electrostatic Potential Representation

2.4 Plotting Interaction Diagrams and Creating Simple Animations

The Next Day

Homology Modeling

1. Introduction to Homology Modeling Principles

1.1 Functions and Use Cases of Homology Modeling

1.2 Methods of Homology Modeling

2. Swiss-Model Homology Modeling;

2.1 Search for Homologous Proteins (Methods such as BLAST)

2.2 Protein Sequence Alignment

2.3 Protein Template Selection

2.4 Protein Model Construction

2.5 Model Evaluation (Protein Raman Spectra)

2.6 Protein Model Optimization

Case Study and Practice: Modeling with the 2019-nCoV Spike Protein Sequence and Evaluating the Model Based on Relevant Parameters and Methods

Small Molecule Construction

1. Introduction to ChemDraw Software

1.1 Small Molecule Structure Construction

1.2 Calculation of Small Molecule Physicochemical Properties (e.g., Molecular Weight, clogP, etc.)

1.3 Construct macromolecules, amino acids, DNA, RNA, and other molecules separately.

Small Molecule Compound Library

2 Small Molecule Database

2.1 DrugBank、ZINC、ChEMBLIntroduction and Use of Databases

2.2 Introduction and Use of Natural Product and Traditional Chinese Medicine Component Databases

Day Three

Basics of Molecular Docking

1.1 Principle of Molecular Docking

1.2 Classification of Molecular Docking

1.3 Molecular Docking Scoring Function

2. Practice of Conventional Molecular Docking

2.1 Implementation of Docking

2.1.1 Preparation of Drug Molecular Ligands

2.1.2 Preparation of Protein Receptors

2.1.3 Receptor Grid Calculation

2.1.3 Perform Semi-Flexible Docking

Result Evaluation

1.2.1 Comparison of Crystal Structure Conformations

1.2.2 Evaluation of Docking Results from an Energy Perspective

1.2.3 Evaluation of Docking Results by Cluster Analysis

1.2.4 Selection of the Optimal Binding Conformation

2 Integration with Other Methods

Day 4

1 Flexible Docking

1.1 Small Molecule Ligand Optimization Preparation

1.2 Preparation of Protein Receptors

1.3 Definition of Flexible Residues

1.4 Protein Receptor Grid Calculation

1.5 Flexible Docking Calculation and Result Evaluation

1.6 Comparison and Selection between Semi-flexible Docking and Flexible Docking

2 Flexible docking implementation through other methods

Afternoon

Receptor-Based Drug Discovery

1 Preparation for Virtual Screening

1.1 Different Formats of Small Molecule Files

1.2 Introduction to the Most Practical Features of OpenBabel

1.3 Conversion of Small Molecules in Different Formats

2. Docking-Based Virtual Screening

2.1 Definition, Workflow Construction, and Demonstration of Virtual Screening

2.2 Target Protein Selection, Compound Library Acquisition

2.3 Virtual Screening

2.4 Result Analysis (Scoring, Energy, and Interaction Analysis)

Day 5

Morning

Some special molecular docking

1.Small molecule-small molecule docking

1.1 Introduction to Small Molecule-Small Molecule Interactions

1.2 Small Molecule Structure Preprocessing

1.3 Small Molecule-Small Molecule Docking (Sugar-Small Molecule as an Example)

1.4 Display and Analysis of Docking Results

2. Protein-Nucleic Acid Docking

3. Protein-Protein Docking

Afternoon

Ligand-Based Drug Discovery

1. Construction of 3D-QSAR Model (Sybyl Software)

1.1 Small Molecule Construction

1.2 Creation of Small Molecule Database

1.3 Small Molecule Charge Addition and Energy Optimization

1.4 Determination and Superposition of Molecular Active Conformations

1.5 Creation of 3D-QSAR Model

1.6 Construction of CoMFA and CoMSIA Models

1.7 Test Set Validation Model

1.8 Model Parameter Analysis

1.9 Model Equipotential Diagram Analysis

1.10 3D-QSAR Model Guided Drug Design

Day Six

Morning

1. Introduction to Linux System

2. Introduction to Common Commands

3. Installation of Programs on Linux (GROMACS)

Afternoon

MD Practice 1: Molecular Dynamics Simulation of Protein under Solvation

Fully familiar with the general process of molecular dynamics simulation

Day Seven

Morning

MD Practice II: Molecular Dynamics Simulation of Protein-Ligand under Solvation

Mastering Force Field Fitting for Non-Standard Residues

Afternoon

Commonly Used Analysis Commands in Molecular Dynamics Simulations

Calculation of Protein-Ligand Binding Free Energy

Partial Model Case Images

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Day One

1. The Importance of Deep Learning in Protein Design

1.1 Overview of the Basic Concepts of Deep Learning

What is Deep Learning

Basic Concepts of Neural Networks

The History and Development of Deep Learning

1.2 Application of Deep Learning in Bioinformatics

Examples of applications in genomics, bio-component modification, and protein design

2. Basic Concepts of Protein Structure and Function

2.1 From Primary to Higher Levels of Protein StructureQuaternary Structure

Amino acid sequence, secondary structure, tertiary structure, quaternary structure

2.2 Relationship between Structure and Function

The Principle of Structure Determines Function (Anfinsen's Principle)

Specific Examples of Protein Functions

2.3 Introduction to Important Protein Databases

Protein Sequence Database

Protein Structure Database (PDB Database, etc.)

2.4 Introduction to Data Representation of Proteins

Protein Sequence Representation

Protein Structure Representation (Including Practical Demonstrations: Protein Structure Manipulation Using BioPython, Pymol, etc.)

The First Day, Afternoon

1. Introduction to Deep Learning Basics

1.1 Introduction to Neural Networks

Basic Components of Neural Networks

Forward Propagation and Backward Propagation

1.2 Commonly Used Deep Learning Algorithms (Convolutional Neural Networks, Recurrent Neural Networks, etc.)

Convolutional Neural Network (CNN)

Recurrent Neural Network (RNN)

Long Short-Term Memory (LSTM)

Transformer Structure

Diffusion Model

2. Deep Learning Tools and Frameworks

2.1 TensorFlow Framework

Introduction to the Basic Concepts of TensorFlow

2.2 PyTorch Framework (Key Focus)

Basic Concepts of PyTorch

Basic Usage of PyTorch (Including Practical Demonstration: Building Neural Networks with PyTorch)

The Next Day

1. Introduction to Protein Sequence Analysis

1.1 Sequence Alignment Tools and Methods

Introduction to Tools such as BLAST and Clustal Omega (Including Practical Demonstrations: How to Use These Tools)

The Importance of Multiple Sequence Alignment (MSA)

Protein Sequence Similarity Analysis Methods Based on Mathematics or Deep Learning (Including Practical Demonstration: Demonstration of Sequence Feature Extraction Methods)

1.2 Application of Deep Learning in Protein Sequence Classification

Automatic Feature Extraction

Protein Family Classification

Protein Function Classification

2. Introduction to Protein Large Language Models (Key Focus)

2.1 Introduction to Language Models

What is Natural Language Processing (NLP)?

What are Large Language Models

2.2 Language Models in Biology

Sequence Language Model kmer2vec, DNABert (Including Practical Demonstration: Simple Training of a kmer2vec Model for Protein Sequences)

ESM Protein Large Language Model

ProGen Large Model

The Next Afternoon

1. Detailed Introduction to Various Knowledge of Natural Language Processing (NLP)

1.1 Word Embedding

  One-hot embedding

  Word2vec embedding

  Glove embedding

Language Large Model Embedding

1.2 Introduction to Commonly Used Loss Functions in Deep Learning

2. Deep Learning Model Optimization Techniques

2.1 Model Evaluation and Optimization Methods

Evaluation Metrics: Accuracy, Recall, F1 Score, etc.

Optimization methods: Regularization, Dropout, etc.

2.2 Hyperparameter Tuning

The Impact of Hyperparameters on the Model

Grid Search, Random Search

Day Three

1. Basic Concepts of Protein Design

1.1 Overview of Protein Engineering and Design

History of Protein Engineering

Methods of Protein Design

1.2 Challenges and Opportunities in Protein Design

The Challenge of Designing New Functional Proteins

Prospects of Protein Design Applications

2. Introduction to the Central Dogma of Protein Design

2.1 Positive Central Dogma

2.2 Reverse Central Dogma

The Third Day, Afternoon

1. Application of Deep Learning in Protein Structure Prediction (Focus)

1.1 The Development Process of Protein Structure Prediction

Summary of David Baker's Research Work

Conversion Method Between Protein 3D Coordinates and Internal Coordinates of Proteins

1.2 AlphaFold and Its Impact

How AlphaFold 2 Works

OpenFold Code Introduction

Differences Between AlphaFold 3 and AlphaFold 2 (Including Practical Demonstration: How to Use the AlphaFold 3 Large Model Open Source Server for Protein Structure Prediction and Interaction Prediction)

Introduction to the RoseTTAFold All-Atom Large Model

Introduction to the ESMFold Large Model

Day 4

1. Application of Deep Learning in Protein Function Prediction for Drug Discovery

1.1 Overview of Deep Learning and Drug Design

The Importance of Deep Learning in Drug Design

1.2 Protein-Small Molecule Interaction Prediction

Introduction to Various Cutting-edge Prediction Methods and Models

How large models play a role in this field

2. Protein Function Prediction: Protein-Protein Interactions

2.1 Model Selection and Training

Introduction to the Characteristics of Different Models

2.2 Evaluation and Interpretation of Prediction Results

Evaluation Indicators and Methods

2.3 Introduction to Protein-Protein Interactions After Point Mutations

Examples of Deep Learning Models Playing a Role in Coronavirus Prediction

The Fourth Day, Afternoon

1. Protein Function Prediction: Protein-Nucleic Acid Interactions

1.1 Protein-DNA Interaction Tasks

1.2 Protein-RNA Interaction Tasks

1.2 Model Selection and Training

Introduction to the Characteristics of Different Models

How large models play a role in this field

2. Molecular Docking Combined with Deep Learning

2.1 Basic Concepts of Molecular Docking

The Principle of Molecular Docking

Introduction to Classic Molecular Docking Software Tools (Including Practical Demonstrations: How to Use Various Molecular Docking Software Tools Such as AutoDock, and Presenting Docking Results Using Tools like Pymol)

2.2 Challenges in Molecular Docking

The Challenge of Computational Complexity

2.3 Application of Deep Learning in Molecular Docking

Introduction to New Methods and New Tools

Combining Deep Learning to Improve Docking Efficiency

Day 5

1. Design of Peptide-Protein Interactions

1.1 Basic Concepts of Peptide Design

Characteristics and Design Methods of Polypeptides

1.2 Application of Deep Learning in Peptide Design

Application Case Analysis

2. Reverse Central Dogma: Inferring Structure from Function

2.1 Division of Protein Functional Motifs

2.2 Introduction to the Method of Deriving Required Sequences from Functions

The Fifth Day, Afternoon

1. Reverse Central Dogma: Designing Structures from Scratch

2. Reverse Central Dogma: Designing Sequences to Define Structure and Function

3. Summary and Outlook

3.1   The Future Development of Deep Learning in Protein Design

Future Development Trends

The Impact of Protein Foundation Models (Including Practical Demonstrations: How to Apply Foundation Models, How to Design Downstream Tasks Based on Foundation Models)

3.2 Course Summary and Review

Review and Summary of Course Content

4. Q&A Session

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Learning Objectives

Introduction of the Lecturer

Time and Location of the Lecture

2024.07.13-2024.07.14All-day teaching (morning9：00-11：30Afternoon13：30-17：00）2024.07.15-2024.07.16Evening Classes (Evening19：00—Night22：00）2024,07.20-2024.07.20-2024.07.21All-day teaching (morning9：00-11：30Afternoon13：30-17：00）

Tencent Meeting Live Stream Format

2024.07.20-2024.07.21All-day teaching (morning9：00-11：30Afternoon13：30-17：00）2024.07.23-2024.07.26Evening Classes (19：00—Night22：00）

2024.07.27-2024.07.28All-day teaching (morning9：00-11：30Afternoon13：30-17：00）

2024.07.29-2024.07.30Evening Class (Evening19：00—Night22：00）

Tencent Meeting Live Streaming Format

2024.07.13-2024.07.14All-day teaching (morning9：00-11：30Afternoon13：30-17：00）2024.07.15-2024.07.16Evening Class (Evening19：00—Evening22：00）2024,07.20-2024.07.20-2024.07.21All-day teaching (morning9：00-11：30Afternoon13：30-17：00）

TengLive broadcast format of teleconference

2024.07.20-2024.07.21All-day teaching (morning9：00-11：30Afternoon13：30-17：00）2024,07.27-2024.07.28All-day teaching (morning9：00-11：30Afternoon13：30-17：00）2024,08.03-2024.08.04All-day teaching (morning9：00-11：30Afternoon13：30-17：00）

TengLive broadcast format of teleconference

2024.07.20-2024.07.21All-day teaching (morning9：00-11：30Afternoon13：30-17：00）2024.07.23-2024.07.24Evening Classes (19：00—Evening22：00）

2024.07.27-2024.07.28All-day teaching (morning9：00-11：30Afternoon13：30-17：00）

TengLive broadcast format of teleconference

2024.07.13-2024.07.14All-day teaching (morning9：00-11：30Afternoon13：30-17：00）2024.07.15-2024.07.16Evening Class (Evening19：00—Evening22：00）2024,07.20-2024.07.20-2024.07.21All-day teaching (morning9：00-11：30Afternoon13：30-17：00）

TengLive broadcast format of teleconference

2024.07.13-2024.07.14All-day teaching (morning9：00-11：30Afternoon13：30-17：00）2024,07.20-2024.07.21All-day teaching (morning9：00-11：30Afternoon13：30-17：00）2024,07.27All-day teaching (morning9：00-11：30Afternoon13：30-17：00）

TengLive broadcast format of teleconference

2024.07.20-2024.07.21All-day teaching (morning9：00-11：30Afternoon13：30-17：00）2024,07.27-2024.07.28All-day teaching (morning9：00-11：30Afternoon13：30-17：00）2024,08.03All-day teaching (morning9：00-11：30Afternoon13：30-17：00）

TengLive broadcast format of teleconference