-
Categories
-
Pharmaceutical Intermediates
-
Active Pharmaceutical Ingredients
-
Food Additives
- Industrial Coatings
- Agrochemicals
- Dyes and Pigments
- Surfactant
- Flavors and Fragrances
- Chemical Reagents
- Catalyst and Auxiliary
- Natural Products
- Inorganic Chemistry
-
Organic Chemistry
-
Biochemical Engineering
- Analytical Chemistry
-
Cosmetic Ingredient
- Water Treatment Chemical
-
Pharmaceutical Intermediates
Promotion
ECHEMI Mall
Wholesale
Weekly Price
Exhibition
News
-
Trade Service
Top journal publication
helps students publish Nature, Science, Cell and other regular and sub-journals! (With the blessing of the new technology of Biotrust analysis, with less money, higher quality articles are published)
Scientific research background
01
of the year by Nature Methods in 2019 and 2020, respectively.
The combination of multi-dimensional research technology in time and space will start from a new research idea, which can not only obtain the heterogeneity between single cells, but also obtain the structural position information of cells in tissue space, and discover more unknown and refined results
.
In summary, single-cell sequencing + spatial transcriptome sequencing: complementary advantages, while obtaining cell type populations, as well as gene expression and spatial location information of cells
.
The spatial transcriptome provides valuable insights
for research and diagnosis by locating and distinguishing the active expression of functional genes within specific tissue regions.
The introduction of 10x Visium has made spatial transcription composition a new research hotspot, favored by researchers, which can not only provide data information such as the transcriptome of the research object, but also locate its spatial position in tissues, which is of great significance
for the study of cancer pathogenesis, neuroscience, developmental biology and many other fields.
04
Train experts
Teaching timeDeep
learning in genomics training time
01
Machine Learning Single Cell Analysis Training Time
02
Single-cell spatial transcriptome training time
03
CAD Computer Aided Drug Design Training Time
04
Registration fee
Finding it was really down-to-earth and at the same time, I needed to look up at the stars occasionally, thank you very much for your recognition of our training! I wish you all the best of luck!
helps students publish Nature, Science, Cell and other regular and sub-journals! (With the blessing of the new technology of Biotrust analysis, with less money, higher quality articles are published)
Scientific research background
01
Machine learning single-cell analysis: The relevant research of cell biology has been limited by the integrity of data and phenotype, and the observation of cell differentiation in stress state and steady state is insufficient
.
In the past five years, the field of computer vision and speech recognition has solved the problem of insufficient data by learning and modeling a large amount of
unlabeled data.
Also in recent research, machine learning methods using single-cell data for perturbation modeling have also pushed the field of cellular biology forward
.
For biologists, whether studying genes, transcripts, modifications, and protein functions, it is necessary to carry out frequent human intervention to achieve positive or reverse changes in the variables of interest and observe changes
in cell phenotypes.
The whole process requires the construction, introduction, and experimental observation of intervention tools to draw phenotypic conclusions
.
The purpose of perturbation modeling is to predict the function of a molecule in the absence of wet experiments through the establishment of mathematical models, through the analysis, induction and summary of existing data, for biologists and drug developers, good models must help deepen the understanding of biological mechanisms and promote the process of drug development
Deep learning in genomics applications: Deep learning has been widely used in genomics research, using known training sets to predict the type of data and response results, deep learning, can perform prediction and dimensionality reduction analysis
.
Deep learning models are more capable and flexible, and with appropriate training data, deep learning can automatically learn features and patterns
with less human involvement.
Regulatory genomics, variant detection, pathogenicity scoring were successfully applied
.
Deep learning can improve the interpretability of genomic data and transform genomic data into actionable clinical information
.
Deep learning is realized by automatically mining the potential features of data from high-dimensional big data through powerful deep neural network models, and in the past 10 years, deep learning has achieved great success
in the fields of computer vision, speech recognition, and natural language processing.
The complex relationship between genomics big data and disease phenotypes is difficult to resolve, and the use of deep learning to mine multi-omics data to explore the pathogenic mechanism and drug response mechanism of complex diseases will greatly improve the progress
of precision medicine and translational medicine 。 In the past two years, top research groups at home and abroad MIT, Harvard University, UPenn, Tsinghua University, Fudan University, etc.
are engaged in the research of deep learning genomics, and this research result has been published in Nature Reviews Genetics, Nature Methods, Science Advances, Cancer Cell, and Nature for many times Biotechnology and other well-known international top journals have identified the basis
for our publication of top journals.
of the year by Nature Methods in 2019 and 2020, respectively.
The combination of multi-dimensional research technology in time and space will start from a new research idea, which can not only obtain the heterogeneity between single cells, but also obtain the structural position information of cells in tissue space, and discover more unknown and refined results
.
In summary, single-cell sequencing + spatial transcriptome sequencing: complementary advantages, while obtaining cell type populations, as well as gene expression and spatial location information of cells
.
The spatial transcriptome provides valuable insights
for research and diagnosis by locating and distinguishing the active expression of functional genes within specific tissue regions.
The introduction of 10x Visium has made spatial transcription composition a new research hotspot, favored by researchers, which can not only provide data information such as the transcriptome of the research object, but also locate its spatial position in tissues, which is of great significance
for the study of cancer pathogenesis, neuroscience, developmental biology and many other fields.
04
CADD (Computer Aided Drug Design): Computer Aided Drug Design, based on biochemistry, enzymology, molecular biology, and genetics The research results of life sciences, in view of the potential drug design targets revealed in these basic studies, including enzymes, receptors, ion channels and nucleic acids, and with reference to the chemical structure characteristics of other genegenous ligands or natural products, based on computer chemistry, through computer simulation, calculation and budget of the interaction between drugs and receptor biological macromolecules, investigate the structural complementarity and complementary properties of drugs and targets, and design reasonable drug molecules
。 It is a method for designing and optimizing lead compounds, and the application of CADD, including structure-based drug design (SBDD), ligand-based drug design (LBDD), high-throughput virtual screening (HTVS) and other technologies, breaks through the traditional lead discovery model and greatly promotes lead discovery and optimization
.
Especially in food, biology, chemistry, medicine, plants, diseases are widely used! The discovery and confirmation of targets is the first step in the research and development of modern new drugs, and it is also one of
the bottlenecks in the process of new drug creation.
The application of CAD can accelerate the speed of target discovery and improve the accuracy of target discovery, thereby promoting the development
of new drugs.
Due to the lack of literature and video tutorial materials on the learning platform, and the technology is not public, it is extremely troubled for researchers with corresponding scientific research tasks and high-quality articles, and training and learning are imminent Machine Learning Single Cell Analysis + Single Cell Spatial Transcriptome" special training course, this has held eleven training sessions, more than 1200 participants, the training arrangement and training quality unanimously evaluated! Learn all the content, learn, learn thoroughly, apply what you have learned, and complete scientific research tasks and high-quality articles!
Train experts
Mr.
Liu, Ph.
D.
in Bioinformatics, has more than ten years of experience
in sequencing data analysis.
His research interests include artificial intelligence, natural language processing, functional genomics, transcriptomics, miRNA and target gene network analysis, single-cell sequencing data analysis, gene regulatory network temporal analysis, protein interaction network analysis, multi-omics joint analysis, etc
.
He has presided over 4 projects such as the Provincial Natural Science Foundation, published the practical medical textbook "Python Medical Practice Analysis", and published 22 SCI papers, including one and 9 parallel works
.
Teacher Chen and Teacher Zhang taught
.
He has published several papers in academic journals at home and abroad, including Nature Communication, Cell Regeneration and other well-known journals, and his research interests are bioinformatics, developmental biology and genetics
.
Using multi-omics data, data analysis and mining through deep learning algorithms, including ChIP-seq, ATAC-seq, RNA-seq, CNV, etc
.
Course 1: Online training course on the application of deep learning in genomics
Day 1
Introduction to deep learning algorithms
Theoretical content:
1.
Neural network algorithm with supervised learning
1.
1 Application examples of fully connected deep neural network DNNs in genomics
1.
2 Examples of the application of convolutional neural network CNNs in genomics
1.
3 Examples of RNNs in genomics
1.
4 Examples of the application of graph convolutional neural network GCN in genomics
2.
Unsupervised neural network algorithms
2.
1 Examples of autoencoder AE applications in genomics
2.
2 Examples of the application of generative adversarial network GAN in genomics
Hands-on content
1.
Linux operating system
1.
1 Common Linux commands
1.
2 Vim editor
1.
3 Genomic data file management, modify file permissions
1.
4 View explore genomic regions
2.
Python language basics
2.
1.
Python package installation and environment construction
2.
2.
Common data structures and data types
The next day
Fundamentals of genomicsTheoretical content
1.
Genomic database
2.
Epigenome
3.
Transcribing the genome
4.
Proteome
5.
Functional genome
Hands-on content
A common deep learning framework for genomics
1.
Install and introduce deep learning toolkits TensorFlow, Keras, PyTorch
2.
Identify deep learning model features in the toolkit
2.
1.
Data Representation
2.
2.
Tensor operations
2.
3.
"Layers" in neural networks
2.
4.
Models composed of layers
2.
5.
Loss Function and Optimizer
2.
6.
Dataset segmentation
2.
7.
Overfitting and Underfitting
3.
Genomic data processing
3.
1 Install and use keras_dna process various gene sequence data such as BED, GFF, GTF, BIGWIG, BEDGRAPH, WIG, etc
3.
2 Design deep learning models using keras_dna
3.
3 Use keras_dna to split the training set and test set
3.
4 Use keras_dna to select the gene sequence of a specific chromosome, etc
4.
Deep neural network DNN is applied in identifying motif features
4.
1 Realize single-layer single-filter DNN identification motif
4.
2 Realize multilayer single-filter DNN identification motif
4.
3 Implement multi-layer multi-filter DNN recognition motif
Day 3
Application of convolutional neural network CNN in gene regulation prediction
Theoretical content
1.
Identify motif feature G4 in Chip-Seq, such as DeepG4
2.
Prediction of DNA methylation in Chip-Seq, DeepSEA
3.
Chip-Seq predicts transcriptional regulator binding, DeepSEA
4.
Predicting chromosomal affinity in DNase-seq, Basset
5.
Predicted gene expression in DNase-seq eQTL, Enformer
Hands-on content
The reproduction convolutional neural network CNN identifies the motif feature DeepG4, the noncoding gene mutation DeepSEA, predicts chromosome affinity Basset, and gene expression eQTL
1.
Reproduce DeepG4 to identify G4 features from Chip-Seq
2.
Install selene_sdk to replicate DeepSEA's prediction of DNA methylation, non-coding gene mutations from Chip-Seq
3.
Reproduce Basset to predict chromosomal affinity from Chip-Seq
4.
Reproduce Enformer, predict gene expression eQT from Chip-Seq
Day 4
Application of deep learning in identifying copy number variation DeePCNV and regulatory factor DeePFactorTheoretical content
1.
Predict copy number variation CNV, DeepCNV in SNP microarray
2.
RNA-Seq predicts premiRNA, dnnMiRPre
3.
Predict the regulator protein from the protein sequence, DeepFactor
Hands-on content
1.
Reproduce DeepCNV using SNP microarray combined image analysis to identify copy number variation
2.
Reproduce the RNN tool dnnMiRPre to predict premiRNAs from RNA-Seq
3.
Reproduce DeepFactor to identify transcriptional regulator proteins from protein sequences
Day 5
Application of deep learning in identifying and disease phenotypes and biomarkers
Theoretical content
1.
DeepType, a deep learning tool for identifying breast cancer typing from gene expression data
2.
Identification of disease phenotypes from high-dimensional multi-omics data, XOmiVAE
3.
DeepHE, a deep learning tool for identifying key genes in gene sequences and protein interaction networks
Hands-on content
1.
Reproduce DeepType to distinguish breast cancer subtypes from METABRIC breast cancer data
2.
Reproduce XOmiVAE to identify breast cancer subtypes from TCGA multidimensional databases
3.
Reproduce DeepHE uses gene sequences and protein interaction networks to identify key genes
Day 6
Application of deep learning in predicting drug response mechanismsTheoretical part
1.
SWnet, a deep learning tool that combines tumor gene markers and drug molecular structure to predict drug response mechanisms
Hands-on content
1.
Pretreatment of drug molecular structure information
2.
Calculate drug similarity
3.
Build self-attention SWnets on different datasets
4.
Evaluate self-attention SWnet
5.
Build SWnet for multitasking
6.
Build a single-layer SWnet
7.
Build SWnet with weight layers
Course 2: Online training course on machine learning single-cell analysis applications
Day <>
Theoretical content:
Principles of single-cell sequencing
2.
Basics of single-cell sequencing
3.
Single cell sequencing methods and data
4.
Single-cell data analysis process
Hands-on content
1.
R language basics
2.
R (4.
1.
3) and installation of Rstudio
3.
R package installation and environment setup
4.
Data structure and data type
5.
R language basic functions
6.
Data Download
7.
Data reading and output
The next day
Theoretical content
1.
Overview of machine learning
2.
Linear model
3.
Decision tree
4.
Support vector machine
5.
Integrated learning
6.
Model selection and performance optimization
Hands-on content
1.
Decision tree algorithm implementation
2.
Random forest algorithm implementation
3.
Support vector machine (SVM) algorithm implementation
4.
Naive Bayes algorithm implementation
5.
Xgboost algorithm implementation
6.
Principal component analysis PCA algorithm implementation
7.
Clustering algorithm implementation
8.
DBSCAN algorithm implementation
9.
Hierarchical clustering algorithm implementation
Day <>
Theoretical content
1.
Fundamentals of multiomics
2.
Common biomics experiments and analysis methods
3.
Introduction to common omics databases
4.
Batch processing of omics data
5.
Biological function analysis
6.
Differential gene screening based on transcriptomics, disease prediction
7.
Analysis of disease pathogenesis based on differential gene combined multiomics
8.
Omics data visualization
Hands-on content
1.
Fundamentals of multiomics
2.
Common biomics experiments and analysis methods
3.
Introduction to common omics databases
4.
Batch processing of omics data
5.
Biological function analysis
6.
Differential gene screening based on transcriptomics, disease prediction
7.
Analysis of disease pathogenesis based on differential gene combined multiomics
8.
Omics data visualization
Day <>
Theoretical content
1.
Common machine learning methods in single-cell analysis
2.
Machine learning algorithm for dimensionality reduction clustering
3.
Machine learning algorithm for group annotation
4.
Common deep learning methods in single-cell analysis
5.
Deep learning algorithm for dimensionality reduction clustering
6.
Deep learning algorithm for group annotation
Hands-on content
1.
Python language basics
2.
Python installation and development environment construction
3.
Basic data types, combined data types
4.
Analyze the environment construction
5.
Use of Jupyter notebooks
6.
Functions, lists, tuples, dictionaries, collections
7.
Control structure, circulation structure
8.
Numpy Module – Scientific Computation of Matrices
9.
Matplotlib module – data processing and plotting
10.
Pandas Module – CSV Data Processing and Analysis
11.
Sklearn module - machine learning model basic package call
Day <>
Theoretical part
1.
Application of machine learning in single-cell analysis
2.
Collect data
3.
Data preparation
4.
Select a model
5.
Model training
6.
Model evaluation
7.
Parameter adjustment
8.
Model prediction
Hands-on content
1.
Create a Seurat object
2.
Data quality control
3.
Sequencing depth difference and standardization
4.
Dimensionality reduction of single-cell data
5.
Batch effect removal
6.
Data integration
7.
Subgroup annotation
8.
GSVA pathway activity analysis
9.
Single cell enrichment analysis
Day <>
Theoretical part
1.
Application of deep learning in single-cell analysis
2.
The basic composition of convolutional neural network
3.
Convolution kernel
4.
Basic composition of recurrent neural network
5.
Circulating nucleus
6.
Basic composition of graph neural network
Hands-on content
1.
Algorithm implementation of convolutional neural network in single-cell analysis
2.
Recurrent neural network algorithm implementation in single-cell analysis
3.
Algorithm implementation of graph neural network analysis in single cells
4.
Automatic annotation of single-cell data
5.
Single-cell data analysis clustering and batch effect
6.
Clustering of single-cell sequencing data
7.
Cell type annotation method for weighted graph neural networks
8.
Deep learning gene relationships in single-cell expression data
Course 3: Online training course on single-cell spatial transcriptome applications
Day <>
Single cell sequencing technology and application
Theoretical content:
1.
Introduction to single-cell omics research
2.
Progress of single-cell transcriptome sequencing technology and its principle: 1992\2009-present
3.
Single-cell multiomics and spatial transcriptome technology;
4.
Common applications and important biological discoveries of single-cell transcriptome sequencing technology;
5.
Introduction
of major single-cell projects and databases.
Practical content:
1.
Linux command introduction and practical training
.
2.
Introduction and installation
of R language.
3.
Simple R syntax and common commands
.
4.
Data mining and its statistical applications
.
5.
R language practical drawing ggplot2 is the mainstay
.
The next day
Single-cell transcriptome data analysis ideas and processes and data analysis practiceTheoretical content:
1.
Introduction to single-cell experiments, common library structure
.
2.
Introduction
to single-cell transcriptome pipeline software and code.
3.
Introduction to
single-cell transcriptome transcription factors and their cellular communication.
4.
Research ideas
of single-cell omics in tumor, development, immunity and other fields.
Practical content:
1.
10X official single-cell software Cellranger explanation and practice
.
2.
Quality control genes and cells
.
3.
Select highly variable genes
.
4.
Dimensionality reduction and grouping
.
5.
Biomarker defines the cell type
.
6.
Look for differential genes
7.
Consolidate multiple samples and eliminate sample heterogeneity
with Seurat.
Day <>
Analysis and mapping practice of single-cell transcriptome trajectories, pathways, transcription factors, etc
Practical content;
1.
Pseudo-chronological analysis
of single-cell transcriptomes by Monocle software.
2.
Functional enrichment analysis
of pathways for individual clusters of single cells.
3.
Score cellular pathways by GSVA, etc
.
4.
CellphoneDB software was used to analyze
cell interactions.
5.
Inference of chromosome copy number variation by typical biocredit software infercnv
.
6.
Comprehensive analysis of SCENIC software for transcription factor prediction analysis
.
Theoretical content:
1.
Introduction to
spatial transcriptome technology.
2.
Application of
spatial transcriptome technology.
3.
Interpretation
of spatial transcriptome article charts.
4.
Research ideas
of spatial transcriptome technology in cancer, development, neuroscience and other fields.
Day <>
Analysis of spatial transcriptome data alignment, dimensionality reduction, and clustering Spatial transcriptome multi-sample and correlated analysis with but single cell dataPractical content:
1.
10x Visium tissue optimization and library preparation
.
2.
10x Visium's official analysis software Space Ranger explains and practices
.
3.
Space Ranger output interpretation
results.
4.
Installation and use
of Loupe Browser software.
5.
Dimensionality reduction, clustering, and visualization
with Seurat software.
6.
Gene expression visualization
by Seurat.
Theoretical + practical content
1.
Identify features of spatial variables with Seurat
.
2.
Association analysis with single-cell data (spatial cell type definition)
3.
Process multiple slices
with Seurat.
4.
Summary
of single-cell and spatial transcriptome data analysis.
Course 4: CAD Computer-Aided Drug Design Online Training Course
Day <>
Background and theoretical knowledge and tool preparation
1.
PDB Introduction and use of databases
1.
1 Introduction to databases
1.
2 Structure query and selection of target proteins
1.
3 Structural sequence download of target protein
1.
4 Download and pretreatment of target proteins
1.
5 Download protein crystal structures in batches
Introduction and use of pymol
2.
1 Introduction to the basic operation and basic knowledge of the software
2.
2 Illustration of protein-ligand interactions
2.
3 Protein-ligand small molecule surface diagram, electrostatic potential representation
2.
4 Protein-ligand structure superposition and alignment
2.
5 Plot the interaction forces
3.
Introduction and use of Notepad
3.
1 Advantages and main functions introduction
3.
2 Introduction to interface and basic operation
3.
3 Plug-in installation and use
General protein-ligand molecular docking explained
1.
Introduction to the relevant theories of docking
1.
1 The concept and basic principle of molecular docking
1.
2 Basic methods of molecular docking
1.
3 Common software for molecular docking
1.
4 General process of molecular docking
2.
Conventional protein-ligand docking
2.
1 Collection of receptor and ligand molecules
2.
2 Treatment of complex pre-conformations
2.
3 Prepare receptors, ligand molecules
2.
4 Protein-ligand docking
2.
5 Analysis of docking results
Take the new coronavirus protein master protease target and related inhibitors as an example
The next day
Virtual filtering
1.
Introduction and download of small molecule database
2.
Introduction of relevant procedures
2.
1 Introduction and use of OpenBabel
2.
2 Introduction and use of Chemdraw
3.
Pre-processing of virtual screening
4.
Virtual screening process and practical demonstration
Case: Screening of novel coronavirus master protease inhibitors
5.
Result analysis and graphing
6.
Drug ADME prediction
6.
1 Introduction to the concept of ADME
6.
2 Introduction to Prediction Related Websites and Software
6.
3 Analysis of forecast results
Day 3:
Expand the use of docking
1.
Protein-protein docking
1.
1 Application scenarios of protein-protein docking
1.
2 Introduction of relevant procedures
1.
3 Collection and pretreatment of target proteins
1.
4 Use examples to perform calculations
1.
5 Presets for critical residues
1.
6 Results obtained with file type
1.
7 Analysis of results
Take the current hot targets PD-1/PD-L1 as an example
.
2.
Involves the docking of metalloenzyme proteins
2.
1 Background introduction to metalloenzyme protein-ligands
2.
2 Collection and pretreatment of protein and ligand molecules
2.
3 Treatment of metal ions
2.
4 Metallocoprotein-ligand docking
2.
5 Analysis of results
Take human farnesyltransferases and their inhibitors as an example
3.
Protein-polysaccharide molecular docking
4.
1 Proteo-polysaccharide interactions
4.
2 Key points of docking processing
4.
3 Process of protein-polysaccharide molecular docking
4.
4 Protein-polysaccharide molecular docking
4.
5 Analysis of relevant results
Take α-glycosidyltransferase and polysaccharide molecular docking as an example
4.
Nucleic acid-small molecule docking
4.
1 Application status of nucleic acid-small molecules
4.
2 Introduction to related procedures
4.
3 Nucleic acid-small molecule binding types
4.
4 Nucleic acid-small molecule docking
4.
5 Analysis of relevant results
Take human telomere g-four strands and ligand molecular docking as an example
.
Introduction to the operation process and practical demonstration
Day <>
Expand the use of docking
1.
Flexible docking
1.
1 Introduction to the use scenarios of flexible docking
1.
2 Advantages of flexible docking
1.
3 Flexible docking of protein-ligands
Focus: How to set up flexible residues
1.
4 Analysis of relevant results
Take cyclin-dependent kinase 2 (CDK2) and ligand 1CK as examples
2.
Covalent docking
2.
1 Introduction of two covalent docking methods
2.
1.
1 Flexible side chain method
2.
1.
2 Two-point attractor method
2.
2 Collection and pretreatment of proteins and ligands
2.
3 Covalent docking of covalent drug molecules and target proteins
2.
4 Comparison of results
Take the current hot new crown covalent drug as an example
.
3.
Protein-hydrate docking
3.
1 Significance and method introduction of hydration in protein-ligand interaction
3.
2 Collection and pretreatment of proteins and ligands
3.
3 Preparation of docking related parameters
Focus: Addition and treatment of water molecules
3.
4 Protein-water molecule-ligand docking
3.
5 Analysis of results
Take acetylcholine-binding protein (AChBP) and nicotine complex as an example
Day <>
Molecular dynamics simulations Linux and gromacs
1.
Introduction and simple use of Linux system
1.
1 Common command lines for Linux
1.
2 Installation of common programs on Linux
1.
3 Experience: How to do virtual filtering on Linux
2.
Theoretical introduction to molecular dynamics
2.
1 Principles of molecular dynamics simulation
2.
2 Methods and related procedures for molecular dynamics simulation
2.
3 Introduction to the relevant force field
3.
Use and introduction of gromacs
Focus: Introduction of main commands and parameters
4.
Introduction and use of origin
Day <>
Execution of solvation molecular dynamics simulations
1.
General solvated protein processing process
2.
Preparation of protein crystals
3.
The energy of the structure is minimized
4.
Pre-balance of the system
5.
Unlimited molecular dynamics simulation
6.
Display and interpretation of molecular dynamics results
Take lysozyme in water as an example
Day 7
Execution of protein-ligand molecular dynamics simulations
1.
Processing flow of protein-ligand in molecular dynamics simulation
2.
Preparation of protein crystals
3.
Preparation of the initial conformation of protein-ligand mimicry
4.
Preparation of ligand molecular force field topology files
4.
1 A brief introduction to Gaussian
4.
2 A brief introduction to AmberTool
4.
3 Generate force field parameter files for small molecules
5.
Pre-equilibrium for temperature and pressure limits of the complex system
6.
Unlimited molecular dynamics simulation
7.
Display and interpretation of molecular dynamics results
8.
Post-trajectory processing and analysis
Take the new coronavirus protein master protease target and related inhibitors as an example
Teaching timeDeep
learning in genomics training time
01
2022.
12.
12-2022.
12.
15 Evening (19.
00-22.
00)
2022.
12.
17-2022.
12.
18 Full Day (9.
00am-11.
30pm 13.
30-17.
00pm)
2022.
12.
19-2022.
12.
22 Evening (19.
00-22.
00)
Machine Learning Single Cell Analysis Training Time
02
2022.
12.
10-2022.
12.
11 Full Day Class (09.
00am-11.
30pm 13.
30-17.
00pm)
2022.
12.
17-2022.
12.
18 Full Day (09.
00am-11.
30pm 13.
30-17.
00pm)
2022.
12.
24-2022.
12.
25 Full Day (09.
00am-11.
30pm 13.
30-17.
00pm)
Single-cell spatial transcriptome training time
03
2022.
12.
17-2022.
12.
18 Full Day (9.
00am-11.
30pm 13.
30-17.
00pm)
2022.
12.
19-2022.
12.
22 Evening (19.
00-22.
00)
CAD Computer Aided Drug Design Training Time
04
2022.
12.
10 -----2022.
12.
11 Full Day (09.
00-11.
30am 13.
30pm-17.
00pm)
2022.
12.
13-----2022.
12.
16 Evening (19.
00-22.
00)
2022.
12.
17-----2022.
12.
18Full day classes (09.
00-11.
30 a.
m.
, 13.
30-17.
00 p.
m.
)
2022.
12.
19 -----2022.
12.
20Evening (19.
00-22.
00pm)
(Tencent Meeting live class, online practice, video playback video for permanent viewing).
Registration fee
For the registration fee, a formal reimbursement invoice can be issued, and relevant payment certificates and invitation letters can be provided
Reimbursement invoices and documents can be issued in advance for reimbursement
Deep learning, genomics, machine learning, single-cell analysis, computer-aided drug design
Public fee: ¥5880 per person per class (including registration fee, training fee, and materials fee)
Self-paid price: ¥5480 per person per class (including registration fee, training fee, and material fee)
Single-cell spatial transcriptome:
Public fee: ¥5080 RMB per person per class (including registration fee, training fee, and materials fee)
Self-paid price: ¥4680 RMB per person per class (including registration fee, training fee, and materials fee)
Preferential
Offer 1: Two classes report: 9880 yuan Three classes report: 13880 yuan Four classes report: 17880 yuan
Offer 2: Students who register and pay in advance + forward to the circle of friends or academic exchange group can enjoy a discount of 400 yuan per person (limited to 15 people).
Offer 3: Register for two classes at the same time and get one free learning place (optional class).
Offer 4: Register for more than five training courses and receive three free training places (optional free class).
Certificate: Students who participate in the training and pass the examination can apply for the post ability adaptation evaluation certificate of "Industrial Power Building Quality Literacy Improvement Action" issued by the Industrial Culture Development Center of the Ministry of Industry and Information Technology
.
The certificate can be found on the official website of the center, which can be used as an important basis
for ability evaluation, assessment and appointment.
Evaluation certificate inquiry website: www.
miit-icdc.
org (voluntary application, additional examination fee of 500 yuan / person)
cultivate
teach
welfare
sharp
After the registration and payment of the registration fee is successfully given a full set of preview videos of the registration class, the whole process of video playback is provided after the end of the course, and the long-term Q&A with the content of the training course, the WeChat problem solving group will never be disbanded, and the students who participate in this course can participate in the same special training course organized in this later stage for free (any one session can be)
Teaching
lesson
Fangli
style
Through the online live broadcast of Tencent Meeting, the teaching mode of theory + practice, the teacher carries the operation hand-in-hand, starting from scratch to explain, more than 800 pages of electronic PPT and tutorial + The preview video is sent to the students one week before the start of the course, all training software will be sent to the students, if there are any questions, take the open microphone to share the screen and WeChat group to solve doubts, students and teachers to communicate, students to communicate with students, after the training to solve long-term doubts The training group is not disbanded, and the previous trainees have a high evaluation of the quality of training and teaching methods
Tencent Meeting questions are answered in real time
(2) The trainees are very cognizant of the training, and we guarantee that the second study is free
Past attendees
Universities abroad; There are students from the Massachusetts Institute of Technology, University of Bristol, University of California, Berkeley, Osaka University, George Mason University, California Institute of Technology, University of Manchester, Rice University, Boston University, Texas A&M University, Drake University, Union University, Princeton University, Stanford University, Imperial College London, KAUSTuniversity, Lehigh University, The University of Queensland, University of Queensland, Yale University, University of Niujin, University of Cambridge, University of Pittsburgh, University of Sydney, University of Toronto, University of Washington in Seattle, University of London, Duke University, University of Tokyo, Columbia University, Cornell University, New York University, Northwestern University, Brown University, University of Washington
Domestic faculties and universities; From Sun Yat-sen University Cancer Center (Sun Yat-sen University Cancer Hospital, Sun Yat-sen University Cancer Institute), Sun Yat-sen University, Peking University First Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College Hospital, Northwest University for Nationalities, Southwest University, Shandong University, University of California, Berkeley, Qiyuan Laboratory, First Medical Center of Chinese People's Liberation Army General Hospital, Henan Normal University, Nanjing University of Technology, Southern University of Science and Technology, Nanjing University, Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences, Qinghai Academy of Agriculture and Forestry Sciences, The First Affiliated Hospital of Tianjin University of Chinese Medicine, Shandong University, Heilongjiang Bayi Agricultural Reclamation University, The Second Affiliated Hospital of Nanchang University, Taizhou Central Hospital (Affiliated Hospital of Taizhou University), Affiliated People's Hospital of Ningbo University, Xinjiang Agricultural University, Beijing Forestry University, Guangxi Medical University, Hunan College of Arts and Sciences, Binzhou Medical College, Binzhou Medical College Yantai Affiliated Hospital, South China Normal University, Chinese Academy of Environmental Sciences, Yunnan Normal University, Kunming University of Science and Technology, Hubei Medical College, Soochow University, Fuzhou University, Nanfang Hospital, The Second Affiliated Hospital of Nanchang University, Shenzhen Hospital of Traditional Chinese Medicine, Hunan College of Arts and Sciences, Henan Academy of Science and Technology, Fujian Provincial Hospital, Xiangya Hospital of Central South University, Shenzhen Hospital of Traditional Chinese Medicine, Provincial Tongde Hospital, Baotou Normal College of Inner Mongolia University of Science and Technology, Urumqi Center for Disease Control and Prevention, Institute of Forestry, Chinese Academy of Forestry, Lanzhou Institute of Animal Husbandry and Veterinary Medicine, Chinese Academy of Agricultural Sciences Ludong University, Hebei University of Engineering, Pearl River Hospital of Southern Medical University, Beijing Obstetrics and Gynecology Hospital Affiliated to Capital Medical University, The Second Affiliated Hospital of Chongqing Medical University, Beijing Puli Zhicheng Biotechnology Co.
, Ltd.
, Shanghai Medical College of Fudan University, Affiliated Hospital of Shaanxi University of Chinese Medicine, Hospital of Hematology, Chinese Academy of Medical Sciences (Institute of Hematology, Chinese Academy of Medical Sciences), Binzhou Kangdaxin Medical Equipment Co.
, Ltd.
, Shenzhen Peking University Hong Kong University of Science and Technology Medical Center, Tianjin Cancer Hospital, Army Characteristic Medical Center, The First Affiliated Hospital of Air Force Military Medical University, Jiangnan University, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Beijing Yuezhong Moment Culture Media Co.
, Ltd.
, Beijing Huikang Jianyi Medical Equipment Co.
, Ltd.
, Hangzhou Aoming Gene Technology Co.
, Ltd.
, Yixin Biotechnology Wuxi Co.
, Ltd.
, Guangzhou Xiaoyue Biotechnology Co.
, Ltd.
, Zhangjiakou Zehan Biotechnology Co.
, Ltd.
, Ping An Technology
.
Thank you for recognizing our training! Many were unable to participate
because of time conflicts.
We cordially invite you to join us this time!
Registration consultation method (please scan the QR code below WeChat)
WeChat/QQ: 766728764
E-mail:
m15238680799@163.
com
Registration Tel: 15238680799
To quote a quote from a previous attendee:
Finding it was really down-to-earth and at the same time, I needed to look up at the stars occasionally, thank you very much for your recognition of our training! I wish you all the best of luck!
