Identify Protein Structures Using Einstein
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Slides I used for my dreamforce presentation. Learn how to use Salesforce Einstein Vision API, precisely the Image Classification API to generate a predictive deep learning model and use the same with some test images to see the prediction working. I use a fictional business and protein 3d structures to illustrate the power of this API - https://metamind.readme.io/docs/introduction-to-the-einstein-predictive-vision-service
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Identify Protein Structures Using Einstein
- 1. Identify Protein Structures using Einstein @gthoppae Gnanasekaran Thoppae, Salesforce Architect | Founder Suisse Romande Salesforce DG
- 2. Use case summary • Suisse Romande Proteomics Lab provides its various services to the academic community in the region. • The lab has developed a custom app (LIMS) on its Salesforce platform. • A community portal allows its external users to login and order services. • Protein structure identification app keeps its customers engaged, business differentiated.
- 4. Deciphering 3D Structures of Proteins Protein Crystal X-Ray Diffraction Electron Density Map 3D Protein Structure
- 5. Demo – Know Your Protein
- 6. Lets Play the “Know Your Protein” Game
- 7. Einstein Platform Services – Authorization Setup 1. Register for the Einstein platform services account – https://api.einstein.ai/signup 2. Download private key – einstein_platform.pem 3. Construct JSON web token (JWT) and sign with your private key - https://api.einstein.ai/token 4. Obtain a time limited OAUTH token 5. Use the token to call the API
- 8. Einstein Vision - Image Classification API Overview 4 easy steps
- 10. Demo – API in action
- 12. Summary • Creative way to use known protein 3d structure image data • Einstein Platform Services overview • Image data used for creating the custom classifier • Einstein Vision API exploration using Postman REST client
- 13. Conclusion • Deep learning methodologies are mature and ready to be exploitable. • Large number of images are required to create a accurate models. • Image analysis has application in almost all domains and new use cases are emerging.
- 14. Next Steps Trailhead Sessions https://github.com/gthoppae/df17 Code Search: “PredictionIO” or “Einstein Vision” in agenda builder
Editor's Notes
- Services: Protein & peptide separation, digestion & labelling, mass spectrometry Lab information management system (LIMS) helps organize the core processes Gathers metadata about samples, experiments as well as provides results Leverages Einstein Image Classification service
- There exists 20 different amino acids in our body. Each amino acid joins together in long chains by forming peptide bond These chains are called polypeptides Through the hydrogen bonding between amino acids in these chains, local conformations can be formed namely alpha-helix and beta-sheets. These are generally called secondary structures The protein structure will bend and fold to form stable structures that allows it to perform a biologically important function.
- Protein molecules are crystallized They are subjected to X-ray beams and the diffraction pattern is recorded Based on the atomic distances between atoms a 3d electron density map is produced A protein model is then built from it
- There are a few ingenious ways Suisse Romande Proteomics Lab keeps its prospects and customers engaged. It has created a game called “know you protein” using Salesforce Einstein AI. It shows a random image of a 3D protein structure and asks what type of protein it is. Given the background of the audience and their capacity to recognize protein folding patterns, people might guess correctly or not. In this example, this protein structure is ovalbumin from egg white and this particular customer guess rightly. Let us see how they built this game using Einstein Image Classification.
- Einstein image classification API is one of capability of the Salesforce Einstein platform services. It simplifies the underlying technology for image based prediction use cases. Anyone can get started using this API by simply creating an account on Einstein.ai website – https://api.Einstein.ai/signup obtaining a one-time RSA private key registering a remote site to allow your org to connect to Einstein service The API communication uses signed JSON Web Token (JWT) payload to generate Oauth token The API is extensively documented with easy to follow CURL snippets that provide “instant gratification”. Image prediction consist of 4 steps: Creating a dataset with labels – in our case, labels are a group of similar 3d protein structure images Training the dataset and generate a predictive model Classifying test data using the generated model Obtaining prediction results out of the The image classification service provides some standard image classifiers and also allows you to build custom classifiers as per your needs. In order to create a custom classifier you must have a good collect of similar, related images. The quality of predictions you receive is as good as the quality of the training dataset you use for the model generation.
- Einstein image classification API is one of capability of the Salesforce Einstein platform services. It simplifies the underlying technology for image based prediction use cases. Anyone can get started using this API by simply creating an account on Einstein.ai website – https://api.Einstein.ai/signup obtaining a one-time RSA private key registering a remote site to allow your org to connect to Einstein service The API communication uses JSON Web Token (JWT) payload to generate Oauth token The API is extensively documented with easy to follow CURL snippets that provide “instant gratification”. Image prediction consist of 4 steps: Creating a dataset with labels – in our case, labels are a group of similar 3d protein structure images Training the dataset and generate a predictive model Classifying test data using the generated model Obtaining prediction results out of the The image classification service provides some standard image classifiers and also allows you to build custom classifiers as per your needs. In order to create a custom classifier you must have a good collect of similar, related images. The quality of predictions you receive is as good as the quality of the training dataset you use for the model generation.
- The “Known your protein” dataset consists of 3 groups of proteins namely Lysozyme, Hemoglobin (globular proteins) and Porin (membrane protein). Each group contains 50 images in different orientation and configurations Based on the secondary structure in this ribbon model the image classifier predicts the similarity and likelihood of a given test image. Now let us see how to interact with the Einstein Image Classification API using a REST UI client
- Artificial Intelligence Basics module – https://trailhead.salesforce.com/modules/ai_basics Quick start: Einstein image classification - https://trailhead.salesforce.com/projects/predictive_vision_apex Build a cat rescue app that recognizes cat breeds - https://trailhead.salesforce.com/projects/build-a-cat-rescue-app-that-recognizes-cat-breeds Search the #DF17 agenda builder for “PredictionIO”