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Description
Echidna is a Haskell-based tool created for fuzzing and property-based testing of Ethereum smart contracts. It employs advanced grammar-driven fuzzing strategies that leverage a contract's ABI to challenge user-defined predicates or Solidity assertions. Designed with a focus on modularity, Echidna allows for easy extensions to incorporate new mutations or to target specific contracts under particular conditions. The tool generates inputs that are specifically adapted to your existing codebase, and it offers optional features for corpus collection, mutation, and coverage guidance to uncover more elusive bugs. It utilizes Slither to extract critical information prior to launching the fuzzing process, ensuring a more effective campaign. With source code integration, Echidna can pinpoint which lines of code are exercised during testing, and it provides an interactive terminal UI along with text-only or JSON output formats. Additionally, it includes automatic test case minimization for efficient triage and integrates seamlessly into the development workflow. The tool also reports maximum gas usage during fuzzing activities and supports complex contract initialization through Etheno and Truffle, enhancing its usability for developers. Ultimately, Echidna stands out as a robust solution for ensuring the reliability and security of Ethereum smart contracts.
Description
The foundational aspect of our immunotherapy approach lies in our comprehension of antigens and neoantigens, particularly in identifying which variations will be transcribed, translated, processed, and subsequently displayed on the surface of cells via Human leukocyte antigen (HLA) molecules, thus making them recognizable to T cells. We achieve this by employing Gritstone EDGETM, a unique platform powered by machine learning. Creating cancer immunotherapies that incorporate tumor-specific neoantigens proves challenging, mainly because tumors consist of numerous mutations, yet only a fraction of these lead to genuine tumor-specific neoantigens. To tackle this complexity, we have developed EDGE's cutting-edge integrated neural network model, trained with millions of data points gathered from a diverse range of tumor and normal tissue samples across various patient ancestries. This extensive training allows us to enhance the accuracy of neoantigen identification and improve the effectiveness of our immunotherapy strategies.
API Access
Has API
API Access
Has API
Integrations
Docker
Etheno
Ethereum
GitHub
Haskell
Homebrew
JSON
Nix
Solidity
Integrations
Docker
Etheno
Ethereum
GitHub
Haskell
Homebrew
JSON
Nix
Solidity
Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Crytic
Website
github.com/crytic/echidna
Vendor Details
Company Name
Gritstone bio
Founded
2015
Country
United States
Website
gritstonebio.com/scientific-platform/