ABYA Genomics
Developer of a computational platform for designing, testing, and validating gene therapies using in silico methods. Describes capabilities in sequence analysis, protein structure prediction with machine learning, genomic visualization, genetic circuit engineering, and computational approaches to assess safety and efficacy that aim to reduce reliance on experimental clinical testing.
Industries
Nr. of Employees
small (1-50)
ABYA Genomics
San Francisco, California, United States, North America
Products
Sequence viewer for large FASTA files
A viewer and analysis tool capable of loading large FASTA files, searching for DNA sequence patterns, and performing sequence calculations on constrained hardware.
Sequence viewer for large FASTA files
A viewer and analysis tool capable of loading large FASTA files, searching for DNA sequence patterns, and performing sequence calculations on constrained hardware.
Services
An integrated software platform intended to design, test and validate gene therapies using computational methods and data analysis workflows.
An integrated software platform intended to design, test and validate gene therapies using computational methods and data analysis workflows.
Expertise Areas
- In silico gene therapy design
- Computational safety and efficacy assessment
- Bioinformatics and genomic visualization
- Machine learning for structural biology
Key Technologies
- In silico simulation and computational modeling
- Machine learning for protein structure prediction
- FASTA sequence analysis
- Genomic visualization (dot plots, synteny detection)
News & Updates
Publication of research sessions, development demos, and behind-the-scenes analyses via video and blog posts.
Company nomination in the Global Startup Awards in the category of Best AI-Enabled Startup.
Demonstrated a custom sequence viewer loading large FASTA files and performing DNA pattern searches on a 2002 Macintosh with 4GB RAM.
Developed and implemented an initial machine learning model to predict secondary protein structures from PDB data.
Publication of research sessions, development demos, and behind-the-scenes analyses via video and blog posts.
Company nomination in the Global Startup Awards in the category of Best AI-Enabled Startup.
Demonstrated a custom sequence viewer loading large FASTA files and performing DNA pattern searches on a 2002 Macintosh with 4GB RAM.
Developed and implemented an initial machine learning model to predict secondary protein structures from PDB data.