NEW RELEASE: v1.3
- Improved visualization incorporating Seaborn and Matplotlib packages
- Increased Machine Learning model types
- Expanded preprocessing for tabular data sets with Pandas integration
Biomedical Machine Learning Using Multiple Data Pipelines
- AugustaTM performs predictive analytics and biomedical machine learning for use cases in bioinformatics, R&D clinical informatics, target discovery, and computational biology:
- MRI/fMRI and other imaging modalities
- Genetics, Proteomics
- Wearables data
- EHR/EMR data
- Real Time Integrative Analytics, e.g. combining genomics with clinical data
- Precision medicine applications in real time
- Drug compound and small molecule activity prediction
- Patient care quality and program analysis
- Drug discovery and development analysis
- Hospital telemetry and operational data
Connect to a Variety of Data Sources
- Through integration with Python, AugustaTM can connect to and read from any data source (g. local storage, databases, cloud storage).
- Modular and customizable pipelines for processing raw phenotypic, imaging, drug, and genomic data sets using any combination of data types.
Integrate with Existing Business Processes
- Augusta can deploy where the data reside, saving valuable time and development effort.
- Seamlessly integrates into existing business processes or embedded directly within user applications.
- Capabilities available for IPython notebooks with Plotly-powered visualizations.
Parallel and Distributed Computing Enabled
- AugustaTM leverages the performance of the most advanced parallel processors – multi-core CPUs and GPUs – for increased performance during model rebuilds.
- Augusta is integrated with Apache Spark for distributed execution.
- Deployable on standalone servers, through the Amazon Web Service and Microsoft Azure, with an Apache Spark distributed framework for all steps above.
- Connection to Apache Kafka to retrieve and organize streaming data.
Using data available in the DrugBank Database we generated binding prediction profiles for every known protein target having 5 or more described ligands (607 protein-drug binding models made, 7,149 potential ligands for each, over 4 million drug-ligand activity predictions total)