Augusta is a biomedical AI (Artificial Intelligence) and ML (Machine Learning) framework designed to transition time from data pre-processing and integration to model building and interrogation using familiar toolsets within Python. Augusta begins with diverse, raw medical data types (e.g. images, chemical structures, genomic data, tabular data), and operates across three modules:

Augusta Pre-Processing
Augusta ML (Machine Learning)
Augusta Architect

Common Use Cases:

Drug discovery and development, incl. small molecule activity prediction
Diagnostics & precision medicine
Patient outcomes prediction and stratification

Capabilities:

Preprocessing
Feature reduction & selection
Data Integration (e.g. combining genomics with clinical data)
Model creation
Model tuning
Model training
Model interrogation
Visualization

Benefits:

Faster, effective data pre-processing, directly integrated with model building
Seamless distributed computing
Flexible architecting of processing pipelines, changing as data type and volume requires
Adaptable to changing needs/preferences over time

AUGUSTA PRE-PROCESSING

Quickly standardize or normalize data
Permute over pre-processing options
Create workflows where critical biases can be reduced
Save, modify, and re-run workflows

Use with:

Any Data Source: BYOD (Bring Your Own Data) local, databases, cloud
Any Combination of Sources: Modular and customizable pipelines for processing raw data in any combination

Sample Data Pipelines:

MRI/fMRI and other imaging modalities
EEG
EKG/ECG
Genomics,
Proteomics
EHR/EMR data
Custom data options available

Feature Optimization

Integrating data of various types (e.g. combining genomics with clinical data), enables the engineering of unique features, providing for greater machine learning insights. Features can be easily grouped, sub grouped, and archived, making them easily accessible to models, increase tuning parameters, and enhanced interrogation capabilities

AUGUSTA ML

Model Creation

Use machine learning models from Tensorflow and Scipy, with a unified syntax and output Model Tuning and Interrogation

Iterate over model-specific parameters, investigate the impact of combinations of pre-processing decisions and model hyperparameters in the context of model performance

Evaluate model performance via cross-validation, using metrics such as accuracy, precision/recall and AUC
Implement multiple feature reduction methods and evaluate impact on model performance

Visualization incorporating Seaborn and Matplotlib packages

AUGUSTA ARCHITECT

Augusta Architect is a simple, Python-based syntax that allows the processing and integration of multiple, diverse data types, and ability to run/compare multiple machine learning algorithms

Augusta

door BioSymetrics