THE FIRST BIOMEDICAL SPECIFIC MACHINE LEARNING FRAMEWORK

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:

  1. Augusta Pre-Processing
  2. Augusta ML (Machine Learning)
  3. 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

Data Integration (sample pipelines)

  • MRI/fMRI and other imaging modalities
  • EEG
  • EKG/ECG
  • Genomics,
  • Proteomics
  • Chemistry
  • EHR/EMR data
  • Streaming/wearables data
  • Tabular 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

Contingent-AI™ (Patent pending) allows data scientists to permute options in the model generation process based on decisions made in pre-processing.

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 Architect uses “code blocks” to construct the data flows and schematic framework for preprocessing and machine learning, reducing the time required to program the flow from data collection through your machine learning engine.  A single toolset from data ingestion to result output, eliminates the need to port data from system to system.  Augusta effectively maintains data integrity and eliminates error prone steps.  The result:

  • Increased speed to market
  • Easy iteration and edits
  • Stronger confidence
  • Greater precision
  • Lower cost of R&D efforts
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Case Studies
March 23, 2020 in Blog Post, Case Study, Drug Discovery

Mitigating Batch Effects in Cell Painting Data

With the advent of high content screening methodologies (e.g. cellular imaging, transcriptomics, etc.), it becomes more challenging to tease apart and visualize batch effects. This is further compounded when building…
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February 12, 2020 in Blog Post, Case Study, Drug Discovery

De-noising CMap L1000 Data

As with any assay, L1000 data is noisy. Experimental replicates (the same compound tested on the same cell line under the same conditions) often result in different levels of expression…
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January 28, 2020 in Blog Post, Case Study, Drug Discovery

When are Two Compounds the Same?

When are two compounds the same? The effect of Simplified Molecular Input Line Entry System (SMILES) format on chemical database overlap including best practice for canonicalization and harmonization to understand…
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January 21, 2020 in Blog Post, Case Study, Drug Discovery

Dealing with Missing Values in Healthcare Data

In this post, we highlight the challenges of missing values when modelling with time-series data of EMRs and discuss some techniques to address it.
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