Collaborative Science

Accelerating time to market and driving innovation with collaboration, knowledge based understanding and prediction.

BIOVIA Pipeline Pilot Analytics and Machine Learning

BIOVIA Pipeline Pilot Analytics and Machine Learning offers a comprehensive set of learning and data modeling capabilities, statistical filters and clustering components optimized for large real-world data sets.

BIOVIA Pipeline Pilot Analytics and Machine Learning Features

  • Rapidly cluster data
  • Employ categorical learning using Bayesian statistics
  • Perform Principal Component Analysis (PCA)
  • Apply regression - Linear, partial least squares (PLS) and k-nearest neighbor (kNN)
  • Utilize Recursive Partitioning methods for building single tree or forest models. The methods can build models for single or multiple response properties.
  • Analyze variable importance to identify the most discriminating descriptors
  • Deploy model applicability domain (MAD) methods and enable cross-validation to assess prediction quality
  • Return multiple models rather than a single "best" model by creating a number of trial models. Combine multiple models into a single ensemble model
  • Employ Genetic Function Approximation (GFA) methods to perform variable selection and build multiple models, which can be combined into a consensus or ensemble model
  • Employ Pareto Optimization for multi-objective optimization problems
  • Generate interactive reports with ROC plots, enrichment plots and other visualization techniques for evaluating model quality and understanding the relationships between descriptors and responses.
  • Integrate with other statistic platforms such as R, JMP and SAS

Analytics and Machine Learning Datasheet

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