Machine Learning

Uncover hidden patterns and relationships, automate analyses and predict future outcomes.
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Discover Trends

Organize your data into meaningful groups based on similarity with these unsupervised ML techniques.

Hierarchical Clustering

Group similar data points together, building a tree-like structure. This allows you to investigate how closely different observations are related to each other and uncover groupings within your data.
Available on Enterprise Plan

K-means Clustering

Specify the number (K) of clusters you expect to find in your data. Prism then takes over, utilizing the K-means++ method, which outperforms alternative versions and intelligently groups your data points into the designated number of clusters.
Available on Enterprise Plan

Simplify Data

Use Dimensionality Reduction to transform your data into more interpretable and efficient formats, while retaining as much information (variance) as possible with these unsupervised ML techniques.

Principal Component Analysis (PCA)

Efficiently reduces variables while preserving crucial information. This unsupervised ML technique tackles overfitting and allows for clearer insights and stronger models.

Principal Component Regression (PCR)

Can be unlocked when combining PCA with regression. PCR is a powerful method for building more accurate and interpretable models.

GLMs for Wide Applicability

Train Generalized Linear Models models using data with known outcomes, allowing you to predict future data behavior and gain valuable insights.

Multiple Linear Regression

Can be used to identify how various factors impact your drug candidates, prioritizing the most promising leads early for efficient drug discovery.

Multiple Logistic Regression

Allows you to uncover biomarkers that predict drug effectiveness in specific patient populations, enabling development of targeted therapies for improved outcomes.

Poisson Regression

Analyzes count data and can be used to optimize cell culture conditions, ensuring consistent and reliable experiments.

Visualizations to Enhance Insights

Prism's visual tools for ML techniques make it easier to interpret clustering results and effectively communicate your findings.

Dendrograms

Provides a visual map of hierarchical clustering results. Visually determine the optimal number of clusters to use to segment the data.
Available on Enterprise Plan

Confidence Ellipses and Convex Hulls

Confidence Ellipses show the variability within each group, highlighting how compact or dispersed your clusters are.
Convex hulls define the boundaries of each cluster, revealing the extent of overlap between different data groups.
Available on Enterprise Plan

Machine Learning Highlights

Highlights:

  • Hierarchical & K-means Clustering
  • Dimensionality Reduction
  • Generalized Linear Models
  • Dendrograms
  • Confidence Ellipses & Convex Hulls

Discover deeper insights from your experiments with Prism’s built in machine learning tools.

Designed specifically for scientists, Prism’s machine learning tools remove unnecessary information, automatically group similar data points, and help you see hidden connections.

Advanced visualizations clarify these relationships, giving you a clearer picture of your research.

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