Machine learning research platform for automatically optimizing life sciences experiments

A system comprising:

• one or more computers;
• and
• one or more storage devices communicatively coupled to the one or more computers, wherein the one or more storage devices store instructions that, when executed by the one or more computers, cause the one or more computers to perform operations comprising:
• receiving, from a user of a life sciences research platform comprising a plurality of computers, input data for a plurality of life sciences experiments within a research domain, the input data comprising, for each life sciences experiment:
• (i) a collection of settings associated with the life sciences experiments,
• (ii) a respective input value for each setting of the collection of settings, wherein the respective input value characterizes a respective experimental condition for the life sciences experiment, and
• (iii) a respective empirical value for an experimental outcome metric for the research domain;
• automatically generating, by the life sciences research platform, a plurality of machine learning models for the research domain, wherein each machine learning model is configured to predict a value for the experimental outcome metric, comprising, for each machine learning model of the plurality of machine learning models within the research domain:
• training a candidate machine learning model from the collection of settings associated with the life sciences experiments;
• and
• determining a performance measure for the candidate machine learning model based on a discrepancy between the respective predicted empirical value of the candidate machine learning model and a target output empirical value for the experimental outcome metric;
• automatically selecting, by the life sciences research platform, a final machine learning model from the plurality of machine learning models within the research domain based on the performance measures of the candidate machine learning models;
• and
• generating, by the life sciences research platform, a user interface presentation that is configured for querying potential experimental setups comprising respective input values for each setting in the collection of settings using the final machine learning model and that provides explainability data comprising contribution scores of the respective input values for each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model,
• wherein the life sciences research platform generates a revised potential experimental setup that can improve the experimental outcome metric by modifying at least one input value of the potential experimental setups to indicate, based on the contribution scores, how the experimental outcome metric varies with different input values for a particular setting.

One or more non-transitory computer storage media storing instructions that when executed by one or more computers cause the one or more computers to perform operations comprising:

• receiving, from a user of a life sciences research platform comprising a plurality of computers, input data for a plurality of life sciences experiments within a research domain, the input data comprising, for each life sciences experiment:
• (i) a collection of settings associated with the life sciences experiments,
• (ii) a respective input value for each setting of the collection of settings, wherein the respective input value characterizes a respective experimental condition for the life sciences experiment, and
• (iii) a respective empirical value for an experimental outcome metric for the research domain;
• automatically generating, by the life sciences research platform, a plurality of machine learning models for the research domain, wherein each machine learning model is configured to predict a value for the experimental outcome metric, comprising, for each machine learning model of the plurality of machine learning models within the research domain:
• training a candidate machine learning model from the collection of settings associated with the life sciences experiments;
• and
• determining a performance measure for the candidate machine learning model based on a discrepancy between the respective predicted empirical value of the candidate machine learning model and a target output empirical value for the experimental outcome metric;
• automatically selecting, by the life sciences research platform, a final machine learning model from the plurality of machine learning models within the research domain based on the performance measures of the candidate machine learning models;
• and
• generating, by the life sciences research platform, a user interface presentation that is configured for querying potential experimental setups comprising respective input values for each setting in the collection of settings using the final machine learning model and that provides explainability data comprising contribution scores of the respective input values for each setting in the collection of settings to the value for the experimental outcome metric predicted by the final machine learning model,
• wherein the life sciences research platform generates a revised potential experimental setup that can improve the experimental outcome metric by modifying at least one input value of the potential experimental setups to indicate, based on the contribution scores, how the experimental outcome metric varies with different input values for a particular setting.