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On the Unreported-Profile-is-Negative Assumption for Predictive Cheminformatics.

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    Assuming unreported compound-target binding profiles are negative can harm predictive model accuracy. Recovering these unknown profiles improves machine learning performance, especially with a joint profile recovery and model learning framework.

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    Area of Science:

    • Cheminformatics
    • Machine Learning
    • Bioinformatics

    Background:

    • Compound-target binding profiles are crucial data in cheminformatics research.
    • Many repositories only provide positive binding profiles, leading to the assumption that unreported profiles are negative.

    Purpose of the Study:

    • To evaluate the effectiveness of assuming unreported binding profiles are negative.
    • To demonstrate the impact of this assumption on predictive model performance.
    • To introduce a novel framework for improving predictive models by recovering missing binding profile data.

    Main Methods:

    • Utilizing compound-target binding profiles as features for predictive model training.
    • Empirically assessing prediction performance degradation when the negative profile assumption fails.
    • Implementing a framework for joint profile recovery and predictive model learning.
    • Applying matrix recovery techniques to address the missing feature problem.

    Main Results:

    • Prediction performance significantly degrades when the assumption of unreported profiles being negative is incorrect.
    • Explicit recovery of unreported binding profiles enhances predictive model performance.
    • The proposed joint recovery and learning framework yields further performance improvements.

    Conclusions:

    • The assumption that unreported compound-target binding profiles are negative is often ineffective and can hinder predictive accuracy.
    • Recovering these unknown profiles is essential for building robust predictive models in cheminformatics.
    • This study introduces 'Learning with Positive and Unknown Features,' a new challenge in machine learning for handling incomplete datasets.