GOBIの紹介: 複雑系における因果的相互作用を推論するための画期的な計算パッケージ(Introducing GOBI: A Breakthrough Computational Package for Inferring Causal Interactions in Complex Systems)

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2023-07-25 韓国基礎科学研究院(IBS)

◆韓国の基礎科学研究所(IBS)の研究者たちは、ダイナミックな自然システムのメカニズムを解明するために因果関係の正確な特定が重要であると認識しました。そこで彼らは「General Ode Based Inference(GOBI)」と呼ばれる革新的なコンピューターパッケージを開発しました。これは、一般的な単調なODEモデルを使って時間系列データを再現するためのテスト条件を導入することで、既存の推論方法の制限を克服しています。
◆GOBIは従来の手法よりも精度が高く、分子から人口までのさまざまなネットワークで因果関係を推論できます。また、直接的な因果関係と間接的な因果関係を区別できるため、ノイズのあるデータでも信頼性があります。GOBIは生物学や生態学、流行病学など、多くの分野で新たな洞察をもたらすことが期待されています。

<関連情報>

一般的なモデルに基づく因果推論手法が同期の呪いと間接効果を克服する A general model-based causal inference method overcomes the curse of synchrony and indirect effect

Se Ho Park,Seokmin Ha & Jae Kyoung Kim
Nature Communications  Published:24 July 2023
DOI:https://doi.org/10.1038/s41467-023-39983-4

figure 1

Abstract

To identify causation, model-free inference methods, such as Granger Causality, have been widely used due to their flexibility. However, they have difficulty distinguishing synchrony and indirect effects from direct causation, leading to false predictions. To overcome this, model-based inference methods that test the reproducibility of data with a specific mechanistic model to infer causality were developed. However, they can only be applied to systems described by a specific model, greatly limiting their applicability. Here, we address this limitation by deriving an easily testable condition for a general monotonic ODE model to reproduce time-series data. We built a user-friendly computational package, General ODE-Based Inference (GOBI), which is applicable to nearly any monotonic system with positive and negative regulations described by ODE. GOBI successfully inferred positive and negative regulations in various networks at both the molecular and population levels, unlike existing model-free methods. Thus, this accurate and broadly applicable inference method is a powerful tool for understanding complex dynamical systems.

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