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Study diagnoses why causal methods fail in gene regulatory network inference

By PulseAugur Editorial · [2 sources] ·

A new study published on arXiv investigates the effectiveness of causal versus correlational methods for inferring gene regulatory networks from single-cell RNA sequencing data. Researchers found that while causal methods excel in ideal conditions, their advantages are neutralized by specific data pathologies like dropout and latent confounders. The study conducted 6,120 controlled experiments to analyze how seven different pathologies impact six inference methods, offering practical guidance for method development and application. AI

IMPACT Provides a nuanced understanding of when different gene regulatory network inference methods succeed or fail, offering actionable insights for method development and practical guidance.

RANK_REASON The cluster contains an academic paper detailing a controlled diagnostic study on gene regulatory network inference methods.

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Study diagnoses why causal methods fail in gene regulatory network inference

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The cluster contains an academic paper detailing a controlled diagnostic study on gene regulatory network inference methods.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Miguel Fernandez-de-Retana, Ruben Sanchez-Corcuera, Unai Zulaika, Aritz Bilbao-Jayo, Aitor Almeida ·

    When Does Gene Regulatory Network Inference Break? A Controlled Diagnostic Study of Causal and Correlational Methods on Single-Cell Data

    arXiv:2605.04930v1 Announce Type: new Abstract: Despite theoretical advantages, causal methods for Gene Regulatory Network (GRN) inference from single-cell RNA-seq data consistently fail to match or outperform correlation-based baselines in many realistic benchmarks, a persistent…

  2. arXiv stat.ML TIER_1 English(EN) · Aitor Almeida ·

    When Does Gene Regulatory Network Inference Break? A Controlled Diagnostic Study of Causal and Correlational Methods on Single-Cell Data

    Despite theoretical advantages, causal methods for Gene Regulatory Network (GRN) inference from single-cell RNA-seq data consistently fail to match or outperform correlation-based baselines in many realistic benchmarks, a persistent puzzle which casts doubt on the value of causal…

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