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New framework ORBIT probes complex interactions in protein fitness landscapes

By PulseAugur Editorial · [1 sources] ·

Researchers have developed ORBIT, a framework designed to analyze the complex interactions within protein fitness landscapes. This method distinguishes between the presence of interactions, how well representations can access them, and the functional recovery achieved. ORBIT was tested on synthetic landscapes and the GB1 fitness landscape, comparing various machine learning models including standard MLPs and a novel Residual Interaction Tokenization (RIT) method. While RIT showed improved pairwise accessibility at the token stage, deeper MLPs demonstrated better prediction and functional recovery for higher-order interactions. AI

IMPACT Introduces a novel method for analyzing complex biological data, potentially improving AI applications in bioinformatics and drug discovery.

RANK_REASON Academic paper detailing a new framework and experimental results. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework ORBIT probes complex interactions in protein fitness landscapes

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Academic paper detailing a new framework and experimental results. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Maryam Rahimimovassagh, Ivan Garibay, Niloofar Yousefi ·

    Beyond Tokens: Probing Higher-Order Epistasis in Learned Protein Representations

    arXiv:2608.24953v1 Announce Type: cross Abstract: Protein fitness landscapes contain nonlinear interactions in which mutation effects depend on other residues. We introduce ORBIT, an Order-Resolved Benchmarking of Interaction Transformations framework that separates interaction p…

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