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1,248 claims from 785 papers are on the record. 45 have been checked so far; the other 1,203 have no check with a result yet.

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Status: Unchecked ×Keyword: interpretability ×Clear all

4 claims from 3 papers

1. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   DeepLoc 2.0: multi-label subcellular localization prediction using protein language models

   Thumuluri, Armenteros, Johansen, Nielsen and Winther · Nucleic Acids Research · 2022

   The authors update the DeepLoc tool to predict multiple subcellular locations per protein, using a protein language model, with better performance and interpretability, and release it as a webserver.

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   1. UncheckedDeepLoc 2.0, which uses a pre-trained protein language model, reports state-of-the-art performance in predicting where proteins sit in cells.“We achieve state-of-the-art performance in DeepLoc 2.0 by using a pre-trained protein language model.”
   2. UncheckedIn DeepLoc 2.0, the model's attention output along a protein sequence lines up well with where sorting signals are located.“We find that the attention output correlates well with the position of sorting signals.”

2. Biochemistry, Genetics and Molecular Biology › Machine Learning in Bioinformatics

   BERTology Meets Biology: Interpreting Attention in Protein Language Models

   Vig, Madani, Varshney, Xiong, Socher and Rajani · bioRxiv (Cold Spring Harbor Laboratory) · 2020

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   1. Unchecked“We show that attention: (1) captures the folding structure of proteins, connecting amino acids that are far apart in the underlying sequence, but spatially close in the three-dimensional structure, (2) targets binding sites, a key functional component of pro…

3. Computer Science › Artificial Intelligence Applications

   On the foundations of Earth foundation models

   Zhu, Xiong, Wang et al. · Communications Earth & Environment · 2026

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   1. Unchecked“Crafting the ideal Earth foundation model, we define eleven features which would allow such a foundation model to be beneficial for any geoscientific downstream application in an environmental- and human-centric manner.”