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Status: Unchecked ×Keyword: self-supervised learning ×Clear all

5 claims from 2 papers

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

   ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning

   Elnaggar, Heinzinger, Dallago et al. · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2021

   The authors trained six language models on huge protein sequence sets and showed their embeddings, used alone, could predict protein structure and location, with the best beating methods that need sequence alignments.

   Unchecked3 claims
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   1. UncheckedSimplifying protein language model embeddings from unlabelled sequences showed they captured some biophysical features of proteins.“Dimensionality reduction revealed that the raw pLM-embeddings from unlabeled data captured some biophysical features of protein sequences.”
   2. UncheckedProtein language model embeddings alone, used as input, predicted secondary structure, cell location and membrane status with the stated accuracies.“We validated the advantage of using the embeddings as exclusive input for several subsequent tasks: (1) a per-residue (per-token) prediction of protein secondary structure (3-state accuracy Q3=81%-87%); (2) per-protein (pooling) predictions of protein sub-cellular location (ten-state accuracy: Q10=…”
   3. Unchecked“For secondary structure, the most informative embeddings (ProtT5) for the first time outperformed the state-of-the-art without multiple sequence alignments (MSAs) or evolutionary information thereby bypassing expensive database searches.”

2. Computer Science › Topic Modeling

   LinkBERT: Pretraining Language Models with Document Links

   Yasunaga, Leskovec and Liang · arXiv (Cornell University) · 2022

   Unchecked2 claims
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   1. Unchecked“We show that LinkBERT outperforms BERT on various downstream tasks across two domains: the general domain (pretrained on Wikipedia with hyperlinks) and biomedical domain (pretrained on PubMed with citation links).”
   2. Unchecked“LinkBERT is especially effective for multi-hop reasoning and few-shot QA (+5% absolute improvement on HotpotQA and TriviaQA), and our biomedical LinkBERT sets new states of the art on various BioNLP tasks (+7% on BioASQ and USMLE).”