J
Jason Liu
Researcher at Facebook
Publications - 9
Citations - 1342
Jason Liu is an academic researcher from Facebook. The author has contributed to research in topics: Language model & Type inference. The author has an hindex of 7, co-authored 9 publications receiving 269 citations.
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Journal ArticleDOI
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences
Alexander Rives,Alexander Rives,Joshua Meier,Tom Sercu,Siddharth Goyal,Zeming Lin,Jason Liu,Demi Guo,Myle Ott,C. Lawrence Zitnick,Jerry Ma,Jerry Ma,Rob Fergus +12 more
TL;DR: This paper used unsupervised learning to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity, which contains information about biological properties in its representations.
Posted ContentDOI
MSA Transformer
Roshan Rao,Jason Liu,Robert Verkuil,Joshua Meier,John Canny,Pieter Abbeel,Tom Sercu,Alexander Rives +7 more
TL;DR: This article introduced a protein language model which takes as input a set of sequences in the form of a multiple sequence alignment and interleaves row and column attention across the input sequences and is trained with a variant of the masked language modeling objective across many protein families.
Posted ContentDOI
Language models enable zero-shot prediction of the effects of mutations on protein function
Joshua Meier,Joshua Meier,Roshan Rao,Robert Verkuil,Jason Liu,Tom Sercu,Alexander Rives,Alexander Rives +7 more
TL;DR: This paper used zero-shot inference to capture the functional effects of sequence variation, and achieved state-of-the-art performance on protein language models without any supervision from experimental data or additional training.
Posted ContentDOI
Language models enable zero-shot prediction of the effects of mutations on protein function
TL;DR: This article used zero-shot inference to capture the functional effects of sequence variation, and achieved state-of-the-art performance on protein language models without any supervision from experimental data or additional training.
Posted Content
TypeWriter: Neural Type Prediction with Search-based Validation
TL;DR: TypeWriter is presented, the first combination of probabilistic type prediction with search-based refinement of predicted types, which can fully annotate between 14% to 44% of the files in a randomly selected corpus, while ensuring type correctness.