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Article summary:
1. Unsupervised learning can be used to train a deep contextual language model on 86 billion amino acids across 250 million protein sequences spanning evolutionary diversity.
2. The resulting model contains information about biological properties in its representations, which can be used for remote homology detection, prediction of secondary structure, long-range residue–residue contacts, and mutational effect.
3. Representation learning produces features that generalize across a range of applications, enabling state-of-the-art supervised prediction of mutational effect and secondary structure and improving state-of-the-art features for long-range contact prediction.
Article analysis:
The article is generally reliable and trustworthy as it provides evidence for the claims made throughout the text. The authors provide detailed descriptions of their methods and results, as well as references to relevant literature to support their arguments. Furthermore, the authors acknowledge potential limitations of their study such as the lack of prior knowledge in the unsupervised learning process and the potential bias in the data set used for training.
However, there are some points that could have been explored further or discussed more thoroughly. For example, while the authors mention that unsupervised representation learning enables state-of-the-art supervised prediction of mutational effect and secondary structure, they do not discuss how this could be applied in practice or what implications this could have for research in biology or medicine. Additionally, while they mention potential biases in their data set due to evolutionary selection pressures, they do not explore this further or discuss any possible counterarguments or alternative explanations for their findings.
In conclusion, overall the article is reliable and trustworthy but there are some points that could have been explored further or discussed more thoroughly to provide a more comprehensive overview of the topic at hand.