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Recent advances in biomedical literature mining.

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TLDR
The goal of this paper is to provide a review of the recent advances in BLM from both communities and inspire new research directions.
Abstract
The recent years have witnessed a rapid increase in the number of scientific articles in biomedical domain. These literature are mostly available and readily accessible in electronic format. The domain knowledge hidden in them is critical for biomedical research and applications, which makes biomedical literature mining (BLM) techniques highly demanding. Numerous efforts have been made on this topic from both biomedical informatics (BMI) and computer science (CS) communities. The BMI community focuses more on the concrete application problems and thus prefer more interpretable and descriptive methods, while the CS community chases more on superior performance and generalization ability, thus more sophisticated and universal models are developed. The goal of this paper is to provide a review of the recent advances in BLM from both communities and inspire new research directions.

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Remdesivir as a possible therapeutic option for the COVID-19

TL;DR: This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record.
Journal ArticleDOI

Artificial Intelligence in Action: Addressing the COVID-19 Pandemic with Natural Language Processing.

TL;DR: This review surveys approximately 150 NLP studies and more than 50 systems and datasets addressing the COVID-19 pandemic and describes work that directly addresses aspects of the pandemic through four additional tasks: topic modeling, sentiment and emotion analysis, case load forecasting and misinformation detection.
Journal ArticleDOI

DrugShot: querying biomedical search terms to retrieve prioritized lists of small molecules

TL;DR: DrugShot as mentioned in this paper is a web-based server application and an Appyter that enables users to enter any biomedical search term into a simple input form to receive ranked lists of drugs and other small molecules based on their relevance to the search term.
Journal ArticleDOI

DrugShot: querying biomedical search terms to retrieve prioritized lists of small molecules

TL;DR: DrugShot as mentioned in this paper is a web-based server application and an Appyter that enables users to enter any biomedical search term into a simple input form to receive ranked lists of drugs and other small molecules based on their relevance to the search term.
Journal ArticleDOI

AI-based language models powering drug discovery and development.

TL;DR: In this paper, the authors highlight the potential role of AI-powered LMs for developing new treatments for Coronavirus 2019 (COVID-19) strategies, including drug repurposing, which can be extrapolated to other infectious diseases that have the potential to cause pandemics.
References
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Journal ArticleDOI

Long short-term memory

TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Proceedings Article

Attention is All you Need

TL;DR: This paper proposed a simple network architecture based solely on an attention mechanism, dispensing with recurrence and convolutions entirely and achieved state-of-the-art performance on English-to-French translation.
Journal ArticleDOI

Deep learning

TL;DR: Deep learning is making major advances in solving problems that have resisted the best attempts of the artificial intelligence community for many years, and will have many more successes in the near future because it requires very little engineering by hand and can easily take advantage of increases in the amount of available computation and data.
Book

Deep Learning

TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Proceedings ArticleDOI

Convolutional Neural Networks for Sentence Classification

TL;DR: The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification, and are proposed to allow for the use of both task-specific and static vectors.
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