Previous work established skip-gram word2vec models could be used to mine knowledge in the materials science literature for the discovery of thermoelectrics. Recent transformer architectures have shown great progress in language modeling and associated fine-tuned tasks, but they have yet to be adapted for drug discovery. We present a RoBERTa transformer-based method that extends the masked language token prediction using query-target conditioning to treat the specificity challenge. The transformer discovery method entails several benefits over the word2vec method including domain-specific (antiviral) analogy performance, negation handling, and flexible query analysis (specific) and is demonstrated on influenza drug discovery. To stimulate COVID-19 research, we release an influenza clinical trials and antiviral analogies dataset used in conjunction with the COVID-19 Open Research Dataset Challenge (CORD-19) literature dataset in the study. We examine k-shot fine-tuning to improve the downstream analogies performance as well as to mine analogies for model explainability. Further, the query-target analysis is verified in a forward chaining analysis against the influenza drug clinical trials dataset, before adapted for COVID-19 drugs (combinations and side-effects) and on-going clinical trials. In consideration of the present topic, we release the model, dataset, and code.
Scientific literature tends to grow as a function of funding and interest in a given field. Mining such literature can reveal trends that may not be immediately apparent. The CORD-19 corpus represents a growing corpus of scientific literature associated with COVID-19. We examined the intersection of a set of candidate therapeutics identified in a drug-repurposing study with temporal instances of the CORD-19 corpus to determine if it was possible to find and measure changes associated with them over time. We propose that the techniques we used could form the basis of a tool to pre-screen new ca…
SRM University, Andhra Pradesh（SRM大学（安得拉邦））；University of Huddersfield（哈德斯菲尔德大学）
The novel coronavirus disease (COVID-19) began in Wuhan, China, in late 2019 and to date has infected over 148M people worldwide, resulting in 3.12M deaths. On March 10, 2020, the World Health Organisation (WHO) declared it as a global pandemic. Many academicians and researchers started to publish papers describing the latest discoveries on covid-19. The large influx of publications made it hard for other researchers to go through a large amount of data and find the appropriate one that helps their research. So, the proposed model attempts to extract relavent titles from the large corpus of re…
Logical reasoning over Knowledge Graphs (KGs) is a fundamental technique that can provide efficient querying mechanism over large and incomplete databases. Current approaches employ spatial geometries such as boxes to learn query representations that encompass the answer entities and model the logical operations of projection and intersection. However, their geometry is restrictive and leads to non-smooth strict boundaries, which further results in ambiguous answer entities. Furthermore, previous works propose transformation tricks to handle unions which results in non-closure and, thus, canno…
DrugReason: Dynamic Multi-View Reasoning over Knowledge Graph and Language Evidence for Drug Repurposing
University of North Carolina at Chapel Hill（北卡罗来纳大学教堂山分校）；Stevens Institute of Technology（史蒂文斯理工学院）；Emory University（埃默里大学）；Massachusetts Institute of Technology（麻省理工学院）
WUSTL（华盛顿大学圣路易斯分校）；Tepper School of Business, CMU（卡内基梅隆大学泰珀商学院）
Active search is a learning paradigm for actively identifying as many members of a given class as possible. A critical target scenario is high-throughput screening for scientific discovery, such as drug or materials discovery. In this paper, we approach this problem in Bayesian decision framework. We first derive the Bayesian optimal policy under a natural utility, and establish a theoretical hardness of active search, proving that the optimal policy can not be approximated for any constant ratio. We also study the batch setting for the first time, where a batch of $b>1$ points can be queried …
We introduce interactive structure discovery, a generic framework that encompasses many interactive learning settings, including active learning, top-k item identification, interactive drug discovery, and others. We adapt a recently developed active learning algorithm of Tosh and Dasgupta (2017) for interactive structure discovery, and show that the new algorithm can be made noise-tolerant and enjoys favorable query complexity bounds.
In the absence of sufficient medication for COVID patients due to the increased demand, disused drugs have been employed or the doses of those available were modified by hospital pharmacists. Some evidences for the use of alternative drugs can be found in the existing scientific literature that could assist in such decisions. However, exploiting large corpus of documents in an efficient manner is not easy, since drugs may not appear explicitly related in the texts and could be mentioned under different brand names. Drugs4Covid combines word embedding techniques and semantic web technologies to…
Scaling Structure Aware Virtual Screening to Billions of Molecules with SPRINT
University of Pittsburgh（匹兹堡大学）；Carnegie Mellon University（卡内基梅隆大学）；Mohamed Bin Zayed University of Artificial Intelligence（穆罕默德·本·扎耶德人工智能大学）；Petuum Inc.（Petuum公司）
Georgia Institute of Technology（佐治亚理工学院）；Emory University（埃默里大学）
Clinical Cohort Studies (CCS), such as randomized clinical trials, are a great source of documented clinical research. Ideally, a clinical expert inspects these articles for exploratory analysis ranging from drug discovery for evaluating the efficacy of existing drugs in tackling emerging diseases to the first test of newly developed drugs. However, more than 100 articles are published daily on a single prevalent disease like COVID-19 in PubMed. As a result, it can take days for a physician to find articles and extract relevant information. Can we develop a system to sift through the long list…
DeepEvidence: Empowering Biomedical Discovery with Deep Knowledge Graph Research
Keiji AI；Institute of Scientific and Industrial Research, Osaka University（大阪大学科学工业研究所）；Bioinformatics Center, Institute for Chemical Research, Kyoto University（京都大学化学研究所生物信息中心）；Division of Intramural Research, National Library of Medicine, National Institutes of Health（国家卫生研究院生物医学图书馆内部研究部）；Department of Population Health Sciences, Weill Cornell Medicine（韦尔·科恩医学中心流行病学与健康科学系）；School of Computing and Data Science, University of Illinois Urbana-Champaign（伊利诺伊大学厄巴纳-香槟分校计算机与数据科学学院）
University of Texas at Austin（得克萨斯大学奥斯汀分校）；Peking University（北京大学）
The emergence of the novel COVID-19 pandemic has had a significant impact on global healthcare and the economy over the past few months. The virus's rapid widespread has led to a proliferation in biomedical research addressing the pandemic and its related topics. One of the essential Knowledge Discovery tools that could help the biomedical research community understand and eventually find a cure for COVID-19 are Knowledge Graphs. The CORD-19 dataset is a collection of publicly available full-text research articles that have been recently published on COVID-19 and coronavirus topics. Here, we u…
This paper presents Macquarie University's participation to the two most recent BioASQ Synergy Tasks (as per June 2022), and to the BioASQ10 Task~B (BioASQ10b), Phase~B. In these tasks, participating systems are expected to generate complex answers to biomedical questions, where the answers may contain more than one sentence. We apply query-focused extractive summarisation techniques. In particular, we follow a sentence classification-based approach that scores each candidate sentence associated to a question, and the $n$ highest-scoring sentences are returned as the answer. The Synergy Task c…
From Large Language Models to Knowledge Graphs for Biomarker Discovery in Cancer
Fraunhofer FIT（弗劳恩霍夫应用研究促进协会信息技术研究所）；University of Siegen（锡根大学）；University of Cologne（科隆大学）；University Hospital Cologne（科隆大学医院）；ZBMED - Information Center for Life Sciences（ZBMED生命科学信息中心）；RWTH Aachen University（亚琛工业大学）
