Text Mining for Drug Discovery

Zheng, S. Q.; Dharssi, Shazia; Wu, Meng; Li, Jiao; Lu, Zhiyong

doi:10.1007/978-1-4939-9089-4_13

Cited by 43 publications

(21 citation statements)

References 64 publications

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“…Text mining combines information from thousands of documents and articles to deliver new meaning and possible answers to complex questions [80]. Text mining has been used prolifically in the medical field and in conjunction with data-driven drug repurposing approaches [81]. A typical biological text mining effort involves four steps: 1) information retrieval, including parsing of relevant information from large data sources; 2) biological name entity recognition, with identification of valuable biological concepts using controlled vocabularies, and the last two steps; 3) biological information extraction; and finally, 4) biological knowledge discovery, which involves extracting useful biological information and constructing a knowledge graph, a compilation of interlinked descriptions of objects [82].…”

Section: Gwas Based Gene Signatures For Repurposingmentioning

confidence: 99%

Integrative Omics for Informed Drug Repurposing: Targeting CNS Disorders

Shukla

Henkel

Alganem

et al. 2020

Preprint

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The treatment of CNS disorders, and in particular psychiatric illnesses, lacks disease-altering therapeutics for many conditions. This is likely due to regulatory challenges involving the high cost and slow-pace of drug development for CNS disorders as well as due to limited understanding of disease causality. Repurposing drugs for new indications have lower cost and shorter development timeline compared to that of de novo drug development. Historically, empirical drug-repurposing is a standard practice in psychiatry; however, recent advances in characterizing molecules with their structural and transcriptomic signatures along with ensemble of data analysis approaches, provides informed and cost-effective repurposing strategies that ameliorate the regulatory challenges. In addition, the potential to incorporate ontological approaches along with signature-based repurposing techniques addresses the various knowledge-based challenges associated with CNS drug development. In this review we primarily discuss signature-based in silico approaches to drug repurposing, and its integration with data science platforms for evidence-based drug repurposing. We contrast various in silico and empirical approaches and discuss possible avenues to improve the clinical relevance.These concepts provide a promising new translational avenue for developing new therapies for difficult to treat disorders, and offer the possibility of connecting drug discovery platforms and big data analytics with personalized disease signatures.3

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Section: Gwas Based Gene Signatures For Repurposingmentioning

confidence: 99%

Integrative Omics for Informed Drug Repurposing: Targeting CNS Disorders

Shukla

Henkel

Alganem

et al. 2020

Preprint

View full text Add to dashboard Cite

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“…For example, given a sentence "Steve Jobs was the co-founder of Apple Inc.", an employee relation instance can be identified between "Steve Jobs" and "Apple Inc.". Finding the relation between two named entities has a wide range of applications, e.g., clinical decision support [2], drug discovery [3] and economic management [4]. This task is seen as foundational to support other natural language processing (NLP) tasks, such as knowledge graph construction [5], question answering [6] and natural language understanding [7].…”

Section: Introductionmentioning

confidence: 99%

Entity Relation Extraction Based on Entity Indicators

et al. 2021

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Relation extraction aims to extract semantic relationships between two specified named entities in a sentence. Because a sentence often contains several named entity pairs, a neural network is easily bewildered when learning a relation representation without position and semantic information about the considered entity pair. In this paper, instead of learning an abstract representation from raw inputs, task-related entity indicators are designed to enable a deep neural network to concentrate on the task-relevant information. By implanting entity indicators into a relation instance, the neural network is effective for encoding syntactic and semantic information about a relation instance. Organized, structured and unified entity indicators can make the similarity between sentences that possess the same or similar entity pair and the internal symmetry of one sentence more obviously. In the experiment, a systemic analysis was conducted to evaluate the impact of entity indicators on relation extraction. This method has achieved state-of-the-art performance, exceeding the compared methods by more than 3.7%, 5.0% and 11.2% in F1 score on the ACE Chinese corpus, ACE English corpus and Chinese literature text corpus, respectively.

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“…Text mining played a significant role in advancing biomedical informatics in general [ 13 ] and computational drug research in particular [ 14 , 15 ]. Wu et al [ 16 ] developed computational models to retrieve drug-drug interaction and drug-gene interaction evidence from PubMed abstracts.…”

Section: Introductionmentioning

confidence: 99%

The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation

Gates¹,

Hamed²

2020

J Med Internet Res

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Background Driven by the COVID-19 pandemic and the dire need to discover an antiviral drug, we explored the landscape of the SARS-CoV-2 biomedical publications to identify potential treatments. Objective The aims of this study are to identify off-label drugs that may have benefits for the coronavirus disease pandemic, present a novel ranking algorithm called CovidX to recommend existing drugs for potential repurposing, and validate the literature-based outcome with drug knowledge available in clinical trials. Methods To achieve such objectives, we applied natural language processing techniques to identify drugs and linked entities (eg, disease, gene, protein, chemical compounds). When such entities are linked, they form a map that can be further explored using network science tools. The CovidX algorithm was based upon a notion that we called “diversity.” A diversity score for a given drug was calculated by measuring how “diverse” a drug is calculated using various biological entities (regardless of the cardinality of actual instances in each category). The algorithm validates the ranking and awards those drugs that are currently being investigated in open clinical trials. The rationale behind the open clinical trial is to provide a validating mechanism of the PubMed results. This ensures providing up to date evidence of the fast development of this disease. Results From the analyzed biomedical literature, the algorithm identified 30 possible drug candidates for repurposing, ranked them accordingly, and validated the ranking outcomes against evidence from clinical trials. The top 10 candidates according to our algorithm are hydroxychloroquine, azithromycin, chloroquine, ritonavir, losartan, remdesivir, favipiravir, methylprednisolone, rapamycin, and tilorone dihydrochloride. Conclusions The ranking shows both consistency and promise in identifying drugs that can be repurposed. We believe, however, the full treatment to be a multifaceted, adjuvant approach where multiple drugs may need to be taken at the same time.

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Text Mining for Drug Discovery

Cited by 43 publications

References 64 publications

Integrative Omics for Informed Drug Repurposing: Targeting CNS Disorders

Integrative Omics for Informed Drug Repurposing: Targeting CNS Disorders

Entity Relation Extraction Based on Entity Indicators

The Anatomy of the SARS-CoV-2 Biomedical Literature: Introducing the CovidX Network Algorithm for Drug Repurposing Recommendation

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