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      HeTDR: Drug repositioning based on heterogeneous networks and text mining

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          Summary

          Using existing knowledge to carry out drug-disease associations prediction is a vital method for drug repositioning. However, effectively fusing the biomedical text and biological network information is one of the great challenges for most current drug repositioning methods. In this study, we propose a drug repositioning method based on heterogeneous networks and text mining (HeTDR). This model can combine drug features from multiple drug-related networks, disease features from biomedical corpora with the known drug-disease associations network to predict the correlation scores between drug and disease. Experiments demonstrate that HeTDR has excellent performance that is superior to that of state-of-the-art models. We present the top 10 novel HeTDR-predicted approved drugs for five diseases and prove our model is capable of discovering potential candidate drugs for disease indications.

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          Highlights

          • We developed a novel DL-based method for drug repositioning (HeTDR)

          • HeTDR succeeds in fusing networks topology information and text mining information

          • HeTDR obtains high accuracy, excessing most state-of-the-art models

          • HeTDR could represent an algorithm integrating multiple sources of information

          The bigger picture

          Traditional drug discovery and development are often time consuming and high risk. Drug repositioning aims to expand existing indications or discover new targets by studying the approved drug compounds, thereby reducing the time, costs, and risk of drug development. We propose a novel method in drug repositioning based on heterogeneous networks and text mining (HeTDR), which combines drugs features from multiple networks and diseases features from biomedical corpora to predict the correlation scores between drugs and diseases. This prediction model has provided a potential solution for multiple information fusion and to exhibit accurate performance leading to the discovery of new drugs for indications. This algorithm could contribute a new idea to the acceleration and development of future drug repositioning by using computational methods and provide computer-aided guidance for biologists in clinical settings.

          Abstract

          Drug repositioning is a useful way to discover new drug candidates for curing diseases. However, integrating multiple networks and text mining information for drug repositioning is still a major challenge. We propose a drug repositioning method based on heterogeneous networks and text mining (HeTDR), which can combine drug features from multiple networks and disease features from biomedical corpora for drug repositioning. HeTDR obtains high accuracy in predicting drug-disease interactions and is capable of finding novel indications of approved drugs.

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          Most cited references64

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          Support-vector networks

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            Drug repurposing: progress, challenges and recommendations

            Given the high attrition rates, substantial costs and slow pace of new drug discovery and development, repurposing of 'old' drugs to treat both common and rare diseases is increasingly becoming an attractive proposition because it involves the use of de-risked compounds, with potentially lower overall development costs and shorter development timelines. Various data-driven and experimental approaches have been suggested for the identification of repurposable drug candidates; however, there are also major technological and regulatory challenges that need to be addressed. In this Review, we present approaches used for drug repurposing (also known as drug repositioning), discuss the challenges faced by the repurposing community and recommend innovative ways by which these challenges could be addressed to help realize the full potential of drug repurposing.
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              BioBERT: a pre-trained biomedical language representation model for biomedical text mining

              Abstract Motivation Biomedical text mining is becoming increasingly important as the number of biomedical documents rapidly grows. With the progress in natural language processing (NLP), extracting valuable information from biomedical literature has gained popularity among researchers, and deep learning has boosted the development of effective biomedical text mining models. However, directly applying the advancements in NLP to biomedical text mining often yields unsatisfactory results due to a word distribution shift from general domain corpora to biomedical corpora. In this article, we investigate how the recently introduced pre-trained language model BERT can be adapted for biomedical corpora. Results We introduce BioBERT (Bidirectional Encoder Representations from Transformers for Biomedical Text Mining), which is a domain-specific language representation model pre-trained on large-scale biomedical corpora. With almost the same architecture across tasks, BioBERT largely outperforms BERT and previous state-of-the-art models in a variety of biomedical text mining tasks when pre-trained on biomedical corpora. While BERT obtains performance comparable to that of previous state-of-the-art models, BioBERT significantly outperforms them on the following three representative biomedical text mining tasks: biomedical named entity recognition (0.62% F1 score improvement), biomedical relation extraction (2.80% F1 score improvement) and biomedical question answering (12.24% MRR improvement). Our analysis results show that pre-training BERT on biomedical corpora helps it to understand complex biomedical texts. Availability and implementation We make the pre-trained weights of BioBERT freely available at https://github.com/naver/biobert-pretrained, and the source code for fine-tuning BioBERT available at https://github.com/dmis-lab/biobert.
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                Author and article information

                Contributors
                Journal
                Patterns (N Y)
                Patterns (N Y)
                Patterns
                Elsevier
                2666-3899
                13 July 2021
                13 August 2021
                13 July 2021
                : 2
                : 8
                : 100307
                Affiliations
                [1 ]Department of Computer Science, Xiamen University, Xiamen 361005, China
                [2 ]National Institute for Data Science in Health and Medicine, Xiamen University, Xiamen 361005, China
                [3 ]Shenzhen Research Institute of Xiamen University, Shenzhen 518000, China
                [4 ]MindRank AI Ltd., Hangzhou, Zhejiang 311113, China
                [5 ]School of Information Science and Engineering, Hunan University, Changsha 410082, China
                Author notes
                []Corresponding author xrliu@ 123456xmu.edu.cn
                [6]

                Lead contact

                Article
                S2666-3899(21)00150-1 100307
                10.1016/j.patter.2021.100307
                8369234
                34430926
                94636e63-f813-40a2-9c48-e287a362e259
                © 2021 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                : 7 March 2021
                : 11 May 2021
                : 14 June 2021
                Categories
                Article

                drug repositioning,heterogeneous networks,text mining,drug-disease associations,feature representation

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