Relation path feature embedding based convolutional neural network method for drug discovery

Zhao, Di; Wang, Jian; Sang, Shengtian; Lin, Hongfei; Wen, Jiabin; Yang, Chunmei

doi:10.1186/s12911-019-0764-5

Cited by 10 publications

(12 citation statements)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The health hazards that ADEs may pose to individuals motivate the extensive work on the application of various computational methods for pharmacovigilance. Authors of this study have either used an open LBD [ 15 , 23 , 24 ] or a closed LBD [ 22 , 25 ] approach for the detection of drug/ADE pairs.…”

Section: Discussionmentioning

confidence: 99%

“…Ranking of LBD-generated hypotheses have been performed by Zhang et al [ 27 ] through a machine learning-based filter (lasso regression filter) and Rastegar-Mojarad et al [ 21 ] by using a binary classifier. Machine learning algorithms like logistic regression [ 22 , 23 , 29 ] and k-Nearest Neighbor (kNN) [ 29 ] have been incorporated in models proposed by authors in this review. Rather et al [ 40 ] integrated Word2vec, a neural network based algorithm, in their LBD approach and showed that the model was able to retrieve strong relationships which were not identified by UMLS.…”

Section: Discussionmentioning

confidence: 99%

“…Knowledge-graph models use graph theory to identify novel associations among various concepts. In their LBD approach for drug discovery, Zhao et al [ 22 ] constructed a biomedical knowledge graph based on semantic predications. A path ranking algorithm was then used to extract drug-disease relation path features.…”

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Literature-based discovery approaches for evidence-based healthcare: a systematic review

Cheerkoot-Jalim

Khedo

2021

Health Technol.

View full text Add to dashboard Cite

Purpose Literature-Based Discovery (LBD) is a text mining technique used to generate novel hypotheses from vast amounts of literature sources, by identifying links between concepts from disparate sources. One of the main areas where it has been predominantly applied is the healthcare domain, whereby promising results, in the form of novel hypotheses, have been reported. The purpose of this work was to conduct a systematic literature review of recent publications on LBD in the healthcare domain in order to assess the trends in the approaches used and to identify issues and challenges for such systems. Methods The review was conducted following the principles of the Kitchenham method. The selected studies have been scrutinized and the derived findings have been reported following the PRISMA guidelines. Results The review results reveal useful information regarding the application areas, the data sources considered, the approaches used, the performance in terms of accuracy and reliability and future research challenges. The results of this review will be beneficial to LBD researchers and other stakeholders in the healthcare domain, by providing them with useful insights on the approaches to adopt, data sources to consider, evaluation model to use and challenges to reflect on. Conclusion The synthesis of the results of this work has shed light on recent issues and challenges that drive new LBD models and provides avenues for their application in other diverse areas in the healthcare domain. To the best of our knowledge, no such recent review has been conducted.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Literature-based discovery approaches for evidence-based healthcare: a systematic review

Cheerkoot-Jalim

Khedo

2021

Health Technol.

View full text Add to dashboard Cite

show abstract

“…Various specially designed AI/ML models have been proposed for detecting novel drug indications. Here, we classify the ML applications for drug repositioning into the following three categories: (i) Similarity‐based methods that employ different types of classifiers like logistic regression, 305,306 SVM, 307–309 RF, 310,311 KNN, 312 and CNN, 313 (ii) feature vector‐based methods that utilize supervised 314–318 and semisupervised 319–321 learning algorithms, and (iii) network‐based methods that mainly use semisupervised learning algorithms (e.g., Laplacian regularized least square, 322–324 label propagation, 325 random walk, 326 and RF 310 ). We provide an in‐depth discussion of these three classes of AI‐based drug repositioning applications in the Supporting Information.…”

Section: Ai/ml Applications In Drug Discoverymentioning

confidence: 99%

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Vatansever

Schlessinger

Wacker

et al. 2020

Medicinal Research Reviews

216

View full text Add to dashboard Cite

Neurological disorders significantly outnumber diseases in other therapeutic areas. However, developing drugs for central nervous system (CNS) disorders remains the most challenging area in drug discovery, accompanied with the long timelines and high attrition rates. With the rapid growth of biomedical data enabled by advanced experimental technologies, artificial intelligence (AI) and machine learning (ML) have emerged as an indispensable tool to draw meaningful insights and improve decision making in drug discovery. Thanks to the advancements in AI and ML algorithms, now the AI/ML‐driven solutions have an unprecedented potential to accelerate the process of CNS drug discovery with better success rate. In this review, we comprehensively summarize AI/ML‐powered pharmaceutical discovery efforts and their implementations in the CNS area. After introducing the AI/ML models as well as the conceptualization and data preparation, we outline the applications of AI/ML technologies to several key procedures in drug discovery, including target identification, compound screening, hit/lead generation and optimization, drug response and synergy prediction, de novo drug design, and drug repurposing. We review the current state‐of‐the‐art of AI/ML‐guided CNS drug discovery, focusing on blood–brain barrier permeability prediction and implementation into therapeutic discovery for neurological diseases. Finally, we discuss the major challenges and limitations of current approaches and possible future directions that may provide resolutions to these difficulties.

show abstract

“…The current tasks of biomedical relation extraction mainly focus on the extraction of binary relations in single sentences, such as protein-protein interaction (PPI), chemical-protein interaction (CPI) and drug-drug interaction (DDI) [ 1 – 3 ]. It is crucial for biomedical relation extraction to automatically construct a knowledge graph, which supports a variety of downstream natural language processing (NLP) tasks such as drug discovery [ 4 ]. An obvious problem is that as the biomedical literature continues to grow, there is a large number of biomedical entities whose binary relations exist not only in a single sentence but also in cross-sentences.…”

Section: Introductionmentioning

confidence: 99%

Incorporating representation learning and multihead attention to improve biomedical cross-sentence n-ary relation extraction

et al. 2020

Self Cite

View full text Add to dashboard Cite

Background: Most biomedical information extraction focuses on binary relations within single sentences. However, extracting n-ary relations that span multiple sentences is in huge demand. At present, in the cross-sentence n-ary relation extraction task, the mainstream method not only relies heavily on syntactic parsing but also ignores prior knowledge. Results: In this paper, we propose a novel cross-sentence n-ary relation extraction method that utilizes the multihead attention and knowledge representation that is learned from the knowledge graph. Our model is built on self-attention, which can directly capture the relations between two words regardless of their syntactic relation. In addition, our method makes use of entity and relation information from the knowledge base to impose assistance while predicting the relation. Experiments on n-ary relation extraction show that combining context and knowledge representations can significantly improve the n-ary relation extraction performance. Meanwhile, we achieve comparable results with state-of-the-art methods. Conclusions: We explored a novel method for cross-sentence n-ary relation extraction. Unlike previous approaches, our methods operate directly on the sequence and learn how to model the internal structures of sentences. In addition, we introduce the knowledge representations learned from the knowledge graph into the cross-sentence n-ary relation extraction. Experiments based on knowledge representation learning show that entities and relations can be extracted in the knowledge graph, and coding this knowledge can provide consistent benefits.

show abstract

Relation path feature embedding based convolutional neural network method for drug discovery

Cited by 10 publications

References 27 publications

Literature-based discovery approaches for evidence-based healthcare: a systematic review

Literature-based discovery approaches for evidence-based healthcare: a systematic review

Artificial intelligence and machine learning‐aided drug discovery in central nervous system diseases: State‐of‐the‐arts and future directions

Incorporating representation learning and multihead attention to improve biomedical cross-sentence n-ary relation extraction

Contact Info

Product

Resources

About