Open AccessPosted Content
Knowledge-based Biomedical Data Science 2019.
TL;DR: The progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs is surveyed.
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Abstract: Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.
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Graph representation learning in biomedicine and healthcare
TL;DR: It is argued that graph representation learning will keep pushing forward machine learning for biomedicine and healthcare applications, including the identification of genetic variants underlying complex traits, the disentanglement of single-cell behaviours and their effects on health, the assistance of patients in diagnosis and treatment, and the development of safe and effective medicines.
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Democratizing knowledge representation with BioCypher.
Sebastian Lobentanzer,Patrick Aloy,Jan Baumbach,Balazs Bohar,Katharina Danhauser,Tunca Dougan,Johann Dreo,Ian Dunham,Adrià Fernández-Torras,Benjamin M. Gyori,Michael Hartung,Charles Tapley Hoyt,Christoph Klein,Tamas Korcsmaros,Andreas Maier,Matthias Mann,David Ochoa,Elena Pareja-Lorente,Martin Preusse,Niklas Probul,Benno Schwikowski,Bunyamin Sen,Maximilian T. Strauss,D. Turei,Erva Ulusoy,Judith A. H. Wodke,Julio Saez-Rodriguez +26 more
TL;DR: This work standardises the framework of knowledge graph creation in BioCypher, a FAIR (findable, accessible, interoperable, reusable) framework to transparently build biomedical knowledge graphs while preserving provenances of the source data.
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The effect of hydroxyapatite nanoparticles on wettability and brine-oil interfacial tension as enhance oil recovery mechanisms
Eugene N. Ngouangna,Mohd Zaidi Jaafar,Mnam Norddin,Agi Augustine,Abdul Rahim Risal,Stanley Chinedu Mamah,Jeffrey O. Oseh +6 more
TL;DR: In this paper , a novel concept of utilizing nanoparticles (NPs) to boost oil recovery and reduce entrapped oil in hydrocarbon reservoirs is explored, and the use of nanofluids (NFs) flooding to change wettability and reduce interfacial tension between oil and water has been shown to be highly effective in experiments.
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A survey of unmanned aerial vehicle flight data anomaly detection: Technologies, applications, and future directions
TL;DR: Several UAV flight data simulation softwares are presented based on a brief presentation of the basic concepts of anomalies, the contents of UAVFlight data, and the public datasets for flight data anomaly detection.
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Creating an Ignorance-Base: Exploring Known Unknowns in the Scientific Literature
Mayla Boguslav,Nourah M. Salem,Elizabeth K. White,Katherine J. Sullivan,Stephanie P. Araki,Michael Bada,Teri L. Hernandez,Sonia M. Leach,Lawrence Hunter +8 more
TL;DR: In this article , a knowledge base of unknowns was created by combining classifiers to recognize ignorance statements and biomedical concepts over the prenatal nutrition literature, which can be used to trace a given topic or experimental result in search of open questions and new avenues for exploration.
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References
Graph embedding on biomedical networks: methods, applications and evaluations.
Xiang Yue,Zhen Wang,Jingong Huang,Srinivasan Parthasarathy,Soheil Moosavinasab,Yungui Huang,Simon Lin,Wen Zhang,Ping Zhang,Huan Sun +9 more
TL;DR: Xiang et al. as mentioned in this paper evaluated graph embedding methods on biomedical networks and found that the learned embeddings can be treated as complementary representations for the biological features, and provided general guidelines for properly selecting graph embeding methods and setting their hyper-parameters for different biomedical tasks.
358
Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks
Ningyu Zhang,Shumin Deng,Zhanlin Sun,Guanying Wang,Xi Chen,Wei Zhang,Huajun Chen +6 more
- 01 Jun 2019
TL;DR: This work proposes to leverage implicit relational knowledge among class labels from knowledge graph embeddings and learn explicit relational knowledge using graph convolution networks and integrates that relational knowledge into relation extraction model by coarse-to-fine knowledge-aware attention mechanism.
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Neuro-symbolic representation learning on biological knowledge graphs.
Mona Alshahrani,Mohammad Asif Khan,Omar Maddouri,Omar Maddouri,Akira R. Kinjo,Núria Queralt-Rosinach,Robert Hoehndorf +6 more
TL;DR: This work develops a novel method for feature learning on biological knowledge graphs that combines symbolic methods, in particular knowledge representation using symbolic logic and automated reasoning, with neural networks to generate embeddings of nodes that encode for related information within knowledge graphs.
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Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network
Md. Rezaul Karim,Michael Cochez,Joao Jares,Mamtaz Uddin,Oya Beyan,Stefan Decker +5 more
- 04 Sep 2019
TL;DR: This work uses 12,000 drug features from DrugBank, PharmGKB, and KEGG drugs, which are integrated using Knowledge Graphs and finds that the best performing combination was a ComplEx embedding method creating using PyTorch-BigGraph with a Convolutional-LSTM network and classic machine learning-based prediction models.
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•Posted Content
Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network
TL;DR: In this paper, the authors proposed a new ML approach for predicting drug-drug interactions based on multiple data sources using knowledge graph (KGs) to deal with skewness in the data.
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