Knowledge-based Biomedical Data Science 2019

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.

[1]  Pabitra Mitra,et al.  Relation Prediction of Co-Morbid Diseases Using Knowledge Graph Completion , 2019, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[2]  Akira R. Kinjo,et al.  Neuro-symbolic representation learning on biological knowledge graphs , 2016, Bioinform..

[3]  Theodosia Togia,et al.  Constructing large scale biomedical knowledge bases from scratch with rapid annotation of interpretable patterns , 2019, BioNLP@ACL.

[4]  Samina Raza Abidi,et al.  Investigating Plausible Reasoning Over Knowledge Graphs for Semantics-Based Health Data Analytics , 2018, 2018 IEEE 27th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE).

[5]  Jing Xie,et al.  Medical Knowledge Embedding Based on Recursive Neural Network for Multi-Disease Diagnosis , 2020, Artif. Intell. Medicine.

[6]  Stefan Decker,et al.  Drug-Drug Interaction Prediction Based on Knowledge Graph Embeddings and Convolutional-LSTM Network , 2019, BCB.

[7]  Haofen Wang,et al.  On building a diabetes centric knowledge base via mining the web , 2019, BMC Medical Informatics and Decision Making.

[8]  Atul J. Butte,et al.  Integrating biomedical research and electronic health records to create knowledge-based biologically meaningful machine-readable embeddings , 2019, Nature Communications.

[9]  Srinivasan Parthasarathy,et al.  Graph embedding on biomedical networks: methods, applications and evaluations , 2019, Bioinform..

[10]  Lawrence Hunter,et al.  OWL-NETS: Transforming OWL representations for improved network inference , 2018, PSB.

[11]  Tianyong Hao,et al.  T-Know: a Knowledge Graph-based Question Answering and Infor-mation Retrieval System for Traditional Chinese Medicine , 2018, COLING.

[12]  Rob Knight,et al.  Challenges in the construction of knowledge bases for human microbiome-disease associations , 2019, Microbiome.

[13]  Karamarie Fecho,et al.  ROBOKOP: an abstraction layer and user interface for knowledge graphs to support question answering , 2019, Bioinform..

[14]  David Koslicki,et al.  Leveraging Distributed Biomedical Knowledge Sources to Discover Novel Uses for Known Drugs , 2019, bioRxiv.

[15]  Nicole Tourigny,et al.  Bio2RDF: Towards a mashup to build bioinformatics knowledge systems , 2008, J. Biomed. Informatics.

[16]  Ignacio J Tripodi,et al.  Applying knowledge-driven mechanistic inference to toxicogenomics. , 2020, Toxicology in vitro : an international journal published in association with BIBRA.

[17]  Juan Martínez-Romo,et al.  Co-occurrence graphs for word sense disambiguation in the biomedical domain , 2018, Artif. Intell. Medicine.

[18]  Oguz Dikenelli,et al.  Evaluation of knowledge graph embedding approaches for drug-drug interaction prediction using Linked Open Data , 2018, SWAT4LS.

[19]  Xin Gao,et al.  OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction , 2018, Bioinform..

[20]  Xiaoxia Liu,et al.  SemaTyP: a knowledge graph based literature mining method for drug discovery , 2018, BMC Bioinformatics.

[21]  Yonghong Xie,et al.  Personalized Diagnostic Modal Discovery of Traditional Chinese Medicine Knowledge Graph , 2018, 2018 14th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).

[22]  Niloy Ganguly,et al.  Incorporating Domain Knowledge into Medical NLI using Knowledge Graphs , 2019, EMNLP.

[23]  Ting Wang,et al.  Using a knowledge graph for hypernymy detection between Chinese symptoms , 2018, 2018 Tenth International Conference on Advanced Computational Intelligence (ICACI).

[24]  Larry Wright,et al.  Overview and Utilization of the NCI Thesaurus , 2004, Comparative and functional genomics.

[25]  Hongfei Lin,et al.  GrEDeL: A Knowledge Graph Embedding Based Method for Drug Discovery From Biomedical Literatures , 2019, IEEE Access.

[26]  Xi Chen,et al.  Long-tail Relation Extraction via Knowledge Graph Embeddings and Graph Convolution Networks , 2019, NAACL.