Interaction Network Analysis Using Semantic Similarity Based on Translation Embeddings

Biomedical knowledge graphs such as STITCH, SIDER, and Drugbank provide the basis for the discovery of associations between biomedical entities, e.g., interactions between drugs and targets. Link prediction is a paramount task and represents a building block for supporting knowledge discovery. Although several approaches have been proposed for effectively predicting links, the role of semantics has not been studied in depth. In this work, we tackle the problem of discovering interactions between drugs and targets, and propose SimTransE, a machine learning-based approach that solves this problem effectively. SimTransE relies on translating embeddings to model drug-target interactions and values of similarity across them. Grounded on the vectorial representation of drug-target interactions, SimTransE is able to discover novel drug-target interactions. We empirically study SimTransE using state-of-the-art benchmarks and approaches. Experimental results suggest that SimTransE is competitive with the state of the art, representing, thus, an effective alternative for knowledge discovery in the biomedical domain

A Study on Drug Repositioning for Rare Diseases based on Biological Pathways

학위논문 (석사) -- 서울대학교 대학원 : 치의학대학원 치의과학과, 2020. 8. 김홍기.서론: 본 연구는 생물학적 패스웨이를 활용하여 희귀질환의 신약재창출 방법론 연구를 목적으로 한다. 전 세계적으로 7,000여 개의 희귀질환이 존재하지만, 치료제는 약 5% 정도만 존재해 더 많은 연구가 필요하다. 희귀질환 치료제 연구에는 전통적인 신약개발 연구보다는 이미 승인된 약물의 새로운 의학적 용도를 찾는 신약재창출이 시간과 비용이 줄어 대안이 될 수 있다. 생물학적 패스웨이는 생체요소 간의 상호작용을 상세히 설명해둔 생물학적 심층 지식으로 유전자들의 정보를 유기적으로 바라볼 때 사용된다. 따라서 신약재창출을 위해 생물학적 패스웨이는 활용하기에 적합하다. 희귀질환의 신약재창출 약물 후보를 찾기 위해 약물관련 유전자들의 정보와 희귀질환 관련 유전자정보를 분석하여 공통 생물학적 패스웨이 목록을 활용한다. 공통 생물학적 패스웨이로 만들어진 희귀질환과 약물의 유사도를 계산하여 희귀질환-약물 후보목록을 만든다. 방법: 희귀질환 유전자정보 데이터베이스 Panel의 희귀질환 309개와 유의미한 관련성을 가진 유전자정보를 활용하였다. 약물 데이터베이스로 DRUGBANK를 사용하였으며, 1888개의 승인된 약물과 관련된 유전자정보를 사용하였다. 패스웨이 데이터베이스로 Reactome에서 제공하는 분석 도구를 사용하여 희귀질환과 약물에 관련된 유전자 목록의 생물학적 패스웨이를 FDR 값을 기준으로 각각 수집하였다. 수집한 생물학적 패스웨이들 중 희귀질환과 약물에 공통으로 관련된 생물학적 패스웨이는 1883개로, 희귀질환과 약물의 유사도를 확인을 위해 활용된다. 희귀질환과 약물의 유사도는 FDR값을 벡터화하여 유클리디안 유사도로 계산하였다. 결과: 본 연구 방법을 통해 희귀질환-약물 후보목록을 만들었다. 희귀질환–약물의 유사도 결과로 나온 값이 작은 값일수록 서로 가까운 거리에 존재한다고 설명할 수 있다. 따라서 유사도 값은 희귀질환의 신약재창출 후보가 될 가능성을 나타낸다. 이를 확인하기 위해 FDR 승인되어 희귀질환 치료제로 쓰이는 약 정보와 값을 비교하였다. Lomitapide 약물은 Homozygous familial hypercholesterolemia 질병 치료제로, 유사도 값이 2.8로 309개의 희귀질환 중 34번째로 약물-희귀질환 목록에 Familial hypercholesterolaemia targeted panel의 이름으로 존재했다. 희귀질환-패스웨이-약물로 분석한 결과가 희귀질환-유전자-약물의 관계보다 유의미한 결과라는 것을 Thalidomide 약을 통해 비교해보았다. 희귀질환-패스웨이-약물에서 Thalidomide 치료제가 어떤 희귀질환에 관련성이높은지를 순서대로 볼 수 있었고, Bladder cancer pertinent cancer susceptibility라는 희귀질환이 가장 가까운 것으로 확인되었고, 관련 연구가 진행되었었음을 확인하였다. 결론: 희귀질환-약물 목록이 희귀질환 치료제와의 비교를 통해 연관성이 있다는 것을 확인하였고, 우선순위목록을 통해 얼마나 연관성이 있는지 알 수 있었다. 또한, 희귀질환-유전자-약물의 관계보다 희귀질환-패스웨이-약물이 더 많은 신약재창출 가능성을 가진 정보라는 것을 알 수 있었다. 따라서 희귀질환 뿐만 아니라, 다른 질병의 관련 유전자 정보를 활용하여 생물학적 패스웨이를 추출하고, 이를 신약재창출 후보를 정렬하여 알 수 있도록 기대해 볼 수 있다.Introduction: The purpose of this study is to utilize biological pathway data for rare disease drug repositioning. There are more than 7,000 rare diseases worldwide, but there is only treatment for 5% of these diseases. While there is a great need for treatments, traditional drug development is a very time consuming and costly process. For rare disease treatment, drug repositioning can potentially be a quicker and cheaper alternative. Biological pathway data describe the interaction between biological elements in detail and can be used to analyze gene data from a wider perspective. Therefore, it is hypothesized that they are suitable to use in drug repositioning. In this study, a common biological pathway list is generated from drug-related and rare disease-related gene data to find new drug candidates for rare diseases. Using the common pathway list and rare disease-drug similarity, a rare disease-drug candidate list is generated. Methods: 309 rare diseases from the Genomics England PanelApp is utilized with the relevant genes. 1,888 approved drugs and related genetic information is used from DrugBank. Using analysis tools provided by Reactome, biological pathways relevant to the rare disease-gene and drug-gene lists were collected based on FDR values. Among the collected biological pathways, there are 1,883 biological pathways commonly associated with the rare diseases and drugs, which are then used to calculate the similarity between the rare diseases and drugs. The Euclidean similarity of the rare diseases and drugs are calculated by vectorizing the FDR values. Results: Through this study, a rare disease-drug candidate list was generated. In the list, it can be interpreted that the smaller the value between a rare disease and drug is the more similar they are. Therefore, the more similar a rare disease and drug is, the more likely it is to be a candidate for rare disease drug repositioning. The results were compared with existing approved drugs used to treat rare diseases, for evaluation. Lomitapide is a drug used to treat Homozygous familial hypercholesterolemia. In the drug-rare disease list it has a similarity value of 2.8 with its PanelApp equivalent disease, which is rank 34 out of 309 rare diseases. The rare disease-pathway-drug results were also compared with the rare disease-gene-drug results with the drug, Thalidomide. In the rare disease-pathway-gene results, it is observed that Bladder cancer pertinent cancer susceptibility was the closest disease to Thalidomide, which coincides with recent literature. Discussion: From the results, it can be confirmed that the rare disease-drug list was relevant with existing rare disease treatments and that this relevance can also be measured. In addition, it is found that rare disease-pathway-drug associations are more applicable to drug repositioning than rare disease-gene-drug associations. Finally, it is believed that biological pathways can be used not just for rare diseases but also for finding drug repositioning candidates in common diseases.Ⅰ. 서 론 1 1. 연구의 필요성 1 2. 신약재창출 4 3. 연구 목적 9 Ⅱ. 연구재료 및 방법 10 1. 연구 재료 10 1) 희귀질환 데이터베이스 10 2) 약물 데이터베이스 11 3) 생물학적 패스웨이 데이터베이스 12 2. 방법 15 1) 전처리과정 15 2) 희귀질환약물의 공통 생물학적 패스웨이 21 3) 거리계산법 23 Ⅲ. 결론 27 1. 희귀질환-약물 유사도 27 2. 신약재창출 약물 후보 28 Ⅳ. 고찰 39 참고문헌 41 Abstract 45Maste

Strategies for Managing Linked Enterprise Data

Data, information and knowledge become key assets of our 21st century economy. As a result, data and knowledge management become key tasks with regard to sustainable development and business success. Often, knowledge is not explicitly represented residing in the minds of people or scattered among a variety of data sources. Knowledge is inherently associated with semantics that conveys its meaning to a human or machine agent. The Linked Data concept facilitates the semantic integration of heterogeneous data sources. However, we still lack an effective knowledge integration strategy applicable to enterprise scenarios, which balances between large amounts of data stored in legacy information systems and data lakes as well as tailored domain specific ontologies that formally describe real-world concepts. In this thesis we investigate strategies for managing linked enterprise data analyzing how actionable knowledge can be derived from enterprise data leveraging knowledge graphs. Actionable knowledge provides valuable insights, supports decision makers with clear interpretable arguments, and keeps its inference processes explainable. The benefits of employing actionable knowledge and its coherent management strategy span from a holistic semantic representation layer of enterprise data, i.e., representing numerous data sources as one, consistent, and integrated knowledge source, to unified interaction mechanisms with other systems that are able to effectively and efficiently leverage such an actionable knowledge. Several challenges have to be addressed on different conceptual levels pursuing this goal, i.e., means for representing knowledge, semantic data integration of raw data sources and subsequent knowledge extraction, communication interfaces, and implementation. In order to tackle those challenges we present the concept of Enterprise Knowledge Graphs (EKGs), describe their characteristics and advantages compared to existing approaches. We study each challenge with regard to using EKGs and demonstrate their efficiency. In particular, EKGs are able to reduce the semantic data integration effort when processing large-scale heterogeneous datasets. Then, having built a consistent logical integration layer with heterogeneity behind the scenes, EKGs unify query processing and enable effective communication interfaces for other enterprise systems. The achieved results allow us to conclude that strategies for managing linked enterprise data based on EKGs exhibit reasonable performance, comply with enterprise requirements, and ensure integrated data and knowledge management throughout its life cycle

A Framework for Semantic Similarity Measures to enhance Knowledge Graph Quality

Precisely determining similarity values among real-world entities becomes a building block for data driven tasks, e.g., ranking, relation discovery or integration. Semantic Web and Linked Data initiatives have promoted the publication of large semi-structured datasets in form of knowledge graphs. Knowledge graphs encode semantics that describes resources in terms of several aspects or resource characteristics, e.g., neighbors, class hierarchies or attributes. Existing similarity measures take into account these aspects in isolation, which may prevent them from delivering accurate similarity values. In this thesis, the relevant resource characteristics to determine accurately similarity values are identified and considered in a cumulative way in a framework of four similarity measures. Additionally, the impact of considering these resource characteristics during the computation of similarity values is analyzed in three data-driven tasks for the enhancement of knowledge graph quality. First, according to the identified resource characteristics, new similarity measures able to combine two or more of them are described. In total four similarity measures are presented in an evolutionary order. While the first three similarity measures, OnSim, IC-OnSim and GADES, combine the resource characteristics according to a human defined aggregation function, the last one, GARUM, makes use of a machine learning regression approach to determine the relevance of each resource characteristic during the computation of the similarity. Second, the suitability of each measure for real-time applications is studied by means of a theoretical and an empirical comparison. The theoretical comparison consists on a study of the worst case computational complexity of each similarity measure. The empirical comparison is based on the execution times of the different similarity measures in two third-party benchmarks involving the comparison of semantically annotated entities. Ultimately, the impact of the described similarity measures is shown in three data-driven tasks for the enhancement of knowledge graph quality: relation discovery, dataset integration and evolution analysis of annotation datasets. Empirical results show that relation discovery and dataset integration tasks obtain better results when considering semantics encoded in semantic similarity measures. Further, using semantic similarity measures in the evolution analysis tasks allows for defining new informative metrics able to give an overview of the evolution of the whole annotation set, instead of the individual annotations like state-of-the-art evolution analysis frameworks