Drug-Target Interaction Prediction Using Semantic Similarity and Edge Partitioning

Palma, Guillermo; Vidal, María-Esther; Raschid, Louiqa

doi:10.1007/978-3-319-11964-9_9

Cited by 45 publications

(34 citation statements)

References 26 publications

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“…• (FI-FC) edge partition using semEP [6]: Unlike node based partitioning, this is a partitioning of the edges. The resulting clusters will provide granular insights into the key relationships among financial institutions across groups of contracts.…”

Section: Analytics Pipeline and Prelimi-nary Resultsmentioning

confidence: 99%

resMBS

Burdick¹,

Raschid

et al. 2016

Proceedings of the Second International Workshop on Data Science for Macro-Modeling

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Understanding the behavior of complex financial supply chains is usually difficult due to a lack of data capturing the interactions between financial institutions (FIs) and the roles that they play in financial contracts (FCs). resMBS is an example supply chain corresponding to the US residential mortgage backed securities that were critical in the 2008 US financial crisis. In this paper, we describe the process of creating the resMBS graph dataset from financial prospectus. We use the SystemT rule-based text extraction platform to develop two tools, ORG NER and Dict NER, for named entity recognition of financial institution (FI) names. The resMBS graph comprises a set of FC nodes (each prospectus) and the corresponding FI nodes that are extracted from the prospectus. A Role-FI extractor matches a role keyword such as originator, sponsor or servicer, with FI names. We study the performance of the Role-FI extractor, and ORG NER and Dict NER, in constructing the resMBS dataset. We also present preliminary results of a clustering based analysis to identify financial communities and their evolution in the resMBS financial supply chain.

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Section: Analytics Pipeline and Prelimi-nary Resultsmentioning

confidence: 99%

resMBS

Burdick¹,

Raschid

et al. 2016

Proceedings of the Second International Workshop on Data Science for Macro-Modeling

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“…After reviewing different approaches such as [2,4], we realize the benefits that integrating the entity-entity similarity (e.g., target-target, drug-drug, and targetdrug) into a learning model can bring. The intuition behind this work is that vector embedding-based approaches effectively combine different dimensions of the input data to learn embeddings.…”

Section: The Simtranse Approachmentioning

confidence: 99%

“…TransE [1] is the baseline of the experiment. Additionally, we utilize the link prediction technique, SemEP [4], that extracts interaction from highly connected partitions of a knowledge graph; these interactions are utilized to enhance the set of input interactions. Furthermore, we compute the drug-drug and target-target similarity matrices; drug similarities are computed using SIMCOMP [3] while target similarities are computed using a normalised Smith-Waterman score [5].…”

Section: Empirical Evaluationmentioning

confidence: 99%

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Interaction Network Analysis Using Semantic Similarity Based on Translation Embeddings

Bajwa

Collarana

Vidal

2019

Lecture Notes in Computer Science

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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-ofthe-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.

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“…In combination with an eigenvalue transformation technique (70), the ATC taxonomy similarity between drugs was computed using a semantic similarity algorithm and used as one drug similarity metric. Likewise, disease terms related to drugs can be applied to evaluate the drug similarity via terminology metrics as shown in the semantics-based edge partitioning approach (semEP) for DTI prediction (71).…”

Section: Therapeutic Effectmentioning

confidence: 99%

Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review

Cheng

Hao

Takeda

et al. 2017

AAPS J

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Abstract.The prediction of drug-target interactions (DTIs) is of extraordinary significance to modern drug discovery in terms of suggesting new drug candidates and repositioning old drugs. Despite technological advances, large-scale experimental determination of DTIs is still expensive and laborious. Effective and low-cost computational alternatives remain in strong need. Meanwhile, open-access resources have been rapidly growing with massive amount of bioactivity data becoming available, creating unprecedented opportunities for the development of novel in silico models for large-scale DTI prediction. In this work, we review the state-of-the-art computational approaches for identifying DTIs from a data-centric perspective: what the underlying data are and how they are utilized in each study. We also summarize popular public data resources and online tools for DTI prediction. It is found that various types of data were employed including properties of chemical structures, drug therapeutic effects and side effects, drug-target binding, drug-drug interactions, bioactivity data of drug molecules across multiple biological targets, and druginduced gene expressions. More often, the heterogeneous data were integrated to offer better performance. However, challenges remain such as handling data imbalance, incorporating negative samples and quantitative bioactivity data, as well as maintaining cross-links among different data sources, which are essential for large-scale and automated information integration.

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Drug-Target Interaction Prediction Using Semantic Similarity and Edge Partitioning

Cited by 45 publications

References 26 publications

resMBS

resMBS

Interaction Network Analysis Using Semantic Similarity Based on Translation Embeddings

Large-Scale Prediction of Drug-Target Interaction: a Data-Centric Review

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