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Motivation Computational approaches for predicting drug–target interactions (DTIs) can provide valuable insights into the drug mechanism of action. DTI predictions can help to quickly identify new promising (on-target) or unintended (off-target) effects of drugs. However, existing models face several challenges. Many can only process a limited number of drugs and/or have poor proteome coverage. The current approaches also often suffer from high false positive prediction rates. Results We propose a novel computational approach for predicting drug target proteins. The approach is based on formulating the problem as a link prediction in knowledge graphs (robust, machine-readable representations of networked knowledge). We use biomedical knowledge bases to create a knowledge graph of entities connected to both drugs and their potential targets. We propose a specific knowledge graph embedding model, TriModel, to learn vector representations (i.e. embeddings) for all drugs and targets in the created knowledge graph. These representations are consequently used to infer candidate drug target interactions based on their scores computed by the trained TriModel model. We have experimentally evaluated our method using computer simulations and compared it to five existing models. This has shown that our approach outperforms all previous ones in terms of both area under ROC and precision–recall curves in standard benchmark tests. Availability and implementation The data, predictions and models are available at: drugtargets.insight-centre.org. Supplementary information Supplementary data are available at Bioinformatics online.

Timely identification of adverse drug reactions (ADRs) is highly important in the domains of public health and pharmacology. Early discovery of potential ADRs can limit their effect on patient lives and also make drug development pipelines more robust and efficient. Reliable in silico prediction of ADRs can be helpful in this context, and thus, it has been intensely studied. Recent works achieved promising results using machine learning. The presented work focuses on machine learning methods that use drug profiles for making predictions and use features from multiple data sources. We argue that despite promising results, existing works have limitations, especially regarding flexibility in experimenting with different data sets and/or predictive models. We suggest to address these limitations by generalization of the key principles used by the state of the art. Namely, we explore effects of: (1) using knowledge graphs machine-readable interlinked representations of biomedical knowledge as a convenient uniform representation of heterogeneous data; and (2) casting ADR prediction as a multi-label ranking problem. We present a specific way of using knowledge graphs to generate different feature sets and demonstrate favourable performance of selected off-the-shelf multi-label learning models in comparison with existing works. Our experiments suggest better suitability of certain multi-label learning methods for applications where ranking is preferred. The presented approach can be easily extended to other feature sources or machine learning methods, making it flexible for experiments tuned toward specific requirements of end users. Our work also provides a clearly defined and reproducible baseline for any future related experiments. The TOMOE project funded by Fujitsu Laboratories Ltd., Japan and Insight Centre for Data Analytics at National University of Ireland Galway (supported by the Science Foundation Ireland (SFI) under Grant Number SFI/12/RC/2289). peer-reviewed 2018-08-18

We introduce KI2NA-LHC (Linked Health Care) a system for data and knowledge integration in life sciences. In particular, we focus on linking clinical resources (electronic patient records) with scientific documents and data (research articles, biomedical ontologies and databases). Our motivation is two-fold. Firstly, we aim to instantly provide scientific context of particular patient cases for clinicians in order for them to propose treatments in a more informed way. Secondly, we want to build a technical infrastructure for researchers that will allow them to semi-automatically formulate and evaluate their hypothesis against longitudinal patient data. This paper outlines the proposed system and its services in a broader context of KI2NA, an ongoing collaboration between the DERI research institute and Fujitsu Laboratories.

The paper presents CORAAL, a novel solution for life science publication search that exploits knowledge locked within unstructured publication texts (apart of possessing the traditional full text functionalities). In contrast to most related state of the art solutions, CORAAL integrally addresses acquisition (i.e., extraction), processing (i.e., integration and extension) and dissemination (i.e., convenient exposure) of the publication knowledge. After detailing the motivations of our research, we outline the representation and processing framework that allows CORAAL to tackle the rather noisy and sparse automatically extracted knowledge. The architecture and core features of the CORAAL prototype itself are described then. Most importantly, we report on an extensive evaluation of the CORAAL tool performed with an assistance of actual sample users. The evaluation illustrates the practical benefits brought by our solution already in the early research prototype stage.

Despite being a flourishing field, the contemporary online scientific publishing properly exploits mostly raw publication data (rather meaningless bags of words) and shallow meta-data (authors, keywords, citations, etc.) regarding search. The much needed economical mass exploitation of the knowledge implicitly contained in publication texts is still largely an uncharted territory. The way towards filling this gap leads through (1) extraction of asserted publication meta-data together with the knowledge implicitly present in the respective text; (2) integration, refinement and extension of the emergent content; (3) release of the processed content via a meaning-sensitive search&browse interface catering for services complementary to the current full-text search. This chapter addresses the scientific and engineering challenges related to the suggested approach and introduces a particular solution that tackles them – CORAAL, a prototype for knowledge-based life science publication search.

We present a lightweight framework for processing uncertain emergent knowledge that comes from multiple resources with varying relevance. The framework is essentially RDF-compatible, but allows also for direct representation of contextual features (e.g., provenance). We support soft integration and robust querying of the represented content based on well-founded notions of aggregation, similarity and ranking. A proof-of-concept implementation is presented and evaluated within large scale knowledge-based search in life science articles. peer-reviewed

Prominent biomedical literature search tools like ScienceDirect, PubMed Central or MEDLINE allow for efficient retrieval of resources based on key words. Due to vast amounts of data available in life sciences, key word search is not always sufficient, though. One would often welcome more intelligent search for knowledge, i.e., for concepts and their mutual relations. This is, however, still a major challenge, since getting the necessary machine-readable knowledge manually is virtually impossible in large scale, while its automatic extraction is not particularly reliable. We have researched a novel framework actually enabling practical exploitation of automatically extracted knowledge, though. On the top of the framework, we implemented CORAAL, a prototype for knowledge-based biomedical literature search. This paper describes its essential principles, innovative capabilities and current results. peer-reviewed