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TRSRD: a database for research on risky substances in tea using natural language processing and knowledge graph-based techniques.

Database : the journal of biological databases and curation
During the production and processing of tea, harmful substances are often introduced. However, they have never been systematically integrated, and it is impossible to understand the harmful substances that may be introduced during tea production and ...

A review of biomedical datasets relating to drug discovery: a knowledge graph perspective.

Briefings in bioinformatics
Drug discovery and development is a complex and costly process. Machine learning approaches are being investigated to help improve the effectiveness and speed of multiple stages of the drug discovery pipeline. Of these, those that use Knowledge Graph...

Implications of topological imbalance for representation learning on biomedical knowledge graphs.

Briefings in bioinformatics
Adoption of recently developed methods from machine learning has given rise to creation of drug-discovery knowledge graphs (KGs) that utilize the interconnected nature of the domain. Graph-based modelling of the data, combined with KG embedding (KGE)...

On the Use of Bayesian Artificial Intelligence for Hypothesis Generation in Psychiatry.

Psychiatria Danubina
In this study, I introduce the use of Bayesian Artificial Intelligence, namely through the probabilistic and structure learning of Bayesian Network models, for hypothesis generation in psychiatry. Bayesian Networks are directed acyclic graphical mode...

There Can Be no Other Reason for this Behavior: Issues in the Ascription of Knowledge to Humans and AI.

Integrative psychological & behavioral science
While machine learning techniques have been used to model categorization/decision making tasks that are beyond the capabilities of traditional AI, these new models are typically uninterpretable, i.e., the reasons for their decisions are not clear. So...

Contexts and contradictions: a roadmap for computational drug repurposing with knowledge inference.

Briefings in bioinformatics
The cost of drug development continues to rise and may be prohibitive in cases of unmet clinical need, particularly for rare diseases. Artificial intelligence-based methods are promising in their potential to discover new treatment options. The task ...

Designing Deep Neural Networks Robust to Sensor Failure in Mobile Health Environments.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Missing data is a very common challenge in health monitoring systems and one reason for that is that they are largely dependent on different types of sensors. A critical characteristic of the sensor-based prediction systems is their dependency on har...

Graph representation learning in bioinformatics: trends, methods and applications.

Briefings in bioinformatics
Graph is a natural data structure for describing complex systems, which contains a set of objects and relationships. Ubiquitous real-life biomedical problems can be modeled as graph analytics tasks. Machine learning, especially deep learning, succeed...

Protein matchmaking through representation learning.

Cell systems
Sledzieski, Singh, Cowen, and Berger employ representation learning to predict protein interactions and associations, additionally identifying binding residues between protein pairs. Generalizability is showcased by training on one organism while eva...