A5084663579- Biomedical Text Mining and Ontologies
- Digital Transformation in Industry
- Advanced Graph Neural Networks
- Bioinformatics and Genomic Networks
- Computational Drug Discovery Methods
- Semantic Web and Ontologies
- Topic Modeling
- Scientific Computing and Data Management
- Big Data and Digital Economy
- Multi-Criteria Decision Making
- Rough Sets and Fuzzy Logic
- Pharmacogenetics and Drug Metabolism
- Knowledge Societies in the 21st Century
- Hepatitis B Virus Studies
- HIV Research and Treatment
- Lung Cancer Treatments and Mutations
- Hepatitis C virus research
- Research Data Management Practices
- Data Mining Algorithms and Applications
- Bayesian Modeling and Causal Inference
Technische Informationsbibliothek (TIB)
2019-2025
L3S Research Center
2020-2023
Leibniz University Hannover
2021-2023
University of Holguín
2019
Tailoring personalized treatments demands the analysis of a patient’s characteristics, which may be scattered over wide variety sources. These features include family history, life habits, comorbidities, and potential treatment side effects. Moreover, services visited most by patient before new diagnosis, as well type requested tests, uncover patterns that contribute to earlier disease detection effectiveness. Built on knowledge-driven ecosystems, we devise DE4LungCancer, health data...
Neuro-Symbolic Artificial Intelligence (AI) focuses on integrating symbolic and sub-symbolic systems to enhance the performance explainability of predictive models. Symbolic approaches differ fundamentally in how they represent data make use features reach conclusions. Neuro-symbolic have recently received significant attention scientific community. However, despite efforts neural-symbolic integration, processing can still be better exploited, mainly when these hybrid are defined top...
Knowledge graphs (KGs) naturally capture the convergence of data and knowledge, making them expressive frameworks for describing integrating heterogeneous in a coherent interconnected manner. However, based on Open World Assumption (OWA), absence information within KGs does not indicate falsity or non-existence; it merely reflects incompleteness. Inductive learning over involves predicting new relationships existing statements KG, using either numerical symbolic models. The Partial...
Chronic hepatitis B virus (HBV) infection is still a global health problem, with over 296 million chronically HBV-infected individuals worldwide. The merging data about clinical parameters, immune phenotyping data, and genetic information, together AI models reliant on this integrated holds promise in effectively predicting the likelihood of functional cure patients. Yet, limited size multidimensional datasets characteristic HBV cases poses challenge for machine learning (ML) systems that...
Capturing knowledge about Drug-Drug Interactions (DDI) is a crucial factor to support clinicians in better treatments. Nowadays, public drug databases provide wealth of information on drugs that can be exploited enhance tasks, e.g., data mining, ranking, and query answering. However, all the interactions database are focused pairs drugs. Since current treatments composed multi-drugs, it extremely challenging know which potential affect effectiveness treatment. In this work, we tackle problem...
Industry 4.0 (I4.0) standards and standardization frameworks provide a unified way to describe smart factories. Standards specify the main components, systems, processes inside factory interaction among all of them. Furthermore, classify according their functions into layers dimensions. Albeit informative, can categorize similar differently. As result, interoperability conflicts are generated whenever factories described with miss-classified standards. Approaches like ontologies knowledge...
Industry~4.0 (I4.0) standards and standardization frameworks have been proposed with the goal of \emph{empowering interoperability} in smart factories. These enable description interaction main components, systems, processes inside a factory. Due to growing number standards, there is an increasing need for approaches that automatically analyze landscape I4.0 standards. Standardization classify according their functions into layers dimensions. However, similar can be classified differently...
In this paper, we present Knowledge4COVID-19, a framework that aims to showcase the power of integrating disparate sources knowledge discover adverse drug effects caused by drug-drug interactions among COVID-19 treatments and pre-existing condition drugs. Initially, focus on constructing Knowledge4COVID-19 graph (KG) from declarative definition mapping rules using RDF Mapping Language. Since valuable information about treatments, interactions, side is in textual descriptions scientific...