The global rise in life expectancy has led to an increased prevalence of multimorbidity and chronic health conditions among older adults, significantly impacting their quality of life. This growing demand for long-term care presents substantial challenges to the sustainability of health and social support systems. Advancements in Artificial Intelligence (AI) and the integration of digital technologies in clinical practice offer promising avenues for enhancing personalized healthcare. In this context, the present study aims to develop a decision-support system to optimize the personalization of care for older adults, thereby promoting healthier and more sustainable ageing. The proposed system integrates machine learning (ML) techniques with a knowledge-based framework to assist healthcare professionals in selecting appropriate monitoring devices (e.g., fall risk, pressure, arrhythmias, weight) and support services (e.g., walking groups, communication activities, recreational programs, motor-functional rehabilitation, nutritional counselling, and transport) tailored to individual needs. Encouraging results were obtained by three ML methods (Logistic regression, Random Forest and XGBoost) validated on 121 real users, providing accurate and reliable classifications of users’ needs. A structured validation consisting in methodological, quantitative sensitivity analysis and clinical validation of the system confirmed the robustness of such a hybrid approach compared to purely ML-based system, pure rule-based system and a traditional MCDA, and its relevance among clinicians. As a future development, it is planned to consider the user’s opinion obtained from usability and user experience tests to optimize the personalization of the assignment and improve the user’s engagement. Moreover, AI techniques based on large language models could automate the devices classification by extracting unstructured data from product documentation (e.g., devices’ leaflets and brochures).

The development of new drugs is a costly and time-consuming process, often spanning over a decade. Drug repurposing has emerged as a promising alternative, leveraging existing compounds to identify novel therapeutic uses. In this context, DRIVE (Data-dRiven platform for dIsease Visualization and Drug rEpurposing) is a platform designed to integrate and exploit heterogeneous biomedical data to support hypothesis generation in drug repurposing. Developed at the Medical Data Analytics Laboratory (MEDAL) of Universidad Politécnica de Madrid, DRIVE combines disease-centered network visualizations and six complementary computational methods, ranging from data-driven-based approaches to graph neural network models. The platform is powered by data from DISNET and integrates phenotypic, molecular, and pharmacological layers of knowledge. Users can interactively explore disease mechanisms, visualize multi-layer disease networks, and obtain ranked repurposing candidates through a web interface. This poster presents the platform architecture, discusses the methodologies implemented, and illustrates the capabilities of DRIVE through representative use cases.

The rapid expansion of genomic and biomedical data has paved the way for constructing complex disease networks, offering new insights into disease mechanisms and therapeutic target identification. Central to these networks are disease modules, which are constructed from seed genes that are prioritized based on their relevance to a given disease. Gene prioritization aims to rank genes based on their strength of association with a disease. Resources like DisGeNET, integrated within DISNET knowledge base, assigns a Gene-Disease Association (GDA) score that reflects the confidence in the association between a gene and a disease. This study investigates how both disease module sizes and GDA scores influence the statistical significance of these modules. By characterizing disease modules, filtering the data based on their GDA score thresholds, and analysing the relationship between their size, GDA score, and significance, potential cutoffs for robust module construction are estimated. Our findings suggest that disease modules filtered to include GDA scores above 0.3 and module sizes greater than 10 tend to be significant. These insights provide guidance for optimal gene prioritization and module selection, ultimately enhancing strategies for target identification and drug repurposing in network medicine.

Lung cancer remains one of the deadliest cancers and a major public health concern. Although numerous studies have identified various risk factors, further research is essential, particularly in the biological domain. Existing data sources compile biological information on lung cancer and its subtypes but differ in structure and format, complicating data extraction and integration for artificial intelligence (AI) models. Ontologies and semantic technologies address this challenge by enabling the construction of unified knowledge graphs that promote interoperability. Lung-CABO is an ontology specifically designed for lung cancer, supporting the creation of a knowledge graph for risk factor identification and AI applications. Its modular design allows expansion to integrate additional data, such as environmental factors, further enhancing its utility and reusability.

Drug repurposing is an effective strategy to identify novel therapeutic options by leveraging existing drugs with known mechanisms of action and safety profiles. This work introduces a pathway-based drug repurposing approach that combines protein-protein interaction (PPI) networks and transcriptomic data to prioritize candidate drugs. The underlying assumption is that if disease 1 and disease 2 are both associated with the same biological pathway, then drugs known to be effective for disease 2 may be potential treatments for disease 1. To explore these relationships, disease–pathway–drug triplets are constructed. Genes shared between disease 1 and the associated pathway are identified, and their proximity is calculated within the PPI network using a Z-score based on random permutations. Candidate drugs for disease 2 are then analyzed using the Connectivity Map data, assessing whether their up- or down-regulated gene signatures are enriched for the genes contributing to the proximity between disease 1 and the pathway. This integration enables the ranking of candidate drugs based on their potential biological impact on disease 1. Finally, top-ranked drug–disease associations are evaluated through manual curation of the scientific literature to assess existing evidence supporting the proposed repurposing hypothesis.

Alzheimer's Disease (AD) is a neurodegenerative disorder characterized by complex, cell-type-specific molecular alterations. This study integrates single-cell RNA sequencing (scRNA-seq) with network-based methodologies to decode transcriptional changes across major brain cell types in AD. Using scRNA-seq data from 432,555 single cells, we constructed ProteinProtein Interaction (PPI) networks specific to each cell type and assessed the differential expression of genes in diseased conditions. Our findings reveal that glutamatergic neurons and inhibitory interneurons exhibit the highest transcriptional dysregulation, while pericytes and endothelial cells show limited changes. The analysis identified significant enrichment of Differentially Expressed Genes (DEGs) within the AD protein module. Network analysis highlights highly connected proteins such as HSPB1, which is implicated in proteostasis, and CXCR4, which is involved in neuroinflammation. Our results underscore the importance of cell-type-specific approaches in AD research, demonstrating that neurons experience more extensive dysregulation, while vascularassociated cells play key roles in maintaining Blood-Brain Barrier (BBB) integrity. These insights emphasize the necessity of tailored therapeutic strategies addressing the heterogeneous molecular landscape of AD.

In silico analysis provides valuable insights into studying macromolecules, particularly proteins. Protein structure prediction models, like AlphaFold (AF), offer a costeffective and time-efficient alternative to traditional methods like X-ray crystallography, NMR spectroscopy, and cryo-EM for determining protein structures. These models are increasingly used in protein-ligand interaction studies, a key aspect of drug discovery. Docking and molecular dynamics simulations facilitate this process, and researchers are continuously developing open-access tools for cavity detection and docking to accelerate protein-ligand interaction studies. However, while many of these tools perform well in specific cases, their strengths and weaknesses in analyzing predicted protein structures remain largely unknown. Therefore, it is crucial to compare docking analyses using experimentally determined protein structures and deep learning-based models. In this study, two well-characterized proteins, dopamine D3 receptor with its ligand ETQ and neprilysin with its ligand sacubitrilat, are used to evaluate docking predictions. The docking tools CB-Dock2 and COACH-D are applied to both Xray crystallography-derived structures and five different AFgenerated models. The objective is to assess the accuracy of these docking approaches and determine whether this strategy can effectively simulate macromolecular behavior in their microenvironment. By doing so, this study aims to generate new insights and contribute to accelerating research in proteinligand interactions.

Since the pandemic, drug repurposing has become a helpful technique for associating treatments to new or already known diseases. Drug repurposing finds new uses for existing drugs, leading to a more affordable solution than de novo drug development. The reduction in time and costs that drug repurposing provides makes it an effective technique to accelerate the process of discovering new treatments. In the present study, we apply a graph deep learning approach to a heterogeneous biomedical graph, aiming to predict a specific link type that connects diseases with drugs, in order to put forward drug repurposing opportunities. In particular, we generate a new model called DRAGON, which builds upon a two-layered Graph Neural Network pipeline. In the encoder stage, drug and protein nodes are initialized with embeddings representing molecular structure and amino acid sequence information. We compare the proposed model to previous baselines that studied the disease-drug prediction approach but did not consider the initialization with embeddings of these two node types. DRAGON reports an improvement of 0.02 in the area under the precision-recall curve when compared to these baselines. We hypothesize that the repurposing model may benefit from the inclusion of multimodal information from different sources.

In the rapidly evolving field of biomedical text mining, the extraction of phenotypic entities from unstructured texts remains a pivotal challenge. This paper introduces a novel method that leverage Large Language Models (LLMs) to extract phenotypical entities from freely available texts such as Wikipedia. Our approach goes beyond traditional Named Entity Recognition (NER) techniques by utilizing both local and cloud-based LLMs. We present a comprehensive comparison with state-of-the-art tools. Our study confirms the significant advantages of LLMs in identifying relevant phenotypic entities, thus enhancing the ability of researchers and clinicians to understand and respond to disease dynamics more effectively. Therefore, this work underscores the potential of next-generation LLMs to redefine the standards for the extraction of phenotypic entities in biomedical research.

The discovery of new drugs poses significant challenges due to the time and cost involved in the traditional drug development process. Drug repurposing (DR) offers a promising strategy by repurposing already approved drugs for new therapeutic purposes beyond their original indications. Among computational methodologies employed in DR, network medicine stands out for integrating network science and systems biology to elucidate complex biomedical relationships. The application of this discipline in the processing of heterogeneous biomedical data enhances the understanding of diseases through complex network structures. This study explores the implementation of network medicine pipelines to formulate DR hypotheses for schizophrenia. By the characterization of disease modules within the interactome and the assessment of disease -drug proximity, potential candidate drugs for repurposing were identified. Integrating data on differential gene expression further refined the selection process. Fourteen drugs emerged as candidates for schizophrenia treatment, notably including antidepressants, antineoplastics, and anti-inflammatory agents. Overall, this investigation underscores the potential of network medicine in accelerating the discovery of treatments by providing novel insights into repurposing existing drugs for new therapeutic indications, holding remarkable promise in the field of neurological diseases.

Drug repurposing has become increasingly important, particularly in light of the COVID-19 pandemic. This process involves identifying new therapeutic uses for existing drugs, which can significantly reduce the cost, risk, and time associated with developing new drugs, de novo development. A previous conducted study proved that Deep Learning can be used to streamline this process by identifying drug repurposing hypotheses. The study presented a model called REDIRECTION, which utilized the rich biomedical information available in graph form and combined it with Geometric Deep Learning to find new indications for existing drugs. The reported metrics for this model were 0.87 for AUROC and 0.83 for AUPRC. In this current study, the importance of node features in GNNs is explored. Specifically, the study used GNNs to embed two-dimensional drug molecular structures and obtain corresponding features. These features were incorporated into the drug repurposing graph, along with some other enhancements, resulting in an improved model called DMSR. Performance score for the reported metrics values raised by 0.0448 in AUROC and 0.0919 in AUPRC. Based on these findings, we believe that the method used for embedding drug molecular structures is interesting and captures valuable information about drugs. Its incorporation in the graph for drug repurposing can significantly benefit the process, leading to improved performance evaluation metrics.

Rare diseases are a collection of unusual pathologies that afflict millions of individuals globally. However, the creation of treatments for these conditions is frequently limited due to the high expenses and lack of profitability associated with drug development. Orphan drugs, which are medications specifically designed for rare diseases, have played a pivotal role in treating these diseases over the past several years. Nevertheless, their creation remains challenging, and many rare diseases lack approved therapies. Therefore, drug repurposing has emerged as a viable strategy for identifying potential new treatments for these pathologies. A technique that consists in using existing drugs to treat a new disease different from the one that they were developed. This approach can significantly reduce the time and cost of drug development while increasing the likelihood of success. In this paper, we examined the temporal progression of orphan drugs since their introduction and assess the impact of drug repositioning on treatments for rare diseases. Additionally, we aim to identify biological patterns that may be unique to rare diseases treated with repurposed orphan drugs. To this end, we analyzed various biological components associated with these diseases, categorized linked diseases, and obtained the type of orphan drug associated with them. Lastly, we evaluated the phenotypic similarity between diseases treated with an orphan drug through repurposing. Through these findings, we have gained insight into the evolution of orphan drug development in recent years and identified specific patterns that characterize rare diseases associated with them.

Drug repurposing, the process of finding new uses for existing drugs, has gained considerable attention due to its potential to reduce the time and costs associated with drug development. Personalized drug repurposing, in which drugs are selected based on the characteristics of individual patients, is an emerging approach that holds promise for improving clinical outcomes. In this context, exploring disease-drug pairs in already conducted clinical trials can provide valuable insights to identify promising patient populations for further study that may lead to personalized drug repositioning. Our analysis aims to shed a light into clinical outcomes by selecting the most appropriate repurposed drug based on clinical trials patient groups' characteristics, such as age and gender. It also gives information about the state of the clinical trials studying these disease-drug pairs, gathering information about the study type, phase and statistical method used to calculate the p-value of the chosen outcome measurement, among others. Overall, this study highlights the importance of using existing knowledge as an initial framework to facilitate further research, particularly in providing patient-specific information. Furthermore, it underlines the importance of building on previous research to facilitate a comprehensive understanding of the research topic, which can eventually improve patient outcomes.

In recent years and due to COVID-19 pandemic, drug repurposing or repositioning has been placed in the spotlight. Giving new therapeutic uses to already existing drugs, this discipline allows to streamline the drug discovery process, reducing the costs and risks inherent to de novo development. Computational approaches have gained momentum, and emerging techniques from the machine learning domain have proved themselves as highly exploitable means for repurposing prediction. Against this backdrop, one can find that biomedical data can be represented in terms of graphs, which allow depicting in a very expressive manner the underlying structure of the information. Combining these graph data structures with deep learning models enhances the prediction of new links, such as potential disease-drug connections. In this paper, we present a new model named REDIRECTION, which aims to predict new disease-drug links in the context of drug repurposing. It has been trained with a part of the DISNET biomedical graph, formed by diseases, symptoms, drugs, and their relationships. The reserved testing graph for the evaluation has yielded to an AUROC of 0.93 and an AUPRC of 0.90. We have performed a secondary validation of REDIRECTION using RepoDB data as the testing set, which has led to an AUROC of 0.87 and a AUPRC of 0.83. In the light of these results, we believe that REDIRECTION can be a meaningful and promising tool to generate drug repurposing hypotheses.

Drug repositioning is a novel, useful, and crucial technique to find new uses for existing drugs. In this field of study, when the clinical trials necessary to obtain successful drug repositioning have been carried out, the female gender has not been given much consideration. Thus far, the participation of women in clinical trials has been very limited. There were several argued reasons to exclude them from trials, like the likelihood of pregnancy or sudden hormonal changes. This has meant that for a long time the adverse effects of a drug on women were unknown. Scientifically, it was known that due to the biological processes of pharmacokinetics and pharmacodynamics, the response to drugs was not the same in both genders, but despite this evidence, there is still no difference in the dosage or form of using a drug between men and women. In this study, we made a preliminary analysis where the main goal is to investigate gender differences within the drug repositioning field through the adverse effects produced by such treatments. A special section on specific cases of drug repositioning in rare diseases will also be considered to carry out the same verification previously mentioned in the text.

This paper presents an original Intelligent and Secure Asset Discovery Tool (ISADT) that uses artificial intelligence and TPM-based technologies to: (i) detect the network assets, and (ii) detect suspicious pattern in the use of the network. The architecture has specifically been designed to discover the assets of medium and large size companies and institutions, such as hospitals, universities, or government buildings. Given the distributed design of the architecture, it can cope with the problem of the isolation of different Virtual Local Area Networks (VLANs). This is done by collecting information from all the VLANs and storing it in a central node, which can be accessed by the network administrator, who may consult and visualize the status in any moment, or even by other authorized applications. The collected data is kept in a secure warehouse by the use of a Trusted Platform Module. Moreover, collected data is processed by the use of artificial intelligence in two ways: (i) the traffic of each network is analysed so that suspicious patterns can be detected, and (ii) identified ports and status are analysed to detect anomalous combinations of open ports in a device.

The simultaneous presence of diseases worsens the prognosis of patients and makes their treatment difficult. Identifying the co-occurrence of diseases is key to improving the situation of patients and designing effective therapeutic strategies. On the one hand, the increasing availability of clinical information opens new ways to unveil hidden relationships between diseases. On the other hand, heterogeneous information networks have been used in recent years to discover novel knowledge from disease data, including symptoms, genes or drugs. The use of meta-paths allows the complex semantics of the relationships between the different types of nodes to be included in heterogeneous networks. In this study, we propose a system to predict disease comorbidities through the use of meta-paths in a heterogeneous network of diseases and symptoms, built from textual sources of public access. The results obtained improve those of similar studies based on biological data, and the predictions calculated for diabetes and Crohn's disease are supported by medical literature. Both the used data and the obtained prediction model are publicly accessible.

While classical disease nosology is based on phenotypical characteristics, the increasing availability of biological and molecular data is providing new understanding of diseases and their underlying relationships, that could lead to a more comprehensive paradigm for modern medicine. In the present work, similarities between diseases are used to study the generation of new possible disease nosologic models that include both phenotypical and biological information. To this aim, disease similarity is measured in terms of disease feature vectors, that stood for genes, proteins, metabolic pathways and PPIs in the case of biological similarity, and for symptoms in the case of phenotypical similarity. An improvement in similarity computation is proposed, considering weighted instead of Booleans feature vectors. Unsupervised learning methods were applied to these data, specifically, density-based DBSCAN clustering algorithm. As evaluation metric silhouette coefficient was chosen, even though the number of clusters and the number of outliers were also considered. As a results validation, a comparison with randomly distributed data was performed. Results suggest that weighted biological similarities based on proteins, and computed according to cosine index, may provide a good starting point to rearrange disease taxonomy and nosology.

Nowadays there is a huge amount of medical information that can be retrieved from different sources, both structured and unstructured. Internet has plenty of textual sources with medical knowledge (books, scientific papers, specialized web pages, etc.), but not all of them are publicly available. Wikipedia is a free, open and worldwide accessible source of knowledge. It contains more than 150,000 articles of medical content in the form of texts (non-structured information) that can be mined. The aim of this work is to study whether the evolution of information contained in Wikipedia medical articles can be used in a research context. The study has been focused on extracting the elements, from Wikipedia disease articles, that can be used to guide a diagnosis process, support the creation of diagnostic systems, or analyze the similarities between diseases, among others.

The increasing availability of biological, clinical and literary sources enables the study of diseases from a more comprehensive approach. However, the interoperability of these sources, particularly of the codes used to identify diseases, poses a major challenge. Because of its role as a hub of multiple medical vocabularies, the Unified Medical Language System (UMLS) has become one of the most widely used resources for mapping diseases from different classification systems. The coverage of these mappings, nevertheless, is still insufficient, so researchers must resort to other methods to fill these gaps. In this article we analyze the limitations in UMLS mappings for the MeSH, ICD-10 and SNOMED CT vocabularies and propose the exploitation of alternative expert-curated sources to complete it. As a result, we demonstrate that this approach allows resolving more than 50% of the missing mappings for these vocabularies. All the findings are shared for validation and reuse.

Over a decade ago, a new discipline called network medicine emerged as an approach to understand human diseases from a network theory point-of-view. Disease networks proved to be an intuitive and powerful way to reveal hidden connections among apparently unconnected biomedical entities such as diseases, physiological processes, signaling pathways, and genes. One of the fields that has benefited most from this improvement is the identification of new opportunities for the use of old drugs, known as drug repurposing. The importance of drug repurposing lies in the high costs and the prolonged time from target selection to regulatory approval of traditional drug development. In this document we analyze the evolution of disease network concept during the last decade and apply a data science pipeline approach to evaluate their functional units. As a result of this analysis, we obtain a list of the most commonly used functional units and the challenges that remain to be solved. This information can be very valuable for the generation of new prediction models based on disease networks.

The increasing availability of biological data is improving our understanding of diseases and providing new insight into their underlying relationships. Thanks to the improvements on both text mining techniques and computational capacity, the combination of biological data with semantic information obtained from medical publications has proven to be a very promising path. However, the limitations in the access to these data and their lack of structure pose challenges to this approach. In this document we propose the use of Wikipedia - the free online encyclopedia - as a source of accessible textual information for disease understanding research. To check its validity, we compare its performance in the determination of relationships between diseases with that of PubMed, one of the most consulted data sources of medical texts. The obtained results suggest that the information extracted from Wikipedia is as relevant as that obtained from PubMed abstracts (i.e. the free access portion of its articles), although further research is proposed to verify its reliability for medical studies.