Gene expression knowledge graph for patient representation and diabetes prediction.

Diabetes is a worldwide health issue affecting millions of people. Machine learning methods have shown promising results in improving diabetes prediction, particularly through the analysis of gene expression data. While gene expression data can provide valuable insights, challenges arise from the fact that the number of patients in expression datasets is usually limited, and the data from different datasets with different gene expressions cannot be easily combined.

Malignant melanoma in Portuguese adult population: a scoping review of the real-world evidence.

Purpose: Malignant melanoma is an aggressive cancer, and there is a notable dearth on epidemiology, clinical and treatment characterization within the Portuguese population. We performed a scoping review to identify real-world evidence studies focused in Portuguese adult patients with malignant melanoma.

Methods: A comprehensive search was conducted.

Explaining protein-protein interactions with knowledge graph-based semantic similarity.

The application of artificial intelligence and machine learning methods for several biomedical applications, such as protein-protein interaction prediction, has gained significant traction in recent decades. However, explainability is a key aspect of using machine learning as a tool for scientific discovery. Explainable artificial intelligence approaches help clarify algorithmic mechanisms and identify potential bias in the data.

Multi-domain knowledge graph embeddings for gene-disease association prediction.

Background: Predicting gene-disease associations typically requires exploring diverse sources of information as well as sophisticated computational approaches. Knowledge graph embeddings can help tackle these challenges by creating representations of genes and diseases based on the scientific knowledge described in ontologies, which can then be explored by machine learning algorithms. However, state-of-the-art knowledge graph embeddings are produced over a single ontology or multiple but disconnected ones, ignoring the impact that considering multiple interconnected domains can have on complex tasks such as gene-disease association prediction.

Breast Sarcomas, Phyllodes Tumors, and Desmoid Tumors: Turning the Magnifying Glass on Rare and Aggressive Entities.

Breast sarcomas (BSs), phyllodes tumors (PTs), and desmoid tumors (DTs) are rare entities that arise from connective tissue. BSs can be classified as either primary or secondary, whether they develop de novo or after radiation exposure or lymphedema. PIK3CA seems to play an important common role in different BS.

Anthracyclines versus No Anthracyclines in the Neoadjuvant Strategy for HER2+ Breast Cancer: Real-World Evidence.

Background And Objectives: Deescalation strategies omitting anthracyclines (AC) have been explored in early human epidermal growth factor receptor 2-positive breast cancer (HER2+ EBC), showing similar efficacy regarding pathological complete response (pCR) and long-term outcomes as AC-containing regimens. The standard treatment for this tumor subtype is based on chemotherapy and dual HER2 blockade with trastuzumab and pertuzumab, with AC-containing regimens remaining a frequent option for these patients, even in non-high-risk cases. The primary aim of this study was to assess and compare the effectiveness of neoadjuvant regimens with and without AC used in the treatment of HER2+ EBC in the clinical practice according to the pCR achieved with each.

Tumor-infiltrating lymphocytes in early breast cancer: an exploratory analysis focused on HER2+ subtype in Portuguese patients.

Tumor-infiltrating lymphocytes (TILs) have shown prognostic value in breast cancer. This study evaluated the TILs scores in 186 Portuguese patients diagnosed with early breast cancer, with special focus on HER2 subtype. Stromal TILs were scored on the core needle biopsies, as well as in the resected specimen in HER2+ patients submitted to neoadjuvant treatment with trastuzumab and pertuzumab.

A Collection of Benchmark Data Sets for Knowledge Graph-based Similarity in the Biomedical Domain.

The ability to compare entities within a knowledge graph is a cornerstone technique for several applications, ranging from the integration of heterogeneous data to machine learning. It is of particular importance in the biomedical domain, where semantic similarity can be applied to the prediction of protein-protein interactions, associations between diseases and genes, cellular localization of proteins, among others. In recent years, several knowledge graph-based semantic similarity measures have been developed, but building a gold standard data set to support their evaluation is non-trivial.

Evolving knowledge graph similarity for supervised learning in complex biomedical domains.

Background: In recent years, biomedical ontologies have become important for describing existing biological knowledge in the form of knowledge graphs. Data mining approaches that work with knowledge graphs have been proposed, but they are based on vector representations that do not capture the full underlying semantics. An alternative is to use machine learning approaches that explore semantic similarity.