[Contribution of cytogenetic in the diagnosis of Edwards's syndrome: about 9 cases].

Introduction: Trisomy 18 is a constitutional chromosomal disorder defined by the presence of a supernumerary chromosome 18. The diagnosis is suspected clinically and confirmed by cytogenetic analysis. Genetic counseling for patients' families is important.

A comprehensive update on CIDO: the community-based coronavirus infectious disease ontology.

Background: The current COVID-19 pandemic and the previous SARS/MERS outbreaks of 2003 and 2012 have resulted in a series of major global public health crises. We argue that in the interest of developing effective and safe vaccines and drugs and to better understand coronaviruses and associated disease mechenisms it is necessary to integrate the large and exponentially growing body of heterogeneous coronavirus data. Ontologies play an important role in standard-based knowledge and data representation, integration, sharing, and analysis.

Simulation and Mechanical Properties of Fine-Grained Heat-Affected Zone Microstructure in 18CrNiMo7-6 Steel.

Heat-affected zones (HAZs) in real welds are usually quite narrow, and consequently most standard mechanical tests are difficult or even impossible. Therefore, simulated microstructures are often used for mechanical tests. However, the most often used weld thermal cycle simulator produces only a few millimeters wide area of simulated microstructure in the middle of specimens.

QAUST: Protein Function Prediction Using Structure Similarity, Protein Interaction, and Functional Motifs.

The number of available protein sequences in public databases is increasing exponentially. However, a significant percentage of these sequences lack functional annotation, which is essential for the understanding of how biological systems operate. Here, we propose a novel method, Quantitative Annotation of Unknown STructure (QAUST), to infer protein functions, specifically Gene Ontology (GO) terms and Enzyme Commission (EC) numbers.

Epidemiology of breast cancer in women based on diagnosis data from oncologists and senologists in Algeria.

Background: Breast cancer (BC) is a major health issue threatening women's life. No reliable epidemiological data on BC diagnosed by oncologists/senologists are available in Algeria.

Methods: The BreCaReAl study, a non-interventional prospective cohort study, included adult women with confirmed BC in Algeria.

Semantic similarity and machine learning with ontologies.

Ontologies have long been employed in the life sciences to formally represent and reason over domain knowledge and they are employed in almost every major biological database. Recently, ontologies are increasingly being used to provide background knowledge in similarity-based analysis and machine learning models. The methods employed to combine ontologies and machine learning are still novel and actively being developed.

Incidence of lung cancer in males and females in Algeria: The lung cancer registry in Algeria (LuCaReAl).

Background: Lung cancer is a major cause of death worldwide. However, few data on incidence, histologic types and mortality rates of lung cancer were available for Algeria.

Methods: LuCaReAl is an ongoing descriptive, non-interventional, national, multicenter, prospective and longitudinal study conducted in Algeria, among oncologists and pulmonologists in public community and university hospitals.

Formal axioms in biomedical ontologies improve analysis and interpretation of associated data.

Motivation: Over the past years, significant resources have been invested into formalizing biomedical ontologies. Formal axioms in ontologies have been developed and used to detect and ensure ontology consistency, find unsatisfiable classes, improve interoperability, guide ontology extension through the application of axiom-based design patterns and encode domain background knowledge. The domain knowledge of biomedical ontologies may have also the potential to provide background knowledge for machine learning and predictive modelling.

OPA2Vec: combining formal and informal content of biomedical ontologies to improve similarity-based prediction.

Motivation: Ontologies are widely used in biology for data annotation, integration and analysis. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation axioms which provide valuable pieces of information that characterize ontology classes. Annotation axioms commonly used in ontologies include class labels, descriptions or synonyms.

Onto2Vec: joint vector-based representation of biological entities and their ontology-based annotations.

Motivation: Biological knowledge is widely represented in the form of ontology-based annotations: ontologies describe the phenomena assumed to exist within a domain, and the annotations associate a (kind of) biological entity with a set of phenomena within the domain. The structure and information contained in ontologies and their annotations make them valuable for developing machine learning, data analysis and knowledge extraction algorithms; notably, semantic similarity is widely used to identify relations between biological entities, and ontology-based annotations are frequently used as features in machine learning applications.

Results: We propose the Onto2Vec method, an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies.