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Introducción: Para la formación del médico general de perfil amplio que necesita la sociedad cubana, las ciencias básicas biomédicas tributan a las bases del fundamento científico, el pensamiento médico y la clínica; así como a actitudes, convicciones y valores, en correspondencia con las tendencias de la educación superior contemporánea. Su evolución en la carrera de medicina en Cuba ha incluido una actualización del diseño y la didáctica de la disciplina y las asignaturas. Objetivo: Realizar un análisis crítico del programa analítico de la asignatura Ontogenia y SOMA en los planes de estudio C, D y E. Métodos: Se realizó un estudio cualitativo de carácter descriptivo. Se emplearon el análisis documental, la sistematización, el análisis y la síntesis para los programas de las asignaturas y las revisiones bibliográficas sobre la temática. Los resultados fueron representados de forma comparativa en figuras y tablas. Resultados: Los aspectos del programa que sufrieron grandes cambios fueron: sistema de habilidades, distribución de tiempo, formas de organización de la enseñanza, estrategias docentes y extracurriculares. Tuvieron algunas modificaciones, pero conservaron elementos de diseños anteriores: objetivos y sistema de evaluación. Los menos modificados resultaron contenido y bibliografía. Conclusiones: Los cambios de diseño de la asignatura han incluido paulatinamente la orientación hacia la atención primaria de salud, el rescate de actividades prácticas, la enseñanza problémica, la utilización del organismo vivo como medio de enseñanza, las tecnologías y el protagonismo del estudiante en la construcción de su conocimiento(AU)

Introduction: For the training of the general physician with a broad profile needed by the Cuban society, basic biomedical sciences contribute to the bases of scientific foundation, medical thinking and clinical practice, as well as attitudes, convictions and values, in correspondence with the tendencies of contemporary higher education. Its evolution in the Cuban medical major has included an update of the design and didactics of the discipline and subjects. Objective: To carry out a critical analysis of the analytical syllabus of the subject Ontogeny and Osteomioarticular System in the study plans C, D and E. Methods: A descriptive and qualitative study was carried out. Document analysis, systematization, analysis and synthesis were used for the subjects' syllabi, as well as for the bibliographic reviews on the topic. The results were represented comparatively in figures and tables. Results: The aspects of the program that underwent major changes were the system of skills, distribution of time, teaching organization forms, as well as teaching and extracurricular strategies. Some modifications were introduced, but other were maintained from previous designs, in the objectives and evaluation system. The least modified aspects were content and bibliography. Conclusions: The changes in the subject's design have gradually included the orientation towards primary healthcare, the recovery of practical activities, problem-solving teaching, the use of the living organism as a teaching aid, technologies, and the student's protagonism in the construction of his or her knowledge(AU)

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High-throughput technologies used in molecular biology have been pivotal to enhance the molecular characterization of human malignancies, allowing multiple omics data types to be available for cancer researchers and practitioners. In this context, appropriate data integration strategies are required to gain new insights from omics high-dimensional data. Yet, in order to extract valuable knowledge from this kind of information in an efficient manner, different approaches to reduce data dimensionality should be considered in multi-omics data integration pipelines. Multi-omics data integration approaches are mainly classified according to the label availability. Unsupervised data integration only draws inference from inputs without prior labels, whereas its supervised counterpart models allow incorporating known phenotype labels to improve the accuracy of high-throughput biomedical data analyses. However, the real value of the above mentioned approaches lies in their sequential combination with machine learning methods. It represents a major challenge for implementing multi-omics data analysis pipelines but it can certainly improve the decision-making process in the diagnosis and clinical management of cancer. The present review addresses the impact of current multi-omics data integration approaches, and their synergy with machine learning approaches, on the precision oncology field.

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