Listar 1. Investigación por título
Mostrando ítems 7354-7373 de 12393
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M-Mode Ultrasound Examination of Soleus Muscle in Healthy Subjects: Intra- and Inter-Rater Reliability Study
(2020-12)[Abstract] Objective: M-mode ultrasound imaging (US) reflects the motion of connective tissue within muscles. The objectives of this study were to evaluate inter-rater and intra-rater reliability of soleus muscle measurements ... -
M. Ferreiro et alii, A edición da Poesía Trobadoresca en Galiza
(Universidade da Coruña, 2009) -
M1 inhibition dependency on slowing of muscle relaxation after brief and fast fatiguing repetitive movements: preliminary results
(Springer, 2018-10-16)[Abstract] This work presents preliminary results on the association between central and peripheral expressions of muscle fatigue induced by unresisted repetitive movements. We tested cortico-spinal excitability and ... -
M3DISEEN: A novel machine learning approach for predicting the 3D printability of medicines
(Elsevier B.V., 2020-11)[Abstract]: Artificial intelligence (AI) has the potential to reshape pharmaceutical formulation development through its ability to analyze and continuously monitor large datasets. Fused deposition modeling (FDM) ... -
Machine Learning Algorithms Reveals Country-Specific Metagenomic Taxa from American Gut Project Data
(NLM (Medline), 2021)[Abstract] In recent years, microbiota has become an increasingly relevant factor for the understanding and potential treatment of diseases. In this work, based on the data reported by the largest study of microbioma in ... -
Machine learning analysis of TCGA cancer data
(PeerJ Inc., 2021)[Abstract] In recent years, machine learning (ML) researchers have changed their focus towards biological problems that are difficult to analyse with standard approaches. Large initiatives such as The Cancer Genome Atlas ... -
Machine Learning Analysis of the Human Infant Gut Microbiome Identifies Influential Species in Type 1 Diabetes
(Elsevier, 2021)[Abstract] Diabetes is a disease that is closely linked to genetics and epigenetics, yet mechanisms for clarifying the onset and/or progression of the disease have sometimes not been fully managed. In recent years and due ... -
Machine learning applied to transcriptomic data to identify genes associated with feed efficiency in pigs
(BioMed Central Ltd., 2019-03-13)[Abstract]: Background: To date, the molecular mechanisms that underlie residual feed intake (RFI) in pigs are unknown. Results from different genome-wide association studies and gene expression analyses are not always ... -
Machine Learning Based Microbiome Signature to Predict Inflammatory Bowel Disease Subtypes
(FRONTIERS MEDIA S.A., 2022)[Abstract] Inflammatory bowel disease (IBD) is a chronic disease with unknown pathophysiological mechanisms. There is evidence of the role of microorganims in this disease development. Thanks to the open access to multiple ... -
Machine Learning Based Moored Ship Movement Prediction
(MDPI, 2021)[Abstract] Several port authorities are involved in the R+D+i projects for developing port management decision-making tools. We recorded the movements of 46 ships in the Outer Port of Punta Langosteira (A Coruña, Spain) ... -
Machine learning classification models for fetal skeletal development performance prediction using maternal bone metabolic proteins in goats
(PeerJ, 2019-10)[Abstract]: Background In developing countries, maternal undernutrition is the major intrauterine environmental factor contributing to fetal development and adverse pregnancy outcomes. Maternal nutrition restriction ... -
Machine Learning for Anomaly Detection: From Surface to Deep
(2024)[Resumo] A detección de anomalías é a rama da aprendizaxe automática encargada de construír modelos capaces de diferenciar entre datos normais e anómalos. A priori, isto converte a detección de anomalías nun problema de ... -
Machine learning for multivariate time series with the R package mlmts
(Elsevier B.V., 2023-06)[Abstract]: Time series data are ubiquitous nowadays. Whereas most of the literature on the topic deals with univariate time series, multivariate time series have typically received much less attention. However, the ... -
Machine Learning in Management of Precautionary Closures Caused by Lipophilic Biotoxins
(Elsevier, 2022)[Abstract] Mussel farming is one of the most important aquaculture industries. The main risk to mussel farming is harmful algal blooms (HABs), which pose a risk to human consumption. In Galicia, the Spanish main producer ... -
Machine learning predicts 3D printing performance of over 900 drug delivery systems
(Elsevier B.V., 2021-09)[Abstract]: Three-dimensional printing (3DP) is a transformative technology that is advancing pharmaceutical research by producing personalized drug products. However, advances made via 3DP have been slow due to the lengthy ... -
Machine Learning Techniques for Single Nucleotide Polymorphism—Disease Classification Models in Schizophrenia
(Molecular Diversity Preservation International, 2010)[Abstract] Single nucleotide polymorphisms (SNPs) can be used as inputs in disease computational studies such as pattern searching and classification models. Schizophrenia is an example of a complex disease with an important ... -
Machine Learning Techniques to Predict Different Levels of Hospital Care of CoVid-19
(Springer, 2022)[Abstract] In this study, we analyze the capability of several state of the art machine learning methods to predict whether patients diagnosed with CoVid-19 (CoronaVirus disease 2019) will need different levels of hospital ... -
Machine Learning to Compute Implied Volatility from European/American Options Considering Dividend Yield
(MDPI AG, 2020-09-15)[Abstract] Computing implied volatility from observed option prices is a frequent and challenging task in finance, even more in the presence of dividends. In this work, we employ a data-driven machine learning approach ... -
Machine Learning-Based Radon Monitoring System
(MDPI, 2022)[Abstract] Radon (Rn) is a biological threat to cells due to its radioactivity. It is capable of penetrating the human body and damaging cellular DNA, causing mutations and interfering with cellular dynamics. Human exposure ... -
Machine learning-based WENO5 scheme
(Elsevier Ltd, 2024-08-15)[Abstract]: Machine learning (ML) is becoming a powerful tool in Computational Fluid Dynamics (CFD) to enhance the accuracy, efficiency, and automation of simulations. Currently, in the design of shock-capturing methods, ...
