Browsing GI-RNASA - Artigos by Title
Now showing items 99-118 of 164
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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 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 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 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-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 ... -
Mapping chemical structure-activity information of HAART-drug cocktails over complex networks of AIDS epidemiology and socioeconomic data of U.S. counties
(Elsevier, 2015-04-24)[Abstract] Using computational algorithms to design tailored drug cocktails for highly active antiretroviral therapy (HAART) on specific populations is a goal of major importance for both pharmaceutical industry and public ... -
Mapping networks of anti-HIV drug cocktails vs. AIDS epidemiology in the US counties
(Elsevier, 2014-08-20)[Abstract] The implementation of the highly active antiretroviral therapy (HAART) and the combination of anti-HIV drugs have resulted in longer survival and a better quality of life for the people infected with the virus. ... -
Markov Mean Properties for Cell Death-Related Protein Classification
(Elsevier, 2014-01-31)[Abstract] The cell death (CD) is a dynamic biological function involved in physiological and pathological processes. Due to the complexity of CD, there is a demand for fast theoretical methods that can help to find new ... -
Mechanical Hyperalgesia but Not Forward Shoulder Posture Is Associated with Shoulder Pain in Volleyball Players: A Cross-Sectional Study
(MDPI, 2022)[Abstract] Shoulder antepulsion, altered scapular kinematics and imbalance of muscle activity are commonly associated with shoulder pain. This study aimed to observe if there is an association between the forward shoulder ... -
MI-NODES multiscale models of metabolic reactions, brain connectome, ecological, epidemic, world trade, and legal-social networks
(Bentham, 2015)[Abstract] Complex systems and networks appear in almost all areas of reality. We find then from proteins residue networks to Protein Interaction Networks (PINs). Chemical reactions form Metabolic Reactions Networks (MRNs) ... -
MIANN models in medicinal, physical and organic chemistry
(Bentham, 2013)[Abstract] Reducing costs in terms of time, animal sacrifice, and material resources with computational methods has become a promising goal in Medicinal, Biological, Physical and Organic Chemistry. There are many computational ... -
MIANN models of networks of biochemical reactions, ecosystems, and U.S. Supreme Court with Balaban-Markov indices
(Bentham Science, 2015)[Abstract] We can use Artificial Neural Networks (ANNs) and graph Topological Indices (TIs) to seek structure-property relationship. Balabans’ J index is one of the classic TIs for chemo-informatics studies. We used here ... -
Microemulsions for colorectal cancer treatments: general considerations and formulation of methotrexate
(Bentham, 2016-04-01)[Abstract] Microemulsions combine the advantages of emulsions with those of nanocarriers, overcoming the stability problems of the former and providing facile scalable systems with compartments adequate for high drug ... -
Modeling complex metabolic reactions, ecological systems, and financial and legal networks with MIANN models based on Markov-Wiener node descriptors
(American Chemical Society, 2013-12-08)[Abstract] The use of numerical parameters in Complex Network analysis is expanding to new fields of application. At a molecular level, we can use them to describe the molecular structure of chemical entities, protein ... -
Modeling of energy efficiency for residential buildings using artificial neuronal networks
(Hindawi, 2018-11-28)[Abstract] Increasing the energy efficiency of buildings is a strategic objective in the European Union, and it is the main reason why numerous studies have been carried out to evaluate and reduce energy consumption in the ... -
Molecular Docking and Machine Learning Analysis of Abemaciclib in Colon Cancer
(BioMed Central Ltd., 2020-07-08)[Abstract] Background - The main challenge in cancer research is the identification of different omic variables that present a prognostic value and personalised diagnosis for each tumour. The fact that the diagnosis is ... -
Nanoinformatics: Developing New Computing Applications for Nanomedicine
(Springer, 2012)Nanoinformatics has recently emerged to address the need of computing applications at the nano level. In this regard, the authors have participated in various initiatives to identify its concepts, foundations and challenges. ... -
NCBO Ontology Recommender 2.0: an enhanced approach for biomedical ontology recommendation
(BioMed Central, 2017-06-07)[Abstract] Background. Ontologies and controlled terminologies have become increasingly important in biomedical research. Researchers use ontologies to annotate their data with ontology terms, enabling better data ...
