Listar RNASA-IMEDIR por título
Mostrando ítems 24-43 de 49
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Gene Prioritization through Consensus Strategy, Enrichment Methodologies Analysis, and Networking for Osteosarcoma Pathogenesis
(M D P I AG, 2020-02-05)[Abstract] Osteosarcoma is the most common subtype of primary bone cancer, affecting mostly adolescents. In recent years, several studies have focused on elucidating the molecular mechanisms of this sarcoma; however, its ... -
Gene prioritization, communality analysis, networking and metabolic integrated pathway to better understand breast cancer pathogenesis
(Nature, 2018-11-12)[Abstract] Consensus strategy was proved to be highly efficient in the recognition of gene-disease association. Therefore, the main objective of this study was to apply theoretical approaches to explore genes and communities ... -
General machine learning model, review, and experimental-theoretic study of magnolol activity in enterotoxigenic induced oxidative stress
(Bentham Science, 2017)[Abstract] This study evaluated the antioxidative effects of magnolol based on the mouse model induced by Enterotoxigenic Escherichia coli (E. coli, ETEC). All experimental mice were equally treated with ETEC suspensions ... -
Improvement of Epitope Prediction Using Peptide Sequence Descriptors and Machine Learning
(MDPI, 2019)[Abstract] In this work, we improved a previous model used for the prediction of proteomes as new B-cell epitopes in vaccine design. The predicted epitope activity of a queried peptide is based on its sequence, a known ... -
Influence of disability on maternal care
(Springer, 2015)[Abstract] The purpose of the study was to examine how women with physical disabilities perceive the influence of their disability on maternal care during the first 3 years of their child’s life, to describe the main ... -
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 ... -
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 ... -
Net-net Auto machine learning (AutoML) prediction of complex ecosystems
(Nature, 2018-08-17)[Abstract] Biological Ecosystem Networks (BENs) are webs of biological species (nodes) establishing trophic relationships (links). Experimental confirmation of all possible links is difficult and generates a huge volume ... -
Net-Net AutoML Selection of Artificial Neural Network Topology for Brain Connectome Prediction
(MDPI, 2020-02-14)[Abstract] Brain Connectome Networks (BCNs) are defined by brain cortex regions (nodes) interacting with others by electrophysiological co-activation (edges). The experimental prediction of new interactions in BCNs ... -
OncoOmics approaches to reveal essential genes in breast cancer: a panoramic view from pathogenesis to precision medicine
(Springer Nature, 2020-03-24)[Abstract] Breast cancer (BC) is the leading cause of cancer-related death among women and the most commonly diagnosed cancer worldwide. Although in recent years large-scale efforts have focused on identifying new therapeutic ... -
Ontologies in medicinal chemistry: current status and future challenges
(Bentham Science, 2013)[Abstract] Recent years have seen a dramatic increase in the amount and availability of data in the diverse areas of medicinal chemistry, making it possible to achieve significant advances in fields such as the design, ... -
Parallel computing for brain simulation
(Bentham Science, 2017-05-01)[Abstract] Background: The human brain is the most complex system in the known universe, it is therefore one of the greatest mysteries. It provides human beings with extraordinary abilities. However, until now it has not ... -
Perturbation theory/machine learning model of ChEMBL data for dopamine targets: docking, synthesis, and assay of new l-prolyl-l-leucyl-glycinamide peptidomimetics
(American Chemical Society, 2018-05-23)[Abstract] Predicting drug–protein interactions (DPIs) for target proteins involved in dopamine pathways is a very important goal in medicinal chemistry. We can tackle this problem using Molecular Docking or Machine Learning ... -
Perturbation-Theory Machine Learning (PTML) Multilabel Model of the ChEMBL Dataset of Preclinical Assays for Antisarcoma Compounds
(American Chemical Society, 2020-10-14)[Abstract] Sarcomas are a group of malignant neoplasms of connective tissue with a different etiology than carcinomas. The efforts to discover new drugs with antisarcoma activity have generated large datasets of multiple ... -
Population Subset Selection for the Use of a Validation Dataset for Overfitting Control in Genetic Programming
(Taylor & Francis Group, 2019-07-31)[Abstract] Genetic Programming (GP) is a technique which is able to solve different problems through the evolution of mathematical expressions. However, in order to be applied, its tendency to overfit the data is one of ... -
Prediction of Breast Cancer Proteins Involved in Immunotherapy, Metastasis, and RNA-Binding Using Molecular Descriptors and Artifcial Neural Networks
(Springer Nature, 2020-05-22)[Abstract] Breast cancer (BC) is a heterogeneous disease where genomic alterations, protein expression deregulation, signaling pathway alterations, hormone disruption, ethnicity and environmental determinants are involved. ... -
Prediction of high anti-angiogenic activity peptides in silico using a generalized linear model and feature selection
(Nature, 2018-10-24)[Abstract] Screening and in silico modeling are critical activities for the reduction of experimental costs. They also speed up research notably and strengthen the theoretical framework, thus allowing researchers to ... -
Prediction of Nucleoitide Binding Peptides Using Star Graph Topological Índices
(Elsevier, 2015-08-05)[Abstract] The nucleotide binding proteins are involved in many important cellular processes, such as transmission of genetic information or energy transfer and storage. Therefore, the screening of new peptides for this ... -
Redesign and performance of an automatic segmentation method
(MDPI, 2018-09-05)[Resumen] La información más relevante de las angiografías coronarias se extrae empleando técnicas de segmentación, que pueden ser automáticas, semiautomáticas o manuales. Existen numerosos algoritmos de segmentación ...