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Mostrando ítems 11-20 de 21
Automatic Assessment of Alzheimer’s Disease Diagnosis Based on Deep Learning Techniques
(Elsevier, 2020-05)
[Abstract]
Early detection is crucial to prevent the progression of Alzheimer’s disease (AD). Thus, specialists can begin preventive treatment as soon as possible. They demand fast and precise assessment in the diagnosis ...
A methodology for the design of experiments in computational intelligence with multiple regression models
(Peer J, 2016-12-01)
[Abstract] The design of experiments and the validation of the results achieved with them are vital in any research study. This paper focuses on the use of different Machine Learning approaches for regression tasks in the ...
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 ...
Bio-AIMS collection of chemoinformatics web tools based on molecular graph information and artificial intelligence models
(Bentham, 2015-09-01)
[Abstract] The molecular information encoding into molecular descriptors is the first step into in silico Chemoinformatics methods in Drug Design. The Machine Learning methods are a complex solution to find prediction ...
Experimental and computational studies of fatty acid distribution networks
(Royal Society of Chemistry, 2015-08-06)
[Abstract] Unbalanced uptake of Omega 6/Omega 3 (ω-6/ω-3) ratios could increase chronic disease occurrences, such as inflammation, atherosclerosis, or tumor proliferation, and methylation methods for measuring the ruminal ...
Carbon nanotubes’ effect on mitochondrial oxygen flux dynamics: polarography experimental study and machine learning models using star graph trace invariants of Raman spectra
(MDPI, 2017-11-11)
[Abstract] This study presents the impact of carbon nanotubes (CNTs) on mitochondrial oxygen mass flux (Jm) under three experimental conditions. New experimental results and a new methodology are reported for the first ...
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 ...
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 ...
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 ...
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 ...