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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. ...
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 ...
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 ...
Automatic multiscale vascular image segmentation algorithm for coronary angiography
(Elsevier, 2018-09)
[Abstract] Cardiovascular diseases, particularly severe stenosis, are the main cause of death in the western world. The primary method of diagnosis, considered to be the standard in the detection and quantification of ...
UAV Swarm Path Planning With Reinforcement Learning for Field Prospecting
(Springer, 2022-03-03)
[Abstract] There has been steady growth in the adoption of Unmanned Aerial Vehicle (UAV) swarms by operators due to their time and cost benefits. However, this kind of system faces an important problem, which is the ...
Convolutional Neural Networks for Sleep Stage Scoring on a Two-Channel EEG Signal
(Springer Nature, 2019-06-26)
[Abstract]
Sleeping problems have become one of the major diseases all over the world. To tackle this issue, the basic tool used by specialists is the Polysomnogram, which is a collection of different signals recorded ...
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 ...
Artificial Neuron–Glia Networks Learning Approach Based on Cooperative Coevolution
(World Scientific, 2015-04-06)
[Abstract] Artificial Neuron–Glia Networks (ANGNs) are a novel bio-inspired machine learning approach. They extend classical Artificial Neural Networks (ANNs) by incorporating recent findings and suppositions about the way ...
