Your browser doesn't support javascript.
loading
Mostrar: 20 | 50 | 100
Resultados 1 - 4 de 4
Filtrar
Mais filtros











Base de dados
Intervalo de ano de publicação
1.
Artigo em Inglês | MEDLINE | ID: mdl-39186430

RESUMO

Identifying circular RNA (circRNA)-drug sensitivity association (CDsA) is crucial for advancing drug development. As conducting traditional wet experiments for determining CDsA is costly and inefficient, calculation methods have already proven to be a valid approach to cope with this problem. However, there exists limited research addressing the prediction of the CDsA prediction problem, and certain discrepancies persist, particularly concerning false-negative associations. As a consequence, we present a multi-view framework, called MAGSDMF, for identifying latent CDsA. Firstly, MAGSDMF applies Multiple Attention mechanisms and Graph learning methods to dynamically extract features and strengthen the features of inside and across multi-similarity networks of circRNA and drug. Secondly, the Stack Deep Matrix Factorization (SDMF) is devised to directly extract features from CDsAs. We consider multi-similarity networks with the original CDsAs as multi-view information. Thirdly, MAGSDMF utilizes a multiattention channel mechanism to integrate these features for the purpose of reconstructing CDsA. Finally, MAGSDMF performs another DMF based on the reconstruction to identify the latent CDsAs. Simultaneously, contrastive learning (CL) is implemented to enhance the generalization capability of MAGSDMF and oversee the learning process of the underlying links prediction task. In comparative experiments, MAGSDMF achieves superior performance on two datasets with AUC values of 0.9743 and 0.9739 based on 5-fold cross-validation. Moreover, in case studies, the achievements further validate the identification reliability of MAGSDMF.

2.
IEEE Trans Nanobioscience ; 22(1): 52-62, 2023 01.
Artigo em Inglês | MEDLINE | ID: mdl-35171775

RESUMO

Memristive technologies are attractive due to their non-volatility, high-density, low-power and compatibility with CMOS. For memristive devices, a model corresponding to practical behavioral characteristics is highly favorable for the realization of its neuromorphic system and applications. This paper presents a novel flexible memristor model with electronic resistive switching memory behavior. Firstly, the Ag-Au / MoSe2-doped Se / Au-Ag memristor is prepared using hydrothermal synthesis method and magnetron sputtering method, and its performance test is conducted on an electrochemical workstation. Then, the mathematical model and SPICE circuit model of the Ag-Au / MoSe2-doped Se / Au-Ag memristor are constructed. The model accuracy is verified by using the electrochemical data derived from the performance test. Furthermore, the proposed model is applied to the circuit implementation of spiking neural network with biological mechanism. Finally, computer simulations and analysis are carried out to verify the validity and effectiveness of the entire scheme.


Assuntos
Eletrônica , Redes Neurais de Computação , Simulação por Computador
3.
Sensors (Basel) ; 15(1): 1312-20, 2015 Jan 12.
Artigo em Inglês | MEDLINE | ID: mdl-25587978

RESUMO

Each year, some 30 percent of global deaths are caused by cardiovascular diseases. This figure is worsening due to both the increasing elderly population and severe shortages of medical personnel. The development of a cardiovascular diseases classifier (CDC) for auto-diagnosis will help address solve the problem. Former CDCs did not achieve quick evaluation of cardiovascular diseases. In this letter, a new CDC to achieve speedy detection is investigated. This investigation incorporates the analytic hierarchy process (AHP)-based multiple criteria decision analysis (MCDA) to develop feature vectors using a Support Vector Machine. The MCDA facilitates the efficient assignment of appropriate weightings to potential patients, thus scaling down the number of features. Since the new CDC will only adopt the most meaningful features for discrimination between healthy persons versus cardiovascular disease patients, a speedy detection of cardiovascular diseases has been successfully implemented.


Assuntos
Doenças Cardiovasculares/classificação , Técnicas de Apoio para a Decisão , Algoritmos , Bases de Dados como Assunto , Eletrocardiografia , Humanos
4.
IEEE Trans Neural Netw Learn Syst ; 23(1): 150-62, 2012 Jan.
Artigo em Inglês | MEDLINE | ID: mdl-24808464

RESUMO

The aim of this paper is to construct a bio-inspired hierarchical neural network that could accurately represent visual images and facilitate follow-up processing. Our computational model adopted a ganglion cell (GC) mechanism with a receptive field that dynamically self-adjusts according to the characteristics of an input image. For each GC, a micro neural circuit and a reverse control circuit were developed to self-adaptively resize the receptive field. An array was also designed to imitate the layer of GCs that perform image representation. Results revealed that this GC array could represent images from the external environment with a low processing cost, and this nonclassical receptive field mechanism could substantially improve both segmentation and integration processing. This model enables automatic extraction of blocks from images, which makes multiscale representation feasible. Importantly, once an original pixel-level image was reorganized into a GC array, semantic-level features emerged. Because GCs, like symbols, are discrete and separable, this GC-grained compact representation is open to operations that can manipulate images partially and selectively. Thus, the GC-array model provides a basic infrastructure and allows for high-level image processing.


Assuntos
Processamento de Imagem Assistida por Computador/métodos , Modelos Neurológicos , Redes Neurais de Computação , Reconhecimento Visual de Modelos , Células Ganglionares da Retina , Campos Visuais , Humanos , Reconhecimento Visual de Modelos/fisiologia , Células Ganglionares da Retina/fisiologia , Campos Visuais/fisiologia
SELEÇÃO DE REFERÊNCIAS
DETALHE DA PESQUISA