Probabilistic Graphical Models and Deep Belief Networks for Prognosis of Breast Cancer

Khademi, Mahmoud; Nedialkov, Nedialko S.

doi:10.1109/icmla.2015.196

Cited by 44 publications

(30 citation statements)

References 12 publications

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“…Ibrahim et al [46] proposed a DBN with an active learning approach to find features in genes and microRNA that resulted in the best classification performance of various cancer diseases such as hepatocellular carcinoma, lung cancer and breast cancer. For breast cancer genetic detection, Khademi et al [47] overcame missing attributes and noise by combining a DBN and Bayesian network to extract features from microarray data. Deep learning approaches have also outperformed SVM in predicting splicing code and understanding how gene expression changes by genetic variants [48], [130].…”

Section: A Translational Bioinformaticsmentioning

confidence: 99%

Deep Learning for Health Informatics

Ravì

Wong

Deligianni

et al. 2017

IEEE J. Biomed. Health Inform.

1,519

776

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With a massive influx of multimodality data, the role of data analytics in health informatics has grown rapidly in the last decade. This has also prompted increasing interests in the generation of analytical, data driven models based on machine learning in health informatics. Deep learning, a technique with its foundation in artificial neural networks, is emerging in recent years as a powerful tool for machine learning, promising to reshape the future of artificial intelligence. Rapid improvements in computational power, fast data storage, and parallelization have also contributed to the rapid uptake of the technology in addition to its predictive power and ability to generate automatically optimized high-level features and semantic interpretation from the input data. This article presents a comprehensive up-to-date review of research employing deep learning in health informatics, providing a critical analysis of the relative merit, and potential pitfalls of the technique as well as its future outlook. The paper mainly focuses on key applications of deep learning in the fields of translational bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health.

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Section: A Translational Bioinformaticsmentioning

confidence: 99%

Deep Learning for Health Informatics

Ravì

Wong

Deligianni

et al. 2017

IEEE J. Biomed. Health Inform.

1,519

776

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“…En la genómica, las ANNs y el DL se han aplicado al diagnóstico de cáncer, la expresión de genes y la secuenciación genómica. Dentro del diagnóstico de cáncer se han desarrollado sistemas para la identificación y extracción de características genéticas presentes en el cáncer [21][22], y sistemas para la detección y clasificación de distintos tipos de cáncer a partir de la expresión genética [23][24] y de la mutación puntual somática en secuencias de ADN [25]. En la expresión de genes, que se refiere al proceso de producción de una proteína a partir de la información codificada en el ADN, se han aplicado a la predicción de la expresión de genes [26][27][28] y a la creación de modelos que predicen el resultado del empalme alternativo a partir de la información del ADN en diferentes contextos celulares [29][30].…”

Section: [15]unclassified

Aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica

Ramos¹

2020

revuin

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Hoy en día, las redes neuronales artificiales y el deep learning, son dos de las herramientas más poderosas del aprendizaje de máquina, que tienen por objetivo desarrollar sistemas que aprenden automáticamente, reconocen patrones, predicen comportamientos y generalizan información a partir de conjuntos de datos. Estasdos herramientas se han convertido en un potencial campo de investigación con aplicaciones a la ingeniería, no siendo la ingeniería biomédica la excepción. En este artículo se presenta una revisión actualizada de las principales aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica en las ramas de la ómica, la imagenología, las interfaces cerebro-máquina y hombre-máquina, y la gestión y administración de la salud pública; ramas que se extienden desde el estudio de procesos a nivel molecular, hasta procesos que involucran grandes poblaciones.Palabras clave:aprendizaje de máquina; inteligencia artificial; reconocimiento de patrones; ómica; bioinformática; biomedicina; imagenología; interfaces cerebro-máquina; interfaces hombre-máquina;salud pública.

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“…Nguyen et al introduce a method for breast cancer prognosis prediction based on random forest (RF) combined with feature selection and achieve the highest classification accuracy than previous methods [6]. Khademi et al propose a probabilistic graphical model (PGM) by integrating two independent models of microarray and clinical data for prognosis and diagnosis of breast cancer [7]. They first apply Principal Component Analysis (PCA) to reduce the dimensionality of microarray data and construct a deep belief network to extract feature representation of the data.…”

Section: Related Workmentioning

confidence: 99%

“…One of the most straightforward approaches for discriminative tasks is to train only one DNN model for all multi-dimensional data. However, different data may have different feature representation, and directly combining the three sources of data as an input of a DNN model may not be efficient [7]. We address this problem by proposing a multimodal DNN model which efficiently integrates multi-dimensional data.…”

Section: Back-end Fusion Of Multi-dimensional Datamentioning

confidence: 99%

Research on Colorectal Cancer Prediction and Survival Analysis with Data Fusion Based on Deep Learning

2019

Proceedings of 2019 the 9th International Workshop on Computer Science and Engineering

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Colorectal cancer is a highly aggressive type of cancer. Accurate prognosis prediction of colorectal cancer can help patients and doctors choose the best treatment and avoid unnecessary costs. Nevertheless, most of previous work relied mostly on selected gene expression data to create a predictive model by traditional machine learning methods to reduce dimensions and predict cancer. Such methods cannot fully consider the correlation between samples to effectively extract feature information, and the cost of calculation is huge. In this paper, we propose a multi-modal neural network which integrates GCN and DNN to train multi-modal data for the prediction of colorectal cancer and its prognosis. The novelty of the method lies in the design of our method's architecture and the fusion of multi-dimensional data, which can give full play to the performance of the neural network. The comprehensive performance evaluation results show that the proposed method achieves a better performance than the widely used prediction methods with single dimensional data and other existing approaches.

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Probabilistic Graphical Models and Deep Belief Networks for Prognosis of Breast Cancer

Cited by 44 publications

References 12 publications

Deep Learning for Health Informatics

Deep Learning for Health Informatics

Aplicaciones de las redes neuronales y el deep learning a la ingeniería biomédica

Research on Colorectal Cancer Prediction and Survival Analysis with Data Fusion Based on Deep Learning

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