Hierarchical System of Gene Selection Based on Deep Learning and Ensemble Approach

2021 ◽  
Author(s):  
Dominik Seweryn ◽  
Stanislaw Osowski
Keyword(s):  
Deep Learning ◽  
Gene Selection ◽  
Hierarchical System ◽  
Ensemble Approach

Deep learning based ensemble approach for probabilistic wind power forecasting

2017 ◽  
Vol 188 ◽  
pp. 56-70 ◽  
Author(s):  
Huai-zhi Wang ◽  
Gang-qiang Li ◽  
Gui-bin Wang ◽  
Jian-chun Peng ◽  
Hui Jiang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Power ◽  
Wind Power Forecasting ◽  
Ensemble Approach ◽  
Power Forecasting

scholarly journals A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 150530-150539 ◽  
Author(s):  
Sehrish Qummar ◽  
Fiaz Gul Khan ◽  
Sajid Shah ◽  
Ahmad Khan ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Ensemble Approach

scholarly journals Deep learning-based gene selection in comprehensive gene analysis in pancreatic cancer

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yasukuni Mori ◽  
Hajime Yokota ◽  
Isamu Hoshino ◽  
Yosuke Iwatate ◽  
Kohei Wakamatsu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Pancreatic Cancer ◽  
Deep Learning ◽  
Gene Selection ◽  
Pancreatic Tissue ◽  
Expression Data ◽  
Tissue Samples ◽  
Normal Pancreatic Tissue ◽  
Model Training ◽  
Selection Of

AbstractThe selection of genes that are important for obtaining gene expression data is challenging. Here, we developed a deep learning-based feature selection method suitable for gene selection. Our novel deep learning model includes an additional feature-selection layer. After model training, the units in this layer with high weights correspond to the genes that worked effectively in the processing of the networks. Cancer tissue samples and adjacent normal pancreatic tissue samples were collected from 13 patients with pancreatic ductal adenocarcinoma during surgery and subsequently frozen. After processing, gene expression data were extracted from the specimens using RNA sequencing. Task 1 for the model training was to discriminate between cancerous and normal pancreatic tissue in six patients. Task 2 was to discriminate between patients with pancreatic cancer (n = 13) who survived for more than one year after surgery. The most frequently selected genes were ACACB, ADAMTS6, NCAM1, and CADPS in Task 1, and CD1D, PLA2G16, DACH1, and SOWAHA in Task 2. According to The Cancer Genome Atlas dataset, these genes are all prognostic factors for pancreatic cancer. Thus, the feasibility of using our deep learning-based method for the selection of genes associated with pancreatic cancer development and prognosis was confirmed.

Ensemble Approach to Deep Learning and Numerical Ocean Modelling of Adriatic Storm Surges

2020 ◽  
Author(s):  
Matjaz Licer ◽  
Lojze Žust ◽  
Matej Kristan
Keyword(s):  
Deep Learning ◽  
Sea Level ◽  
Ocean Circulation ◽  
Adriatic Sea ◽  
State Of The Art ◽  
Storm Surges ◽  
Phase Lag ◽  
Deterministic Models ◽  
Northern Adriatic ◽  
Ensemble Approach

<p>Storm surges are among the most serious threats to Venice, Chioggia, Piran and other historic coastal towns in Northern Adriatic. Adriatic Sea has a well defined lowest seiche period of approximately 22 hours and its amplitude decays on the scale of several days, reinforcing (or diminishing) the tidal signal, depending on the relative phase lag between tides and surges. This makes prediction of Adriatic sea level extremely difficult using conventional deterministic models. The current state-of-the-art predictions of sea surface height (SSH) hence involve numerical ocean models using ensemble forcing. These simulations are computationally-demanding and time consuming, making the method unsuitable for operational or civil rescue services with limited access to dedicated high-performance computing facilities.</p><p>Ensemble approach to deep learning offers a possible solution to the challenges described above. Even though training a deep network may involve substantial computational resources, the subsequent forecasting -- even ensemble forecasting -- is fast and delivers near-realtime SSH predictions (and associated error variances) on a personal computer. In this work we present an ensemble SSH forecast using new deep convolutional neural network for sea-level prediction in the Adriatic basin and compare it to the standard approach using state-of-the-art publicly available modelling components (NEMO ocean circulation model and TensorFlow libraries for deep learning).</p>

scholarly journals A three-stage, deep learning, ensemble approach for prognosis in patients with Parkinson’s disease

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Kevin H. Leung ◽  
Steven P. Rowe ◽  
Martin G. Pomper ◽  
Yong Du
Keyword(s):  
Parkinson’S Disease ◽  
Deep Learning ◽  
Correlation Coefficient ◽  
Motor Symptoms ◽  
Pearson’S Correlation ◽  
Pearson’S Correlation Coefficient ◽  
Ensemble Approach ◽  
Pearson's Correlation ◽  
Clinical Measures ◽  
Pearson's Correlation Coefficient

Abstract Background Diagnosis of Parkinson’s disease (PD) is informed by the presence of progressive motor and non-motor symptoms and by imaging dopamine transporter with [123I]ioflupane (DaTscan). Deep learning and ensemble methods have recently shown promise in medical image analysis. Therefore, this study aimed to develop a three-stage, deep learning, ensemble approach for prognosis in patients with PD. Methods Retrospective data of 198 patients with PD were retrieved from the Parkinson’s Progression Markers Initiative database and randomly partitioned into the training, validation, and test sets with 118, 40, and 40 patients, respectively. The first and second stages of the approach extracted features from DaTscan and clinical measures of motor symptoms, respectively. The third stage trained an ensemble of deep neural networks on different subsets of the extracted features to predict patient outcome 4 years after initial baseline screening. The approach was evaluated by assessing mean absolute percentage error (MAPE), mean absolute error (MAE), Pearson’s correlation coefficient, and bias between the predicted and observed motor outcome scores. The approach was compared to individual networks given different data subsets as inputs. Results The ensemble approach yielded a MAPE of 18.36%, MAE of 4.70, a Pearson’s correlation coefficient of 0.84, and had no significant bias indicating accurate outcome prediction. The approach outperformed individual networks not given DaTscan imaging or clinical measures of motor symptoms as inputs, respectively. Conclusion The approach showed promise for longitudinal prognostication in PD and demonstrated the synergy of imaging and non-imaging information for the prediction task.

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