[PDF] Top 20 Deep Learning for genomic data analysis
Has 10000 "Deep Learning for genomic data analysis" found on our website. Below are the top 20 most common "Deep Learning for genomic data analysis".
Deep Learning for genomic data analysis
... of data presents a big challenge for most classification algorithms and often leads to ...In deep learning, particularly, the low number of samples presents a big challenge as this method is very ... See full document
101
Improving eQTL Analysis Using a Machine Learning Approach for Data Integration
... eQTL analysis, that is, to establish the rela- tions in each regulatory network by exploiting the information of the simulated gene expression levels of the 1000 genes and the simulated genotype ...the ... See full document
42
Developing deep learning computational tools for cancer using omics data
... chine learning models, as well as an explanation about the used the ...sion data origin is the Neuroblastoma Data Integration Challenge, part of the 16th Annual International Conference on Critical ... See full document
106
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support
... on Deep Learning in Medical Image Analysis (DLMIA). Deep learning methods have experienced an immense growth in interest from the medical image analysis community because of ... See full document
16
Deep Learning in Melanoma Detection
... machine learning workflow, from data management to hyperparameter training to de- ployment solutions" [ 12 ...with deep learning due to being user-friendly, having quality APIs and some ... See full document
77
Contributions on Deep Transfer Learning
... By the analysis of the plots, independent of the chosen target dataset, a few interesting con- clusions can be drawn. First, there seems to be no significant performance change, regarding the target dataset, ... See full document
141
A Live IDE for Deep Learning Architectures
... of data that was collected: time that the participants required to com- plete every task, whether or not a participant managed to complete certain parts of a task and Likert-scale based answers [Lik32] to the ... See full document
105
Sleep Stage Classification: A Deep Learning Approach
... Lajnef et al. [56] proposed a sleep staging framework based on a DSVM classification. Its performance was evaluated using polysomnographic data from 15 subjects’ EEG, EOG and EMG recordings. They mentioned that ... See full document
187
Uma abordagem deep learning para reconhecimento de expressões faciais.
... curve analysis and matrix of ...Cohn-Kanade data set for the seven basic expressions, demonstrating the best performance against all state-of-the-art methods and, in absolute terms, the proposed approach ... See full document
82
Mobile app recommendations using deep learning and big data
... network data. Social network data is also not structured in the traditional relational database sense, which makes it difficult to represent in SQL ...network data is ...network analysis ... See full document
67
Deep learning for identification of pathogenic genetic mutations
... Functional Analysis through Hidden Markov Models (FatHMM), MutationTaster-2 (MT2) [8], MutationAssessor (MASS) [9], Combined Annotation Dependent Depletion (CADD) [10], Likelihood Ratio Test (LRT) [11] and ... See full document
132
Environmental adaptation: genomic analysis of the piezotolerant and psychrotolerant deep-sea iron reducing bacterium Shewanella piezotolerans WP3.
... detailed genomic and proteomic investigations, the COG (Clusters of Orthologous Groups) [25] functional classification of WP3 was compared with that of ...The data revealed that the percentage of genes ... See full document
12
Detection of abnormalities in ECG using Deep Learning
... machine learning algorithms, scientists have considerations in terms of computational load, data require- ments, classification time and the possibility to integrate in a device to use during daily ... See full document
80
Adam Deep Learning with SOM for Human Sentiment Classification
... media data so that immediate action or legal reaction can be ...network data has become truly ...sentiment analysis including feature selection, data integration, data cleaning, and ... See full document
23
Deep learning for cardiac MR images analysis
... collected data used during this project is made of (cine) functional CMR images, coming from a group of 55 patients, with 39 males and 16 females and ages between 12 and 92 years, collected along different imaging ... See full document
80
Deep Learning for EEG Analysis in Epilepsy
... Comparing these results with the ones obtained with Set B (cf. Figs. 7.4 and C.5 ), it is possible to conclude that there was a decrease in the performance of all models. On the test set, the VGG went from an AUC of 0.96 ... See full document
144
Deep learning for multi-class skin lesion diagnosis
... More recently, in 2019, Ly et al. [105], trained multiple models from scratch intending to deploy such models for offline usage in smartphones. They justified the decision of training a model from scratch by arguing that ... See full document
144
"Deep Reinforcement Learning" na Otimização de Políticas de Encaminhamento na Manufatura
... Em 1999, [35] utilizaram o algoritmo de Q-learning para atribuir inteligência a agen- tes, de modo a que estes tivessem a capacidade de aprender e otimizar uma política de execução local dinâmica para resolver ... See full document
171
Exploring deep learning representations for biometric multimodal systems.
... Today, deep learning techniques represent the state-of-the-art for robust feature rep- ...of deep learning to represent other biometric modalities (Ghosh ...of deep learning on ... See full document
161
Moving deep learning to the edge
... of deep learning ...specific deep learning ...run deep learning ...robust deep learning models with a life-cycle that justifies the ASIC ... See full document
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