A predictive model of gene expression using a deep learning framework

2016 ◽  
Author(s):  
Rui Xie ◽  
Andrew Quitadamo ◽  
Jianlin Cheng ◽  
Xinghua Shi
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Predictive Model ◽  
Learning Framework

scholarly journals Correlating predicted epigenetic marks with expression data to find interactions between SNPs and genes

2020 ◽  
Author(s):  
Antoine Despinasse ◽  
Yongjin Park ◽  
Michael Lapi ◽  
Manolis Kellis
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Dna Sequence ◽  
Target Genes ◽  
Eqtl Analysis ◽  
Expression Data ◽  
Learning Framework ◽  
Coding Regions ◽  
Gwas Snps ◽  
Work Done

ABSTRACTDespite all the work done, mapping GWAS SNPs in non-coding regions to their target genes remains a challenge. The SNP can be associated with target genes by eQTL analysis. Here we introduce a method to make these eQTLs more robust. Instead of correlating the gene expression with the SNP value like in eQTLs, we correlate it with epigenomic data. This epigenomic data is very expensive and noisy. We therefore predict the epigenomic data from the DNA sequence using the deep learning framework DeepSEA (Zhou and Troyanskaya, 2015).

scholarly journals GNE: A deep learning framework for gene network inference by aggregating biological information

2018 ◽  
Author(s):  
K C Kishan ◽  
Rui Li ◽  
Feng Cui ◽  
Qi Yu ◽  
Anne R. Haake
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Network Inference ◽  
Gene Interaction ◽  
Interaction Networks ◽  
Biological Information ◽  
Gene Interactions ◽  
Learning Framework ◽  
Gene Interaction Networks ◽  
Low Dimensional

AbstractThe topological landscape of gene interaction networks provides a rich source of information for inferring functional patterns of genes or proteins. However, it is still a challenging task to aggregate heterogeneous biological information such as gene expression and gene interactions to achieve more accurate inference for prediction and discovery of new gene interactions. In particular, how to generate a unified vector representation to integrate diverse input data is a key challenge addressed here. We propose a scalable and robust deep learning framework to learn embedded representations to unify known gene interactions and gene expression for gene interaction predictions. These low-dimensional embeddings derive deeper insights into the structure of rapidly accumulating and diverse gene interaction networks and greatly simplify downstream modeling. We compare the predictive power of our deep embeddings to the strong baselines. The results suggest that our deep embeddings achieve significantly more accurate predictions. Moreover, a set of novel gene interaction predictions are validated by up-to-date literature-based database entries. GNE is freely available under the GNU General Public License and can be downloaded from Github (https://github.com/kckishan/GNE)

scholarly journals Prediction of Air Flight Cancellation during COVID-19 using Deep Learning Methods

2020 ◽  
Author(s):  
Samir Kumar Bandyopadhyay ◽  
Vishal Goyel ◽  
SHAWNI DUTTA
Keyword(s):  
Deep Learning ◽  
Predictive Model ◽  
Short Term Memory ◽  
Disease Outbreaks ◽  
Air Traffic ◽  
Aviation Industry ◽  
Learning Framework ◽  
History Of ◽  
Flight Cancellations ◽  
Oil Crises

Air traffic is vulnerable to external factors, such as oil crises, natural disasters, economic recessions and disease outbreaks due to COVID-19. This reason seems to have a more severe and more rapid impact on air traffic numbers as sudden increases in flight cancellations, aircraft groundings and travel bans. Various Airways loose revenues and it is difficult for them to sustain for a long period. This problem as been facing the entire world. The reductions in passenger numbers are significant. It is due to flights being cancelled or planes flying empty between airports. It is in turn massively reducing revenues for airlines and forced many airlines to lay off employees or declare bankruptcy. Airways also have to attempt refunding cancelled trips in order to diminish their losses. The airliner manufacturers and airport operators have also laid off employees. According to some commentators, this crisis is the worst ever encountered in the history of the aviation industry. Aircraft cancellation prediction is accomplished by utilising deep learning framework. In this framework, two dissimilar recurrent neural networks are assembled as a single entity while inferring the prediction results. Long-short term memory (LSTM) and Gated Recurrent Unit (GRU) are employed to design the proposed predictive model. This predictive model is compared against traditional neural network based Multi-layer perceptron model. Experimental results indicated an accuracy of 98.7% by the proposed model.

A Deep Learning Framework for Prediction of Retinal Disorders

2020 ◽  
Author(s):  
Raniyaharini R ◽  
Madhumitha K ◽  
Mishaa S ◽  
Virajaravi R
Keyword(s):  
Deep Learning ◽  
Learning Framework ◽  
Retinal Disorders

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction

2020 ◽  
Vol 26 ◽  
Author(s):  
Xiaoping Min ◽  
Fengqing Lu ◽  
Chunyan Li
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Dna Sequences ◽  
Experimental Methods ◽  
Learning Methods ◽  
Comprehensive Review ◽  
Genomic Features ◽  
Disease Mechanisms ◽  
Evaluation Strategies ◽  
Learning Frameworks

: Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation which tightly controls gene expression. Identification of EPIs can help us better deciphering gene regulation and understanding disease mechanisms. However, experimental methods to identify EPIs are constrained by the fund, time and manpower while computational methods using DNA sequences and genomic features are viable alternatives. Deep learning methods have shown promising prospects in classification and efforts that have been utilized to identify EPIs. In this survey, we specifically focus on sequence-based deep learning methods and conduct a comprehensive review of the literatures of them. We first briefly introduce existing sequence-based frameworks on EPIs prediction and their technique details. After that, we elaborate on the dataset, pre-processing means and evaluation strategies. Finally, we discuss the challenges these methods are confronted with and suggest several future opportunities.

COVID-19 pneumonia diagnosis using a simple 2D deep learning framework with a single chest CT image (Preprint)

2020 ◽  
Author(s):  
Jinseok Lee
Keyword(s):  
Deep Learning ◽  
Diagnostic Performance ◽  
Ct Images ◽  
Chest Ct ◽  
University Hospital ◽  
Detection Accuracy ◽  
Ct Image ◽  
Test Dataset ◽  
Learning Framework ◽  
Testing Dataset

BACKGROUND The coronavirus disease (COVID-19) has explosively spread worldwide since the beginning of 2020. According to a multinational consensus statement from the Fleischner Society, computed tomography (CT) can be used as a relevant screening tool owing to its higher sensitivity for detecting early pneumonic changes. However, physicians are extremely busy fighting COVID-19 in this era of worldwide crisis. Thus, it is crucial to accelerate the development of an artificial intelligence (AI) diagnostic tool to support physicians. OBJECTIVE We aimed to quickly develop an AI technique to diagnose COVID-19 pneumonia and differentiate it from non-COVID pneumonia and non-pneumonia diseases on CT. METHODS A simple 2D deep learning framework, named fast-track COVID-19 classification network (FCONet), was developed to diagnose COVID-19 pneumonia based on a single chest CT image. FCONet was developed by transfer learning, using one of the four state-of-art pre-trained deep learning models (VGG16, ResNet50, InceptionV3, or Xception) as a backbone. For training and testing of FCONet, we collected 3,993 chest CT images of patients with COVID-19 pneumonia, other pneumonia, and non-pneumonia diseases from Wonkwang University Hospital, Chonnam National University Hospital, and the Italian Society of Medical and Interventional Radiology public database. These CT images were split into a training and a testing set at a ratio of 8:2. For the test dataset, the diagnostic performance to diagnose COVID-19 pneumonia was compared among the four pre-trained FCONet models. In addition, we tested the FCONet models on an additional external testing dataset extracted from the embedded low-quality chest CT images of COVID-19 pneumonia in recently published papers. RESULTS Of the four pre-trained models of FCONet, the ResNet50 showed excellent diagnostic performance (sensitivity 99.58%, specificity 100%, and accuracy 99.87%) and outperformed the other three pre-trained models in testing dataset. In additional external test dataset using low-quality CT images, the detection accuracy of the ResNet50 model was the highest (96.97%), followed by Xception, InceptionV3, and VGG16 (90.71%, 89.38%, and 87.12%, respectively). CONCLUSIONS The FCONet, a simple 2D deep learning framework based on a single chest CT image, provides excellent diagnostic performance in detecting COVID-19 pneumonia. Based on our testing dataset, the ResNet50-based FCONet might be the best model, as it outperformed other FCONet models based on VGG16, Xception, and InceptionV3.

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