scholarly journals Analysis of Extremely Obese Individuals Using Deep Learning Stacked Autoencoders and Genome-Wide Genetic Data

2020 ◽  
pp. 262-276
Author(s):  
Casimiro A. Curbelo Montañez ◽  
Paul Fergus ◽  
Carl Chalmers ◽  
Jade Hind
Keyword(s):  
Deep Learning ◽  
Genetic Data ◽  
Genome Wide ◽  
Stacked Autoencoders

scholarly journals Integrative oncogene-dependency mapping identifies RIT1 vulnerabilities and synergies in lung cancer

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Athea Vichas ◽  
Amanda K. Riley ◽  
Naomi T. Nkinsi ◽  
Shriya Kamlapurkar ◽  
Phoebe C. R. Parrish ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Spindle Assembly Checkpoint ◽  
Spindle Assembly ◽  
Genetic Data ◽  
Multiple Models ◽  
Ras Pathway ◽  
Functional Interactions ◽  
Genome Wide ◽  
A Genome ◽  
Mutant Cells

AbstractCRISPR-based cancer dependency maps are accelerating advances in cancer precision medicine, but adequate functional maps are limited to the most common oncogenes. To identify opportunities for therapeutic intervention in other rarer subsets of cancer, we investigate the oncogene-specific dependencies conferred by the lung cancer oncogene, RIT1. Here, genome-wide CRISPR screening in KRAS, EGFR, and RIT1-mutant isogenic lung cancer cells identifies shared and unique vulnerabilities of each oncogene. Combining this genetic data with small-molecule sensitivity profiling, we identify a unique vulnerability of RIT1-mutant cells to loss of spindle assembly checkpoint regulators. Oncogenic RIT1M90I weakens the spindle assembly checkpoint and perturbs mitotic timing, resulting in sensitivity to Aurora A inhibition. In addition, we observe synergy between mutant RIT1 and activation of YAP1 in multiple models and frequent nuclear overexpression of YAP1 in human primary RIT1-mutant lung tumors. These results provide a genome-wide atlas of oncogenic RIT1 functional interactions and identify components of the RAS pathway, spindle assembly checkpoint, and Hippo/YAP1 network as candidate therapeutic targets in RIT1-mutant lung cancer.

scholarly journals Deep learning identifies genome-wide DNA binding sites of long noncoding RNAs

RNA Biology ◽  
2018 ◽  
Vol 15 (12) ◽  
pp. 1468-1476 ◽  
Author(s):  
Fan Wang ◽  
Pranik Chainani ◽  
Tommy White ◽  
Jin Yang ◽  
Yu Liu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Dna Binding ◽  
Binding Sites ◽  
Noncoding Rnas ◽  
Long Noncoding Rnas ◽  
Dna Binding Sites ◽  
Genome Wide

Genetic Data Analysis

2021 ◽  
pp. 358-372
Author(s):  
M. Shamila ◽  
Amit Kumar Tyagi
Keyword(s):  
Data Analysis ◽  
Association Studies ◽  
Single Gene ◽  
Genetic Data ◽  
Building Blocks ◽  
Genome Wide Association ◽  
Genome Wide Association Studies ◽  
Human Beings ◽  
Genome Wide ◽  
Genetic Data Analysis

Genome-wide association studies (GWAS) or genetic data analysis is used to discover common genetic factors which influence the health of human beings and become a part of a disease. The concept of using genomics has increased in recent years, especially in e-healthcare. Today there is huge improvement required in this field or genomics. Note that the terms genomics and genetics are not similar terms here. Basically, the human genome is made up of DNA, which consists of four different chemical building blocks (called bases and abbreviated A, T, C, and G). Based on this, we differentiate each and every human being living on earth. The term ‘genetics' originated from the Greek word ‘genetikos'. It means ‘origin'. In simple terms, genetics can be defined as a branch of biology, which deals with the study of the functionalities and composition of a single gene in an organism. There are mainly three branches of genetics, which include classical genetics, molecular genetics, and population genetics.

scholarly journals Application of Model-Based Deep Learning Algorithm in Fault Diagnosis of Coal Mills

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Yifan Jian ◽  
Xianguo Qing ◽  
Yang Zhao ◽  
Liang He ◽  
Xiao Qi
Keyword(s):  
Deep Learning ◽  
Fault Diagnosis ◽  
Learning Algorithm ◽  
Mechanism Model ◽  
Model Based ◽  
Deep Learning Algorithm ◽  
Stacked Autoencoders ◽  
Different Types ◽  
Operation Status ◽  
Types Of Faults

The coal mill is one of the important auxiliary engines in the coal-fired power station. Its operation status is directly related to the safe and steady operation of the units. In this paper, a model-based deep learning algorithm for fault diagnosis is proposed to effectively detect the operation state of coal mills. Based on the system mechanism model of coal mills, massive fault data are obtained by analyzing and simulating the different types of faults. Then, stacked autoencoders (SAEs) are established by combining the said data with the deep learning algorithm. The SAE model is trained by the fault data, which provide it with the learning and identification capability of the characteristics of faults. According to the simulation results, the accuracy of fault diagnosis of coal mills based on SAE is high at 98.97%. Finally, the proposed SAEs can well detect the fault in coal mills and generate the warnings in advance.

scholarly journals Predicting the Landscape of Recombination Using Deep Learning

2020 ◽  
Vol 37 (6) ◽  
pp. 1790-1808 ◽  
Author(s):  
Jeffrey R Adrion ◽  
Jared G Galloway ◽  
Andrew D Kern
Keyword(s):  
Deep Learning ◽  
Evolutionary History ◽  
Model Misspecification ◽  
Natural Populations ◽  
High Accuracy ◽  
Demographic Model ◽  
Recombination Rates ◽  
Genome Wide ◽  
A Genome ◽  
The Face

Abstract Accurately inferring the genome-wide landscape of recombination rates in natural populations is a central aim in genomics, as patterns of linkage influence everything from genetic mapping to understanding evolutionary history. Here, we describe recombination landscape estimation using recurrent neural networks (ReLERNN), a deep learning method for estimating a genome-wide recombination map that is accurate even with small numbers of pooled or individually sequenced genomes. Rather than use summaries of linkage disequilibrium as its input, ReLERNN takes columns from a genotype alignment, which are then modeled as a sequence across the genome using a recurrent neural network. We demonstrate that ReLERNN improves accuracy and reduces bias relative to existing methods and maintains high accuracy in the face of demographic model misspecification, missing genotype calls, and genome inaccessibility. We apply ReLERNN to natural populations of African Drosophila melanogaster and show that genome-wide recombination landscapes, although largely correlated among populations, exhibit important population-specific differences. Lastly, we connect the inferred patterns of recombination with the frequencies of major inversions segregating in natural Drosophila populations.

Brain Tumor Detection by Using Stacked Autoencoders in Deep Learning

2019 ◽  
Vol 44 (2) ◽  
Author(s):  
Javaria Amin ◽  
Muhammad Sharif ◽  
Nadia Gul ◽  
Mudassar Raza ◽  
Muhammad Almas Anjum ◽  
...  
Keyword(s):  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Detection ◽  
Stacked Autoencoders
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