scholarly journals MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning

2021 ◽  
Vol 22 (8) ◽  
pp. 4217
Author(s):  
Vladimir Nosi ◽  
Alessandrì Luca ◽  
Melissa Milan ◽  
Maddalena Arigoni ◽  
Silvia Benvenuti ◽  
...  
Keyword(s):  
Neural Networks ◽  
Exon Skipping ◽  
Driver Genes ◽  
Cancer Driver ◽  
Rnaseq Data ◽  
The Neural Networks ◽  
Long Interspersed Nuclear Element ◽  
Mesenchymal Epithelial Transition

Background: Disruption of alternative splicing (AS) is frequently observed in cancer and might represent an important signature for tumor progression and therapy. Exon skipping (ES) represents one of the most frequent AS events, and in non-small cell lung cancer (NSCLC) MET exon 14 skipping was shown to be targetable. Methods: We constructed neural networks (NN/CNN) specifically designed to detect MET exon 14 skipping events using RNAseq data. Furthermore, for discovery purposes we also developed a sparsely connected autoencoder to identify uncharacterized MET isoforms. Results: The neural networks had a Met exon 14 skipping detection rate greater than 94% when tested on a manually curated set of 690 TCGA bronchus and lung samples. When globally applied to 2605 TCGA samples, we observed that the majority of false positives was characterized by a blurry coverage of exon 14, but interestingly they share a common coverage peak in the second intron and we speculate that this event could be the transcription signature of a LINE1 (Long Interspersed Nuclear Element 1)-MET (Mesenchymal Epithelial Transition receptor tyrosine kinase) fusion. Conclusions: Taken together, our results indicate that neural networks can be an effective tool to provide a quick classification of pathological transcription events, and sparsely connected autoencoders could represent the basis for the development of an effective discovery tool.

scholarly journals MET Exon 14 Skipping: A Case Study for the Detection of Genetic Variants in Cancer Driver Genes by Deep Learning

2021 ◽  
Author(s):  
Vladimir Nosi ◽  
Alessandrì Luca ◽  
Melissa Milan ◽  
Maddalena Arigoni ◽  
Silvia Benvenuti ◽  
...  
Keyword(s):  
Genetic Variants ◽  
Detection Rate ◽  
Exon Skipping ◽  
Small Cell ◽  
Small Cell Lung ◽  
Driver Genes ◽  
Cancer Driver ◽  
Rnaseq Data

Background: Disruption of alternative splicing (AS) is frequently observed in cancer and it might represent an important signature for tumor progression and therapy. Exon skipping (ES) represents one of the most frequent AS events and in non-small cell lung cancer (NSCLC) MET exon 14 skipping was shown to be targetable. Methods: We constructed a neural network (NN) specifically designed to detect MET exon 14 skipping events using RNAseq data. Furthermore, for discovery purpose we also developed a sparsely connected autoencoder to identify uncharacterized MET isoforms. Results: The NN had 100% Met exon 14 skipping detection rate, when tested on a manually curated set of 690 TCGA bronchus and lung samples. When globally applied to 2605 TCGA samples, we observed that the majority of false positives was characterized by a blurry coverage of exon 14, but interesting they share a common coverage peak in the second intron and we speculate that this event could be the transcription signature of a LINE1-MET fusion. Conclusions: Taken together our results indicate that neural networks can be an effective tool to provide a quick classification of pathological transcription events and sparsely connected autoencoders could represent the basis for the development of an effective discovery tool.

scholarly journals Machine learning algorithm improved automated droplet classification of ddPCR for detection of BRAF V600E in paraffin-embedded samples

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gabriel A. Colozza-Gama ◽  
Fabiano Callegari ◽  
Nikola Bešič ◽  
Ana C. de J. Paviza ◽  
Janete M. Cerutti
Keyword(s):  
Machine Learning ◽  
Sanger Sequencing ◽  
Learning Algorithm ◽  
Absolute Quantification ◽  
Braf V600e Mutation ◽  
Braf V600e ◽  
Driver Genes ◽  
Quantitative Classification ◽  
Cancer Driver

AbstractSomatic mutations in cancer driver genes can help diagnosis, prognosis and treatment decisions. Formalin-fixed paraffin-embedded (FFPE) specimen is the main source of DNA for somatic mutation detection. To overcome constraints of DNA isolated from FFPE, we compared pyrosequencing and ddPCR analysis for absolute quantification of BRAF V600E mutation in the DNA extracted from FFPE specimens and compared the results to the qualitative detection information obtained by Sanger Sequencing. Sanger sequencing was able to detect BRAF V600E mutation only when it was present in more than 15% total alleles. Although the sensitivity of ddPCR is higher than that observed for Sanger, it was less consistent than pyrosequencing, likely due to droplet classification bias of FFPE-derived DNA. To address the droplet allocation bias in ddPCR analysis, we have compared different algorithms for automated droplet classification and next correlated these findings with those obtained from pyrosequencing. By examining the addition of non-classifiable droplets (rain) in ddPCR, it was possible to obtain better qualitative classification of droplets and better quantitative classification compared to no rain droplets, when considering pyrosequencing results. Notable, only the Machine learning k-NN algorithm was able to automatically classify the samples, surpassing manual classification based on no-template controls, which shows promise in clinical practice.

Automate Labeling Of Bugs and Tickets Using Attention-Based Mechanism in Recurrent Neral Networks

2021 ◽  
Vol 9 (11) ◽  
pp. 1411-1418
Author(s):  
Ravi Kauthale
Keyword(s):  
Neural Networks ◽  
Text Classification ◽  
Recurrent Neural Networks ◽  
Support Systems ◽  
The Neural Networks

Abstract: The aim here is to explore the methods to automate the labelling of the information that is present in bug trackers and client support systems. This is majorly based on the classification of the content depending on some criteria e.g., priority or product area. Labelling of the tickets is important as it helps in effective and efficient handling of the ticket and help is quicker and comprehensive resolution of the tickets. The main goal of the project is to analyze the existing methodologies used for automated labelling and then use a newer approach and compare the results. The existing methodologies are the ones which are based of the neural networks and without neural networks. In this project, a newer approach based on the recurrent neural networks which are based on the hierarchical attention paradigm will be used. Keywords: Automate Labeling, Recurrent Neural Networks, Hierarchical Attention, Multi-class Text Classification, GRU

Neural networks application for phasechronometric measurement information processing

2020 ◽  
pp. 31-35
Author(s):  
D. D. Boldasov ◽  
J. V. Drozdova ◽  
A. S. Komshin ◽  
A. B. Syritskii
Keyword(s):  
Neural Networks ◽  
Information Processing ◽  
Binary Classification ◽  
Processing Technique ◽  
Measurement Information ◽  
Perceptron Algorithm ◽  
Classification Feature ◽  
Operation Modes ◽  
The Neural Networks

This article describes the processing technique of measuring phasechronometric information based on the neural networks use. The novelty of the proposed approach lies in the choice of a classification feature and the perceptron algorithm use as an algorithm for binary classification performing. In this article, to assess the concept operability, the simplest binary classification of the lathe operation modes is made: idle or cutting.

scholarly journals ContrastRank: a new method for ranking putative cancer driver genes and classification of tumor samples

2014 ◽  
Vol 30 (17) ◽  
pp. i572-i578 ◽  
Author(s):  
Rui Tian ◽  
Malay K. Basu ◽  
Emidio Capriotti
Keyword(s):  
New Method ◽  
Driver Genes ◽  
Cancer Driver ◽  
Cancer Driver Genes

Lettuce Images Features Extraction and Intelligent Classification of Growth Period

2013 ◽  
Vol 846-847 ◽  
pp. 1351-1354
Author(s):  
Yan Wang ◽  
Jun Sun ◽  
Hong Li Wang ◽  
Ze Gao Dai ◽  
Wen Xia Lv
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Recognition Accuracy ◽  
Growth Period ◽  
Modeling And Analysis ◽  
Good Image ◽  
Visible Images ◽  
The Neural Networks ◽  
Lettuce Growth

Classification of lettuce growth peroid is the premise of records of lettuce growth information. In this study, lettuce images in every growth period are collected. And visible images are preprocessed to extract features to establish initial feature library of lettuce images. Through R cluster analysis on many features, good image eigenvector are obtained. Classification of the lettuce samples are obtained by modeling and analysis of the neural networks. The experimental classification results compared with practical classification results, the recognition accuracy is up to 88.4%.

scholarly journals 10.5937/industrija42-5686 = The implementation of the neural networks to the problem of economic classification of countries

Industrija ◽  
2014 ◽  
Vol 42 (4) ◽  
pp. 25-42
Author(s):  
Sasa Obradovic ◽  
Miljan Lekovic ◽  
Milos Marinkovic
Keyword(s):  
Neural Networks ◽  
The Neural Networks
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