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10.1155/2021/1835056 http://scihub22266oqcxt.onion/10.1155/2021/1835056 34306171!8285202!34306171     free     free     free Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534 Deprecated: Implicit conversion from float 227.6 to int loses precision in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 534       Comput+Math+Methods+Med 2021 ; 2021 (ä): 1835056 Nephropedia Template TP {{tp|p=34306171|t=2021. Analysis of DNA Sequence Classification Using CNN and Hybrid Models |pdf=|usr=}}{{34306171}} gab.com Text Analysis of DNA Sequence Classification Using CNN and Hybrid Models JO: Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=34306171 #FOAvip In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days, deep learning (DL) models can automatically extract the features from the input. In this work, we employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and K-mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with K-mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data. Twit Text FOAVip Analysis of DNA Sequence Classification Using CNN and Hybrid Models ????Comput Math Methods Med http://www.kidney.de/pget.php?&tw=1&pmid=34306171 #FOAvip Twit Text # Free Paper on # # # # # LINK http://www.kidney.de/pget.php?&tw=1&pmid=34306171 #FOAvip English Wikipedia <ref>{{Cite pmid|34306171}}</ref> Analysis of DNA Sequence Classification Using CNN and Hybrid Models #MMPMID34306171 Gunasekaran H; Ramalakshmi K; Rex Macedo Arokiaraj A; Deepa Kanmani S; Venkatesan C; Suresh Gnana Dhas C Comput Math Methods Med 2021[]; 2021 (ä): 1835056 PMID34306171show ga In a general computational context for biomedical data analysis, DNA sequence classification is a crucial challenge. Several machine learning techniques have used to complete this task in recent years successfully. Identification and classification of viruses are essential to avoid an outbreak like COVID-19. Regardless, the feature selection process remains the most challenging aspect of the issue. The most commonly used representations worsen the case of high dimensionality, and sequences lack explicit features. It also helps in detecting the effect of viruses and drug design. In recent days, deep learning (DL) models can automatically extract the features from the input. In this work, we employed CNN, CNN-LSTM, and CNN-Bidirectional LSTM architectures using Label and K-mer encoding for DNA sequence classification. The models are evaluated on different classification metrics. From the experimental results, the CNN and CNN-Bidirectional LSTM with K-mer encoding offers high accuracy with 93.16% and 93.13%, respectively, on testing data. |*Neural Networks, Computer[MESH] |Base Sequence[MESH] |COVID-19/*virology[MESH] |Computational Biology[MESH] |DNA, Viral/classification/genetics[MESH] |Databases, Nucleic Acid/statistics & numerical data[MESH] |Deep Learning[MESH] |High-Throughput Nucleotide Sequencing/*statistics & numerical data[MESH] |Humans[MESH] |Pandemics[MESH] |SARS-CoV-2/classification/*genetics[MESH]Analysis of DNA Sequence Classification Using CNN and Hybrid Models (epmc).pdf DeepDyve Pubget Overpricing