Sequence classification of water channels and related proteins in view of functional predictions

1999 ◽  
Vol 101 (1-3) ◽  
pp. 77-81
Author(s):  
B. Tallur ◽  
J. Nicolas ◽  
A. Froger ◽  
D. Thomas ◽  
C. Delamarche
Keyword(s):  
Water Channels ◽  
Sequence Classification ◽  
Related Proteins

Neurodegenerative Disorders

2019 ◽  
pp. 195-216
Author(s):  
Mohamed M. Amin
Keyword(s):  
Dna Binding ◽  
Binding Protein ◽  
Clinical Signs ◽  
Amyloid Β ◽  
Molecular Classification ◽  
Dna Binding Protein ◽  
Fused In Sarcoma ◽  
Protein Deposits ◽  
Related Proteins

Neurodegenerative diseases (NDs) are characterized by specific dysfunction and damage of neurons related to pathologically changed proteins that deposit in the patient brain but also in peripheral organs. These proteins can be used for therapy or used as biomarkers. Except for a plethora of alterations revealed for dissimilar neurodegeneration-related proteins, amyloid-β, prion protein, TAR DNA-binding protein 43 (TDP-43, transactive response DNA binding protein 43 kDa), tau and α-synuclein, or fused in sarcoma protein (FUS), molecular classification of NDs depend on the full morphological assessment of protein deposits, their spreading in the brain, and their correspondence to clinical signs with specific genetic modifications. The current chapter represents the etiology of neurodegeneration, classification of NDs, concentrating on the maximum applicable biochemical and anatomical characteristics and most imperative NDs.

scholarly journals Deep Learning for Image Sequence Classification of Astronomical Events

2019 ◽  
Vol 131 (1004) ◽  
pp. 108006 ◽  
Author(s):  
Rodrigo Carrasco-Davis ◽  
Guillermo Cabrera-Vives ◽  
Francisco Förster ◽  
Pablo A. Estévez ◽  
Pablo Huijse ◽  
...  
Keyword(s):  
Deep Learning ◽  
Image Sequence ◽  
Sequence Classification

scholarly journals FOCUS2: agile and sensitive classification of metagenomics data using a reduced database

2016 ◽  
Author(s):  
Genivaldo Gueiros Z. Silva ◽  
Bas E. Dutilh ◽  
Robert A. Edwards
Keyword(s):  
Microbial Community ◽  
Dna Sequences ◽  
Computational Method ◽  
Environmental Research ◽  
Supplementary Information ◽  
Sequence Classification ◽  
Computationally Efficient ◽  
Link Type ◽  
Metagenomics Data

ABSTRACTSummaryMetagenomics approaches rely on identifying the presence of organisms in the microbial community from a set of unknown DNA sequences. Sequence classification has valuable applications in multiple important areas of medical and environmental research. Here we introduce FOCUS2, an update of the previously published computational method FOCUS. FOCUS2 was tested with 10 simulated and 543 real metagenomes demonstrating that the program is more sensitive, faster, and more computationally efficient than existing methods.AvailabilityThe Python implementation is freely available at https://edwards.sdsu.edu/FOCUS2.Supplementary informationavailable at Bioinformatics online.

scholarly journals Analysis of DNA Sequence Classification Using CNN and Hybrid Models

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Hemalatha Gunasekaran ◽  
K. Ramalakshmi ◽  
A. Rex Macedo Arokiaraj ◽  
S. Deepa Kanmani ◽  
Chandran Venkatesan ◽  
...  
Keyword(s):  
Dna Sequence ◽  
Selection Process ◽  
High Accuracy ◽  
Machine Learning Techniques ◽  
Biomedical Data ◽  
Sequence Classification ◽  
Learning Techniques ◽  
Testing Data ◽  
Bidirectional Lstm

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.

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