Biological sequence classification: A review on data and general methods
C Ao, S Jiao, Y Wang, L Yu, Q Zou - Research, 2022 - spj.science.org
With the rapid development of biotechnology, the number of biological sequences has
grown exponentially. The continuous expansion of biological sequence data promotes the …
grown exponentially. The continuous expansion of biological sequence data promotes the …
MathFeature: feature extraction package for DNA, RNA and protein sequences based on mathematical descriptors
One of the main challenges in applying machine learning algorithms to biological sequence
data is how to numerically represent a sequence in a numeric input vector. Feature …
data is how to numerically represent a sequence in a numeric input vector. Feature …
iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets
The rapid accumulation of molecular data motivates development of innovative approaches
to computationally characterize sequences, structures and functions of biological and …
to computationally characterize sequences, structures and functions of biological and …
Predicting Parkinson disease related genes based on PyFeat and gradient boosted decision tree
Identifying genes related to Parkinson's disease (PD) is an active research topic in
biomedical analysis, which plays a critical role in diagnosis and treatment. Recently, many …
biomedical analysis, which plays a critical role in diagnosis and treatment. Recently, many …
TACOS: a novel approach for accurate prediction of cell-specific long noncoding RNAs subcellular localization
Long noncoding RNAs (lncRNAs) are primarily regulated by their cellular localization, which
is responsible for their molecular functions, including cell cycle regulation and genome …
is responsible for their molecular functions, including cell cycle regulation and genome …
BioAutoML: automated feature engineering and metalearning to predict noncoding RNAs in bacteria
Recent technological advances have led to an exponential expansion of biological
sequence data and extraction of meaningful information through Machine Learning (ML) …
sequence data and extraction of meaningful information through Machine Learning (ML) …
Prediction of linear cationic antimicrobial peptides active against gram-negative and gram-positive bacteria based on machine learning models
Antimicrobial peptides (AMPs) are considered as promising alternatives to conventional
antibiotics in order to overcome the growing problems of antibiotic resistance …
antibiotics in order to overcome the growing problems of antibiotic resistance …
ProPythia: a Python package for protein classification based on machine and deep learning
The field of protein data mining has been growing rapidly in the last years. To characterize
proteins and determine their function from their amino acid sequences are challenging and …
proteins and determine their function from their amino acid sequences are challenging and …
Identification of DNA modification sites based on elastic net and bidirectional gated recurrent unit with convolutional neural network
B Yu, Y Zhang, X Wang, H Gao, J Sun, X Gao - … Signal Processing and …, 2022 - Elsevier
Abstract DNA N4-methylcytosine (4mC) and DNA N6-methyladenine (6mA) are significant
epigenetic modifications. 4mC is closely related to the restriction modification system, and …
epigenetic modifications. 4mC is closely related to the restriction modification system, and …
Geographic encoding of transcripts enabled high-accuracy and isoform-aware deep learning of RNA methylation
As the most pervasive epigenetic mark present on mRNA and lncRNA, N6-methyladenosine
(m6A) RNA methylation regulates all stages of RNA life in various biological processes and …
(m6A) RNA methylation regulates all stages of RNA life in various biological processes and …