Machine learning approaches and their current application in plant molecular biology: A systematic review
Abstract Machine learning (ML) is a field of artificial intelligence that has rapidly emerged in
molecular biology, thus allowing the exploitation of Big Data concepts in plant genomics. In …
molecular biology, thus allowing the exploitation of Big Data concepts in plant genomics. In …
iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences
Structural and physiochemical descriptors extracted from sequence data have been widely
used to represent sequences and predict structural, functional, expression and interaction …
used to represent sequences and predict structural, functional, expression and interaction …
BioSeq-Analysis2. 0: an updated platform for analyzing DNA, RNA and protein sequences at sequence level and residue level based on machine learning …
B Liu, X Gao, H Zhang - Nucleic acids research, 2019 - academic.oup.com
As the first web server to analyze various biological sequences at sequence level based on
machine learning approaches, many powerful predictors in the field of computational …
machine learning approaches, many powerful predictors in the field of computational …
iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization
Sequence-based analysis and prediction are fundamental bioinformatic tasks that facilitate
understanding of the sequence (-structure)-function paradigm for DNAs, RNAs and proteins …
understanding of the sequence (-structure)-function paradigm for DNAs, RNAs and proteins …
Identifying SNARE proteins using an alignment-free method based on multiscan convolutional neural network and PSSM profiles
Background: SNARE proteins play a vital role in membrane fusion and cellular physiology
and pathological processes. Many potential therapeutics for mental diseases or even cancer …
and pathological processes. Many potential therapeutics for mental diseases or even cancer …
iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data
With the explosive growth of biological sequences generated in the post-genomic era, one
of the most challenging problems in bioinformatics and computational biology is to …
of the most challenging problems in bioinformatics and computational biology is to …
Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE
Background Protein solubility is a precondition for efficient heterologous protein expression
at the basis of most industrial applications and for functional interpretation in basic research …
at the basis of most industrial applications and for functional interpretation in basic research …
AlgPred: prediction of allergenic proteins and mapping of IgE epitopes
S Saha, GPS Raghava - Nucleic acids research, 2006 - academic.oup.com
In this study a systematic attempt has been made to integrate various approaches in order to
predict allergenic proteins with high accuracy. The dataset used for testing and training …
predict allergenic proteins with high accuracy. The dataset used for testing and training …
AMYPred-FRL is a novel approach for accurate prediction of amyloid proteins by using feature representation learning
Amyloid proteins have the ability to form insoluble fibril aggregates that have important
pathogenic effects in many tissues. Such amyloidoses are prominently associated with …
pathogenic effects in many tissues. Such amyloidoses are prominently associated with …
ACPred-Fuse: fusing multi-view information improves the prediction of anticancer peptides
B Rao, C Zhou, G Zhang, R Su… - Briefings in bioinformatics, 2020 - academic.oup.com
Fast and accurate identification of the peptides with anticancer activity potential from large-
scale proteins is currently a challenging task. In this study, we propose a new machine …
scale proteins is currently a challenging task. In this study, we propose a new machine …