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 …
Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
S Basith, B Manavalan, T Hwan Shin… - Medicinal research …, 2020 - Wiley Online Library
Discovery and development of biopeptides are time‐consuming, laborious, and dependent
on various factors. Data‐driven computational methods, especially machine learning (ML) …
on various factors. Data‐driven computational methods, especially machine learning (ML) …
SBSM-pro: support bio-sequence machine for proteins
Proteins play a pivotal role in biological systems. The use of machine learning algorithms for
protein classification can assist and even guide biological experiments, offering crucial …
protein classification can assist and even guide biological experiments, offering crucial …
POSREG: proteomic signature discovered by simultaneously optimizing its reproducibility and generalizability
Mass spectrometry-based proteomic technique has become indispensable in current
exploration of complex and dynamic biological processes. Instrument development has …
exploration of complex and dynamic biological processes. Instrument development has …
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 …
MRMD2. 0: a python tool for machine learning with feature ranking and reduction
S He, F Guo, Q Zou - Current Bioinformatics, 2020 - ingentaconnect.com
Aims: The study aims to find a way to reduce the dimensionality of the dataset. Background:
Dimensionality reduction is the key issue of the machine learning process. It does not only …
Dimensionality reduction is the key issue of the machine learning process. It does not only …
Improving protein-protein interactions prediction accuracy using XGBoost feature selection and stacked ensemble classifier
C Chen, Q Zhang, B Yu, Z Yu, PJ Lawrence… - Computers in biology …, 2020 - Elsevier
Protein-protein interactions (PPIs) are involved with most cellular activities at the proteomic
level, making the study of PPIs necessary to comprehending any biological process …
level, making the study of PPIs necessary to comprehending any biological process …
mCSM-PPI2: predicting the effects of mutations on protein–protein interactions
Protein–protein Interactions are involved in most fundamental biological processes, with
disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a …
disease causing mutations enriched at their interfaces. Here we present mCSM-PPI2, a …
A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information
Recently, language representation models have drawn a lot of attention in the natural
language processing field due to their remarkable results. Among them, bidirectional …
language processing field due to their remarkable results. Among them, bidirectional …
iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data
Z Chen, P Zhao, F Li, TT Marquez-Lago… - Briefings in …, 2020 - academic.oup.com
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 …