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 …
Bioactive milk peptides: An updated comprehensive overview and database
Partial digestion of milk proteins leads to the formation of numerous bioactive peptides.
Previously, our research team thoroughly examined the decades of existing literature on …
Previously, our research team thoroughly examined the decades of existing literature on …
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
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 …
PAtbP-EnC: Identifying anti-tubercular peptides using multi-feature representation and genetic algorithm-based deep ensemble model
Mycobacterium tuberculosis, a highly perilous pathogen in humans, serves as the causative
agent of tuberculosis (TB), affecting nearly 33% of the global population. With the increasing …
agent of tuberculosis (TB), affecting nearly 33% of the global population. With the increasing …
Machine intelligence in peptide therapeutics: A next‐generation tool for rapid disease screening
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) …
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 …