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) …

Artificial intelligence techniques for predictive modeling of vector-borne diseases and its pathogens: a systematic review

I Kaur, AK Sandhu, Y Kumar - Archives of Computational Methods in …, 2022 - Springer
Vector-borne diseases (VBDs) have a significant impact on human and animal health. VBD
has been emerging or re-emerging in a variety of geographic regions, raising alarming new …

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 …

sgRNACNN: identifying sgRNA on-target activity in four crops using ensembles of convolutional neural networks

M Niu, Y Lin, Q Zou - Plant molecular biology, 2021 - Springer
Key message We proposed an ensemble convolutional neural network model to identify
sgRNA high on-target activity in four crops and we used one-hot encoding and k-mers for …

BBPpred: sequence-based prediction of blood-brain barrier peptides with feature representation learning and logistic regression

R Dai, W Zhang, W Tang, E Wynendaele… - Journal of Chemical …, 2021 - ACS Publications
Blood-brain barrier peptides (BBPs) have a large range of biomedical applications since
they can cross the blood-brain barrier based on different mechanisms. As experimental …

Convolutional neural network-based annotation of bacterial type IV secretion system effectors with enhanced accuracy and reduced false discovery

J Hong, Y Luo, M Mou, J Fu, Y Zhang… - Briefings in …, 2020 - academic.oup.com
The type IV bacterial secretion system (SS) is reported to be one of the most ubiquitous SSs
in nature and can induce serious conditions by secreting type IV SS effectors (T4SEs) into …

[HTML][HTML] New model combination meta-learner to improve accuracy prediction P2P lending with stacking ensemble learning

MA Muslim, TL Nikmah, DAA Pertiwi, Y Dasril - Intelligent Systems with …, 2023 - Elsevier
Abstract Peer-to-peer (P2P) Lending is a type of financial innovation that offers loans without
intermediaries to individuals and companies. In the P2P lending system, there is a risk of …

Ensemble machine learning techniques using computer simulation data for wild blueberry yield prediction

HR Seireg, YMK Omar, FE Abd El-Samie… - IEEE …, 2022 - ieeexplore.ieee.org
Precision agriculture is a challenging task to achieve. Several studies have been conducted
to forecast agricultural yields using machine learning algorithms (MLA), but few studies have …

4mCpred-EL: An Ensemble Learning Framework for Identification of DNA N4-Methylcytosine Sites in the Mouse Genome

B Manavalan, S Basith, TH Shin, DY Lee, L Wei, G Lee - Cells, 2019 - mdpi.com
DNA N 4-methylcytosine (4mC) is one of the key epigenetic alterations, playing essential
roles in DNA replication, differentiation, cell cycle, and gene expression. To better …

RF-PseU: a random forest predictor for RNA pseudouridine sites

Z Lv, J Zhang, H Ding, Q Zou - Frontiers in Bioengineering and …, 2020 - frontiersin.org
One of the ubiquitous chemical modifications in RNA, pseudouridine modification is crucial
for various cellular biological and physiological processes. To gain more insight into the …