Deep learning in omics: a survey and guideline

Z Zhang, Y Zhao, X Liao, W Shi, K Li… - Briefings in functional …, 2019 - academic.oup.com
Omics, such as genomics, transcriptome and proteomics, has been affected by the era of big
data. A huge amount of high dimensional and complex structured data has made it no …

Empirical comparison and analysis of web-based cell-penetrating peptide prediction tools

R Su, J Hu, Q Zou, B Manavalan… - Briefings in …, 2020 - academic.oup.com
Cell-penetrating peptides (CPPs) facilitate the delivery of therapeutically relevant molecules,
including DNA, proteins and oligonucleotides, into cells both in vitro and in vivo. This unique …

Gene2vec: gene subsequence embedding for prediction of mammalian N6-methyladenosine sites from mRNA

Q Zou, P **ng, L Wei, B Liu - Rna, 2019 - rnajournal.cshlp.org
N 6-Methyladenosine (m6A) refers to methylation modification of the adenosine nucleotide
acid at the nitrogen-6 position. Many conventional computational methods for identifying N 6 …

MK-FSVM-SVDD: a multiple kernel-based fuzzy SVM model for predicting DNA-binding proteins via support vector data description

Y Zou, H Wu, X Guo, L Peng, Y Ding… - Current …, 2021 - ingentaconnect.com
Background: Detecting DNA-binding proteins (DBPs) based on biological and chemical
methods is time-consuming and expensive. Objective: In recent years, the rise of …

StackIL6: a stacking ensemble model for improving the prediction of IL-6 inducing peptides

P Charoenkwan, W Chiangjong… - Briefings in …, 2021 - academic.oup.com
The release of interleukin (IL)-6 is stimulated by antigenic peptides from pathogens as well
as by immune cells for activating aggressive inflammation. IL-6 inducing peptides are …

Classification of autism spectrum disorder by combining brain connectivity and deep neural network classifier

Y Kong, J Gao, Y Xu, Y Pan, J Wang, J Liu - Neurocomputing, 2019 - Elsevier
Autism spectrum disorder (ASD) is a common neurodevelopmental disorder that seriously
affects communication and sociality of patients. It is crucial to accurately identify patients with …

Anticancer peptides prediction with deep representation learning features

Z Lv, F Cui, Q Zou, L Zhang, L Xu - Briefings in bioinformatics, 2021 - academic.oup.com
Anticancer peptides constitute one of the most promising therapeutic agents for combating
common human cancers. Using wet experiments to verify whether a peptide displays …

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 …

DeePromoter: robust promoter predictor using deep learning

M Oubounyt, Z Louadi, H Tayara, KT Chong - Frontiers in genetics, 2019 - frontiersin.org
The promoter region is located near the transcription start sites and regulates transcription
initiation of the gene by controlling the binding of RNA polymerase. Thus, promoter region …

Predicting thermophilic proteins by machine learning

XF Wang, P Gao, YF Liu, HF Li, F Lu - Current Bioinformatics, 2020 - ingentaconnect.com
Background: Thermophilic proteins can maintain good activity under high temperature,
therefore, it is important to study thermophilic proteins for the thermal stability of proteins …