Deep learning in omics: a survey and guideline
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 …
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
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 …
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
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 …
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
Background: Detecting DNA-binding proteins (DBPs) based on biological and chemical
methods is time-consuming and expensive. Objective: In recent years, the rise of …
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 …
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
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 …
affects communication and sociality of patients. It is crucial to accurately identify patients with …
Anticancer peptides prediction with deep representation learning features
Anticancer peptides constitute one of the most promising therapeutic agents for combating
common human cancers. Using wet experiments to verify whether a peptide displays …
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
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 …
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
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 …
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 …
therefore, it is important to study thermophilic proteins for the thermal stability of proteins …