Impact of word embedding models on text analytics in deep learning environment: a review
The selection of word embedding and deep learning models for better outcomes is vital.
Word embeddings are an n-dimensional distributed representation of a text that attempts to …
Word embeddings are an n-dimensional distributed representation of a text that attempts to …
iPro-WAEL: a comprehensive and robust framework for identifying promoters in multiple species
Promoters are consensus DNA sequences located near the transcription start sites and they
play an important role in transcription initiation. Due to their importance in biological …
play an important role in transcription initiation. Due to their importance in biological …
CLNN-loop: a deep learning model to predict CTCF-mediated chromatin loops in the different cell lines and CTCF-binding sites (CBS) pair types
Abstract Motivation Three-dimensional (3D) genome organization is of vital importance in
gene regulation and disease mechanisms. Previous studies have shown that CTCF …
gene regulation and disease mechanisms. Previous studies have shown that CTCF …
A comprehensive revisit of the machine‐learning tools developed for the identification of enhancers in the human genome
LT Phan, C Oh, T He, B Manavalan - Proteomics, 2023 - Wiley Online Library
Enhancers are non‐coding DNA elements that play a crucial role in enhancing the
transcription rate of a specific gene in the genome. Experiments for identifying enhancers …
transcription rate of a specific gene in the genome. Experiments for identifying enhancers …
Integrative machine learning framework for the identification of cell-specific enhancers from the human genome
Enhancers are deoxyribonucleic acid (DNA) fragments which when bound by transcription
factors enhance the transcription of related genes. Due to its sporadic distribution and …
factors enhance the transcription of related genes. Due to its sporadic distribution and …
Genomic benchmarks: a collection of datasets for genomic sequence classification
Background Recently, deep neural networks have been successfully applied in many
biological fields. In 2020, a deep learning model AlphaFold won the protein folding …
biological fields. In 2020, a deep learning model AlphaFold won the protein folding …
XG-ac4C: identification of N4-acetylcytidine (ac4C) in mRNA using eXtreme gradient boosting with electron-ion interaction pseudopotentials
Abstract N4-acetylcytidine (ac4C) is a post-transcriptional modification in mRNA which plays
a major role in the stability and regulation of mRNA translation. The working mechanism of …
a major role in the stability and regulation of mRNA translation. The working mechanism of …
DNA sequences performs as natural language processing by exploiting deep learning algorithm for the identification of N4-methylcytosine
N4-methylcytosine is a biochemical alteration of DNA that affects the genetic operations
without modifying the DNA nucleotides such as gene expression, genomic imprinting …
without modifying the DNA nucleotides such as gene expression, genomic imprinting …
DeepCap-Kcr: accurate identification and investigation of protein lysine crotonylation sites based on capsule network
Lysine crotonylation (Kcr) is a posttranslational modification widely detected in histone and
nonhistone proteins. It plays a vital role in human disease progression and various cellular …
nonhistone proteins. It plays a vital role in human disease progression and various cellular …
A robust drug–target interaction prediction framework with capsule network and transfer learning
Drug–target interactions (DTIs) are considered a crucial component of drug design and drug
discovery. To date, many computational methods were developed for drug–target …
discovery. To date, many computational methods were developed for drug–target …