Intelligent and robust computational prediction model for DNA N4-methylcytosine sites via natural language processing

A Convolution Neural Network-based computational model to identify the occurrence sites of various RNA modifications by fusing varied features

(2021) Muhammad Tahir et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

DNA sequences performs as natural language processing by exploiting deep learning algorithm for the identification of N4-methylcytosine

(2021) Abdul Wahab et al.   Scientific Reports

A Deep Neural Network for Identifying DNA N4-Methylcytosine Sites

(2020) Feng Zeng et al.   Frontiers in Genetics

Deep4mC: systematic assessment and computational prediction for DNA N4-methylcytosine sites by deep learning

(2020) Haodong Xu et al.   BRIEFINGS IN BIOINFORMATICS

Prediction of N6-methyladenosine sites using convolution neural network model based on distributed feature representations

(2020) Muhammad Tahir et al.   NEURAL NETWORKS

A Convolutional Neural Network Using Dinucleotide One-hot Encoder for identifying DNA N6-Methyladenine Sites in the Rice Genome

(2020) Zhibin Lv et al.   NEUROCOMPUTING

kDeepBind: Prediction of RNA-Proteins binding sites using convolution neural network and k-gram features

(2020) Muhammad Tahir et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Iterative feature representations improve N4-methylcytosine site prediction

(2019) Leyi Wei et al.   BIOINFORMATICS

Meta-4mCpred: A Sequence-Based Meta-Predictor for Accurate DNA 4mC Site Prediction Using Effective Feature Representation

(2019) Balachandran Manavalan et al.   Molecular Therapy-Nucleic Acids

iN6-Methyl (5-step): Identifying RNA N6-methyladenosine sites using deep learning mode via Chou's 5-step rules and Chou's general PseKNC

(2019) Iman Nazari et al.   CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS

Prediction of enhancer-promoter interactions via natural language processing

(2018) Wanwen Zeng et al.   BMC GENOMICS

Selective recognition of N 4-methylcytosine in DNA by engineered transcription-activator-like effectors

(2018) Preeti Rathi et al.   PHILOSOPHICAL TRANSACTIONS OF THE ROYAL SOCIETY B-BIOLOGICAL SCIENCES

4mCPred: Machine Learning Methods for DNA N4-methylcytosine sites Prediction

(2018) Wenying He et al.   BIOINFORMATICS

Exploring sequence-based features for the improved prediction of DNA N4-methylcytosine sites in multiple species

(2018) Leyi Wei et al.   BIOINFORMATICS

DeepM6ASeq: prediction and characterization of m6A-containing sequences using deep learning

(2018) Yiqian Zhang et al.   BMC BIOINFORMATICS

iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou’s PseAAC to pseudo-tri-nucleotide composition

(2018) Muhammad Tahir et al.   MOLECULAR GENETICS AND GENOMICS

iRNA-PseKNC(2methyl): Identify RNA 2'-O-methylation sites by convolution neural network and Chou's pseudo components

(2018) Muhammad Tahir et al.   JOURNAL OF THEORETICAL BIOLOGY

iNuc-STNC: a sequence-based predictor for identification of nucleosome positioning in genomes by extending the concept of SAAC and Chou's PseAAC

(2016) Muhammad Tahir et al.   Molecular BioSystems

3-methylcytosine in cancer: an underappreciated methyl lesion?

(2016) Thomas Pataillot-Meakin et al.   Epigenomics

Direct detection of DNA methylation during single-molecule, real-time sequencing

(2010) Benjamin A Flusberg et al.   NATURE METHODS