Fractal and DNA sequence analysis

Genome-wide Pervasiveness and Localized Variation of k-mer-based Genomic Signatures in Eukaryotes

This study investigates the pervasiveness and variation of genomic signatures across eukaryotic genomes using k-mer-based FCGR analysis, identifying localized exceptions and developing tools for improved taxonomic classification.

Solving the gene classification problem with a novel Gene Space approach

This paper introduces a Gene Space framework to characterize genome organization by analyzing nucleotide token frequencies, enabling species-specific gene classification and evolutionary pathway constraints.

Positional frequency chaos game representation for machine learning-based classification of crop lncRNAs

This paper introduces PFCGR, a new encoding method for classifying plant lncRNAs using machine learning, achieving high accuracy with significantly reduced computational time compared to DNABERT.

Alignment Free Viral Sequence Classification

This study evaluates alignment-free methods for viral sequence classification, showing high accuracy and efficiency on SARS-CoV-2, dengue, and HIV datasets.

DNA walk of specific fused oncogenes exhibit distinct fractal geometric characteristics in nucleotide patterns

The paper uses fractal geometry and DNA walk analysis to identify distinct geometric features in nucleotide patterns of oncogenes and tumour-suppressor genes, highlighting their potential in cancer research.

Preserving Hidden Hierarchical Structure: Poincare Distance for Enhanced Genomic Sequence Analysis

This paper introduces Poincare distance measures for genomic and proteomic sequence analysis, showing improved performance in classification tasks compared to existing methods.

DNA N-gram Analysis Framework (DNAnamer): A generalized N-gram frequency analysis framework for the supervised classification of DNA sequences

DNAnamer is a framework that uses N-gram frequency analysis to classify DNA sequences, identifying species, functional elements, and contamination.

Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning

The paper provides a database of quantum mechanical and geometric features for DNA k-mers and demonstrates their use in predicting A to C mutation rate constants.

Laws of genome nucleotide composition

The paper proposes and validates three mathematical laws of genome nucleotide composition across three domains of life.

The determinants of the rarity of nucleic and peptide short sequences in nature

The paper calculates the rarity of short nucleic and peptide sequences across genomes and proteomes, finds dipeptide rarity in peptides but not dinucleotides, and develops predictive models for sequence frequency.

New distance measure for comparing protein using cellular automata image

The paper introduces a new distance measure using cellular automata to compare protein sequences and identify common ancestors across species.

Kmer-Node2Vec: a Fast and Efficient Method for Kmer Embedding from the Kmer Co-occurrence Graph, with Applications to DNA Sequences

The paper introduces Kmer-Node2Vec, a fast graph-based method for kmer embedding in DNA sequences, achieving faster training times than DNA2Vec while maintaining comparable accuracy.

Visual and Quantitative Analyses of Virus Genomic Sequences using a Metric-based Algorithm

This paper introduces a metric-based algorithm to analyze virus genomic sequences, finding stable and deviant patterns in atg-triplet distributions across different viruses.

Accurate and Fast Clade Assignment via Deep Learning and Frequency Chaos Game Representation

The paper develops CouGaR-g, a deep learning tool using FCGR and CNNs for accurate and fast clade assignment of SARS-CoV-2 sequences, achieving 96.29% accuracy on GISAID data.

GenomeBits Characterization of MPXV

The paper uses GenomeBits signal mapping to analyze MPXV genome sequences and characterize its evolution through statistical analysis.

Prediction of DNA from context using neural networks

The paper trains a neural network to predict DNA bases from context, achieving 54% accuracy on the human genome, outperforming Markov models and revealing periodic signals linked to nucleosome positioning.

Classifying COVID-19 variants based on genetic sequences using deep learning models

This paper proposes an alignment-free LSTM model for classifying 20 COVID-19 variants using k-mer sequences, achieving up to 92.5% test accuracy with one-vs-all classifiers.

GenomeBits insight into omicron and delta variants of coronavirus pathogen

The paper applies the GenomeBits method to analyze genomic features of Omicron and Delta coronavirus variants, revealing transitions in order/disorder around the S-spike protein region.

Fast Whole-Genome Phylogeny of the COVID-19 Virus SARS-CoV-2 by Compression

The paper uses compression-based methods to analyze SARS-CoV-2 phylogeny, finding it closest to RaTG13 and two bat coronaviruses.

DeLUCS: Deep Learning for Unsupervised Clustering of DNA Sequences

DeLUCS clusters DNA sequences without alignment or homology, achieving high accuracy on diverse datasets and outperforming traditional methods.

A graph-theoretical approach to DNA similarity analysis

This paper introduces a graph-theoretical method for DNA similarity analysis, showing comparable accuracy and efficiency across multiple viral and mammalian datasets.

A new method to study genome mutations using the information entropy

The paper introduces a mathematical method using information entropy to detect genetic mutations in genomes and tests it on SARS-CoV-2 sequences.

Significant non-existence of sequences in genomes and proteomes

The paper identifies significant absent oligomers (nullomers) in genomes and proteomes across diverse species, suggesting negative selection and potential biological significance.

Feature Extraction Approaches for Biological Sequences: A Comparative Study of Mathematical Models

This paper compares mathematical models for feature extraction from biological sequences, showing their effectiveness in classifying Long Non-Coding RNA and other sequence types.

D-graph clusters flaviviruses and β-coronaviruses according to their hosts, disease type and human cell receptors

The paper introduces DGraph, a tool that clusters viral protein sequences based on physico-chemical properties, revealing host, disease, and receptor relationships.

Fractal Genomics of SOD1 Evolution

This study analyzes SOD1 orthologs to show that their fractal organization arises from convergent evolution rather than point mutations or segmental duplications.

Machine learning using intrinsic genomic signatures for rapid classification of novel pathogens: COVID-19 case study

This paper uses machine learning and genomic signatures to classify novel pathogens, demonstrating high accuracy in identifying the origin and taxonomy of the COVID-19 virus.

Synonymous Dinucleotide Usage: A Codon-Aware Metric for Quantifying Dinucleotide Representation in Viruses

The paper introduces a new metric, Synonymous Dinucleotide Usage (SDU), to quantify dinucleotide representation in viral genomes and demonstrates its application on flaviviruses and rhabdoviruses.

Learning supervised embeddings for large scale sequence comparisons

This paper introduces SuperVec and H-SuperVec, supervised methods for learning sequence embeddings that improve retrieval performance and speed compared to existing approaches.

Natural versus Random Proteins: Nouvel Neural Network Approach Based on Time Series Analysis

The paper uses a neural network to classify natural versus random protein sequences based on hydropathy time series analysis, achieving 88% accuracy.