Fractal and DNA sequence analysis

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.

Fractal signatures of SARS-CoV2 coronavirus, the indicator matrix, the fractal dimension and the 2D directional wavelet transform: A comparative study with SARS-CoV, MERS-CoV and SARS-like coronavirus.

This paper compares fractal signatures of SARS-CoV2 with SARS-CoV, MERS-CoV, and SARS-like coronavirus using indicator matrices and 2D directional wavelet transforms, finding similar patterns and fractal dimensions.

Ubiquitous Forbidden Order in R-group classified protein sequence of SARS-CoV-2 and other viruses

The paper identifies unique forbidden order patterns in viral protein sequences, suggesting a novel method for species characterization.

Multifractal Analysis of SARS-CoV-2 Coronavirus genomes using the wavelet transform.

This study uses the wavelet transform to analyze SARS-CoV-2 genome sequences and finds long-range correlations and varying Hurst exponents across isolates.

Mutual information reveals a homonucleotide bias at 6 bp distance in promoters

The paper uses mutual information to analyze nucleotide dependencies in promoter regions and finds a 6 bp homonucleotide bias in multicellular species.

Fourier spectral density of the coronavirus genome

The paper analyzes the coronavirus genome's Fourier spectral density and identifies a new scaling law relating genome complexity to the power-law exponent of the low-frequency domain.

Optimizing the molecular diagnosis of Covid-19 by combining RT-PCR and a pseudo-convolutional machine learning approach to characterize virus DNA sequences

This paper combines RT-PCR with a pseudo-convolutional machine learning approach to improve the molecular diagnosis of SARS-CoV-2 by enhancing sensitivity and specificity in identifying virus DNA sequences.

Analysis of SARS-CoV-2 RNA-Sequences by Interpretable Machine Learning Models

The paper applies interpretable machine learning to classify and analyze SARS-CoV-2 sequences, identifying potential new virus types through rejected classifications.

Complexity in SARS-CoV-2 genome data: Price theory of mutant isolates

The paper analyzes SARS-CoV-2 genome data using multifractal methods to study mutation patterns and host-virus interactions, finding high mutational rates and complex genomic organization.

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.

Genetic Distance Calculation Based on Locality Sensitive Hashing

This paper introduces an alignment-free genetic distance calculation method using locality sensitive hashing to efficiently compare DNA sequences across species.

Automatic Identification of SARS Coronavirus using Compression-Complexity Measures

This paper introduces a compression-complexity based method to identify SARS coronavirus strains using short nucleotide sequences, distinguishing SARS-CoV-2 from SARS-CoV-1.

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.

Investigation of Codon Alternation Patterns in Genetic Diseases through Numerical Representation and Codon Classification

The paper investigates codon alteration patterns in two neurodegenerative diseases using numerical representation and classification, finding strong class codons are most mutated and mutations in the first or second positions are more deleterious.

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.

Sequence Comparison without Alignment: The SpaM approaches

This paper reviews alignment-free sequence comparison methods, focusing on SpaM approaches that use spaced word matches to estimate phylogenetic distances.

MLDSP-GUI: An alignment-free standalone tool with an interactive graphical user interface for DNA sequence comparison and analysis

MLDSP-GUI is an open-source, alignment-free tool for DNA sequence comparison and analysis, offering taxonomic and evolutionary applications.

Global atlas tree of natural proteins based on sorted composition vectors

This paper introduces a non-parametric global sequence comparison method using sorted composition vectors to efficiently classify and compare large protein sequence datasets.

The number of k-mer matches between two DNA sequences as a function of k and applications to estimate phylogenetic distances

The paper develops a method to estimate phylogenetic distances using k-mer matches and introduces Slope-SpaM, showing high accuracy for up to 0.5 substitutions per site.

Magnus Representation of Genome Sequences

The paper introduces the Magnus Representation, an alignment-free method for analyzing genome sequences, and applies it to phylogenetic analysis of viruses and bacteria, confirming known relationships and classifications.

Deep Learning on Chaos Game Representation for Proteins

This study compares machine learning methods for protein sequence classification using FCGR encoding, finding that deep neural networks outperform others.

DNA energy constraints shape biological evolutionary trajectories

This paper shows that DNA sequence patterns arise from physical constraints of dsDNA and maximum entropy, not evolutionary pressure.

Short exon prediction based on multiscale products of a genomic-inspired multiscale bilateral filtering

This paper introduces a nonlinear genomic-inspired multiscale bilateral filtering method for short exon prediction, showing significant improvements in exon length prediction across multiple ranges.

Chemical Characterization of Interacting Genes in Few Subnetworks of Alzheimer’s Disease

The paper investigates whether hub genes and their neighbors in Alzheimer’s disease subnetworks show chemical proximity in DNA sequences and amino acid properties.

The common origin of symmetry and structure in genetic sequences

The paper shows symmetry and structure in genetic sequences arise from the same biological processes and presents a model validated on human genomes.

Effective Variations of related Physicochemical Properties of Nucleotides Leading to Amino Acids for Characterizing Genes and Proteins

The paper quantitatively maps physicochemical properties of DNA sequences to their corresponding amino acid sequences, showing DNA signatures persist through translation.

Fast and global detection of periodic sequence repeats in large genomic resources

The paper introduces SPADE, a software that detects periodic DNA and protein repeats in large genomic datasets with high-throughput and unsupervised methods, outperforming existing tools.

Distribution of Purines and Pyrimidines over miRNAs of Human, Gorilla and Chimpanzee

The paper analyzes the distribution of purines and pyrimidines in miRNAs across human, gorilla, and chimpanzee, identifying non-random patterns and clustering based on quantitative metrics.

The variations of human miRNAs and Ising like base pairing models

The paper analyzes nucleotide base distributions in human miRNAs and proposes an Ising-like model to explain their non-random sequence variations.