Background: The first objective for realizing and handling biological systems is to choose a suitable model prototype and then perform structure and parameter identification. Afterwards, a theoretical analysis is needed to understand the characteristics, abilities, and limitations of the underlying systems. Generalized Michaelis–Menten kinetics (MM) and S-systems are two well-known biochemical system

Viral infection is a complicated dynamic process, in which viruses intrude into cells to duplicate themselves and trigger succeeding biological processes regulated by genes. It may lead to a serious disaster to human’s health. A scheme is proposed to monitor the response of cells after being infected by viruses. Co-expression levels of genes measured at successive time points form a gene expression

Prostate Specific Antigen (PSA) level in the serum is one of the most widely used markers in monitoring prostate cancer (PCa) progression, treatment response, and disease relapse. Although significant efforts have been taken to analyze various socioeconomic and cultural factors that contribute to the racial disparities in PCa, limited research has been performed to quantitatively understand how and

In the field of comparative genomics, one way of comparing two genomes is through the analysis of how they distinguish themselves based on a set of mutations called rearrangement events. When considering that genomes undergo different types of rearrangements, it can be assumed that some events are more common than others. To model this assumption, one can assign different weights to different events

DNA-binding proteins (DBPs) perform an influential role in diverse biological activities like DNA replication, slicing, repair, and transcription. Some DBPs are indispensable for understanding many types of human cancers (i.e. lung, breast, and liver cancer) and chronic diseases (i.e. AIDS/HIV, asthma), while other kinds are involved in antibiotics, steroids, and anti-inflammatory drugs designing.

In this paper, we study the limitations imposed on the transcription process by the presence of short ubiquitous pauses and crowding. These effects are especially pronounced in highly transcribed genes such as ribosomal genes (rrn) in fast growing bacteria. Our model indicates that the quantity and duration of pauses reported for protein-coding genes is incompatible with the average elongation rate

Quantifying the hemolytic activity of peptides is a crucial step in the discovery of novel therapeutic peptides. Computational methods are attractive in this domain due to their ability to guide wet-lab experimental discovery or screening of peptides based on their hemolytic activity. However, existing methods are unable to accurately model various important aspects of this predictive problem such

Accurate detection of N6-methyladenine (6mA) sites by biochemical experiments will help to reveal their biological functions, still, these wet experiments are laborious and expensive. Therefore, it is necessary to introduce a powerful computational model to identify the 6mA sites on a genomic scale, especially for plant genomes. In view of this, we proposed a model called iDNA6mA-Rice-DL for the effective

Glioma is one particular type of brain malignancy which is highly complex and usually has a poor prognosis. Despite the limited diagnostic level of glioma, the survival time of affected patients broadly varies. Here, we conducted a detailed analysis, regarding the differences in patient survival time, to discover potential survival-related genes in glioma as well as their putative regulatory mechanisms

The pandemic caused by SARS-CoV-2 has had a significant impact on the whole world. In a theory of the origin of SARS-CoV-2, pangolins are considered as a potential intermediate host. To assemble the genome of suspicious coronavirus (CoV) found in pangolins, SARS-CoV-2 was used as a reference in most of the previous studies, implicitly assuming the pangolin CoV and SARS-CoV-2 are the closest neighbors

Large amounts of research efforts have been focused on learning gene regulatory networks (GRNs) based on gene expression data to understand the functional basis of a living organism. Under the assumption that the joint distribution of the gene expressions of interest is a multivariate normal distribution, such networks can be constructed by assessing the nonzero elements of the inverse covariance matrix

Defining protein complexes in the cell is important for learning about cellular processes mechanisms as they perform many of the molecular functions in these processes. Most of the proposed algorithms predict a complex as a dense area in a Protein–Protein Interaction (PPI) network. Others, on the other hand, weight the network using gene expression or geneontology (GO). These approaches, however, eliminate

Accurately determining a change in protein binding affinity upon mutations is important to find novel therapeutics and to assist mutagenesis studies. Determination of change in binding affinity upon mutations requires sophisticated, expensive, and time-consuming wet-lab experiments that can be supported with computational methods. Most of the available computational prediction techniques depend upon

Most of the current computational models for splice junction prediction are based on the identification of canonical splice junctions. However, it is observed that the junctions lacking the consensus dimers GT and AG also undergo splicing. Identification of such splice junctions, called the non-canonical splice junctions, is also essential for a comprehensive understanding of the splicing phenomenon

Neuroimaging genetics has become an important research topic since it can reveal complex associations between genetic variants (i.e. single nucleotide polymorphisms (SNPs) and the structures or functions of the human brain. However, existing kernel mapping is difficult to directly use the sparse representation method in the kernel feature space, which makes it difficult for most existing sparse canonical

COVID-19 pandemic has caused a global health crisis. Developing vaccines would need a good knowledge of genetic properties of SARS-CoV-2. The most fundamental approach is to look into the structures of its RNA, in particular, the nucleotides and amino acids. This motivates our research on this topic. We study the occurrence structures of nitrogenous bases and amino acids. To this aim, we devise a structural

A central problem of systems biology is the reconstruction of Gene Regulatory Networks (GRNs) by the use of time series data. Although many attempts have been made to design an efficient method for GRN inference, providing a best solution is still a challenging task. Existing noise, low number of samples, and high number of nodes are the main reasons causing poor performance of existing methods. The

High-throughput chromosome conformation capture (Hi-C) is one of the most popular methods for studying the three-dimensional organization of genomes. However, Hi-C protocols can be expensive since they require large amounts of sample material and may be time-consuming. Most commonly used Hi-C data are low-resolution. Such data can only be used to identify large-scale genomic interactions and are not

This paper proposes a new algorithm for prophage loci prediction in bacteria. Prophages are defined in Bioinformatics as viral nucleotide sequences that are found intermixed with host nucleotide sequences in bacteria. The proposed algorithm uses machine learning patterns and processing methodologies in order to provide a highly efficient system for loci prediction, thereby reducing the time-space complexity

Molecular docking is a fast and efficient computational method for the prediction of the binding mode and binding affinity between a ligand and a target protein at the atomic level. However, the performance of current docking programs is less than satisfactory. Herein, with a focus on free programs and scoring functions, the performances of LeDock and three standalone scoring functions were tested

The three helical Histone Fold Motif (HFM) of core histone proteins provides an evolutionarily favored site for the protein–DNA interface. Despite significant variation in sequence, the HFM retains a distinctive structural fold that has diversified into several non-histone protein families. In this work, we explore the ancestry of non-histone HFM containing families in the plant kingdom. A sequence

Specific functions in biological processes are dependent on protein–protein interactions. Hot spot residues play a key role in the determination of these interactions and have wide applications in engineering proteins and drug discovery. Experimental techniques to identify hotspots are often labor intensive and expensive. Also, most of the computational methods which have been developed are structure

Binding site prediction for new proteins is important in structure-based drug design. The identified binding sites may be helpful in the development of treatments for new viral outbreaks in the world when there is no information available about their pockets with COVID-19 being a case in point. Identification of the pockets using computational methods, as an alternative method, has recently attracted

The de Bruijn Graph algorithm (DBG) as one of the cornerstones algorithms in short read assembly has extended with the rapid advancement of the Next Generation Sequencing (NGS) technologies and low-cost production of millions of high-quality short reads. Erroneous reads, non-uniform coverage, and genomic repeats are three major problems that influence the performance of short read assemblers. To encounter

Discovering motifs and repeats in data sequences is of great importance in biology and a large number of efficient tools for their finding have been developed. As the number of results found can be very large, our goal is to provide a tool that, on a mathematical basis, can precisely find all motifs and repeats, filter them according to input arguments and output the results in a convenient way. RepeatsPlus

Understanding the molecular mechanisms that correlate pathologies with missense mutations is of critical importance for disease risk estimations and for devising personalized therapies. Thus, we have performed a bioinformatic survey of ClinVar, a database of human genomic variations, to find signals that can account for missense mutation pathogenicity. Arginine resulted as the most frequently replaced

Many regions of the protein universe remain inaccessible by wet-laboratory or computational structure determination methods. A significant challenge in elucidating these dark regions in silico relates to the ability to discriminate relevant structure(s) among many structures/decoys computed for a protein of interest, a problem known as decoy selection. Clustering decoys based on geometric similarity

In many cancers, mechanisms of gene regulation can be severely altered. Identification of deregulated genes, which do not follow the regulation processes that exist between transcription factors and their target genes, is of importance to better understand the development of the disease. We propose a methodology to detect deregulation mechanisms with a particular focus on cancer subtypes. This strategy

Non-negative Matrix Factorization (NMF) is a popular data dimension reduction method in recent years. The traditional NMF method has high sensitivity to data noise. In the paper, we propose a model called Sparse Robust Graph-regularized Non-negative Matrix Factorization based on Correntropy (SGNMFC). The maximized correntropy replaces the traditional minimized Euclidean distance to improve the robustness

Multivariate simple interval mapping (SIM) is one of the most popular approaches for multiple quantitative trait locus (QTL) analysis. Both maximum likelihood (ML) and least squares (LS) multivariate regression (MVR) are widely used methods for multi-trait SIM. ML-based MVR (MVR-ML) is an expectation maximization (EM) algorithm based iterative and complex time-consuming approach. Although the LS-based

Several mathematical models have been developed to understand the interactions of microorganisms in foods and predict their growth. The resulting model equations for the growth of interacting cells include several parameters that must be determined for the specific conditions to be modeled. In this study, these parameters were determined by using inverse engineering and a multi-objective optimization

Prediction of Adverse Drug Reactions (ADRs) has been an important aspect of Pharmacovigilance because of its impact in the pharma industry. The standard process of introduction of a new drug into a market involves a lot of clinical trials and tests. This is a tedious and time consuming process and also involves a lot of monetary resources. The faster approval of a drug helps the patients who are in

FASTA data sets of short reads are usually generated in tens or hundreds for a biomedical study. However, current compression of these data sets is carried out one-by-one without consideration of the inter-similarity between the data sets which can be otherwise exploited to enhance compression performance of de novo compression. We show that clustering these data sets into similar sub-groups for a

The application of graph theory in structural biology offers an alternative means of studying 3D models of large macromolecules such as proteins. The radius of gyration, which scales with exponent ∼0.4, provides quantitative information about the compactness of the protein structure. In this study, we combine two proven methods, the graph-theoretical and the fundamental scaling laws, to study 3D protein

Phylogenetic inference proposes an evolutionary hypothesis for a group of taxa which is usually represented as a phylogenetic tree. The use of several distinct biological evidence has shown to produce more resolved phylogenies than single evidence approaches. Currently, two conflicting paradigms are applied to combine biological evidence: taxonomic congruence (TC) and total evidence (TE). Although

Membrane proteins are a major focus for new drug discovery. Transmembrane beta-barrel (TMB) proteins play key roles in the translocation machinery, pore formation, membrane anchoring and ion exchange. Given their key roles and the difficulty in membrane protein structure determination, the use of computational modeling is essential. This paper focuses on the topology prediction of TMB proteins. In

Biomarkers are used for clinical diagnostic purposes, but existing indexes exhibit limitations in terms of the resolving power of biomarkers. This paper proposes a new index, the magnitude-standardized index (MSI), to describe the quantitative variations and resolving powers of different biomarkers. In MSI analysis models, variation scales for ratios and differences are considered simultaneously, and

Sequence-specific and consequential interactions within or between proteins and/or RNAs can be predicted by identifying co-evolution of residues in these molecules. Different algorithms have been used to detect co-evolution, often using biological data to benchmark a methods ability to discriminate against indirect co-evolution. Such a benchmark is problematic, because not all the interactions and

Using a prior biological knowledge of relationships and genetic functions for gene similarity, from repository such as the Gene Ontology (GO), has shown good results in multi-objective gene clustering algorithms. In this scenario and to obtain useful clustering results, it would be helpful to know which measure of biological similarity between genes should be employed to yield meaningful clusters that

16S rRNA gene sequencing and whole microbiome sequencing make it possible and stable to quantitatively analyze the composition of microbial communities and the relationship among microbial communities, microbes, and hosts. One essential step in the analysis of microbiome compositional data is inferring the direct interaction network among microbial species, bringing to light the potential underlying

A front-end method based on random forest proximity distance (PD) is used to screen the test set to improve protein–protein interaction site (PPIS) prediction. The assessment of a distance metric is done under the assumption that a distance definition of higher quality leads to higher classification. On an independent test set, the numerical analysis based on statistical inference shows that the PD

This paper has developed and described a detailed method for selecting inhibitors based on modified natural peptides for the SARS-CoV BJ01 spike-glycoprotein. The selection of inhibitors is carried out by increasing the affinity of the peptide to the active center of the protein. This paper also provides a step-by-step algorithm for analyzing the affinity of protein interactions and presents an analysis

MicroRNAs (miRNA) are a type of non-coding RNA molecules that are effective on the formation and the progression of many different diseases. Various researches have reported that miRNAs play a major role in the prevention, diagnosis, and treatment of complex human diseases. In recent years, researchers have made a tremendous effort to find the potential relationships between miRNAs and diseases. Since

Accurately identifying organisms based on their partially available genetic material is an important task to explore the phylogenetic diversity in an environment. Specific fragments in the DNA sequence of a living organism have been defined as DNA barcodes and can be used as markers to identify species efficiently and effectively. The existing DNA barcode-based classification approaches suffer from

Prokaryote adaptive immunity (CRISPR-Cas systems) can be a threat to its carriers. We analyze the risks of autoimmune reactions related to adaptive immunity in prokaryotes by computational methods. We found important differences between bacteria and archaea with respect to autoimmunity potential. According to the results of our analysis, CRISPR-Cas systems in bacteria are more prone to self-targeting

Lymphoma is the most complicated cancer that can be divided into several tens of subtypes. It may occur in any part of body that has lymphocytes, and is closely correlated with diverse environmental factors such as the ionizing radiation, chemocarcinogenesis, and virus infection. All the environmental factors affect the lymphoma through genes. Identifying pathogenic genes for lymphoma is consequently

Gleason score (GS) is a powerful prognostic factor in prostate cancer (PCa). A GS-7 tumor typically has the primary Gleason (architectural) pattern and secondary prevalent one being graded with 3 and 4 (or 4 and 3), respectively. Due to the well-known intratumoral multifocal occurrence of different patterns, a biological sample from a GS-7 tumor used in a molecular experiment will be uncertain regarding

As interactions among genetic variants in different genes can be an important factor for predicting complex diseases, many computational methods have been proposed to detect if a particular set of genes has interaction with a particular complex disease. However, even though many such methods have been shown to be useful, they can be made more effective if the properties of gene–gene interactions can

The amount of sequencing data is growing at a fast pace due to a rapid revolution in sequencing technologies. Quality scores, which indicate the reliability of each of the called nucleotides, take a significant portion of the sequencing data. In addition, quality scores are more challenging to compress than nucleotides, and they are often noisy. Hence, a natural solution to further decrease the size

Clustering analysis has been widely applied to single-cell RNA-sequencing (scRNA-seq) data to discover cell types and cell states. Algorithms developed in recent years have greatly helped the understanding of cellular heterogeneity and the underlying mechanisms of biological processes. However, these algorithms often use different techniques, were evaluated on different datasets and compared with some

Many biological and biomedical research areas such as drug design require analyzing the Gene Regulatory Networks (GRNs) to provide clear insight and understanding of the cellular processes in live cells. Under normality assumption for the genes, GRNs can be constructed by assessing the nonzero elements of the inverse covariance matrix. Nevertheless, such techniques are unable to deal with non-normality

Predicting structural properties of proteins plays a key role in predicting the 3D structure of proteins. In this study, new structural profile matrices (SPM) are developed for protein secondary structure, solvent accessibility and torsion angle class predictions, which could be used as input to 3D prediction algorithms. The structural templates employed in computing SPMs are detected by eight alignment

Splice site prediction is crucial for understanding underlying gene regulation, gene function for better genome annotation. Many computational methods exist for recognizing the splice sites. Although most of the methods achieve a competent performance, their interpretability remains challenging. Moreover, all traditional machine learning methods manually extract features, which is tedious job. To address

Protein complexes are the cornerstones of most of the biological processes. Identifying protein complexes is crucial in understanding the principles of cellular organization with several important applications, including in disease diagnosis. Several computational techniques have been developed to identify protein complexes from protein–protein interaction (PPI) data (equivalently, from PPI networks)

Conformational plasticity of the functionally important regions and binding sites in protein/enzyme structures is one of the key factors affecting their function and interaction with substrates/ligands. Molecular dynamics (MD) can address the challenge of accounting for protein flexibility by predicting the time-dependent behavior of a molecular system. It has a potential of becoming a particularly

In addition to tumor cells, a large number of immune cells are found in the tumor microenvironment (TME) of cancer patients. Tumor-infiltrating immune cells play an important role in tumor progression and patient outcome. We improved the relative proportion estimation algorithm of immune cells based on RNA-seq gene expression profiling and solved the multiple linear regression model by support vector