The new type of corona virus (SARS-COV-2) emerging in Wuhan, China has spread rapidly to the world and has become a pandemic. In addition to having a significant impact on daily life, it also shows its effect in different areas, including public health and economy. Currently, there is no vaccine or antiviral drug available to prevent the COVID-19 disease. Therefore, determination of protein interactions

Diabetic retinopathy (DR) is one of the most prevalent genetic diseases in human and it is caused by damage to the blood vessels in the eye retina. If it is undetected and untreated at right time, it can lead to vision loss. There are many medical imaging and processing technologies to improve the diagnostic process of DR to overcome the lack of human experts. In the existing image processing methods

Corona virus disease (COVID-19) acknowledged as a pandemic by the WHO and mankind all over the world is vulnerable to this virus. Alternative tools are needed that can help in diagnosis of the coronavirus. Researchers of this article investigated the potential of machine learning methods for automatic diagnosis of corona virus with high accuracy from X-ray images. Two most commonly used classifiers

White matter magnetic resonance hyperintensities of presumed vascular origin, which could be widely observed in elderly people, and has significant importance in multiple neurological studies. Quantitative measurement usually relies heavily on manual or semi-automatic delineation and intuitive localization, which is time-consuming and observer-dependent. Current automatic quantification methods focus

A substantial body of research is focused to improve the understanding of the relationship between genotypes and phenotypes. Genotype–phenotype studies have shown promise in improving disease diagnosis in humans and identification of specific clinical phenotypes may be helpful in developing more effective therapeutic and diagnostic strategies. To expand on the existing paradigm of evaluating genotypes

Background Studies have shown that extracts from Lycium barbarum exerted protective effects against colorectal cancer (CRC) cells. We used the network pharmacology method to determine the effects of L. barbarum on CRC and to predict core targets, biological functions, pathways, and mechanisms of action. Method We obtained the active compounds and their targets in L. barbarum via use of the Traditional

With fast-evolving next-generation sequencing technology, a great amount of plant genome and transcriptome data are becoming available. Due to the availability of mature microRNA (miRNA) sequence information from the miRBase (release 21) database, it is possible to predict endogenous target mimics (eTMs) in plant by searching seed-matched target sites. We identified a total of 2669 non-redundant eTM

Using Gaussian and Orca, UV and fluorescence spectra of three amino acids (Tyr: Tyrosine, Trp: Tryptophan, Phe: Phenylalanine) were calculated by different functionals (B3LYP, BP86, wB97X). The spectra calculated by BP86 are consistent with the experiments. UV spectra peak of Tyr is 255 nm (Exp. 275 nm, Δλ = 20 nm), Trp is 279 nm (Exp. 277 nm, Δλ = 2 nm), and Phe is 275 nm (Exp. 257 nm, Δλ = 18 nm)

Background Variations in the human genome have been studied extensively. However, little is known about the role of micro-inversions (MIs), generally defined as small (< 100 bp) inversions, in human evolution, diversity, and health. Depicting the pattern of MIs among diverse populations is critical for interpreting human evolutionary history and obtaining insight into genetic diseases. Results In this

The selection of gene identifier from microarray databases is a challenging task since microarray contains large number of gene attributes for a few samples. This article proposes a novel fuzzy-rough set-based gene expression features selection using fuzzy-rough reduct under multi-granular space for human diabetes patient. Firstly, fuzzy multi-granular gain has been computed from the expression datasets

In China, Banxia Xiexin decoction (BXD) is applied to treat diabetic gastroparesis (DGP), but its key active ingredients and mechanisms against DGP are unclear. This study is designated to reveal the molecular mechanisms of BXD in treating DGP by adopting a creative approach known as network pharmacology to explore the active ingredients and therapeutic targets of BXD. In our study, 730 differentially

Breast cancer, as one of the most common diseases threatening the women's life, has attracted serious attention of the clinical and biomedical researchers worldwide. The genome-based studies along with their registered GEO datasets are frequent in the literature. Since several methodologies have been developed for analyzing and identifying gene biomarkers, it is necessary to evaluate their robustness

The human brain organoid is a miniature three-dimensional tissue culture that can simulate the structure and function of the brain in an in vitro culture environment. Although we consider that human brain organoids could be used to understand brain development and diseases, experimental models of human brain organoids are so highly variable that we apply artificial intelligence (AI) techniques to investigate

Retinal image contains rich information on the blood vessel and is highly related to vascular diseases. Fully automatic and accurate identification of arteries and veins from the complex background of retinal images is essential for analyzing eye-relevant diseases, and monitoring progressive eye diseases. However, popular methods, including deep learning-based models, performed unsatisfactorily in

Abstract RNA-seq analysis has become one of the most widely used methods for biological and medical experiments, aiming to identify differentially expressed genes at a large scale. However, due to lack of programming skills and statistical background, it is difficult for biologists including faculty and students to fully understand what the RNA-seq results are and how to interpret them. In recent years

Background Mutations in the core CVR region of hepatitis C virus (HCV) and polymorphisms of interleukin 28B (IL28B) are associated with progression toward liver disease and in response to therapy. In addition, interactions of the core protein with some cell interactors can be related to HCV liver damage. Aim This study aimed to evaluate the effect of core mutations as well as IL28B polymorphism on

Comparison of system-wide metabolic pathways among microbes provides valuable insights of organisms’ metabolic capabilities that can further assist in rationally screening organisms in silico for various applications. In this work, we present a much needed, efficient and user-friendly Genome to KEGG Orthology (G2KO) pipeline tool that facilitates efficient comparison of system wide metabolic networks

Abstract Most recently, an outbreak of severe pneumonia caused by the infection of SARS-CoV-2, a novel coronavirus first identified in Wuhan, China, imposes serious threats to public health. Upon infecting host cells, coronaviruses assemble a multi-subunit RNA-synthesis complex of viral non-structural proteins (nsp) responsible for the replication and transcription of the viral genome. Therefore, the

Long non-coding RNA(lncRNA) is one of the non-coding RNAs longer than 200 nucleotides and it has no protein encoding function. LncRNA plays a key role in many biological processes. Studying the RNA-binding protein (RBP) binding sites on the lncRNA chain helps to reveal epigenetic and post-transcriptional mechanisms, to explore the physiological and pathological processes of cancer, and to discover

In the tremendous field of the bioinformatics look into, enormous volume of genetic information has been produced. Higher throughput gadgets are made accessible at lower cost made the age of Big data. In a time of developing information multifaceted nature and volume and the approach of huge information, feature selection has a key task to carry out in decreasing high dimensionality in AI issues. Dealing

Nowadays, the manipulation and analysis of genomic data stored in publicly accessible repositories have become a daily task in genomics and bioinformatics laboratories. Due to the enormous advancement in the field of genome sequencing and the emergence of many projects, bioinformaticians have pushed for the creation of a variety of programs and pipelines that will automatically analyze such big data

Protein sequence is a wealth of experimental information which is yet to be exploited to extract information on protein homologues. Consequently, it is observed from publications that dynamic programming, heuristics and HMM profile-based alignment techniques along with the alignment free techniques do not directly utilize ordered profile of physicochemical properties of a protein to identify its homologue

Next-generation sequencing (NGS) is related to massively parallel or deep deoxyribonucleic acid (DNA) sequencing technology which has revolutionized genomic researches in recent years. Although the cost of generating NGS data was decreased compared to the one at the time of emerging this technology, its cost might still be somewhat a problem. Hence, new strategies as pool-seq and low-coverage NGS data

Medical image processing is now gaining a significant momentum in clinical situation to undertake diagnosis of different anatomical defects. However, with regard to eye diseases, there is no such well-defined image processing technique in medical image analysis. The scope of this study is to automate computer analysis of ocular disease-related retinal images, which may ease the job of ophthalmologists

A novel coronavirus, called 2019-nCoV, was recently found in Wuhan, Hubei Province of China, and now is spreading across China and other parts of the world. Although there are some drugs to treat 2019-nCoV, there is no proper scientific evidence about its activity on the virus. It is of high significance to develop a drug that can combat the virus effectively to save valuable human lives. It usually

Protein Complexes plays important role in key functional processes in cells by forming Protein Protein Interaction (PPI) networks. Conventionally, they were determined through experimental approaches. For the sake of saving time and cost reduction, many computational methods have been proposed. Fewer computational approaches take into account significant biological information contained within protein

Due to the presence of irrelevant or redundant data in microarray datasets, capturing potential patterns accurately and directly via existing models is difficult. Feature selection (FS) has become a necessary strategy to identify and screen out the most relevant attributes. However, the high dimensionality of microarray datasets poses a serious challenge to most existing FS algorithms. For this purpose

There are a huge amount of neural units in brain networks. Some of the neural units have tight connection and form neural unit modules. These unit modules are helpful to the disease detection and target therapy. A good method can find neural unit modules accurately and effectively. The study proposes a new algorithm to analyze a brain network and obtain its neural unit modules. The proposed algorithm

Malaria is one of the epidemics that can cause human death. Accurate and rapid diagnosis of malaria is important for treatment. Due to the limited number of data and human factors, the prediction performance and reliability of traditional classification methods are often affected. In this study, we propose an efficient and novel classification network named Attentive Dense Circular Net (ADCN) which

In the era of big data analysis, genomics data analysis is highly needed to extract the hidden information present in the DNA sequences. One of the important hidden features present in the DNA sequences is CpG islands. CpG Islands are important as these are used as gene markers and also these are associated with cancer etc. Therefore, various methods have been reported for the identification of CpG

The human body immune system, metabolism and homeostasis are affected by microbes. Dysbiosis occurs when the homeostatic equilibrium is disrupted due to an alteration in the normal microbiota of the intestine. Dysbiosis can cause cancer, and also affect a patient's ability to respond to treatment. Metataxonomics seeks to identify the bacteria present in a biological sample, based on the sequencing

Hepatocellular carcinoma (HCC) is a common cancer of high mortality, mainly due to the difficulty in diagnosis during its clinical stage. Here we aim to find the gene biomarkers, which are of important significance for diagnosis and treatment. In this work, 3682 differentially expressed genes on HCC were firstly differentiated based on the Cancer Genome Atlas database (TCGA). Co-expression modules

The activity of post-marketing surveillance results in a collection of large amount of data. The analysis of data is very useful for raising early warnings on possible adverse reactions of drugs. Association rule mining techniques have been heavily explored by the research community for identifying binary association between drugs and their adverse effects. But these techniques perform poorly and miss

Many kinds of disease-related data are now available and researchers are constantly attempting to mine useful information out of these. Medical data are not always homogeneous and in structured form, and mostly they are time-stamped data. Thus, special care is required to prevent any kind of information loss during mining such data. Mining medical data is challenging as predicting the non-accurate

Protein-protein interactions are important for most biological processes and have been studied for decades. However, the detailed formation mechanism of protein-protein interaction interface is still ambiguous, which makes it difficult to accurately predict the protein-protein interaction interface residue pairs. Here, we extract the interface residue-residue contacts from the decoys in the ZDOCK protein-protein

Pseudouridine represents one of the most prevalent post-transcriptional RNA modifications. The identification of pseudouridine sites is an essential step toward understanding RNA functions, RNA structure stabilization, translation process, and RNA stability; however, high-throughput experimental techniques remain expensive and time-consuming in lab explorations and biochemical processes. Thus, how

Clustering of single-cell RNA sequencing (scRNA-seq) data enables discovering cell subtypes, which is helpful for understanding and analyzing the processes of diseases. Determining the weight of edges is an essential component in graph-based clustering methods. While several graph-based clustering algorithms for scRNA-seq data have been proposed, they are generally based on k-nearest neighbor (KNN)

Breast cancer is the most common cause of death in women worldwide. Approximately 5%-10% of instances are attributed to mutations acquired from the parents. Therefore, it is highly recommended to design more potential drugs and drug targets to eradicate such complex diseases. Network-based gene expression profiling is a suggested tool for discovering drug targets by incorporating various factors such

Alzheimer's disease (AD) is a neurodegenerative disorder partly induced by dysregulation of different brain regions. Prefrontal cortex (PFC) dysregulation has been reported to associate with mental symptoms such as delusion, apathy, and depression in AD patients. However, the internal mechanisms have not yet been well-understood. This study aims to identify the potential therapeutic target genes and

Parkinson's disease (PD) is another major neurodegenerative disorder following Alzheimer's disease, which not only seriously reduces the survival in patients, affecting patient's quality of life, but also imposes a tremendous burden on families and even the whole society. It is urgent to find out effective drugs without side effects. The present study applied a creative approach called network pharmacology

BACKGROUND Although microsatellite instability (MSI) is most commonly detected in colorectal cancer (CRC), improvement in MSI analysis method can always help us better assessing MSI phenotypes and gaining useful information in challenging cases. The purpose of current study is to explore whether the ProDx® MSI analysis System (ProDx® MSI) can improve MSI classification in CRC. METHODS We compared the

Potassium channels play a key role in regulating the flow of ions through the plasma membrane, orchestrating many cellular processes including cell volume regulation, hormone secretion and electrical impulse formation. Ligand peptides of potassium channels are molecules used in basic and applied research and are now considered promising alternatives in the treatment of many diseases, such as cardiovascular

To inhibit hIAPP aggregation and reduce toxicity of its oligomers are one of the potential strategies for the treatment of Type 2 diabetes (T2D). It has been reported that there is an effective inhibitory effect on hIAPP aggregation by five natural flavonoids, including Genistein, Rutin, Quercetin, Epigallocatechin gallate (EGCG), and Silibinin, which are widely found in our daily food. However, the

Nucleosomal profiling is an effective method to determine the positioning and occupancy of nucleosomes, which is essential to understand their roles in genomic processes. However, the positional randomness across the genome and its relationship with nucleosome occupancy remains poorly understood. Here we present a computational method that segments the profile into nucleosomal domains and quantifies

CytoMegaloVirus (CMV) is known to cause infection in humans and may remain dormant throughout the life span of an individual. CMV infection has been reported to be fatal in patients with weak immunity. It is transmitted through blood, saliva, urine, semen and breast milk. Although medications are available to treat the infected patients, there is no cure for CMV. This concern prompted us to construct

Non-coding RNA (ncRNA) plays an important role in regulating biological activities of animals and plants, and the representative ones are microRNA (miRNA) and long non-coding RNA (lncRNA). Recent research has found that predicting the interaction between miRNA and lncRNA is the primary task for elucidating their functional mechanisms. Due to the small scale of data, a large amount of noise, and the

The argonaute protein (Ago) exists in almost all organisms. In eukaryotes, it functions as a regulatory system for gene expression. In prokaryotes, it is a type of defense system against foreign invasive genomes. The Ago system has been engineered for gene silencing and genome editing and plays an important role in biological studies. With an increasing number of genomes and proteomes of various microbes

Counting the abundance of all the distinct kmers in biological sequence data is a fundamental step in bioinformatics. These applications include de novo genome assembly, error correction, etc. With the development of sequencing technology, the sequence data in a single project can reach Petabyte-scale or Terabyte-scale nucleotides. Counting demand for the abundance of these sequencing data is beyond

BACKGROUND Computational prediction of inhibition efficiency (IE) for inhibitor molecules is a crucial supplementary way to design novel molecules that can efficiently inhibit corrosion onto metallic surfaces. PURPOSE Here we are dedicated to developing a new machine learning-based predictor for the inhibition efficiency (IE) of benzimidazole derivatives. METHODS First, a comprehensively numerical

In recent years, the application of Whole Genome Sequencing (WGS) on plants has generated sufficient data for the identification of trait-associated genomic loci or genes. A high-throughput genome-assisted QTL-seq strategy, combined with bulked-segregant analysis and WGS of two bulked populations from a segregating progeny with opposite phenotypic trait values, has gained increasing popularities in

Nonparametric estimation of cumulative distribution function and probability density function of continuous random variables is a basic and central problem in probability theory and statistics. Although many methods such as kernel density estimation have been presented, it is still quite a challenging problem to be addressed to researchers. In this paper, we proposed a new method of spline regression

The web application "Shiny App for Frequency Analysis on Homo sapiens Genome (SAFA-HsG)" was developed using R programming-based bioconductor packages and shiny framework. Through the app, preliminary descriptive data analysis on nucleotide frequency, and CpG island, CpG non-island, and CpG island shores and shelves (downstream and upstream) of human reference genome can be carried out, which will