Cancer is the second leading cause of death globally, and use therapeutic peptides to target kill cancer cells has received considerable attention in recent years. Identification anticancer (ACPs) through wet-lab experimentation expensive often time consuming; therefore, development an efficient computational method essential identify potential ACP candidates prior vitro experimentation. In this study, we developed support vector machine- random forest-based machine-learning methods for...

Abstract Motivation Cardiovascular disease is the primary cause of death globally accounting for approximately 17.7 million deaths per year. One stakes linked with cardiovascular diseases and other complications hypertension. Naturally derived bioactive peptides antihypertensive activities serve as promising alternatives to pharmaceutical drugs. So far, there no comprehensive analysis, assessment diverse features implementation various machine-learning (ML) algorithms applied peptide (AHTP)...

DNA N4-methylcytosine (4mC) is an important genetic modification and plays crucial roles in differentiation between self non-self controlling replication, cell cycle, gene-expression levels. Accurate 4mC site identification fundamental to improve the understanding of biological functions mechanisms. Hence, it necessary develop silico approaches for efficient high-throughput identification. Although some bioinformatic tools have been developed this regard, their prediction accuracy...

The use of therapeutic peptides in various inflammatory diseases and autoimmune disorders has received considerable attention; however, the identification anti-inflammatory (AIPs) through wet-lab experimentation is expensive often time consuming. Therefore, development novel computational methods needed to identify potential AIP candidates prior vitro experimentation. In this study, we proposed a random forest (RF)-based method for predicting AIPs, called AIPpred (anti-inflammatory peptide...

Cell-penetrating peptides (CPPs) can enter cells as a variety of biologically active conjugates and have various biomedical applications. To offset the cost effort designing novel CPPs in laboratories, computational methods are necessitated to identify candidate before vitro experimental studies. We developed two-layer prediction framework called machine-learning-based cell-penetrating (MLCPPs). The first-layer predicts whether given peptide is CPP or non-CPP, whereas second-layer uptake...

Abstract Motivation Therapeutic peptides failing at clinical trials could be attributed to their toxicity profiles like hemolytic activity, which hamper further progress of as drug candidates. The accurate prediction (HLPs) and its activity from the given is one challenging tasks in immunoinformatics, essential for development basic research. Although there are a few computational methods that have been proposed this aspect, none them able identify HLPs activities simultaneously. Results In...

Accurately identifying bacteriophage virion proteins from uncharacterized sequences is important to understand interactions between the phage and its host bacteria in order develop new antibacterial drugs. However, identification of such using experimental techniques expensive often time consuming; hence, development an efficient computational algorithm for prediction (PVPs) prior vitro experimentation needed. Here, we describe a support vector machine (SVM)-based PVP predictor, called...

Anticancer peptides (ACPs) are promising therapeutic agents for targeting and killing cancer cells. The accurate prediction of ACPs from given peptide sequences remains as an open problem in the field immunoinformatics. Recently, machine learning algorithms have emerged a tool helping experimental scientists predict ACPs. However, performance existing methods still needs to be improved. In this study, we present novel approach ACPs, which involves following two steps: (i) We applied two-step...

DNA N6-adenine methylation (6mA) is an epigenetic modification in prokaryotes and eukaryotes. Identifying 6mA sites rice genome important epigenetics breeding, but non-random distribution biological functions of these remain unclear. Several machine-learning tools can identify show limited prediction accuracy, which limits their usability research. Here, we developed a novel computational predictor, called the Sequence-based N6-methyladenine predictor (SDM6A), two-layer ensemble approach for...

The identification of bitter peptides through experimental approaches is an expensive and time-consuming endeavor. Due to the huge number newly available peptide sequences in post-genomic era, development automated computational models for novel highly desirable.In this work, we present BERT4Bitter, a bidirectional encoder representation from transformers (BERT)-based model predicting directly their amino acid sequence without using any structural information. To best our knowledge, first...

Abstract The release of interleukin (IL)-6 is stimulated by antigenic peptides from pathogens as well immune cells for activating aggressive inflammation. IL-6 inducing are derived and can be used diagnostic biomarkers predicting various stages disease severity being inhibitors the suppression multi-signaling responses. Thus, accurate identification great importance investigating their mechanism action developing immunotherapeutic applications. This study proposes a novel stacking ensemble...

Identification of B-cell epitopes (BCEs) is a fundamental step for epitope-based vaccine development, antibody production, and disease prevention diagnosis. Due to the avalanche protein sequence data discovered in postgenomic age, it essential develop an automated computational method enable fast accurate identification novel BCEs within vast number candidate proteins peptides. Although several methods have been developed, their accuracy unreliable. Thus, developing reliable model with...

The accurate ranking of predicted structural models and selecting the best model from a given candidate pool remain as open problems in field bioinformatics. quality assessment (QA) methods used to address these can be grouped into two categories: consensus single-model methods. Consensus general perform better attain higher correlation between true measures. However, frequently fail generate proper scores for native-like structures which are distinct rest pool. Conversely, do not suffer...

Abstract Motivation Accurate identification of N4-methylcytosine (4mC) modifications in a genome wide can provide insights into their biological functions and mechanisms. Machine learning recently have become effective approaches for computational 4mC sites genome. Unfortunately, existing methods cannot achieve satisfactory performance, owing to the lack DNA feature representations that are capable capture characteristics modifications. Results In this work, we developed new predictor named...

Abstract DNA N6-methyladenine (6mA) represents important epigenetic modifications, which are responsible for various cellular processes. The accurate identification of 6mA sites is one the challenging tasks in genome analysis, leads to an understanding their biological functions. To date, several species-specific machine learning (ML)-based models have been proposed, but majority them did not test model other species. Hence, practical application plant species quite limited. In this study,...

Abstract Origins of replication sites (ORIs), which refers to the initiative locations genomic DNA replication, play essential roles in process. Detection ORIs’ distribution genome scale is one key steps in-depth understanding their regulation mechanisms. In this study, we presented a novel machine learning-based approach called Stack-ORI encompassing 10 cell-specific prediction models for identifying ORIs from four different eukaryotic species (Homo sapiens, Mus musculus, Drosophila...

Proinflammatory cytokines have the capacity to increase inflammatory reaction and play a central role in first line of defence against invading pathogens. inducing peptides (PIPs) been used as an antineoplastic agent, antibacterial agent vaccine immunization therapies. Due advancement sequence technologies that resulted avalanche protein data. Therefore, it is necessary develop automated computational method enable fast accurate identification novel PIPs within vast number candidate proteins...

Highlights•We developed an identification method for GHBPs using extremely randomized tree.•iGHBP displayed superior performance compared to the existing method.•We constructed a user-friendly web server that implements proposed iGHBP method.AbstractA soluble carrier growth hormone binding protein (GHBP) can selectively and non-covalently interact with hormone, thereby acting as modulator or inhibitor of signalling. Accurate GHBP from given sequence also provides important clues...