Current Bioinformatics - Volume 16, Issue 3, 2021

Structure-based drug discovery has become a promising and efficient approach for identifying novel and potent drug candidates with less time and cost than conventional drug discovery approaches. It has been widely used in the pharmaceutical industry since it uses the 3D structure of biological protein targets and thereby allows us to understand the molecular basis of diseases. For the virtual identification of drug candi Read More

Background: Presynaptic and postsynaptic neurotoxins are two important categories of neurotoxins. Due to the important role of presynaptic and postsynaptic neurotoxins in pharmacology and neuroscience, their identification has become very important biologically. Methods: In this study, statistical tests and F-scores were used to calculate differences between amino acids and biological properties. The support vector mac Read More

Background: MicroRNAs (miRNAs) are a class of endogenous non-coding RNAs with about 22 nucleotides, and they play a significant role in a variety of complex biological processes. Many researches have shown that miRNAs are closely related to human diseases. Although the biological experiments are reliable in identifying miRNA-disease associations, they are timeconsuming and costly. Objective: Thus, computational Read More

Background: Recently, ample researches show that microRNAs (miRNAs) not only interact with coding genes but interact with a pool of different RNAs. Those RNAs are called miRNA sponges, including long non-coding RNAs (lncRNAs), circular RNA, pseudogenes and various messenger RNAs. Understanding regulatory networks of miRNA sponges can better help researchers to study the mechanisms of breast cancers. Objectiv Read More

Background: Gene regulation represents a very complex mechanism in the cell initiated to increase or decrease gene expression. This regulation of genes forms a Gene regulatory Network GRN composed of a collection of genes and products of genes in interaction. The high throughput technologies that generate a huge volume of gene expression data are useful for analyzing the GRN. The biologists are interested in the Read More

Background: Gene set enrichment analyses (GSEA) provide a useful and powerful approach to identify differentially expressed gene sets with prior biological knowledge. Several GSEA algorithms have been proposed to perform enrichment analyses on groups of genes. However, many of these algorithms have focused on the identification of differentially expressed gene sets in a given phenotype. Objective: In this paper, we pro Read More

Background: Preventing adverse drug reactions (ADRs) is imperative for the safety of the people. The problem of under-reporting the ADRs has been prevalent across the world, making it difficult to develop the prediction models, which are unbiased. As a result, most of the models are skewed to the negative samples leading to high accuracy but poor performance in other metrics such as precision, recall, F1 score, and Read More

Objective: It is a known fact that numerous complex disorders do not happen in isolation indicating the plausible set of shared causes common to several different sicknesses. Hence, analysis of comorbidity can be utilized to explore the association between several disorders. In this study, we have proposed a network-based computational approach, in which genes are organized based on the topological characteristics of the c Read More

Background: Proteins contribute significantly in every task of cellular life. Their functions encompass the building and repairing of tissues in human bodies and other organisms. Hence they are the building blocks of bones, muscles, cartilage, skin, and blood. Similarly, antifreeze proteins are of prime significance for organisms that live in very cold areas. With the help of these proteins, the cold water organisms can survive belo Read More

Background: With the rapid development of the sequencing methods in recent years, binding sites have been systematically identified in such projects as Nested-MICA and MEME. Prediction of DNA motifs with higher accuracy and precision has been a very important task for bioinformaticians. Nevertheless, experimental approaches are still time-consuming for big data set, making computational identification Read More

Background: Bacterial pathogens are deadly for animals and humans. The ease of their dissemination, coupled with their high capacity for ailments and death in infected individuals, makes them a threat to society. Objective: Due to the high similarity among genera and species of pathogens, it is sometimes difficult for microbiologists to differentiate between them. Their automatic classification using deeplearning models can help Read More

Objective: A gene interaction network, along with its related biological features, has an important role in computational biology. Bayesian network, as an efficient model, based on probabilistic concepts is able to exploit known and novel biological casual relationships between genes. The success of Bayesian networks in predicting the relationships greatly depends on selecting priors. Methods: K-mers have been applied as the pro Read More