Improved Fuzzy Cognitive Maps for Gene Regulatory Networks Inference based on time series data.

Microarray data provide lots of information regarding gene expression levels. Due to the large amount of such data, their analysis requires sufficient computational methods for identifying and analyzing gene regulation networks; however, researchers in this field are faced with numerous challenges such as consideration for too many genes and at the same time, the limited number of samples and their noisy nature of the data. In this paper, a hybrid method base on fuzzy cognitive map and compressed sensing is used to identify interactions between genes.

Improving prediction of drug-target interactions based on fusing multiple features with data balancing and feature selection techniques.

Drug discovery relies on predicting drug-target interaction (DTI), which is an important challenging task. The purpose of DTI is to identify the interaction between drug chemical compounds and protein targets. Traditional wet lab experiments are time-consuming and expensive, that's why in recent years, the use of computational methods based on machine learning has attracted the attention of many researchers.

Drug-target interaction prediction based on protein features, using wrapper feature selection.

Drug-target interaction prediction is a vital stage in drug development, involving lots of methods. Experimental methods that identify these relationships on the basis of clinical remedies are time-taking, costly, laborious, and complex introducing a lot of challenges. One group of new methods is called computational methods.

A hybrid feature extraction scheme for efficient malonylation site prediction.

Lysine malonylation is one of the most important post-translational modifications (PTMs). It affects the functionality of cells. Malonylation site prediction in proteins can unfold the mechanisms of cellular functionalities.

Drug-target interaction prediction using unifying of graph regularized nuclear norm with bilinear factorization.

Background: Wet-lab experiments for identification of interactions between drugs and target proteins are time-consuming, costly and labor-intensive. The use of computational prediction of drug-target interactions (DTIs), which is one of the significant points in drug discovery, has been considered by many researchers in recent years. It also reduces the search space of interactions by proposing potential interaction candidates.

KFGRNI: A robust method to inference gene regulatory network from time-course gene data based on ensemble Kalman filter.

A central problem of systems biology is the reconstruction of Gene Regulatory Networks () by the use of time series data. Although many attempts have been made to design an efficient method for 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.

RCOVID19: Recurrence-based SARS-CoV-2 features using chaos game representation.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic. It was first detected in China and was rapidly spread to other countries. Several thousands of whole genome sequences of SARS-CoV-2 have been reported and it is important to compare them and identify distinctive evolutionary/mutant markers.

An Efficient hybrid filter-wrapper metaheuristic-based gene selection method for high dimensional datasets.

Feature selection problem is one of the most significant issues in data classification. The purpose of feature selection is selection of the least number of features in order to increase accuracy and decrease the cost of data classification. In recent years, due to appearance of high-dimensional datasets with low number of samples, classification models have encountered over-fitting problem.

TIGRNCRN: Trustful inference of gene regulatory network using clustering and refining the network.

In this study, in order to deal with the noise and uncertainty in gene expression data, learning networks, especially Bayesian networks, that have the ability to use prior knowledge, were used to infer gene regulatory network. Learning networks are methods that have the structure of the network and a learning process to obtain relationships. One of the methods which have been used for measuring the relationship between genes is the correlation metrics, but the high correlated genes not necessarily mean that they have causal effect on each other.

A robust gene regulatory network inference method base on Kalman filter and linear regression.

The reconstruction of the topology of gene regulatory networks (GRNs) using high throughput genomic data such as microarray gene expression data is an important problem in systems biology. The main challenge in gene expression data is the high number of genes and low number of samples; also the data are often impregnated with noise. In this paper, in dealing with the noisy data, Kalman filter based method that has the ability to use prior knowledge on learning the network was used.