Biology subjects

Olyaee, M. H.

Publications and source records attributed to Olyaee, M. H..

4 recordsLinked to original sources

bioRxiv2021

EnGRNT: Inference of gene regulatory networks using ensemble methods and topological feature extraction

The development of computational methods to predict gene regulatory networks (GRNs) from gene expression data is a challenging task. Many machine learning methods have been developed, including supervised, unsupervised, and semi-supervised to infer gene regulatory networks. Most of these methods ignore the class imbalance problem which can lead to decreasing the accuracy of predicting regulatory interactions in the network. Therefore, developing an effective method considering imbalanced data is a challenging task. In this paper, we propose EnGRNT approach to infer GRNs with high accuracy that uses ensemble-based methods. The proposed approach, as well as the gene expression data, considers the topological features of GRN. We applied our approach to the simulated Escherichia coli dataset. Experimental results demonstrate that the appropriateness of the inference method relies on the size and type of expression profiles in microarray data. Except for multifactorial experimental conditions, the proposed approach outperforms unsupervised methods. The obtained results recommend the application of EnGRNT on the imbalanced datasets.

bioinformatics↗

bioRxiv2020

ProCbA: Protein Function Prediction based on Clique Analysis

Protein function prediction based on protein-protein interactions (PPI) is one of the most important challenges of the Post-Genomic era. Due to the fact that determining protein function by experimental techniques can be costly, function prediction has become an important challenge for computational biology and bioinformatics. Some researchers utilize graph- (or network-) based methods using PPI networks for un-annotated proteins. The aim of this study is to increase the accuracy of the protein function prediction using two proposed methods. To predict protein functions, we propose a Protein Function Prediction based on Clique Analysis (ProCbA) and Protein Function Prediction on Neighborhood Counting using functional aggregation (ProNC-FA). Both ProCbA and ProNC-FA can predict the functions of unknown proteins. In addition, in ProNC-FA which is not including new algorithm; we try to address the essence of incomplete and noisy data of PPI era in order to achieving a network with complete functional aggregation. The experimental results on MIPS data and the 17 different explained datasets validate the encouraging performance and the strength of both ProCbA and ProNC-FA on function prediction. Experimental result analysis as can be seen in Section IV, the both ProCbA and ProNC-FA are generally able to outperform all the other methods.

bioinformatics↗

bioRxiv2020

Single Individual Haplotype Reconstruction Using Fuzzy C-Means Clustering With Minimum Error Correction

Evolution of human genetics is one of the most interesting areas for researchers. Determination of Haplotypes not only makes valuable information for this purpose but also performs a major role in investigating the probable relation between diseases and genomes. Determining haplotypes by experimental methods is a time-consuming and expensive task. Recent progress in high throughput sequencing allows researchers to use computational methods for this purpose. Although, several algorithms have been proposed but they are less accurate when the error rate of input fragments increases. In this paper, first, a fuzzy conflict graph is constructed based on the similarities of all input fragments and next, the cluster centers are used as initial centers by fuzzy c-means (FCM) algorithm. The proposed method has been tested on several real datasets and compared with some current methods. The comparison with the existing approaches shows that our method can be a complementary role among the others.

bioinformatics↗

bioRxiv2020

A chaotic viewpoint-based approach to solve haplotype assembly using hypergraph model

Decreasing the cost of high-throughput DNA sequencing technologies, provides a huge amount of data that enables researchers to determine haplotypes for diploid and polyploid organisms. Although various methods have been developed to reconstruct haplotypes in diploid form, their accuracy is still a challenging task. Also, most of the current methods cannot be applied to polyploid form. In this paper, an iterative method is proposed, which employs hypergraph to reconstruct haplotype. The proposed method by utilizing chaotic viewpoint can enhance the obtained haplotypes. For this purpose, a haplotype set was randomly generated as an initial estimate, and its consistency with the input fragments was described by constructing a weighted hypergraph. Partitioning the hypergraph specifies those positions in the haplotype set that need to be corrected. This procedure is repeated until no further improvement could be achieved. Each element of the finalized haplotype set is mapped to a line by chaos game representation, and a coordinate series is defined based on the position of mapped points. Then, some positions with low qualities can be assessed by applying a local projection. Experimental results on both simulated and real datasets demonstrate that this method outperforms most other approaches, and is promising to perform the haplotype assembly.

bioinformatics↗