Browsing by Author "Haberal, Ismail"

Now showing 1 - 3 of 3

DeepMBS: Prediction of Protein Metal Binding-Site Using Deep Learning Networks
(2017) Haberal, Ismail; Ogul, Hasan; 0000-0002-8647-4295; AAJ-8956-2021
The tertiary structure of a protein indicates what vital function that protein fulfills in the cell. Prediction of the metal binding comformation of a protein from its sequence is a crucial step in predicting its tertiary structure. In this study, a computational method was developed for predicting the binding of Histidine and Cysteine to metals. We propose a deep convolutional neural network architecture, DeepMBS, to predict protein metal binding sites. To our knowledge, this study is the first realization of deep learning idea for the problem of predicting metal binding site. The method allows automatic extraction of complex interactions between important features using only sequence information by utilizing PAM120 scoring matrix. Features were extracted from protein sequences obtained from the Protein Data Bank and deep convolutional neural network was applied to these features. According to experimental results on a benchmark dataset, metal binding states can be predicted with 82% recall and 79% precision. These results show that a better performance can be achived with deep learning approach compared with previous studies on the same dataset.
Detection of Microrna Clusters Associated with Prostate Cancer
(2014) Haberal, Ismail; Ogul, Hasan; https://orcid.org/0000-0002-8647-4295; AAJ-8956-2021
MicroRNAs (miRNAs) are a class of small non-coding RNAs of 22 nucleotides which normally function as negative regulators of target mRNA expression at the posttranscriptional level. miRNAs play a role for one or more target genes by suppressing in processes as growth, differentiation, proliferation and cell death. Recent evidence has shown that miRNA mutations or mis-expression correlate with various human cancers and indicates that miRNAs can function as tumour suppressors and oncogenes. MicroRNAs have been shown to repress the expression of important cancer-related genes and might prove useful in the diagnosis and treatment of cancer. In this study, hierarchical microRNA clusters are obtained through microarray expression data in order to analyze the microRNA prostate cancer relationships. Clustering results are evaluated by their biological relevance. It is seen that such approach can be useful in detectitn relationships between microRNAs and diseases.
Prediction of Protein Metal Binding Sites Using Deep Neural Networks
(2019) Haberal, Ismail; Ogul, Hasan; 0000-0002-8647-4295; 30977960
Metals have crucial roles for many physiological, pathological and diagnostic processes. Metal binding proteins or metalloproteins are important for metabolism functions. The proteins that reach the three-dimensional structure by folding show which vital function is fulfilled. The prediction of metal-binding in proteins will be considered as a step-in function assignment for new proteins, which helps to obtain functional proteins in genomic studies, is critical to protein function annotation and drug discovery. Computational predictions made by using machine learning methods from the data obtained from amino acid sequences are widely used in the protein metal-binding and various bioinformatics fields. In this work, we present three different deep learning architectures for prediction of metal-binding of Histidines (HIS) and Cysteines (CYS) amino acids. These architectures are as follows: 2D Convolutional Neural Network, Long-Short Term Memory and Recurrent Neural Network. Their comparison is carried out on the three different sets of attributes derived from a public dataset of protein sequences. These three sets of features extracted from the protein sequence were obtained using the PAM scoring matrix, protein composition server, and binary representation methods. The results show that a better performance for prediction of protein metal- binding sites is obtained through Convolutional Neural Network architecture.