PubMed İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/397

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Now showing 1 - 4 of 4

Citation - WoS: 8
Citation - Scopus: 9
Developing Structural Profile Matrices for Protein Secondary Structure and Solvent Accessibility Prediction
(Oxford Univ Press, 2019) Aydin, Zafer; Azginoglu, Nuh; Bilgin, Halil Ibrahim; Celik, Mete; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
Motivation: Predicting secondary structure and solvent accessibility of proteins are among the essential steps that preclude more elaborate 3D structure prediction tasks. Incorporating class label information contained in templates with known structures has the potential to improve the accuracy of prediction methods. Building a structural profile matrix is one such technique that provides a distribution for class labels at each amino acid position of the target. Results: In this paper, a new structural profiling technique is proposed that is based on deriving PFAM families and is combined with an existing approach. Cross-validation experiments on two benchmark datasets and at various similarity intervals demonstrate that the proposed profiling strategy performs significantly better than Homolpro, a state-of-the-art method for incorporating template information, as assessed by statistical hypothesis tests.
Citation - WoS: 41
Citation - Scopus: 42
HomSI: A Homozygous Stretch Identifier From Next-Generation Sequencing Data
(Oxford Univ Press, 2014) Gormez, Zeliha; Bakir-Gungor, Burcu; Sagiroglu, Mahmut Samil; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
In consanguineous families, as a result of inheriting the same genomic segments through both parents, the individuals have stretches of their genomes that are homozygous. This situation leads to the prevalence of recessive diseases among the members of these families. Homozygosity mapping is based on this observation, and in consanguineous families, several recessive disease genes have been discovered with the help of this technique. The researchers typically use single nucleotide polymorphism arrays to determine the homozygous regions and then search for the disease gene by sequencing the genes within this candidate disease loci. Recently, the advent of next-generation sequencing enables the concurrent identification of homozygous regions and the detection of mutations relevant for diagnosis, using data from a single sequencing experiment. In this respect, we have developed a novel tool that identifies homozygous regions using deep sequence data. Using*.vcf (variant call format) files as an input file, our program identifies the majority of homozygous regions found by microarray single nucleotide polymorphism genotype data.
Citation - WoS: 46
Citation - Scopus: 59
Node Similarity-Based Graph Convolution for Link Prediction in Biological Networks
(Oxford Univ Press, 2021) Coskun, Mustafa; Koyuturk, Mehmet; 01. Abdullah Gül University
Background: Link prediction is an important and well-studied problem in network biology. Recently, graph representation learning methods, including Graph Convolutional Network (GCN)-based node embedding have drawn increasing attention in link prediction. Motivation: An important component of GCN-based network embedding is the convolution matrix, which is used to propagate features across the network. Existing algorithms use the degree-normalized adjacency matrix for this purpose, as this matrix is closely related to the graph Laplacian, capturing the spectral properties of the network. In parallel, it has been shown that GCNs with a single layer can generate more robust embeddings by reducing the number of parameters. Laplacian-based convolution is not well suited to single-layered GCNs, as it limits the propagation of information to immediate neighbors of a node. Results: Capitalizing on the rich literature on unsupervised link prediction, we propose using node similarity-based convolution matrices in GCNs to compute node embeddings for link prediction. We consider eight representative node-similarity measures (Common Neighbors, Jaccard Index, Adamic-Adar, Resource Allocation, Hub- Depressed Index, Hub-Promoted Index, Sorenson Index and Salton Index) for this purpose. We systematically compare the performance of the resulting algorithms against GCNs that use the degree-normalized adjacency matrix for convolution, as well as other link prediction algorithms. In our experiments, we use three-link prediction tasks involving biomedical networks: drug-disease association prediction, drug-drug interaction prediction and protein-protein interaction prediction. Our results show that node similarity-based convolution matrices significantly improve the link prediction performance of GCN-based embeddings. Conclusion: As sophisticated machine-learning frameworks are increasingly employed in biological applications, historically well-established methods can be useful in making a head-start.
Citation - WoS: 24
Citation - Scopus: 27
PANOGA: a Web Server for Identification of SNP-Targeted Pathways From Genome-Wide Association Study Data
(Oxford Univ Press, 2014) Bakir-Gungor, Burcu; Egemen, Ece; Sezerman, Osman Ugur; 01. Abdullah Gül University; 02. 04. Bilgisayar Mühendisliği; 02. Mühendislik Fakültesi
Genome-wide association studies (GWAS) have revolutionized the search for the variants underlying human complex diseases. However, in a typical GWAS, only a minority of the single-nucleotide polymorphisms (SNPs) with the strongest evidence of association is explained. One possible reason of complex diseases is the alterations in the activity of several biological pathways. Here we present a web server called Pathway and Network-Oriented GWAS Analysis to devise functionally important pathways through the identification of SNP-targeted genes within these pathways. The strength of our methodology stems from its multidimensional perspective, where we combine evidence from the following five resources: (i) genetic association information obtained through GWAS, (ii) SNP functional information, (iii) protein-protein interaction network, (iv) linkage disequilibrium and (v) biochemical pathways.

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Browsing PubMed İndeksli Yayınlar Koleksiyonu by Journal "Bioinformatics"