Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
Browse
3 results
Search Results
Article Citation - WoS: 1Citation - Scopus: 2Identifying Promoter and Enhancer Sequences by Graph Convolutional Networks(Elsevier Ltd, 2024) Tenekeci,S.; Tekir,S.Identification of promoters, enhancers, and their interactions helps understand genetic regulation. This study proposes a graph-based semi-supervised learning model (GCN4EPI) for the enhancer-promoter classification problem. We adopt a graph convolutional network (GCN) architecture to integrate interaction information with sequence features. Nodes of the constructed graph hold word embeddings of DNA sequences while edges hold the Enhancer-Promoter Interaction (EPI) information. By means of semi-supervised learning, much less data (16%) and time are needed in model training. Comparisons on a benchmark dataset of six human cell lines show that the proposed approach outperforms the state-of-the-art methods by a large margin (10% higher F1 score) and has the fastest training time (up to 3 times). Moreover, GCN4EPI's performance on cross-cell line data is also better than the baselines (3% higher F1 score). Our qualitative analyses with graph explainability models prove that GCN4EPI learns from both text and graph structure. The results suggest that integrating interaction information with sequence features improves predictive performance and compensates for the number of training instances. © 2024Conference Object 2-D Thresholding of the Connectivity Map Following the Multiple Sequence Alignments of Diverse Datasets(ACTA Press, 2013) Doğan, Tunca; Karaçalı, BilgeMultiple sequence alignment (MSA) is a widely used method to uncover the relationships between the biomolecular sequences. One essential prerequisite to apply this procedure is to have a considerable amount of similarity between the test sequences. It's usually not possible to obtain reliable results from the multiple alignments of large and diverse datasets. Here we propose a method to obtain sequence clusters of significant intragroup similarities and make sense out of the multiple alignments containing remote sequences. This is achieved by thresholding the pairwise connectivity map over 2 parameters. The first one is the inferred pairwise evolutionary distances and the second parameter is the number of gapless positions on the pairwise comparisons of the alignment. Threshold curves are generated regarding the statistical parameter values obtained from a shuffled dataset and probability distribution techniques are employed to select an optimum threshold curve that eliminate as much of the unreliable connectivities while keeping the reliable ones. We applied the method on a large and diverse dataset composed of nearly 18000 human proteins and measured the biological relevance of the recovered connectivities. Our precision measure (0.981) was nearly 20% higher than the one for the connectivities left after a classical thresholding procedure displaying a significant improvement. Finally we employed the method for the functional clustering of protein sequences in a gold standard dataset. We have also measured the performance, obtaining a higher F-measure (0.882) compared to a conventional clustering operation (0.827).Article Citation - WoS: 7Citation - Scopus: 9Automatic Identification of Highly Conserved Family Regions and Relationships in Genome Wide Datasets Including Remote Protein Sequences(Public Library of Science, 2013) Doğan, Tunca; Karaçalı, BilgeIdentifying shared sequence segments along amino acid sequences generally requires a collection of closely related proteins, most often curated manually from the sequence datasets to suit the purpose at hand. Currently developed statistical methods are strained, however, when the collection contains remote sequences with poor alignment to the rest, or sequences containing multiple domains. In this paper, we propose a completely unsupervised and automated method to identify the shared sequence segments observed in a diverse collection of protein sequences including those present in a smaller fraction of the sequences in the collection, using a combination of sequence alignment, residue conservation scoring and graph-theoretical approaches. Since shared sequence fragments often imply conserved functional or structural attributes, the method produces a table of associations between the sequences and the identified conserved regions that can reveal previously unknown protein families as well as new members to existing ones. We evaluated the biological relevance of the method by clustering the proteins in gold standard datasets and assessing the clustering performance in comparison with previous methods from the literature. We have then applied the proposed method to a genome wide dataset of 17793 human proteins and generated a global association map to each of the 4753 identified conserved regions. Investigations on the major conserved regions revealed that they corresponded strongly to annotated structural domains. This suggests that the method can be useful in predicting novel domains on protein sequences.