İstatistiksel metotlar ile genom-ölçekli protein ağlarının oluşturulması ve analizi

Download
2010
Söyler, Alper
Can, Tolga
Pamuk, Bahar
Bokar, Awadh H. Ali
Yaveroğlu, Nebil Ömer
Akkoyun, Emrah
Baloğlu, Onur
Turanalp, Emin Mehmet
Güney, Doğacan Tacettin
Yaşar, Sevgi
Bu projede büyük ölçekli protein-protein etkileşim ağlarının işlemsel metotlar ile tahmin edilmesi ve elde edilen ağların yine işlemsel metotlar ile analiz edilmesi amaçlanmıştır. Proje kapsamında biyologlara ilgilendikleri organizma hakkında sistem biyolojisi seviyesinde bulgular sağlayacak çeşitli teknikler geliştirilmiştir. Özellikle yeni sekanslanmış organizmaları hedefleyen teknikler sayesinde son yıllarda artan sekanslama projelerinden elde edilen verilerin etkili bir şekilde kullanılmasına olanak sağlanmıştır. Sadece protein sekansı kullanılarak protein etkileşimlerinin tahmin edilmesi, büyük ölçekli ağların görselleştirilmesi, ağların işlemsel yollarla otomatik olarak analiz edilerek birbiri ile çok etkileşen protein modüllerinin bulunması ve etkileşim ağlarında sık geçen fonksiyonel örüntülerin bulunması için metotlar geliştirilmiştir. Proje kapsamında geliştirilen metotlar çeşitli yayın ve tezler aracılığıyla bilim dünyasına sunulmuştur. Proje sonucu A. Fumigatus organizması için elde edilen ağ modülleri de ayrıca http://bioserver.ceng.metu.edu.tr/AFumigatus/ adresinde sunulmaktadır.

Suggestions

A mathematical contribution of statistical learning and continuous optimization using infinite and semi-infinite programming to computational statistics
Özöğür-Akyüz, Süreyya; Weber, Gerhard Wilhelm; Department of Scientific Computing (2009)
A subfield of artificial intelligence, machine learning (ML), is concerned with the development of algorithms that allow computers to “learn”. ML is the process of training a system with large number of examples, extracting rules and finding patterns in order to make predictions on new data points (examples). The most common machine learning schemes are supervised, semi-supervised, unsupervised and reinforcement learning. These schemes apply to natural language processing, search engines, medical diagnosis,...
Implementation of crossbar web service for interactive visualizations of biological networks
Atakan , Ahmet; Atalay, Mehmet Volkan; Department of Computer Engineering (2020-9)
Existing computational tools, resources, and services to assist experimental biomedical research lack data diversity and data connectivity and therefore, they are limited in helping to solve real world problems. Within the framework of CROssBAR project aiming for drug discovery, CROssBAR database (CROssBAR-DB) integrated diverse biomedical resources with the predictions of a comprehensive computing resource developed using machine learning and deep learning-based methods. It is crucial for the users to ...
Bilişsel durum analizi için beyin Ağı modeli
ÖNAL, ITIR; Aksan, Emre; VELİOĞLU, BURAK; Fırat, Orhan; Özay, Mete; Yarman Vural, Fatoş Tunay (2015-05-19)
We suggest a new approach to estimate a brain network to model cognitive tasks and explore the node degree distribution of this network in anatomic regions. Functional Magnetic Resonance Images are used to estimate the relationship among the voxels. First, a local mesh is formed around each voxel in a predefined neighborhood system. Then, the edge weights of meshes, called Mesh Arc Descriptors (MAD) are estimated using a linear regression model. In order to estimate the optimal mesh size for voxels, the err...
Distance matrices as protein representations
Dinç, Mehmet; Atalay, Mehmet Volkan; Department of Computer Engineering (2022-9-02)
Representing protein sequences is a crucial problem in the field of bioinformatics since any data-driven model's performance is limited by the information contained in its input features. A protein's biological function is dictated by its structure and knowing a protein's structure can potentially help predict its interactions with drug candidates or predict its Gene Ontology (GO) term. Yet, off-the-shelf protein representations do not contain such information since only a small fraction of the billions of ...
Optimization of time-cost-resource trade-off problems in project scheduling using meta-heuristic algorithms
Bettemir, Önder Halis; Sönmez, Rifat; Department of Civil Engineering (2009)
In this thesis, meta-heuristic algorithms are developed to obtain optimum or near optimum solutions for the time-cost-resource trade-off and resource leveling problems in project scheduling. Time cost trade-off, resource leveling, single-mode resource constrained project scheduling, multi-mode resource constrained project scheduling and resource constrained time cost trade-off problems are analyzed. Genetic algorithm simulated annealing, quantum simulated annealing, memetic algorithm, variable neighborhood ...
Citation Formats
A. Söyler et al., “İstatistiksel metotlar ile genom-ölçekli protein ağlarının oluşturulması ve analizi,” 2010. Accessed: 00, 2020. [Online]. Available: https://app.trdizin.gov.tr/publication/project/detail/T1RVME9Uaz0.