Loading...
Search for:
protein-interaction-network
0.011 seconds
Protein Interaction Prediction Through Efficient FPGA and GPU Implementation
, M.Sc. Thesis Sharif University of Technology ; Koohi, Somayyeh (Supervisor)
Abstract
Alignment of genetic sequences is a fundamental part of genetic and bio-science. Alignment of DNA and protein sequences has an effective role in accelerating and simplifying problems in Bioinformatics like predicting protein interactions. Smith-Waterman algorithm is a precise algorithm for performing local alignment, suffering from computation complexity. There are some implementations on CPU, GPU, and FPGA platforms in order to reduce the run time of this algorithm. FPGA implementation is considered because of low power consumption and high degree of parallelism. With using pipeline and hardware redundancy techniques, various architectures have been proposed and implemented. In the best...
Protein Function Prediction using Protein Interaction Networks
, M.Sc. Thesis Sharif University of Technology ; Fatemizadeh, Emadoddin (Supervisor)
Abstract
Predicting protein function accurately is an important issue in the post genomic era. To achieve this goal, several approaches have been proposed deduce the function of unclassified proteins through sequence similarity, co expression profiles, and other information. Among these methods, the Global Optimization Method is an interesting and powerful tool that assigns functions to unclassified proteins based on their positions in a physical interaction network. To boost both the accuracy and speed of global optimization method, a new prediction method, Accurate Global Optimization Method (AGOM), is presented in this thesis, which employs optimal repetition method enhanced with frequency of...
Overcoming drug resistance by co-targeting
, Article Proceedings - 2010 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2010, 18 December 2010 through 21 December 2010 ; December , 2010 , Pages 198-202 ; 9781424483075 (ISBN) ; Taheri, G ; Arab, S ; Wong, L ; Eslahchi, C ; Sharif University of Technology
2010
Abstract
Removal or suppression of key proteins in an essential pathway of a pathogen is expected to disrupt the pathway and prohibit the pathogen from performing a vital function. Thus disconnecting multiple essential pathways should disrupt the survival of a pathogen even when it has multiple pathways to drug resistance. We consider a scenario where the drug-resistance pathways are unknown. To disrupt these pathways, we consider a cut set S of G, where G is a connected simple graph representing the protein interaction network of the pathogen, so that G-S splits to two partitions such that the endpoints of each pathway are in different partitions. If the difference between the sizes of the two...
Finding correlation between protein protein interaction modules using semantic web techniques
, Article 13th International Computer Society of Iran Computer Conference on Advances in Computer Science and Engineering, CSICC 2008, Kish Island, 9 March 2008 through 11 March 2008 ; Volume 6 CCIS , 2008 , Pages 1009-1012 ; 18650929 (ISSN); 3540899847 (ISBN); 9783540899846 (ISBN) ; Moaven, S ; Abolhassani, H ; Sharif University of Technology
2008
Abstract
Many complex networks such as social networks and computer show modular structures, where edges between nodes are much denser within modules than between modules. It is strongly believed that cellular networks are also modular, reflecting the relative independence and coherence of different functional units in a cell. In this paper we used a human curated dataset. In this paper we consider each module in the PPI network as ontology. Using techniques in ontology alignment, we compare each pair of modules in the network. We want to see that is there a correlation between the structure of each module or they have totally different structures. Our results show that there is no correlation...
TripletProt: Deep Representation Learning of Proteins Based On Siamese Networks
, Article IEEE/ACM Transactions on Computational Biology and Bioinformatics ; Volume 19, Issue 6 , 2022 , Pages 3744-3753 ; 15455963 (ISSN) ; Asgari, E ; McHardy, A. C ; Mofrad, M. R. K ; Sharif University of Technology
Institute of Electrical and Electronics Engineers Inc
2022
Abstract
Pretrained representations have recently gained attention in various machine learning applications. Nonetheless, the high computational costs associated with training these models have motivated alternative approaches for representation learning. Herein we introduce TripletProt, a new approach for protein representation learning based on the Siamese neural networks. Representation learning of biological entities which capture essential features can alleviate many of the challenges associated with supervised learning in bioinformatics. The most important distinction of our proposed method is relying on the protein-protein interaction (PPI) network. The computational cost of the generated...