Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 15 of 15 for “"protein function prediction"”.
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Protein Function Prediction Using Graph Convolutional Network
This project advances protein function prediction by integrating protein language models (PLMs) and graph convolutional networks (GCNs), addressing the limitations of traditional methods that rely heavily on sequence similarity. The proposed model leverages diverse protein features, including …
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Leveraging expression and network data for protein function prediction
Protein function prediction is one of the prominent problems in bioinformatics today. Protein annotation is slowly falling behind as more and more genomes are being sequenced. Experimental methods are expensive and time consuming, which leaves computational methods to fill the gap. While …
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Accurate Protein Function Prediction with Graph Transformer-Based Function Localization
Protein function prediction is a fundamental challenge in biology, crucial for understanding biological processes, disease mechanisms, and accelerating drug discovery. While computational methods leveraging sequence or structural information have advanced, accurately translating protein structure …
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Hierarchical multi-label classification for protein function prediction going beyond traditional approaches
… that are in turn organized in a hierarchy. Functional classification of genes is a challenging problem in functional genomics due to several reasons. First, each gene participates in multiple biological activities. Hence, prediction models should support multi-label classification. Second, …
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Multi-Modal Protein Function Prediction using a Joint Embedding Space from Two Graph Neural Networks
In bioinformatics and proteomics, determining protein functions experimentally is expensive and slow. There’s a growing need for precise and quick computational prediction methods, filling the gap between sequence discovery and functional understanding. Over recent years there has been an influx of …
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Multi-target Prediction Methods for Bioinformatics: Approaches for Protein Function Prediction and Candidate Discovery for Gene Regulatory Network Expansion
… of their workflow. Given the central role of proteins in living organisms, in this thesis we focus on their functional analysis and the intrinsic multi-target nature of this task. To this end, we propose different predictive methods, specifically developed to exploit side knowledge among …
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Improving Profile Similarity Search and Alignment of Protein Sequences
Protein function prediction is one of the most important problems in the field of computational biology. The most reliable method to predict protein function is to detect homologs. Homologous proteins tend to possess conserved sequence motifs, the same structure folds, and similar functional sites. …
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Graph diffusions and matrix functions: fast algorithms and localization results
… applications in graphs such as webpage ranking, protein-function prediction, and product categorization and recommendation. As real-world networks grow to have millions of nodes and billions of edges, the scalability of network analysis algorithms becomes increasingly important. Whereas many …
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MPrompt: A Pretraining-Prompting Scheme for Enhanced Fewshot Subgraph Classification
… synthetic and real-world datasets, including protein function prediction and social network analysis. Our method demonstrated performance improvement under few-shot experiment setting and maintained comparable performance in full-shot settings while requiring less computation.
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Genome data analysis, protein function and structure prediction by machine learning techniques
… them, and extract useful structure and function information, such as the function of genes, the structure of proteins encoded by gene, and the function of proteins. Understanding these information is crucial for us to improve longevity and quality of life, and has a lot of applications, …
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Assessing the Role of Clusters Derived from Large Sequence Similarity Networks for Gene Function Predictions
… for biological experiments, computational predictions of gene functions can aid in reducing a large list of candidate genes to a few promising targets. Various computational solutions have been proposed and developed for gene function prediction. These solutions utilize various forms of …
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Computational Labeling, Partitioning, and Balancing of Molecular Networks
… with high accuracy, including mRNAs, proteins and metabolites. Differential expression of these molecules in case and control samples provides a way to select phenotype-associated molecules with statistically significant changes. However, given the significance ranking list of …
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Design and Evaluation of Network Algorithms and Deep Learning Models in Systems Biology and Biomedicine
… enhances the interpretability of diffusion-based predictions, a key requirement for their reliable application in biomedical contexts. (ii) Recognizing that the utility of network diffusion and other network-based algorithms depends on the quality of the underlying networks, we present ICoN, an …
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Data Mining Algorithms for Classification of Complex Biomedical Data
… (2) Multi-label classification of gene and protein prediction from multi-source biological data; (3) Spatial scan for movement data. In microarray classification, samples belong to several predefined categories (e.g., cancer vs. control tissues) and the goal is to build a predictor that …
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An Interdisciplinary Approach: Computational Sequence Motif Search and Prediction of Protein Function with Experimental Validation
… harbors a superfamily of effector genes whose protein products enter the cells of the host, soybean. Many of the effectors contain an RXLR-dEER motif in their N-terminus. More than 400 members belonging to this family have been previously identified using a Hidden Markov Model. Amino acids …