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Protein Function Prediction Using Dependence
"... Abstract. Protein function prediction is one of the fundamental tasks in the post genomic era. The vast amount of available proteomic data makes it possible to computationally annotate proteins. Most computa-tional approaches predict protein functions by using the labeled proteins and assuming that ..."
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Abstract. Protein function prediction is one of the fundamental tasks in the post genomic era. The vast amount of available proteomic data makes it possible to computationally annotate proteins. Most computa-tional approaches predict protein functions by using the labeled proteins and assuming that the annotation of labeled proteins is complete, and without any missing functions. However, partially annotated proteins are common in real-world scenarios, that is a protein may have some confirmed functions, and whether it has other functions is unknown. In this paper, we make use of partially annotated proteomic data, and propose an approach called Protein Function Prediction using Dependency M aximization (ProDM). ProDM works by leveraging the correlation between different function labels, the ‘guilt by association ’ rule between proteins, and maximizes the dependency between function labels and feature expression of proteins. ProDM can replenish the missing func-tions of partially annotated proteins (a seldom studied problem), and can predict functions for completely unlabeled proteins using partially anno-tated ones. An empirical study on publicly available protein-protein inter-action (PPI) networks shows that, when the number of missing functions is large, ProDM performs significantly better than other related methods with respect to various evaluation criteria. 1
Protein Function Prediction with Incomplete Annotations
"... Abstract—Automated protein function prediction is one of the grand challenges in computational biology. Multi-label learning is widely used to predict functions of proteins. Most of multi-label learning methods make prediction for unlabeled proteins under the assump-tion that the labeled proteins ar ..."
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Abstract—Automated protein function prediction is one of the grand challenges in computational biology. Multi-label learning is widely used to predict functions of proteins. Most of multi-label learning methods make prediction for unlabeled proteins under the assump-tion that the labeled proteins are completely annotated, i.e., without any missing functions. However, in practice, we may have a subset of the ground-truth functions for a protein, and whether the protein has other functions is unknown. To predict protein functions with incomplete annotations, we propose a Protein Function Prediction method with Weak-label Learning (ProWL) and its variant ProWL-IF. Both ProWL and ProWL-IF can replenish the missing functions of proteins. In addition, ProWL-IF makes use of the knowledge that a protein cannot have certain functions, which can further boost the performance of protein function prediction. Our experimental results on protein-protein interaction networks and gene expression benchmarks validate the effectiveness of both ProWL and ProWL-IF.
Multi-Instance Multilabel Learning with Weak-Label for Predicting Protein Function in Electricigens
"... Nature often brings several domains together to form multidomain and multifunctional proteins with a vast number of possibilities. In our previous study, we disclosed that the protein function prediction problem is naturally and inherently Multi-Instance Multilabel (MIML) learning tasks. Automated ..."
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Nature often brings several domains together to form multidomain and multifunctional proteins with a vast number of possibilities. In our previous study, we disclosed that the protein function prediction problem is naturally and inherently Multi-Instance Multilabel (MIML) learning tasks. Automated protein function prediction is typically implemented under the assumption that the functions of labeled proteins are complete; that is, there are no missing labels. In contrast, in practice just a subset of the functions of a protein are known, and whether this protein has other functions is unknown. It is evident that protein function prediction tasks suffer from weak-label problem; thus protein function prediction with incomplete annotation matches well with the MIML with weak-label learning framework. In this paper, we have applied the state-of-the-art MIML with weak-label learning algorithm MIMLwel for predicting protein functions in two typical real-world electricigens organisms which have been widely used in microbial fuel cells (MFCs) researches. Our experimental results validate the effectiveness of MIMLwel algorithm in predicting protein functions with incomplete annotation.
