True Path Rule Hierarchical Ensembles for Genome-Wide Gene Function Prediction

Valentini, Giorgio

doi:10.1109/tcbb.2010.38

Cited by 127 publications

(130 citation statements)

References 45 publications

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“…positives). This issue is particularly relevant in problems like GFP, where the imbalance in data requires the adoption of cost-sensitive strategies (Mostafavi et al, 2008;Cesa-Bianchi & Valentini, 2010;Valentini, 2011). Moreover, many of the described approaches may not preserve the prior knowledge coded in the initial labeling and in the pairwise similarities, and this is a relevant issue when we assume that the prior knowledge is not affected by noise.…”

Section: Introductionmentioning

confidence: 99%

A neural network algorithm for semi-supervised node label learning from unbalanced data

et al. 2013

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Given a weighted graph and a partial node labeling, the graph classification problem consists in predicting the labels of all the nodes. In several application domains, from gene to social network analysis, the labeling is unbalanced: for instance positive labels may be much less than negatives. In this paper we present COSNet (COst Sensitive neural Network), a neural algorithm for predicting node labels in graphs with unbalanced labels. COSNet is based on a 2-parameters family of Hopfield networks, and consists of two main steps: 1) the network parameters are learned through a costsensitive optimization procedure; 2) a suitable Hopfield network restricted to the unlabeled nodes is considered and simulated. The reached equilibrium point induces the classification of the unlabeled nodes. The restriction of the dynamics leads to a significant reduction in time complexity and allows the algorithm to nicely scale with large networks. An experimental analysis on real-world unbalanced data, in the context of the genome-wide prediction of gene functions, shows the effectiveness of the proposed approach.

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Section: Introductionmentioning

confidence: 99%

A neural network algorithm for semi-supervised node label learning from unbalanced data

et al. 2013

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“…In the bottom-up step, the positive predictions are moved towards the parent nodes and gradually towards the ancestor nodes. In the top-down step, negative predictions are propagated to the children nodes and the descendant nodes of each node, aiming to ensure the consistency of the final results and also give more precise predictions [17].…”

Section: Experiments On Eight Data Setsmentioning

confidence: 99%

“…The results of these base classifiers were processed via a Bayesian network to guarantee the hierarchy constraint for the final results. Valentini et al [17] presented a true path rule (TPR) hierarchical ensemble method to deal with the tree structure by using probabilistic SVMs for binary classifications. Chen et al [15] improved the TPR approach for the tree structure.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Multi-Label Classification Algorithm for Gene Function Prediction

Feng

Zheng

2017

Algorithms

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Gene function prediction is a complicated and challenging hierarchical multi-label classification (HMC) task, in which genes may have many functions at the same time and these functions are organized in a hierarchy. This paper proposed a novel HMC algorithm for solving this problem based on the Gene Ontology (GO), the hierarchy of which is a directed acyclic graph (DAG) and is more difficult to tackle. In the proposed algorithm, the HMC task is firstly changed into a set of binary classification tasks. Then, two measures are implemented in the algorithm to enhance the HMC performance by considering the hierarchy structure during the learning procedures. Firstly, negative instances selecting policy associated with the SMOTE approach are proposed to alleviate the imbalanced data set problem. Secondly, a nodes interaction method is introduced to combine the results of binary classifiers. It can guarantee that the predictions are consistent with the hierarchy constraint. The experiments on eight benchmark yeast data sets annotated by the Gene Ontology show the promising performance of the proposed algorithm compared with other state-of-the-art algorithms.

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“…There is a DAG relationship between GO terms. According to the true path rule [36], a protein annotated with a descendant term is also annotated with its ancestor terms. By this way, we process our dataset to expand GO terms of a protein.…”

Section: Human Datasetmentioning

confidence: 99%

Predicting protein function via multi-label supervised topic model on gene ontology

Liu

Tang

et al. 2017

Biotechnology & Biotechnological Equipment

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As the biological datasets accumulate rapidly, computational methods designed to automate protein function prediction are critically needed. The problem of protein function prediction can be considered as a multi-label classification problem resulting in protein functional annotations. Nevertheless, biologists prefer to discover the correlations between protein attributes and functions. We introduce a multi-label supervised topic model into protein function prediction and investigate the advantages of this approach. This topic model can not only work out the function probability distributions over protein instances effectively, but also directly provide the words probability distributions over functions. To the best of our knowledge, this is the first effort to apply a multi-label supervised topic model to the protein function prediction. In this paper, we model a protein as a document and a function label as a topic. First, a set of protein sequences is formalized into a bag of words. Then, we perform inference and estimate the model parameters to predict protein functions. Experimental results on yeast and human datasets demonstrate the effectiveness of this multi-label supervised topic model on protein function prediction. Meanwhile, the experiments also show that this multi-label supervised topic model delivers superior results over the compared algorithms. In summary, the method discussed in this paper provides a new efficient approach to protein function prediction and reveals more information about functions.

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True Path Rule Hierarchical Ensembles for Genome-Wide Gene Function Prediction

Cited by 127 publications

References 45 publications

A neural network algorithm for semi-supervised node label learning from unbalanced data

A neural network algorithm for semi-supervised node label learning from unbalanced data

A Hierarchical Multi-Label Classification Algorithm for Gene Function Prediction

Predicting protein function via multi-label supervised topic model on gene ontology

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