Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions

Barros, Rodrigo C.; Cerri, Ricardo; Freitas, Alex A.; Carvalho, André C. P. L. F. de

doi:10.1007/978-3-642-40991-2_25

Cited by 14 publications

(6 citation statements)

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“…As its name suggests, the algorithm adaptation strategy consists of adapting a traditional algorithm to handle hierarchical constraints. Masera and Blanzieri [6] created a neural network whose architecture incorporates the underlying hierarchy, making gradient updates flow from the neurons associated to the leaves up neurons associated to their parent nodes; Sun et al [8] proposed to use Partial Least Squares to reduce both label and feature dimension, followed by an optimal path selection algorithm; Barros et al [17] proposed a centroid based method where the training data is initially clustered, then predictions are performed by measuring the distance between the new instance and all clusters, the label set associated to the closest cluster is given as the prediction; Borges and Nievola [31] developed a competitive neural network whose architecture replicates the hierarchy; Vens et al [2] also proposed to train a single Predictive Clustering Tree for the entire hierarchy; as an extension of [2], Schietgat et al [21] proposed to use ensemble of Predictive Clustering Trees; Stojanova et al [18] proposed a slight modification for Predictive Clustering Trees in which the correlation between the proteins is also used to build the tree.…”

Section: Related Workmentioning

confidence: 99%

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Machine learning for discovering missing or wrong protein function annotations

Nakano

Lietaert²,

Vens

2019

BMC Bioinformatics

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Background A massive amount of proteomic data is generated on a daily basis, nonetheless annotating all sequences is costly and often unfeasible. As a countermeasure, machine learning methods have been used to automatically annotate new protein functions. More specifically, many studies have investigated hierarchical multi-label classification (HMC) methods to predict annotations, using the Functional Catalogue (FunCat) or Gene Ontology (GO) label hierarchies. Most of these studies employed benchmark datasets created more than a decade ago, and thus train their models on outdated information. In this work, we provide an updated version of these datasets. By querying recent versions of FunCat and GO yeast annotations, we provide 24 new datasets in total. We compare four HMC methods, providing baseline results for the new datasets. Furthermore, we also evaluate whether the predictive models are able to discover new or wrong annotations, by training them on the old data and evaluating their results against the most recent information. Results The results demonstrated that the method based on predictive clustering trees, Clus-Ensemble, proposed in 2008, achieved superior results compared to more recent methods on the standard evaluation task. For the discovery of new knowledge, Clus-Ensemble performed better when discovering new annotations in the FunCat taxonomy, whereas hierarchical multi-label classification with genetic algorithm (HMC-GA), a method based on genetic algorithms, was overall superior when detecting annotations that were removed. In the GO datasets, Clus-Ensemble once again had the upper hand when discovering new annotations, HMC-GA performed better for detecting removed annotations. However, in this evaluation, there were less significant differences among the methods. Conclusions The experiments have showed that protein function prediction is a very challenging task which should be further investigated. We believe that the baseline results associated with the updated datasets provided in this work should be considered as guidelines for future studies, nonetheless the old versions of the datasets should not be disregarded since other tasks in machine learning could benefit from them.

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Section: Related Workmentioning

confidence: 99%

“…Some work, such as [5,10,11,13,17,22], have also decided to not include them. Table 12 presents the datasets evaluated in this work.…”

Section: Evaluated Datasetsmentioning

confidence: 99%

Machine learning for discovering missing or wrong protein function annotations

Nakano

Lietaert²,

Vens

2019

BMC Bioinformatics

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“…Alaydie et al (2012) proposed a boosting-based method for HMC, where at each iteration the label hierarchy is used to select the training set for each classifier. Recently, Barros et al proposed a method for HMC based on the probabilistic clustering with expectation-maximization algorithm (Barros et al 2013).…”

Section: Related Workmentioning

confidence: 99%

The importance of the label hierarchy in hierarchical multi-label classification

2014

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We address the task of hierarchical multi-label classification (HMC). HMC is a task of structured output prediction where the classes are organized into a hierarchy and an instance may belong to multiple classes. In many problems, such as gene function prediction or prediction of ecological community structure, classes inherently follow these constraints. The potential for application of HMC was recognized by many researchers and several such methods were proposed and demonstrated to achieve good predictive performances in the past. However, there is no clear understanding when is favorable to consider such relationships (hierarchical and multi-label) among classes, and when this presents unnecessary burden for classification methods. To this end, we perform a detailed comparative study over 8 datasets that have HMC properties. We investigate two important influences in HMC: the multiple labels per example and the information about the hierarchy. More specifically, we consider four machine learning tasks: multi-label classification, hierarchical multi-label classification, single-label classification and hierarchical single-label classification. To construct the predictive models, we use predictive clustering trees (a generalized form of decision trees), which are able to tackle each of the modelling tasks listed. Moreover, we investigate whether the influence of the hierarchy and the multiple labels carries over for ensemble models. For each of the tasks, we construct a single tree and two ensembles (random forest and bagging). The results reveal that the hierarchy and the multiple labels do help to obtain a better single tree model, while this is not preserved for the ensemble models.

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“…There has also been recent interest in multi-label text classification [51], [52], [8], [53], [54] Recently, progress has also been made in incorporating hierarchical knowledge to single label classifiers to add additional semantics to the models' learning capabilities such that even when the model makes mistakes dues to data ambiguities, it is able to make semantically better mistakes. Hierarchical information is important in many other applications such as food recognition [55], [25], protein function prediction [6], [7], [56], [57], [58], [59] , image annotation [60], text classification [61], [62], [63]. Some major approaches include imposing logical constraints [4], using hyperbolic embeddings [64], prototype learning [14], label smearing and soft labels, loss modifications [3], multiple learning heads for different levels of the hierarchy [5], hierarchical post-processing [65] and others [66], [67], [68].…”

Section: Related Workmentioning

confidence: 99%

All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP)

Vaswani¹,

Aggarwal²,

Netrapalli³

et al. 2022

Preprint

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This paper considers the problem of Hierarchical Multi-Label Classification (HMC), where (i) several labels can be present for each example, and (ii) labels are related via a domain-specific hierarchy tree. Guided by the intuition that all mistakes are not equal, we present Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP), a framework that penalizes a misprediction depending on its severity as per the hierarchy tree. While there have been works that apply such an idea to singlelabel classification, to the best of our knowledge, there are limited such works for multilabel classification focusing on the severity of mistakes. The key reason is that there is no clear way of quantifying the severity of a misprediction a priori in the multilabel setting. In this work, we propose a simple but effective metric to quantify the severity of a mistake in HMC, naturally leading to CHAMP. Extensive experiments on six public HMC datasets across modalities (image, audio, and text) demonstrate that incorporating hierarchical information leads to substantial gains as CHAMP improves both AUPRC (2.6% median percentage improvement) and hierarchical metrics (2.85% median percentage improvement), over stand-alone hierarchical or multilabel classification methods. Compared to standard multilabel baselines, CHAMP provides improved AUPRC in both robustness (8.87% mean percentage improvement) and less data regimes. Further, our method provides a framework to enhance existing multilabel classification algorithms with better mistakes (18.1% mean percentage improvement).Preprint. Under review.

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Probabilistic Clustering for Hierarchical Multi-Label Classification of Protein Functions

Cited by 14 publications

References 23 publications

Machine learning for discovering missing or wrong protein function annotations

Machine learning for discovering missing or wrong protein function annotations

The importance of the label hierarchy in hierarchical multi-label classification

All Mistakes Are Not Equal: Comprehensive Hierarchy Aware Multi-label Predictions (CHAMP)

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