Ontological function annotation of long non-coding RNAs through hierarchical multi-label classification

Zhang, Jingpu; Zhang, Zuping; Wang, Zixiang; Liu, Yuting; Deng, Lei

doi:10.1093/bioinformatics/btx833

Cited by 54 publications

(23 citation statements)

References 39 publications

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“…Other application domains have also explored HMC, such as managing IT services [37,38], text classification on social media [39], large scale document classification [40] and annotation of non-coding RNA [41]. It can even be applied to non-hierarchical multi-label problems where artificial hierarchies are created [42].…”

Section: Related Workmentioning

confidence: 99%

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 Most of these studies employed benchmark datasets created more than a decade ago, and thus train their models on outdated information.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%

Machine learning for discovering missing or wrong protein function annotations

Nakano

Lietaert²,

Vens

2019

BMC Bioinformatics

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“…With the rapid development of machine learning and artificial intelligence in recent years, new algorithms based on machine learning have been applied to predict candidate pathogenic genes; they have shown good predictive performance (Zou et al, 2018;Peng et al, 2018;Liao et al, 2018;Zhang et al, 2018;Xiong et al, 2018;He et al, 2018;Ding et al, 2019;Liu, 2019;Liu et al, 2019a;Zhu et al, 2019). In 2011, Mordelet et al (Mordelet and Vert, 2011) considered the problem of genetic prediction as a supervised machine learning problem and proposed the ProDiGe method.…”

Section: Introductionmentioning

confidence: 99%

Pathogenic Gene Prediction Algorithm Based on Heterogeneous Information Fusion

et al. 2020

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Complex diseases seriously affect people's physical and mental health. The discovery of disease-causing genes has become a target of research. With the emergence of bioinformatics and the rapid development of biotechnology, to overcome the inherent difficulties of the long experimental period and high cost of traditional biomedical methods, researchers have proposed many gene prioritization algorithms that use a large amount of biological data to mine pathogenic genes. However, because the currently known genedisease association matrix is still very sparse and lacks evidence that genes and diseases are unrelated, there are limits to the predictive performance of gene prioritization algorithms. Based on the hypothesis that functionally related gene mutations may lead to similar disease phenotypes, this paper proposes a PU induction matrix completion algorithm based on heterogeneous information fusion (PUIMCHIF) to predict candidate genes involved in the pathogenicity of human diseases. On the one hand, PUIMCHIF uses different compact feature learning methods to extract features of genes and diseases from multiple data sources, making up for the lack of sparse data. On the other hand, based on the prior knowledge that most of the unknown gene-disease associations are unrelated, we use the PU-Learning strategy to treat the unknown unlabeled data as negative examples for biased learning. The experimental results of the PUIMCHIF algorithm regarding the three indexes of precision, recall, and mean percentile ranking (MPR) were significantly better than those of other algorithms. In the top 100 global prediction analysis of multiple genes and multiple diseases, the probability of recovering true gene associations using PUIMCHIF reached 50% and the MPR value was 10.94%. The PUIMCHIF algorithm has higher priority than those from other methods, such as IMC and CATAPULT.

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“…Different machine learning classifiers have been employed, including support vector machine [ 41 , 42 ], random forests [ 43 ], ANN [ 44 ], etc. There are also some special classifiers for different conditions, such as ensemble classifier [ 42 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ], multi-label classifier [ 55 , 56 , 57 , 58 ], imbalance classifier [ 59 , 60 ], hierarchical classifier [ 61 , 62 , 63 ], etc. All these previous works guide us to build a frame for amyloid protein identification.…”

Section: Introductionmentioning

confidence: 99%

RFAmyloid: A Web Server for Predicting Amyloid Proteins

Niu

Wang

et al. 2018

IJMS

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Amyloid is an insoluble fibrous protein and its mis-aggregation can lead to some diseases, such as Alzheimer’s disease and Creutzfeldt–Jakob’s disease. Therefore, the identification of amyloid is essential for the discovery and understanding of disease. We established a novel predictor called RFAmy based on random forest to identify amyloid, and it employed SVMProt 188-D feature extraction method based on protein composition and physicochemical properties and pse-in-one feature extraction method based on amino acid composition, autocorrelation pseudo acid composition, profile-based features and predicted structures features. In the ten-fold cross-validation test, RFAmy’s overall accuracy was 89.19% and F-measure was 0.891. Results were obtained by comparison experiments with other feature, classifiers, and existing methods. This shows the effectiveness of RFAmy in predicting amyloid protein. The RFAmy proposed in this paper can be accessed through the URL .

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Ontological function annotation of long non-coding RNAs through hierarchical multi-label classification

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 54 publications

References 39 publications

Machine learning for discovering missing or wrong protein function annotations

Machine learning for discovering missing or wrong protein function annotations

Pathogenic Gene Prediction Algorithm Based on Heterogeneous Information Fusion

RFAmyloid: A Web Server for Predicting Amyloid Proteins

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