An effective biomedical document classification scheme in support of biocuration: addressing class imbalance

Jiang, Xiangying; Ringwald, Martin; Blake, Judith A.; Arighi, Cecilia N.; Zhang, Gongbo; Shatkay, Hagit

doi:10.1093/database/baz045

Cited by 16 publications

(10 citation statements)

References 41 publications

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“…Other work focuses on the task of associating genes with diseases [267] or finding associations between metabolites, proteins, genes, and diseases [258]. Another challenge is document triage, such as finding documents relevant to a context or field of study [268][269][270]. For a thorough review of NLP techniques, as applied to the biomedical literature, we invite the reader to reference [271].…”

Section: Computationally Predicted Resourcesmentioning

confidence: 99%

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

et al. 2020

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As researchers are increasingly able to collect data on a large scale from multiple clinical and omics modalities, multi-omics integration is becoming a critical component of metabolomics research. This introduces a need for increased understanding by the metabolomics researcher of computational and statistical analysis methods relevant to multi-omics studies. In this review, we discuss common types of analyses performed in multi-omics studies and the computational and statistical methods that can be used for each type of analysis. We pinpoint the caveats and considerations for analysis methods, including required parameters, sample size and data distribution requirements, sources of a priori knowledge, and techniques for the evaluation of model accuracy. Finally, for the types of analyses discussed, we provide examples of the applications of corresponding methods to clinical and basic research. We intend that our review may be used as a guide for metabolomics researchers to choose effective techniques for multi-omics analyses relevant to their field of study.

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Section: Computationally Predicted Resourcesmentioning

confidence: 99%

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

et al. 2020

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“…The achieved F1 score for this method was 84.0% while using a multi-layer perceptron with sigmoid functions. A biomedical document classification was carried out in [18], where an imbalanced bio-dataset was used for a cluster-based classification on the under-sampled dataset GXD. Overall precision of 0.72 was achieved.…”

Section: Literature Reviewmentioning

confidence: 99%

Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training

Nasir

Khan

Yasmin

et al. 2020

Sensors

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Documents are stored in a digital form across several organizations. Printing this amount of data and placing it into folders instead of storing digitally is against the practical, economical, and ecological perspective. An efficient way of retrieving data from digitally stored documents is also required. This article presents a real-time supervised learning technique for document classification based on deep convolutional neural network (DCNN), which aims to reduce the impact of adverse document image issues such as signatures, marks, logo, and handwritten notes. The proposed technique’s major steps include data augmentation, feature extraction using pre-trained neural network models, feature fusion, and feature selection. We propose a novel data augmentation technique, which normalizes the imbalanced dataset using the secondary dataset RVL-CDIP. The DCNN features are extracted using the VGG19 and AlexNet networks. The extracted features are fused, and the fused feature vector is optimized by applying a Pearson correlation coefficient-based technique to select the optimized features while removing the redundant features. The proposed technique is tested on the Tobacco3482 dataset, which gives a classification accuracy of 93.1% using a cubic support vector machine classifier, proving the validity of the proposed technique.

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“…However, they did not lead to overall improvement. An alternative would be to use a classifier ensemble, as proposed in [15]. However, this approach is too expensive for deep learning models due to the learning cost.…”

Section: Classification Performance Per Category Document Size and Omentioning

confidence: 99%

“…LocText, for example, implements a NER and RE for proteins based on SVM, achieving 86% precision (56% F1-score) [14]. To address the common issue of class imbalance in biocuration, an ensemble of SVM classifiers along with random under-sampling were proposed for automatically identifying relevant papers for curation in the Gene Expression Database [15].…”

Section: Introductionmentioning

confidence: 99%

UPCLASS: a Deep Learning-based Classifier for UniProtKB Entry Publications

Teodoro

Knafou

Naderi

et al. 2019

Preprint

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AbstractIn the UniProt Knowledgebase (UniProtKB), publications providing evidence for a specific protein annotation entry are organized across different categories, such as function, interaction and expression, based on the type of data they contain. To provide a systematic way of categorizing computationally mapped bibliography in UniProt, we investigate a Convolution Neural Network (CNN) model to classify publications with accession annotations according to UniProtKB categories. The main challenge to categorize publications at the accession annotation level is that the same publication can be annotated with multiple proteins, and thus be associated to different category sets according to the evidence provided for the protein. We propose a model that divides the document into parts containing and not containing evidence for the protein annotation. Then, we use these parts to create different feature sets for each accession and feed them to separate layers of the network. The CNN model achieved a F1-score of 0.72, outperforming baseline models based on logistic regression and support vector machine by up to 22 and 18 percentage points, respectively. We believe that such approach could be used to systematically categorize the computationally mapped bibliography in UniProtKB, which represents a significant set of the publications, and help curators to decide whether a publication is relevant for further curation for a protein accession.

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An effective biomedical document classification scheme in support of biocuration: addressing class imbalance

Cited by 16 publications

References 41 publications

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

Metabolomics and Multi-Omics Integration: A Survey of Computational Methods and Resources

Pearson Correlation-Based Feature Selection for Document Classification Using Balanced Training

UPCLASS: a Deep Learning-based Classifier for UniProtKB Entry Publications

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