A co‐training based entity recognition approach for cross‐disease clinical documents

Chen, Dehua; Che, Nannan; Le, Jiajin; Pan, Qiao

doi:10.1002/cpe.4505

Cited by 3 publications

(3 citation statements)

References 17 publications

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“…Finally, deep neural networks (DNNs) [74] could be employed under a co-training scheme to boost the predictive performance, fed with either raw data or other generic kinds of datasets. More specifically, long short term memory (LSTM) networks have already proven efficient enough when combined with SSL methods for constructing clinical support decision systems [75]. In case DNNs should be exploited, creation of new insights into inserted data could take place, providing either totally new view (s) or augmenting the existing one (s).…”

Section: Discussionmentioning

confidence: 99%

A Soft-Voting Ensemble Based Co-Training Scheme Using Static Selection for Binary Classification Problems

2020

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In recent years, a forward-looking subfield of machine learning has emerged with important applications in a variety of scientific fields. Semi-supervised learning is increasingly being recognized as a burgeoning area embracing a plethora of efficient methods and algorithms seeking to exploit a small pool of labeled examples together with a large pool of unlabeled ones in the most efficient way. Co-training is a representative semi-supervised classification algorithm originally based on the assumption that each example can be described by two distinct feature sets, usually referred to as views. Since such an assumption can hardly be met in real world problems, several variants of the co-training algorithm have been proposed dealing with the absence or existence of a naturally two-view feature split. In this context, a Static Selection Ensemble-based co-training scheme operating under a random feature split strategy is outlined regarding binary classification problems, where the type of the base ensemble learner is a soft-Voting one composed of two participants. Ensemble methods are commonly used to boost the predictive performance of learning models by using a set of different classifiers, while the Static Ensemble Selection approach seeks to find the most suitable structure of ensemble classifier based on a specific criterion through a pool of candidate classifiers. The efficacy of the proposed scheme is verified through several experiments on a plethora of benchmark datasets as statistically confirmed by the Friedman Aligned Ranks non-parametric test over the behavior of classification accuracy, F1-score, and Area Under Curve metrics.

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Section: Discussionmentioning

confidence: 99%

A Soft-Voting Ensemble Based Co-Training Scheme Using Static Selection for Binary Classification Problems

2020

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“…We encourage the reader to review the works of Ye et al, Chen et al, and He et al to get more information about the topics of modeling, algorithm development, implementation, and execution.…”

Section: Introductionmentioning

confidence: 99%

Foreword to the special issue of green cloud computing: Methodology and practice

Zheng

Rong²,

Badarch

2019

Concurrency and Computation

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“…Investigation of disease-associated genes can improve the understanding of disease etiology and development, thereby facilitating design and development of novel preventive and treatment strategies (7,8). Cross disease-gene studies and further pathway analyses provide an opportunity to resolve overlapping associations into discrete pathways and investigate possible shared etiologies (9,10).…”

Section: Introductionmentioning

confidence: 99%

Cross‑disease analysis identified novel common genes for both lung adenocarcinoma and lung squamous cell carcinoma

Zhang¹,

Wang²,

Huang³

et al. 2019

Oncol Lett

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Lung squamous cell carcinoma (LSCC) exhibits a number of similarities with lung adenocarcinoma (LA) in terms of copy number alterations. However, compared with LA, the range of genetic alterations in LSCC is less understood. In the present study, a large-scale literature-based search of LA-associated genes and LSCC-associated genes was performed to identify the genetic basis in common with these two diseases. For each of the LA-associated genes, a mega-analysis was performed to test its expression variations in LSCC using 11 RNA expression datasets, with significant genes identified using statistical analysis. Subsequently, a functional pathway analysis was performed to identify a possible association between any of the significant genes identified from the mega-analysis and LSCC, followed by a co-expression analysis. A multiple linear regression (MLR) model was employed to investigate the possible influence of sample size, country of origin and study date on gene expression in patients with LSCC. Disease-gene association data analysis identified 1,178 genes involved in LA, 334 in LSCC, with a significant overlap of 187 genes (P<1.02× −161 ). Mega-analysis revealed that three LA-associated genes, such as solute carrier family 2 member 1 (SLC2A1), endothelial PAS domain protein 1 (EPAS1) and cyclin-dependent kinase 4 (CDK4), were significantly associated with LSCC (P<1.60×10 −8 ), with multiple potential pathways identified by functional pathway analysis, which were further validated by co-expression analysis. The present MLR analysis suggested that the country of origin was a significant factor for the levels of expression of all three genes in patients with LSCC (P<4.0×10 −3 ). Collectively, the present results suggested that genes associated with LA should be further investigated for their association with LSCC. In addition, SLC2A1, EPAS1 and CDK4 may be novel risk genes associated with LA and LSCC.

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A co‐training based entity recognition approach for cross‐disease clinical documents

Cited by 3 publications

References 17 publications

A Soft-Voting Ensemble Based Co-Training Scheme Using Static Selection for Binary Classification Problems

A Soft-Voting Ensemble Based Co-Training Scheme Using Static Selection for Binary Classification Problems

Foreword to the special issue of green cloud computing: Methodology and practice

Cross‑disease analysis identified novel common genes for both lung adenocarcinoma and lung squamous cell carcinoma

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