The Deep Learning–Based Recommender System “Pubmender” for Choosing a Biomedical Publication Venue: Development and Validation Study

Feng, Xiaoyue; Zhang, Hao; Ren, Yijie; Shang, Penghui; Zhu, Yi; Guan, Renchu; Xu, Dong

doi:10.2196/12957

Cited by 55 publications

(28 citation statements)

References 20 publications

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“…For clinicians or biological researchers, rapid and effective acquisition of cutting-edge information on research advances from tens of thousands publications is a huge challenge 24. However, because of the complexity of the multi-disciplinary study, there is no research to summarize these findings to keep pace with the research trends of AD using machine learning and natural language processing models.…”

Section: Introductionmentioning

confidence: 99%

Trends in Alzheimer's Disease Research Based upon Machine Learning Analysis of PubMed Abstracts

Guan¹,

Wen²,

Xu³

et al. 2019

Int. J. Biol. Sci.

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About 29.8 million people worldwide had been diagnosed with Alzheimer's disease (AD) in 2015, and the number is projected to triple by 2050. In 2018, AD was the fifth leading cause of death in Americans with 65 years of age or older, but the progress of AD drug research is very limited. It is helpful to identify the key factors and research trends of AD for guiding further more effective studies. We proposed a framework named as LDAP, which combined the latent Dirichlet allocation model and affinity propagation algorithm to extract research topics from 95,876 AD-related papers published from 2007 to 2016. Trends and hotspots analyses were performed on LDAP results. We found that the focus points of AD research for the past 10 years include 15 diseases, 15 amino acids, peptides, and proteins, 9 enzymes and coenzymes, 7 hormones, 7 carbohydrates, 5 lipids, 2 organophosphonates, 18 chemicals, 11 compounds, 13 symptoms, and 20 phenomena. Our LDAP framework allowed us to trace the evolution of research trends and the most popular areas of interest (hotspots) on disease, protein, symptom, and phenomena. Meanwhile, 556 AD related-genes were identified, which are enriched in 12 KEGG pathways including the AD pathway and nitrogen metabolism pathway. Our results are freely available at https://www.keaml.cn/Alzheimer.

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Section: Introductionmentioning

confidence: 99%

Trends in Alzheimer's Disease Research Based upon Machine Learning Analysis of PubMed Abstracts

Guan¹,

Wen²,

Xu³

et al. 2019

Int. J. Biol. Sci.

Self Cite

View full text Add to dashboard Cite

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“…In [47,88,[91][92][93][94][95][96], DM techniques are applied independently in RS and reviewed in detail. A comprehensive review of the use of ML and DL for health informatics can be found in [91].…”

Section: Machine Learning (Ml) and Deep Learning (Dl) Approachesmentioning

confidence: 99%

“…A comprehensive review of the use of ML and DL for health informatics can be found in [91]. A RS based on DL approach for choosing publication venue is introduced in [92]. Additionally, a comprehensive review of the use of DL in RS can be found in [93,95].…”

Section: Machine Learning (Ml) and Deep Learning (Dl) Approachesmentioning

confidence: 99%

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

Guruge

Kadel

Halder

2021

Data

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In recent years, education institutions have offered a wide range of course selections with overlaps. This presents significant challenges to students in selecting successful courses that match their current knowledge and personal goals. Although many studies have been conducted on Recommender Systems (RS), a review of methodologies used in course RS is still insufficiently explored. To fill this literature gap, this paper presents the state of the art of methodologies used in course RS along with the summary of the types of data sources used to evaluate these techniques. This review aims to recognize emerging trends in course RS techniques in recent research literature to deliver insights for researchers for further investigation. We provide a systematic review process followed by research findings on the current methodologies implemented in different course RS in selected research journals such as: collaborative, content-based, knowledge-based, Data Mining (DM), hybrid, statistical and Conversational RS (CRS). This study analyzed publications between 2016 and June 2020, in three repositories; IEEE Xplore, ACM, and Google Scholar. These papers were explored and classified based on the methodology used in recommending courses. This review has revealed that there is a growing popularity in hybrid course RS and followed by DM techniques in recent publications. However, few CRS-based course RS were present in the selected publications. Finally, we discussed future avenues based on the research outcome, which might lead to next-generation course RS.

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“…The application of deep learning to recommender systems has recently received more attention; due to the limitations faced by the traditional methods in precisely modeling complex relationships (e.g., non-linear relationships) among users and items. There are several deep learning techniques that are utilized today by different studies in the recommendation literature, such as Multilayer Perceptron (MLP) [24], [25], Convolutional Neural Network (CNN) [26], [27], Recurrent Neural Network (RNN) [28], [29], and Generative Adversarial Network (GAN) [30]- [32].…”

Section: Deep Learning-based Recommendationsmentioning

confidence: 99%

“…For instance, NCF [24] is a MLP-based model that generalizes the matrix factorization (MF) method by replacing the dot product of MF with a neural network architecture. Moreover, Pubmender [26] uses a deep CNN to recommend venues for publishing biomedical articles by using the abstract of each article. In addition, AskTheGRU [29] uses a RNN-based technique to represent textual data as latent vectors for the task of scientific article recommendation.…”

Section: Deep Learning-based Recommendationsmentioning

confidence: 99%

CATA++: A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles

Alfarhood

Cheng

2020

IEEE Access

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Matrix Factorization (MF) method is widely popular for personalized recommendations. However, the natural data sparsity problem limits its performance, where users generally only interact with a small fraction of available items. Accordingly, several hybrid models have been proposed recently to optimize MF performance by incorporating additional contextual information in the MF's learning process. Although these models improved recommendation quality, there are two primary aspects for further enhancements: (1) multiple models focus only on some portion of the available contextual information and neglect other portions, and (2) learning the feature space of the contextual information needs to be further improved. Therefore, we introduce a Collaborative Dual Attentive Autoencoder (CATA++) method that utilizes an item's content and learns its latent space via two parallel autoencoders. We employ the attention mechanism in the middle of our autoencoders to capture the most significant segments of contextual information, which leads to a better representation of the items in the latent space. Extensive experiments on three scientific-article datasets have shown that our dual-process learning strategy has significantly improved MF performance in comparison with other state-of-the-art MF-based models using various experimental evaluations. The source code of our method is available at: https://github.com/jianlin-cheng/CATA.

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The Deep Learning–Based Recommender System “Pubmender” for Choosing a Biomedical Publication Venue: Development and Validation Study

Cited by 55 publications

References 20 publications

Trends in Alzheimer's Disease Research Based upon Machine Learning Analysis of PubMed Abstracts

Trends in Alzheimer's Disease Research Based upon Machine Learning Analysis of PubMed Abstracts

The State of the Art in Methodologies of Course Recommender Systems—A Review of Recent Research

CATA++: A Collaborative Dual Attentive Autoencoder Method for Recommending Scientific Articles

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