Journal of Medical Internet Research - International Scientific Journal for Medical Research, Information and Communication on the Internet #Preprint #PeerReviewMe: Warning: This is a unreviewed preprint. Readers are warned that the document has not been peer-reviewed by expert/patient reviewers or an academic editor, may contain misleading claims, and is likely to undergo changes before final publication, if accepted, or may have been rejected/withdrawn. Readers with interest and expertise are encouraged to sign up as peer-reviewer, if the paper is within an open peer-review period. Please cite this preprint only for review purposes or for grant applications and CVs (if you are the author). Background: It is of great importance for researchers to publish research results in high-quality journals. However, it is often challenging to choose a most suitable publication venue given the exponential growth of journals and conferences. Although recommender systems have achieved success in promoting movies, music and products, very few studies have explored recommendation of publication venues, especially for biomedical research. No recommender system exists that can specifically recommend journals in PubMed, the largest collection of biomedical literature. Objective: We proposed a publication recommender system, named Pubmender, to suggest suitable PubMed journals based on a paper’s Methods: In Pubmender, pretrained word2vec is first used to construct the start-up feature space. Then, a deep convolutional neural network is constructed to achieve the high-level representation of abstracts, and a fully connected softmax model is adopted to recommend the best journals. Results: We collected 880,165 papers from 1,130 journals in PubMed Central (PMC) and extracted abstracts from these papers as an empirical dataset. We compared different recommendation models such as Cavnar-Trenkleon on the Microsoft Academic Search engine (MAS), a collaborative-filtering-based recommender system on the digital library of the Association for Computing Machinery (ACM) and CiteSeer. We found the accuracy of our system for the top-10 recommendtations to be 87.0%, 22.9% and 196.0% higher than those of MAS, ACM, and CiteSeer, respectively. In addition, we compared our system with Journal Finder and Journal Suggester, which are tools of Elsevier and Springer to help authors find suitable journals in their series. The results revealed that the accuracy of our system was 329% higher than Journal Finder and 406% higher than Journal Suggester for the top-10 recommendations. Our web service is freely available at http://www.keaml.cn:8081/. Conclusions: Our deep learning based recommender system can suggest an appropriate journal list to help biomedical scientists and clinicians to choose suitable venues for their papers.

With recent advances in single-cell RNA sequencing, enormous transcriptome datasets have been generated. These datasets have furthered our understanding of cellular heterogeneity and its underlying mechanisms in homogeneous populations. Single-cell RNA sequencing (scRNA-seq) data clustering can group cells belonging to the same cell type based on patterns embedded in gene expression. However, scRNA-seq data are high-dimensional, noisy, and sparse, owing to the limitation of existing scRNA-seq technologies. Traditional clustering methods are not effective and efficient for high-dimensional and sparse matrix computations. Therefore, several dimension reduction methods have been introduced. To validate a reliable and standard research routine, we conducted a comprehensive review and evaluation of four classical dimension reduction methods and five clustering models. Four experiments were progressively performed on two large scRNA-seq datasets using 20 models. Results showed that the feature selection method contributed positively to high-dimensional and sparse scRNA-seq data. Moreover, feature-extraction methods were able to promote clustering performance, although this was not eternally immutable. Independent component analysis (ICA) performed well in those small compressed feature spaces, whereas principal component analysis was steadier than all the other feature-extraction methods. In addition, ICA was not ideal for fuzzy C-means clustering in scRNA-seq data analysis. K-means clustering was combined with feature-extraction methods to achieve good results.

Attributed networks, as a manifestation of data in non-Euclidean domains, have a wide range of applications in the real world, such as molecular property prediction, social network analysis and anomaly detection. Node classification, as a fundamental research problem in attributed networks, has attracted increasing attention among research communities. However, most existing models cannot be directly applied to the data with limited labeled instances (\textiti.e., the few-shot scenario). Few-shot node classification on attributed networks is gradually becoming a research hotspot. Although several methods aim to integrate meta-learning with graph neural networks to address this problem, some limitations remain. First, they all assume node representation learning using graph neural networks in homophilic graphs. %Hence, suboptimal performance is obtained when these models are applied to heterophilic graphs. Second, existing models based on meta-learning entirely depend on instance-based statistics. %which in few-shot settings are unavoidably degraded by data noise or outliers. Third, most previous models treat all sampled tasks equally and fail to adapt their uniqueness. %which has a significant impact on the overall performance of the model. To solve the above three limitations, we propose a novel graph Meta -learning framework called G raph learning based on P rototype and S caling & shifting transformation (Meta-GPS ). More specifically, we introduce an efficient method for learning expressive node representations even on heterophilic graphs and propose utilizing a prototype-based approach to initialize parameters in meta-learning. Moreover, we also leverage S$^2$ (scaling & shifting) transformation to learn effective transferable knowledge from diverse tasks. Extensive experimental results on six real-world datasets demonstrate the superiority of our proposed framework, which outperforms other state-of-the-art baselines by up to 13% absolute improvement in terms of related metrics.

Knowledge extracted from the protein-protein interaction (PPI) network can help researchers reveal the molecular mechanisms of biological processes. With the rapid growth in the volume of the biomedical literature, manually detecting and annotating PPIs from raw literature has become increasingly difficult. Hence, automatically extracting PPIs by machine learning methods from raw literature has gained significance in the biomedical research. In this paper, we propose a novel PPI extraction method based on the residual convolutional neural network (CNN). This is the first time that the residual CNN is applied to the PPI extraction task. In addition, the previous state-of-the-art PPI extraction models heavily rely on parsing results from natural language processing tools, such as dependence parsers. Our model does not rely on any parsing tools. We evaluated our model based on five benchmark PPI extraction corpora, AIMed, BioInfer, HPRD50, IEPA, and LLL. The experimental results showed that our model achieved the best results compared with the previous kernel-based and CNN-based PPI extraction models. Compared with the previous recurrent neural network-based PPI extraction models, our model achieved better or comparable performance.