Gene2DisCo: Gene to disease using disease commonalities

Graph Neural Networks and Their Current Applications in Bioinformatics

Graph neural networks (GNNs), as a branch of deep learning in non-Euclidean space, perform particularly well in various tasks that process graph structure data. With the rapid accumulation of biological network data, GNNs have also become an important tool in bioinformatics. In this research, a systematic survey of GNNs and their advances in bioinformatics is presented from multiple perspectives. We first introduce some commonly used GNN models and their basic principles. Then, three representative tasks are proposed based on the three levels of structural information that can be learned by GNNs: node classification, link prediction, and graph generation. Meanwhile, according to the specific applications for various omics data, we categorize and discuss the related studies in three aspects: disease prediction, drug discovery, and biomedical imaging. Based on the analysis, we provide an outlook on the shortcomings of current studies and point out their developing prospect. Although GNNs have achieved excellent results in many biological tasks at present, they still face challenges in terms of low-quality data processing, methodology, and interpretability and have a long road ahead. We believe that GNNs are potentially an excellent method that solves various biological problems in bioinformatics research.

IsoDA: Isoform-Disease Association Prediction by Multiomics Data Fusion

A gene can be spliced into different isoforms by alternative splicing, which contributes to the functional diversity of protein species. Computational prediction of gene-disease associations (GDAs) has been studied for decades. However, the process of identifying the isoform-disease associations (IDAs) at a large scale is rarely explored, which can decipher the pathology at a more granular level. The main bottleneck is the lack of IDAs in current databases and the multilevel omics data fusion. To bridge this gap, we propose a computational approach called Isoform-Disease Association prediction by multiomics data fusion (IsoDA) to predict IDAs. Based on the relationship between a gene and its spliced isoforms, IsoDA first introduces a dispatch and aggregation term to dispatch gene-disease associations to individual isoforms, and reversely aggregate these dispatched associations to their hosting genes. At the same time, it fuses the genome, transcriptome, and proteome data by joint matrix factorization to improve the prediction of IDAs. Experimental results show that IsoDA significantly outperforms the related state-of-the-art methods at both the gene level and isoform level. A case study further shows that IsoDA credibly identifies three isoforms spliced from apolipoprotein E, which have individual associations with Alzheimer's disease, and two isoforms spliced from vascular endothelial growth factor A, which have different associations with coronary heart disease. The codes of IsoDA are available at http://mlda.swu.edu.cn/codes.php?name=IsoDA.

SemBioNLQA: A semantic biomedical question answering system for retrieving exact and ideal answers to natural language questions

BACKGROUND AND OBJECTIVE

Question answering (QA), the identification of short accurate answers to users questions written in natural language expressions, is a longstanding issue widely studied over the last decades in the open-domain. However, it still remains a real challenge in the biomedical domain as the most of the existing systems support a limited amount of question and answer types as well as still require further efforts in order to improve their performance in terms of precision for the supported questions. Here, we present a semantic biomedical QA system named SemBioNLQA which has the ability to handle the kinds of yes/no, factoid, list, and summary natural language questions.

METHODS

This paper describes the system architecture and an evaluation of the developed end-to-end biomedical QA system named SemBioNLQA, which consists of question classification, document retrieval, passage retrieval and answer extraction modules. It takes natural language questions as input, and outputs both short precise answers and summaries as results. The SemBioNLQA system, dealing with four types of questions, is based on (1) handcrafted lexico-syntactic patterns and a machine learning algorithm for question classification, (2) PubMed search engine and UMLS similarity for document retrieval, (3) the BM25 model, stemmed words and UMLS concepts for passage retrieval, and (4) UMLS metathesaurus, BioPortal synonyms, sentiment analysis and term frequency metric for answer extraction.

RESULTS AND CONCLUSION

Compared with the current state-of-the-art biomedical QA systems, SemBioNLQA, a fully automated system, has the potential to deal with a large amount of question and answer types. SemBioNLQA retrieves quickly users' information needs by returning exact answers (e.g., "yes", "no", a biomedical entity name, etc.) and ideal answers (i.e., paragraph-sized summaries of relevant information) for yes/no, factoid and list questions, whereas it provides only the ideal answers for summary questions. Moreover, experimental evaluations performed on biomedical questions and answers provided by the BioASQ challenge especially in 2015, 2016 and 2017 (as part of our participation), show that SemBioNLQA achieves good performances compared with the most current state-of-the-art systems and allows a practical and competitive alternative to help information seekers find exact and ideal answers to their biomedical questions. The SemBioNLQA source code is publicly available at https://github.com/sarrouti/sembionlqa.