Updated review of advances in microRNAs and complex diseases: taxonomy, trends and challenges of computational models. Issue 5 (2nd September 2022)

• Record Type: Journal Article
• Authors: Huang, Li; Zhang, Li; Chen, Xing
• Is Part Of: Briefings in bioinformatics. Volume 23:Issue 5(2022)
• Publication Date: 2022-09-02
• Subjects: microRNA -- complex diseases -- microRNA–disease association prediction -- computational model -- model fusion -- machine learning; Genetics -- Data processing -- Periodicals; Molecular biology -- Data processing -- Periodicals; Genomes -- Data processing -- Periodicals; 572.80285
• DOI: 10.1093/bib/bbac358
• Languages: English
• ISSNs: 1467-5463

Abstract

Abstract: Since the problem proposed in late 2000s, microRNA–disease association (MDA) predictions have been implemented based on the data fusion paradigm. Integrating diverse data sources gains a more comprehensive research perspective, and brings a challenge to algorithm design for generating accurate, concise and consistent representations of the fused data. After more than a decade of research progress, a relatively simple algorithm like the score function or a single computation layer may no longer be sufficient for further improving predictive performance. Advanced model design has become more frequent in recent years, particularly in the form of reasonably combing multiple algorithms, a process known as model fusion. In the current review, we present 29 state-of-the-art models and introduce the taxonomy of computational models for MDA prediction based on model fusion and non-fusion. The new taxonomy exhibits notable changes in the algorithmic architecture of models, compared with that of earlier ones in the 2017 review by Chen et al . Moreover, we discuss the progresses that have been made towards overcoming the obstacles to effective MDA prediction since 2017 and elaborated on how future models can be designed according to a set of new schemas. Lastly, we analysed the strengths and weaknesses of each model category in the proposed taxonomy and proposed future research directions from diverse perspectives for enhancing model performance.