Abstract
This special issue of the Journal of Computational Biology includes a selection of articles presented at the 14th International Symposium on Bioinformatics Research and Applications (ISBRA 2018) that was held in Beijing, China, on June 8–11, 2018. ISBRA provides a forum for the exchange of ideas and results among researchers, developers, and practitioners working on all aspects of bioinformatics and computational biology and their applications.
In 2018, 138 extended abstracts were submitted in response to the call for articles, out of which 24 extended abstracts appeared in the ISBRA proceedings published as volume 10847 of Springer Verlag's Lecture Notes in Bioinformatics series. Authors of six articles were invited to submit extended versions of their abstracts to this special issue.
In “A Dynamic Scale-Free Network Particle Swarm Optimization for Extracting Features on Multi-Omics Data,” the authors propose an improved method for extracting features on multi-omics data such as the Cancer Genome Atlas from which they can efficiently extract cancer-associated genes. The article “Truncated Robust Principal Component Analysis and Noise Reduction for Single Cell RNA-seq Data” develops a truncated robust principal component analysis with noise reduction making it much faster and memory efficient, which is necessary for high-dimensional and noisy data routinely generated in genomics. In the article “Extending the Evolvability Model to the Prokaryotic World: Simulations and Results on Real Data,” the evolvability framework of valiant is extended to accommodate horizontal gene transfer between unrelated organisms focusing on the evolutionary process of developing a trait and model it as the conjunction function.
The article “Bounds on Identification of Genome Evolution Pacemakers” studies the model of Universal PaceMaker explaining the tight correlation between genes' evolutionary rate and provides theoretical bounds for the problem of finding the gene–pacemaker association only from the gene sequence data. In “Locality Sensitive Imputation for Single-Cell RNA-Seq Data,” the authors address the transcriptome “drop-out” in single cell RNA-Seq data analysis comprehensively assessing existing and proposed imputation methods for correcting the “drop-out” effect on real scRNA-Seq data sets with varying per cell sequencing depth. In “Identifying Interactions between Kinases and Substrates Based on Protein–Protein Interaction Network,” the authors propose a new computational method to predict kinase–substrate interactions based on protein–protein interaction network measuring kinase–kinase and substrate–substrate similarities.