This special issue includes a selection of articles presented at the 13th International Symposium on Bioinformatics Research and Applications (ISBRA 2017), which was held in Honolulu, Hawaii, on May 30–June 2, 2017. 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 2017, 118 articles were submitted in response to the call for articles, out of which 27 articles appeared in the ISBRA proceedings published as volume 10330 of Springer Verlag's Lecture Notes in Bioinformatics series. Authors of nine articles were invited to submit extended versions of their proceeding articles to this special issue.
In “Reconstructing One-Articulated Networks with Distance Matrices,” the authors consider one-articulated phylogenetic networks that form a proper superset of galled trees and give efficient algorithms for reconstructing corresponding tree for ultrametric one-articulated networks.
The article “ICON-MIC: Implementing a CPU/MIC Collaboration Parallel Framework for ICON on Tianhe-2 Supercomputer” develops new strategies for parallelization of the key validation procedure in electron tomography achieving 13.3 × acceleration.
In the article “Estimation of Rates of Reactions Triggered by Electron Transfer in Top-Down Mass Spectrometry,” a new method for analyzing results of the electron transfer dissociation technique is presented.
The article “A Median Solver and Phylogenetic Inference Based on DCJ Sorting” proposes a new median solver for gene order data that combines double-cut-and-join sorting with the simulated annealing and applies it to the small and big parsimony problems.
In “Convolutional Neural Network for Histopathological Analysis of Osteosarcoma,” the authors apply convolutional neural network to improve efficiency and accuracy of osteosarcoma tumor classification into tumor classes versus nontumor.
In “Beta-Barrel Detection for Medium Resolution Cryo-EM Density Maps Using Genetic Algorithms and Ray Tracing,” the authors present a new approach utilizing a genetic algorithm and ray tracing to automatically identify and extract β-barrels from cryo-EM density maps.
The article “An Optimized Method for Bayesian Connectivity Change Point Model” proposes a method that can automatically mine the hidden information from protein sequences and generate highly representative features through iterations of multiple layers.
The article “Unbiased Taxonomic Annotation of Metagenomic Samples” shows that the Rand index is a better indicator of classification error than the often used area under the ROC curve and F-measure for both balanced and imbalanced reference taxonomies.
In “A Computational Based Method for Predicting Drug–Target Interactions by Using Stacked Auto-Encoder Deep Neural Network,” the authors propose a new computational method for predicting drug–target interactions from drug molecular structure and protein sequence by using the stacked autoencoder of deep learning, which can adequately extract the raw data information.