Preface Special Issue: 15th International Symposium on Bioinformatics Research and Applications (ISBRA 2019)
Preface Special Issue: 15th International Symposium on Bioinformatics Research and Applications (ISBRA 2019)
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序言专刊：第十五届生物信息学研究与应用国际研讨会（ISBRA 2019）
DOI：
10.1089/cmb.2019.29024.zc
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发表时间：
2020
影响因子：
1.7
通讯作者：
Zelikovsky, Alexander
中科院分区：
文献类型：
--
作者：
Cai, Zhipeng;Skums, Pavel;Zelikovsky, Alexander
本特刊包括2019年6月3日至6日在西班牙巴塞罗那举行的第15届生物信息学研究与应用国际研讨会（ISBRA 2019）上发表的文章。ISBRA为从事生物信息学和计算生物学及其应用各个方面的研究人员、开发人员和从业人员提供了一个交流思想和成果的论坛。2019年，有95篇扩展摘要响应论文征集而提交，其中22篇扩展摘要出现在ISBRA 2019会议录中，作为Springer Verlag生物信息学系列讲座笔记的第11，490卷出版。10篇文章的作者被邀请提交他们摘要的扩展版本。此外，在ISBRA 2019上接受简短口头演讲的三篇文章的作者也被邀请向这个特刊提交完整的文章。前两篇文章专门讨论单元格中的布尔模型。”细胞电路的动态布尔模型的鲁棒性分析“使用随机模拟和整数线性规划研究这种模型的鲁棒性，”使用稳定状态观测的调节网络的布尔模型的修订“找到最小修复集，使模型与实验观测一致。三篇文章专门讨论进化模型。根据基因顺序和基因间大小的基因组重排进行排序“给出了当允许各种基因组重排和indel时估计基因组之间进化距离的常数因子近似算法。通过叶移除计算共识系统发生“有效地为给定的一组树找到精确和启发式的共识系统发生树”。通过粗粒模型的进化开关结构转换”提出了一种快速的方法，用于系统和广泛的探索多稳定蛋白质的转换途径。文章“最大堆叠碱基对：非线性LP舍入的硬度和近似”处理基于具有平行和相邻伙伴的碱基对数量最大化的RNA二级结构预测问题。两篇文章为耗时的生物信息学问题提供了加速。电子断层扫描中分布式多倾斜重建的共识框架”与电子断层扫描中的原始多倾斜重建相比，速度提高了5.4倍，内存减少了7倍。”迭代间隔种子哈希：缩小间隔种子散列和k-mer散列之间的差距“与现有技术相比，可以以6.2倍的速度计算间隔种子的散列值。三篇文章将AI方法应用于分类问题。”可解释的深度学习增强sRNA表达谱“从小RNA-seq表达数据中可靠地生成人类组织和细胞系的组织和性别注释”。“使用神经网络进行16 S核糖体基因分类的比较研究”提出了三种不同的深度学习架构来对细菌进行科、属和种的分类。峰值通过：自动化ChIP-Seq黑名单创建“通过有效分类ChIP-Seq实验中经常产生伪影和噪声的区域，提高了ChIP-Seq数据质量。
This special issue includes a selection of articles presented at the 15th International Symposium on Bioinformatics Research and Applications (ISBRA 2019), which was held in Barcelona, Spain, on June 3–6, 2019. 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 2019, 95 extended abstracts were submitted in response to the call for articles, out of which 22 extended abstracts appeared in the ISBRA 2019 proceedings published as volume 11,490 of Springer Verlag’s Lecture Notes in Bioinformatics series. Authors of 10 articles were invited to submit extended versions of their abstracts to this special issue. In addition, authors of three articles accepted for a short oral presentation at ISBRA 2019 were also invited to submit full articles to this special issue. The first two articles are devoted to Boolean models in cells.‘‘A Robustness Analysis of Dynamic Boolean Models of Cellular Circuits’’studies the robustness of such models using stochastic simulation and integer linear programming and ‘‘Revision of Boolean Models of Regulatory Networks Using Stable State Observations’’finds the set of minimum repairs to make a model consistent with the experimental observations. Three articles are devoted to evolutionary models.‘‘Sorting by Genome Rearrangements on both Gene Order and Intergenic Sizes’’give constant-factor approximation algorithms for estimation of evolutionary distance between genomes when various genome rearrangements and indels are allowed.‘‘Computing a Consensus Phylogeny via Leaf Removal’’efficiently finds an exact and heuristic consensus phylogenetic tree for a given set of trees.‘‘Evolutionary Switches Structural Transitions via Coarse Grained Models’’proposes a fast method for systematic and extensive exploration of the multistable proteins transition pathways. The article ‘‘Maximum Stacking Base Pairs: Hardness and Approximation by Nonlinear LP-Rounding’’deals with problem RNA secondary structure prediction based on maximization of number of base pairs with parallel and adjacent partners.Two articles offer speedup for time-consuming bioinformatics problems.‘‘A Consensus Framework of Distributed Multiple-Tilt Reconstruction in Electron Tomography’’gives a 5.4× speedup and 7× memory reduction compared with the raw multitilt reconstruction in electron tomography.‘‘Iterative Spaced Seed Hashing: Closing the Gap Between Spaced Seed Hashing and k-mer Hashing’’can compute the hashing values of spaced seeds with a speedup of 6.2× compared with state-of-the-art. Three articles apply AI methods to classification problems.‘‘Explainable Deep Learning for Augmentation of sRNA Expression Profiles’’reliably generates tissue and sex annotations for human tissues and cell lines from small RNA-seq expression data.‘‘Comparative Study Using Neural Networks for 16S Ribosomal Gene Classification’’proposes three different deep learning architectures to classify bacteria at a family, genus, and species.‘‘PeakPass: Automating ChIP-Seq Blacklist Creation’’improves ChIP-Seq data quality by efficiently classifying regions that frequently produce artifacts and noise in ChIP-Seq experiments.