Special Issue, Part I 18th International Symposium on Bioinformatics Research and Applications (ISBRA 2022)
Special Issue, Part I 18th International Symposium on Bioinformatics Research and Applications (ISBRA 2022)
复制标题
特刊，第一部分第十八届生物信息学研究与应用国际研讨会 (ISBRA 2022)
DOI：
10.1089/cmb.2023.29095.az
复制
发表时间：
2023
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
在“A Clonal Evolution Simulator for Planning Somatic Evolution Studies”中，作者提出了一个广泛的克隆谱系，变体类别和测序技术选择的模拟器，旨在为体细胞谱系分析中的有效研究设计提供平台。“ARGLRR：结合新的图正则化的稀疏低秩表示单细胞RNA测序数据聚类方法”提出了一种实用的稀疏子空间聚类方法来识别细胞类型。论文“Reflection and Transposition Distance on Unbalanced Genomes using Intergenic Information”研究了不平衡基因组上的基因间转座距离，该论文也将插入和缺失视为可能重排集合中的非保守重排。论文“A Gene Correlation Measurement Method for Spatial Transcriptome Data Based on Partitioning and Distribution”提出计算基因对（X，Y）的高斯分布的联合概率密度以排除离群值。在“Multi-View Enhanced Tensor Nuclear Norm and Local Constraint Model for Cancer Clustering and Feature Gene Selection”中，作者改进了张量核范数的灵活性，并引入了各向异性时空全变差正则化。论文“Multi-Granularity Label Prediction Model for Automatic ICD Coding in Clinical Text”和“Automatic ICD coding via Note-Code Interaction Network with Denoising Mechanism”提出了使用学习模型进行国际疾病分类的代码预测，这些模型分别针对不同的代码级别显式地训练多个分类器，并引入去噪机制以减少噪声的影响。论文“Identification of Disease-Associated MiRNAs via Locality-Constrained Linear Coding-Based Encoding Learning”集成了多源生物信息，并引入超图正则项来建立样本之间的复杂关系。论文“A Graph Representation Approach Based on Light Gradient Boosting Machine for Predicting Drug-Disease Associations”的作者提出了一种基于新的异构蛋白质网络和从网络拓扑和生物知识前景中提取特征的药物-疾病关联预测机器学习方法。
In “A Clonal Evolution Simulator for Planning Somatic Evolution Studies”, authors present a simulator of a wide range of clonal lineages, variant classes, and sequencing technology choices, intended to provide a platform for effective study design in somatic lineage analysis. The paper “ARGLRR: A Sparse Low-rank Representation Single-cell RNA-sequencing Data Clustering Method Combined with a New Graph Regularization” proposes a practical sparse subspace clustering method to identify cell types. The paper “Reversal and Transposition Distance on Unbalanced Genomes using Intergenic Information” studies the intergenic transposition distance on unbalanced genomes, which also considers insertions and deletions as non-conservative rearrangements in the set of possible rearrangements. The paper “A Gene Correlation Measurement Method for Spatial Transcriptome Data Based on Partitioning and Distribution” proposes to calculate the joint probability density of Gaussian distributions of the gene pair (X, Y) to exclude outliers. In “Multi-View Enhanced Tensor Nuclear Norm and Local Constraint Model for cancer clustering and feature gene selection”, authors improve the flexibility of the tensor nuclear norm and introduce the anisotropic spatial-temporal total variation regularization. The papers “Multi-Granularity Label Prediction Model for Automatic ICD Coding in Clinical Text” and “Automatic ICD coding via Note-Code Interaction Network with Denoising Mechanism” propose code prediction for the International Classification of Disease using learning models, which explicitly train multiple classifiers for different code levels and introduce a denoising mechanism to reduce the influence of noise, respectively. The paper “Identification of Disease-Associated MiRNAs via Locality-Constrained Linear Coding-Based Ensemble Learning” ensembles multi-source biological information and introduces the hypergraph-regular term to establish complex relationships between samples. Authors of the paper “A Graph Representation Approach Based on Light Gradient Boosting Machine for Predicting Drug-Disease Associations” propose a machine learning approach for Drug-Disease Association prediction based on a new heterogeneous network of proteins and feature extraction from network topology and biological knowledge prospectibes.