Innovating Computational Biology and Intelligent Medicine: ICIBM 2019 Special Issue

The International Association for Intelligent Biology and Medicine (IAIBM) is a nonprofit organization that promotes intelligent biology and medical science. It hosts an annual International Conference on Intelligent Biology and Medicine (ICIBM), which was established in 2012. The ICIBM 2019 was held from 9 to 11 June 2019 in Columbus, Ohio, USA. Out of the 105 original research manuscripts submitted to the conference, 18 were selected for publication in a Special Issue in Genes. The topics of the selected manuscripts cover a wide range of current topics in biomedical research including cancer informatics, transcriptomic, computational algorithms, visualization and tools, deep learning, and microbiome research. In this editorial, we briefly introduce each of the manuscripts and discuss their contribution to the advance of science and technology.

[1]  S. Handelman,et al.  Identifying Interaction Clusters for MiRNA and MRNA Pairs in TCGA Network , 2019, Genes.

[2]  Jianlin Cheng,et al.  A Super-Clustering Approach for Fully Automated Single Particle Picking in Cryo-EM , 2019, Genes.

[4]  Xinghua Shi,et al.  Sparse Convolutional Denoising Autoencoders for Genotype Imputation , 2019, Genes.

[5]  Lang Li,et al.  Computational Cancer Cell Models to Guide Precision Breast Cancer Medicine , 2020, Genes.

[6]  Zhongming Zhao,et al.  Multi-Objective Optimized Fuzzy Clustering for Detecting Cell Clusters from Single-Cell Expression Profiles , 2019, Genes.

[7]  J. Schwartz,et al.  Kinetic Modeling of DUSP Regulation in Herceptin-Resistant HER2-Positive Breast Cancer , 2019, Genes.

[8]  William C. Ray,et al.  Forming Big Datasets through Latent Class Concatenation of Imperfectly Matched Databases Features , 2019, Genes.

[9]  Zhandong Liu,et al.  A Portal to Visualize Transcriptome Profiles in Mouse Models of Neurological Disorders , 2019, Genes.

[10]  K. Nan,et al.  CNV Detection from Circulating Tumor DNA in Late Stage Non-Small Cell Lung Cancer Patients , 2019, Genes.

[11]  Zhongming Zhao,et al.  Changes in the Microbial Community Diversity of Oil Exploitation , 2019, Genes.

[12]  Jianhua Ruan,et al.  Network-Based Single-Cell RNA-Seq Data Imputation Enhances Cell Type Identification , 2020, Genes.

[13]  S. Janga,et al.  Long Non-Coding RNA Expression Levels Modulate Cell-Type-Specific Splicing Patterns by Altering Their Interaction Landscape with RNA-Binding Proteins , 2019, bioRxiv.

[14]  J. Postlethwait,et al.  The Molecular Evolution of Circadian Clock Genes in Spotted Gar (Lepisosteus oculatus) , 2019, Genes.

[15]  Kun Huang,et al.  Gene Co-Expression Networks Restructured Gene Fusion in Rhabdomyosarcoma Cancers , 2019, Genes.

[16]  W. Shih,et al.  Tumor-Infiltrating Leukocyte Composition and Prognostic Power in Hepatitis B- and Hepatitis C-Related Hepatocellular Carcinomas , 2019, Genes.

[17]  Weixing Feng,et al.  Identification of Alternatively-Activated Pathways between Primary Breast Cancer and Liver Metastatic Cancer Using Microarray Data , 2019, Genes.

[18]  Qi Liu,et al.  Network as a Biomarker: A Novel Network-Based Sparse Bayesian Machine for Pathway-Driven Drug Response Prediction , 2019, Genes.

[19]  S. Li,et al.  DNA Methylation Markers for Pan-Cancer Prediction by Deep Learning , 2019, Genes.