Recent Advances in Genomics Research

A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Cellular and Molecular Bioengineering".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1324

Special Issue Editor


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Guest Editor
Division of Biostatistics, Data Science Institute, Medical College of Wisconsin, Milwaukee, WI, USA
Interests: biostatistics; statistical genetics; bioinformatics; omics data analysis; proteomics; metabolomics; machine learning; integrative analysis; medical data analysis; computational biology
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Special Issue Information

Dear Colleagues,

With the advent of technological advancements in sequencing technology, computational algorithms and bioinformatic tools, the field of genomics research has evolved rapidly. This Special Issue aims to explore cutting-edge developments, particularly in genomics data analysis, offering insights into novel methodologies, tools, and applications that have transformed the perspective of genomic data analysis in medical research. The Issue welcomes a wide range of topics related to data analysis, including but not limited to next-generation sequencing technology, multi-omics integration technique, artificial intelligence (AI) and machine learning algorithms using genomics data, single-cell sequencing, spatial transcriptomics, population genomics, and precision medicine. This Special Issue highlights the advancements from basic science to clinical applications and aims to serve as a valuable resource for scientists to leverage the cutting-edge tools and strategies for genomics data analysis.

Dr. Chien-Wei (Masaki) Lin
Guest Editor

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Keywords

  • genomics data analysis
  • NGS
  • AI
  • machine learning
  • multi-omics integration
  • single-cell sequencing
  • precision medicine

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Published Papers (1 paper)

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Research

25 pages, 8509 KiB  
Article
CCI: A Consensus Clustering-Based Imputation Method for Addressing Dropout Events in scRNA-Seq Data
by Wanlin Juan, Kwang Woo Ahn, Yi-Guang Chen and Chien-Wei Lin
Bioengineering 2025, 12(1), 31; https://doi.org/10.3390/bioengineering12010031 - 3 Jan 2025
Viewed by 959
Abstract
Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technique in molecular biology and genomics, revealing the cellular heterogeneity. However, scRNA-seq data often suffer from dropout events, meaning that certain genes exhibit very low or even zero expression levels due to technical limitations. Existing imputation [...] Read more.
Single-cell RNA sequencing (scRNA-seq) is a cutting-edge technique in molecular biology and genomics, revealing the cellular heterogeneity. However, scRNA-seq data often suffer from dropout events, meaning that certain genes exhibit very low or even zero expression levels due to technical limitations. Existing imputation methods for dropout events lack comprehensive evaluations in downstream analyses and do not demonstrate robustness across various scenarios. In response to this challenge, we propose a consensus clustering-based imputation (CCI) method. CCI performs clustering on each subset of data sampling across genes and summarizes clustering outcomes to define cellular similarities. CCI leverages the information from similar cells and employs the similarities to impute gene expression levels. Our comprehensive evaluations demonstrate that CCI not only reconstructs the original data pattern, but also improves the performance of downstream analyses. CCI outperforms existing methods for data imputation under different scenarios, exhibiting accuracy, robustness, and generalization. Full article
(This article belongs to the Special Issue Recent Advances in Genomics Research)
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