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Bioengineering
Special Issues
Recent Advances in Genomics Research
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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 1697

Special Issue Editor

Dr. Chien-Wei (Masaki) Lin

E-Mail Website
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
Special Issues, Collections and Topics in MDPI journals

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Bioengineering is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

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

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

24 pages, 5751 KiB  
Article
In Silico Identification of LSD1 Inhibition-Responsive Targets in Small Cell Lung Cancer
by Ihsan Nalkiran, Hatice Sevim Nalkiran, Neslihan Ozcelik and Mehmet Kivrak
Bioengineering 2025, 12(5), 504; https://doi.org/10.3390/bioengineering12050504 - 10 May 2025
Viewed by 237
Abstract
Small cell lung cancer (SCLC) is an aggressive neuroendocrine malignancy characterized by rapid progression, high metastatic potential, and limited therapeutic options. Lysine-specific demethylase 1 (LSD1) has been identified as a promising epigenetic target in SCLC. RG6016 (ORY-1001) is a selective LSD1 inhibitor currently [...] Read more.
Small cell lung cancer (SCLC) is an aggressive neuroendocrine malignancy characterized by rapid progression, high metastatic potential, and limited therapeutic options. Lysine-specific demethylase 1 (LSD1) has been identified as a promising epigenetic target in SCLC. RG6016 (ORY-1001) is a selective LSD1 inhibitor currently under clinical investigation for its antitumor activity. In this study, publicly available RNA-Seq datasets from SCLC patient-derived xenograft (PDX) models treated with RG6016 were reanalyzed using bioinformatic approaches. Differential gene expression analysis was conducted to identify genes responsive to LSD1 inhibition. Candidate genes showing significant downregulation were further evaluated by molecular docking to assess their potential interaction with RG6016. The analysis identified a set of differentially expressed genes following RG6016 treatment, including notable downregulation of MYC, UCHL1, and TSPAN8. In silico molecular docking revealed favorable docking poses between RG6016 and the proteins encoded by these genes, suggesting potential direct or indirect targeting. These findings support a broader mechanism of action for RG6016 beyond its known interaction with LSD1. This study demonstrates that RG6016 may exert its antitumor effects through the modulation of additional molecular targets such as MYC, UCHL1, and TSPAN8 in SCLC. The combined bioinformatic and molecular docking analyses provide new insights into the potential multi-target profile of RG6016 and indicate the need for further experimental validation. Full article
(This article belongs to the Special Issue Recent Advances in Genomics Research)
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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 1050
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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