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Huo Q, Song R, Ma Z. Recent advances in exploring transcriptional regulatory landscape of crops. FRONTIERS IN PLANT SCIENCE 2024; 15:1421503. [PMID: 38903438 PMCID: PMC11188431 DOI: 10.3389/fpls.2024.1421503] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/22/2024] [Accepted: 05/23/2024] [Indexed: 06/22/2024]
Abstract
Crop breeding entails developing and selecting plant varieties with improved agronomic traits. Modern molecular techniques, such as genome editing, enable more efficient manipulation of plant phenotype by altering the expression of particular regulatory or functional genes. Hence, it is essential to thoroughly comprehend the transcriptional regulatory mechanisms that underpin these traits. In the multi-omics era, a large amount of omics data has been generated for diverse crop species, including genomics, epigenomics, transcriptomics, proteomics, and single-cell omics. The abundant data resources and the emergence of advanced computational tools offer unprecedented opportunities for obtaining a holistic view and profound understanding of the regulatory processes linked to desirable traits. This review focuses on integrated network approaches that utilize multi-omics data to investigate gene expression regulation. Various types of regulatory networks and their inference methods are discussed, focusing on recent advancements in crop plants. The integration of multi-omics data has been proven to be crucial for the construction of high-confidence regulatory networks. With the refinement of these methodologies, they will significantly enhance crop breeding efforts and contribute to global food security.
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Affiliation(s)
| | | | - Zeyang Ma
- State Key Laboratory of Maize Bio-breeding, Frontiers Science Center for Molecular Design Breeding, Joint International Research Laboratory of Crop Molecular Breeding, National Maize Improvement Center, College of Agronomy and Biotechnology, China Agricultural University, Beijing, China
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2
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Lei Y, Huang XT, Guo X, Hang Katie Chan K, Gao L. DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations. Brief Bioinform 2024; 25:bbae334. [PMID: 38980373 PMCID: PMC11232306 DOI: 10.1093/bib/bbae334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2024] [Revised: 06/03/2024] [Accepted: 07/01/2024] [Indexed: 07/10/2024] Open
Abstract
Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. However, many methods for inferring individual GRNs from scRNA-seq data are limited because they overlook intercellular heterogeneity and similarities between different cell subpopulations, which are often present in the data. Here, we propose a deep learning-based framework, DeepGRNCS, for jointly inferring GRNs across cell subpopulations. We follow the commonly accepted hypothesis that the expression of a target gene can be predicted based on the expression of transcription factors (TFs) due to underlying regulatory relationships. We initially processed scRNA-seq data by discretizing data scattering using the equal-width method. Then, we trained deep learning models to predict target gene expression from TFs. By individually removing each TF from the expression matrix, we used pre-trained deep model predictions to infer regulatory relationships between TFs and genes, thereby constructing the GRN. Our method outperforms existing GRN inference methods for various simulated and real scRNA-seq datasets. Finally, we applied DeepGRNCS to non-small cell lung cancer scRNA-seq data to identify key genes in each cell subpopulation and analyzed their biological relevance. In conclusion, DeepGRNCS effectively predicts cell subpopulation-specific GRNs. The source code is available at https://github.com/Nastume777/DeepGRNCS.
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Affiliation(s)
- Yahui Lei
- School of Computer Science and Technology, Xidian University, Xi’an 710071, Shaanxi, China
| | - Xiao-Tai Huang
- School of Computer Science and Technology, Xidian University, Xi’an 710071, Shaanxi, China
| | - Xingli Guo
- School of Computer Science and Technology, Xidian University, Xi’an 710071, Shaanxi, China
| | - Kei Hang Katie Chan
- Department of Electrical Engineering, City University of Hong Kong, Hong Kong SAR, China
- Department of Biomedical Sciences, City University of Hong Kong, Hong Kong SAR, China
- Department of Epidemiology and Center for Global Cardiometabolic Health, Brown University, Providence, RI, United States
| | - Lin Gao
- School of Computer Science and Technology, Xidian University, Xi’an 710071, Shaanxi, China
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3
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Kim D, Tran A, Kim HJ, Lin Y, Yang JYH, Yang P. Gene regulatory network reconstruction: harnessing the power of single-cell multi-omic data. NPJ Syst Biol Appl 2023; 9:51. [PMID: 37857632 PMCID: PMC10587078 DOI: 10.1038/s41540-023-00312-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2023] [Accepted: 10/02/2023] [Indexed: 10/21/2023] Open
Abstract
Inferring gene regulatory networks (GRNs) is a fundamental challenge in biology that aims to unravel the complex relationships between genes and their regulators. Deciphering these networks plays a critical role in understanding the underlying regulatory crosstalk that drives many cellular processes and diseases. Recent advances in sequencing technology have led to the development of state-of-the-art GRN inference methods that exploit matched single-cell multi-omic data. By employing diverse mathematical and statistical methodologies, these methods aim to reconstruct more comprehensive and precise gene regulatory networks. In this review, we give a brief overview on the statistical and methodological foundations commonly used in GRN inference methods. We then compare and contrast the latest state-of-the-art GRN inference methods for single-cell matched multi-omics data, and discuss their assumptions, limitations and opportunities. Finally, we discuss the challenges and future directions that hold promise for further advancements in this rapidly developing field.
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Affiliation(s)
- Daniel Kim
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
| | - Andy Tran
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia
| | - Hani Jieun Kim
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
| | - Yingxin Lin
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia
| | - Jean Yee Hwa Yang
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia.
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia.
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia.
| | - Pengyi Yang
- School of Mathematics and Statistics, University of Sydney, Camperdown, NSW, Australia.
- Computational Systems Biology Unit, Children's Medical Research Institute, University of Sydney, Camperdown, NSW, Australia.
- Sydney Precision Data Science Centre, University of Sydney, Camperdown, NSW, Australia.
- Charles Perkins Centre, University of Sydney, Camperdown, NSW, Australia.
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Ellis D, Wu D, Datta S. SAREV: A review on statistical analytics of single-cell RNA sequencing data. WILEY INTERDISCIPLINARY REVIEWS. COMPUTATIONAL STATISTICS 2022; 14:e1558. [PMID: 36034329 PMCID: PMC9400796 DOI: 10.1002/wics.1558] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Accepted: 04/09/2021] [Indexed: 06/15/2023]
Abstract
Due to the development of next-generation RNA sequencing (NGS) technologies, there has been tremendous progress in research involving determining the role of genomics, transcriptomics and epigenomics in complex biological systems. However, scientists have realized that information obtained using earlier technology, frequently called 'bulk RNA-seq' data, provides information averaged across all the cells present in a tissue. Relatively newly developed single cell (scRNA-seq) technology allows us to provide transcriptomic information at a single-cell resolution. Nevertheless, these high-resolution data have their own complex natures and demand novel statistical data analysis methods to provide effective and highly accurate results on complex biological systems. In this review, we cover many such recently developed statistical methods for researchers wanting to pursue scRNA-seq statistical and computational research as well as scientific research about these existing methods and free software tools available for their generated data. This review is certainly not exhaustive due to page limitations. We have tried to cover the popular methods starting from quality control to the downstream analysis of finding differentially expressed genes and concluding with a brief description of network analysis.
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Affiliation(s)
- Dorothy Ellis
- Department of Biostatistics, University of Florida, School of Public Health and Health Professions, Gainesville, FL
| | - Dongyuan Wu
- Department of Biostatistics, University of Florida, School of Public Health and Health Professions, Gainesville, FL
| | - Susmita Datta
- Department of Biostatistics, University of Florida, School of Public Health and Health Professions, Gainesville, FL
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Panditrao G, Bhowmick R, Meena C, Sarkar RR. Emerging landscape of molecular interaction networks: Opportunities, challenges and prospects. J Biosci 2022. [PMID: 36210749 PMCID: PMC9018971 DOI: 10.1007/s12038-022-00253-y] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Network biology finds application in interpreting molecular interaction networks and providing insightful inferences using graph theoretical analysis of biological systems. The integration of computational bio-modelling approaches with different hybrid network-based techniques provides additional information about the behaviour of complex systems. With increasing advances in high-throughput technologies in biological research, attempts have been made to incorporate this information into network structures, which has led to a continuous update of network biology approaches over time. The newly minted centrality measures accommodate the details of omics data and regulatory network structure information. The unification of graph network properties with classical mathematical and computational modelling approaches and technologically advanced approaches like machine-learning- and artificial intelligence-based algorithms leverages the potential application of these techniques. These computational advances prove beneficial and serve various applications such as essential gene prediction, identification of drug–disease interaction and gene prioritization. Hence, in this review, we have provided a comprehensive overview of the emerging landscape of molecular interaction networks using graph theoretical approaches. With the aim to provide information on the wide range of applications of network biology approaches in understanding the interaction and regulation of genes, proteins, enzymes and metabolites at different molecular levels, we have reviewed the methods that utilize network topological properties, emerging hybrid network-based approaches and applications that integrate machine learning techniques to analyse molecular interaction networks. Further, we have discussed the applications of these approaches in biomedical research with a note on future prospects.
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Affiliation(s)
- Gauri Panditrao
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
| | - Rupa Bhowmick
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
| | - Chandrakala Meena
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
| | - Ram Rup Sarkar
- Chemical Engineering and Process Development Division, CSIR-National Chemical Laboratory, Pune, 411008 India
- Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002 India
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Wang M, Song WM, Ming C, Wang Q, Zhou X, Xu P, Krek A, Yoon Y, Ho L, Orr ME, Yuan GC, Zhang B. Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application. Mol Neurodegener 2022; 17:17. [PMID: 35236372 PMCID: PMC8889402 DOI: 10.1186/s13024-022-00517-z] [Citation(s) in RCA: 36] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2021] [Accepted: 01/18/2022] [Indexed: 12/13/2022] Open
Abstract
Alzheimer's disease (AD) is the most common form of dementia, characterized by progressive cognitive impairment and neurodegeneration. Extensive clinical and genomic studies have revealed biomarkers, risk factors, pathways, and targets of AD in the past decade. However, the exact molecular basis of AD development and progression remains elusive. The emerging single-cell sequencing technology can potentially provide cell-level insights into the disease. Here we systematically review the state-of-the-art bioinformatics approaches to analyze single-cell sequencing data and their applications to AD in 14 major directions, including 1) quality control and normalization, 2) dimension reduction and feature extraction, 3) cell clustering analysis, 4) cell type inference and annotation, 5) differential expression, 6) trajectory inference, 7) copy number variation analysis, 8) integration of single-cell multi-omics, 9) epigenomic analysis, 10) gene network inference, 11) prioritization of cell subpopulations, 12) integrative analysis of human and mouse sc-RNA-seq data, 13) spatial transcriptomics, and 14) comparison of single cell AD mouse model studies and single cell human AD studies. We also address challenges in using human postmortem and mouse tissues and outline future developments in single cell sequencing data analysis. Importantly, we have implemented our recommended workflow for each major analytic direction and applied them to a large single nucleus RNA-sequencing (snRNA-seq) dataset in AD. Key analytic results are reported while the scripts and the data are shared with the research community through GitHub. In summary, this comprehensive review provides insights into various approaches to analyze single cell sequencing data and offers specific guidelines for study design and a variety of analytic directions. The review and the accompanied software tools will serve as a valuable resource for studying cellular and molecular mechanisms of AD, other diseases, or biological systems at the single cell level.
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Affiliation(s)
- Minghui Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Won-min Song
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Chen Ming
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Qian Wang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Xianxiao Zhou
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Peng Xu
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Azra Krek
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Yonejung Yoon
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Lap Ho
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
| | - Miranda E. Orr
- Department of Internal Medicine, Section of Gerontology and Geriatric Medicine, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
- Sticht Center for Healthy Aging and Alzheimer’s Prevention, Wake Forest School of Medicine, Winston-Salem, North Carolina USA
| | - Guo-Cheng Yuan
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, One Gustave L. Levy Place, New York, NY 10029 USA
| | - Bin Zhang
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Mount Sinai Center for Transformative Disease Modeling, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Icahn Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
- Department of Pharmacological Sciences, Icahn School of Medicine at Mount Sinai, 1470 Madison Avenue, Room S8-111, New York, NY 10029 USA
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Sekula M, Gaskins J, Datta S. Single-Cell Differential Network Analysis with Sparse Bayesian Factor Models. Front Genet 2022; 12:810816. [PMID: 35186014 PMCID: PMC8855158 DOI: 10.3389/fgene.2021.810816] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Accepted: 12/21/2021] [Indexed: 11/13/2022] Open
Abstract
Differential network analysis plays an important role in learning how gene interactions change under different biological conditions, and the high resolution of single-cell RNA (scRNA-seq) sequencing provides new opportunities to explore these changing gene-gene interactions. Here, we present a sparse hierarchical Bayesian factor model to identify differences across network structures from different biological conditions in scRNA-seq data. Our methodology utilizes latent factors to impact gene expression values for each cell to help account for zero-inflation, increased cell-to-cell variability, and overdispersion that are unique characteristics of scRNA-seq data. Condition-dependent parameters determine which latent factors are activated in a gene, which allows for not only the calculation of gene-gene co-expression within each group but also the calculation of the co-expression differences between groups. We highlight our methodology’s performance in detecting differential gene-gene associations across groups by analyzing simulated datasets and a SARS-CoV-2 case study dataset.
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Affiliation(s)
- Michael Sekula
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, United States
| | - Jeremy Gaskins
- Department of Bioinformatics and Biostatistics, University of Louisville, Louisville, KY, United States
| | - Susmita Datta
- Department of Biostatistics, University of Florida, Gainesville, FL, United States
- *Correspondence: Susmita Datta,
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Wu G, Li Y. Distinct characteristics of correlation analysis at the single-cell and the population level. Stat Appl Genet Mol Biol 2022; 21:sagmb-2022-0015. [PMID: 35918809 DOI: 10.1515/sagmb-2022-0015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2022] [Accepted: 06/13/2022] [Indexed: 11/15/2022]
Abstract
Correlation analysis is widely used in biological studies to infer molecular relationships within biological networks. Recently, single-cell analysis has drawn tremendous interests, for its ability to obtain high-resolution molecular phenotypes. It turns out that there is little overlap of co-expressed genes identified in single-cell level investigations with that of population level investigations. However, the nature of the relationship of correlations between single-cell and population levels remains unclear. In this manuscript, we aimed to unveil the origin of the differences between the correlation coefficients at the single-cell level and that at the population level, and bridge the gap between them. Through developing formulations to link correlations at the single-cell and the population level, we illustrated that aggregated correlations could be stronger, weaker or equal to the corresponding individual correlations, depending on the variations and the correlations within the population. When the correlation within the population is weaker than the individual correlation, the aggregated correlation is stronger than the corresponding individual correlation. Besides, our data indicated that aggregated correlation is more likely to be stronger than the corresponding individual correlation, and it was rare to find gene-pairs exclusively strongly correlated at the single-cell level. Through a bottom-up approach to model interactions between molecules in a signaling cascade or a multi-regulator-controlled gene expression, we surprisingly found that the existence of interaction between two components could not be excluded simply based on their low correlation coefficients, suggesting a reconsideration of connectivity within biological networks which was derived solely from correlation analysis. We also investigated the impact of technical random measurement errors on the correlation coefficients for the single-cell level and the population level. The results indicate that the aggregated correlation is relatively robust and less affected. Because of the heterogeneity among single cells, correlation coefficients calculated based on data of the single-cell level might be different from that of the population level. Depending on the specific question we are asking, proper sampling and normalization procedure should be done before we draw any conclusions.
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Affiliation(s)
- Guoyu Wu
- School of Clinical Pharmacy, Guangdong Pharmaceutical University, Guangzhou, China
- Key Specialty of Clinical Pharmacy, The First Affiliated Hospital of Guangdong Pharmaceutical University, Guangzhou, China
- NMPA Key Laboratory for Technology Research and Evaluation of Pharmacovigilance, Guangdong Pharmaceutical University, Guangzhou, China
| | - Yuchao Li
- Max Planck Institute for Molecular Genetics, Berlin, Germany
- MegaLab, MegaRobo Technologies Co., Ltd, Beijing, China
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Yu CY, Mitrofanova A. Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer. Front Genet 2021; 12:687813. [PMID: 34408770 PMCID: PMC8365516 DOI: 10.3389/fgene.2021.687813] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 06/28/2021] [Indexed: 12/18/2022] Open
Abstract
Biomarker discovery is at the heart of personalized treatment planning and cancer precision therapeutics, encompassing disease classification and prognosis, prediction of treatment response, and therapeutic targeting. However, many biomarkers represent passenger rather than driver alterations, limiting their utilization as functional units for therapeutic targeting. We suggest that identification of driver biomarkers through mechanism-centric approaches, which take into account upstream and downstream regulatory mechanisms, is fundamental to the discovery of functionally meaningful markers. Here, we examine computational approaches that identify mechanism-centric biomarkers elucidated from gene co-expression networks, regulatory networks (e.g., transcriptional regulation), protein-protein interaction (PPI) networks, and molecular pathways. We discuss their objectives, advantages over gene-centric approaches, and known limitations. Future directions highlight the importance of input and model interpretability, method and data integration, and the role of recently introduced technological advantages, such as single-cell sequencing, which are central for effective biomarker discovery and time-cautious precision therapeutics.
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Affiliation(s)
- Christina Y. Yu
- Department of Biomedical and Health Informatics, School of Health Professions, Rutgers, The State University of New Jersey, Newark, NJ, United States
| | - Antonina Mitrofanova
- Department of Biomedical and Health Informatics, School of Health Professions, Rutgers, The State University of New Jersey, Newark, NJ, United States
- Rutgers Cancer Institute of New Jersey, Rutgers, The State University of New Jersey, New Brunswick, NJ, United States
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