Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine Learning Article Swipe
Gustavo Sganzerla Martinez
,
Alexis Garduno
,
Ali Toloue Ostadgavahi
,
Benjamin Hewins
,
Mansi Dutt
,
Anuj Kumar
,
Ignacio Martín‐Loeches
,
David J. Kelvin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ijms25115920
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/ijms25115920
A common result of infection is an abnormal immune response, which may be detrimental to the host. To control the infection, the immune system might undergo regulation, therefore producing an excess of either pro-inflammatory or anti-inflammatory pathways that can lead to widespread inflammation, tissue damage, and organ failure. A dysregulated immune response can manifest as changes in differentiated immune cell populations and concentrations of circulating biomarkers. To propose an early diagnostic system that enables differentiation and identifies the severity of immune-dysregulated syndromes, we built an artificial intelligence tool that uses input data from single-cell RNA sequencing. In our results, single-cell transcriptomics successfully distinguished between mild and severe sepsis and COVID-19 infections. Moreover, by interpreting the decision patterns of our classification system, we identified that different immune cells upregulating or downregulating the expression of the genes CD3, CD14, CD16, FOSB, S100A12, and TCRɣδ can accurately differentiate between different degrees of infection. Our research has identified genes of significance that effectively distinguish between infections, offering promising prospects as diagnostic markers and providing potential targets for therapeutic intervention.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/ijms25115920
- https://www.mdpi.com/1422-0067/25/11/5920/pdf?version=1716965656
- OA Status
- gold
- Cited By
- 3
- References
- 32
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4399138585
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399138585Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/ijms25115920Digital Object Identifier
- Title
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Identification of Marker Genes in Infectious Diseases from ScRNA-seq Data Using Interpretable Machine LearningWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-05-29Full publication date if available
- Authors
-
Gustavo Sganzerla Martinez, Alexis Garduno, Ali Toloue Ostadgavahi, Benjamin Hewins, Mansi Dutt, Anuj Kumar, Ignacio Martín‐Loeches, David J. KelvinList of authors in order
- Landing page
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https://doi.org/10.3390/ijms25115920Publisher landing page
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https://www.mdpi.com/1422-0067/25/11/5920/pdf?version=1716965656Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1422-0067/25/11/5920/pdf?version=1716965656Direct OA link when available
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Identification (biology), Computational biology, Gene, Biology, Artificial intelligence, Computer science, Machine learning, Genetics, EcologyTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 1, 2024: 2Per-year citation counts (last 5 years)
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32Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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