Novel progressive deep learning algorithm for uncovering multiple single nucleotide polymorphism interactions to predict paclitaxel clearance in patients with nonsmall cell lung cancer Article Swipe
Wei Chen
,
Haiyan Zhou
,
Mingyu Zhang
,
Yafei Shi
,
Taifeng Li
,
Qian Di
,
Jun Yang
,
Feng Yu
,
Guohui Li
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/cai2.110
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1002/cai2.110
Background The rate at which the anticancer drug paclitaxel is cleared from the body markedly impacts its dosage and chemotherapy effectiveness. Importantly, paclitaxel clearance varies among individuals, primarily because of genetic polymorphisms. This metabolic variability arises from a nonlinear process that is influenced by multiple single nucleotide polymorphisms (SNPs). Conventional bioinformatics methods struggle to accurately analyze this complex process and, currently, there is no established efficient algorithm for investigating SNP interactions. Methods We developed a novel machine‐learning approach called GEP‐CSIs data mining algorithm. This algorithm, an advanced version of GEP, uses linear algebra computations to handle discrete variables. The GEP‐CSI algorithm calculates a fitness function score based on paclitaxel clearance data and genetic polymorphisms in patients with nonsmall cell lung cancer. The data were divided into a primary set and a validation set for the analysis. Results We identified and validated 1184 three‐SNP combinations that had the highest fitness function values. Notably, SERPINA1 , ATF3 and EGF were found to indirectly influence paclitaxel clearance by coordinating the activity of genes previously reported to be significant in paclitaxel clearance. Particularly intriguing was the discovery of a combination of three SNPs in genes FLT1 , EGF and MUC16 . These SNPs‐related proteins were confirmed to interact with each other in the protein–protein interaction network, which formed the basis for further exploration of their functional roles and mechanisms. Conclusion We successfully developed an effective deep‐learning algorithm tailored for the nuanced mining of SNP interactions, leveraging data on paclitaxel clearance and individual genetic polymorphisms.
Related Topics
Concepts
Single-nucleotide polymorphism
Paclitaxel
Lung cancer
Algorithm
Polymorphism (computer science)
Computational biology
Computer science
Oncology
Biology
Cancer research
Medicine
Genotype
Genetics
Cancer
Gene
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/cai2.110
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cai2.110
- OA Status
- diamond
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4396869529
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4396869529Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/cai2.110Digital Object Identifier
- Title
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Novel progressive deep learning algorithm for uncovering multiple single nucleotide polymorphism interactions to predict paclitaxel clearance in patients with nonsmall cell lung cancerWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-05-12Full publication date if available
- Authors
-
Wei Chen, Haiyan Zhou, Mingyu Zhang, Yafei Shi, Taifeng Li, Qian Di, Jun Yang, Feng Yu, Guohui LiList of authors in order
- Landing page
-
https://doi.org/10.1002/cai2.110Publisher landing page
- PDF URL
-
https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cai2.110Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
diamondOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/cai2.110Direct OA link when available
- Concepts
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Single-nucleotide polymorphism, Paclitaxel, Lung cancer, Algorithm, Polymorphism (computer science), Computational biology, Computer science, Oncology, Biology, Cancer research, Medicine, Genotype, Genetics, Cancer, GeneTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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28Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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