Exploring potential gene signatures in dengue through machine learning and deep learning approaches.
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| Title: | Exploring potential gene signatures in dengue through machine learning and deep learning approaches. |
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| Authors: | Josyula JVN; Department of Applied Biology, CSIR-Indian Institute of Chemical Technology, Hyderabad, India.; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India., Jangili S; Department of Applied Biology, CSIR-Indian Institute of Chemical Technology, Hyderabad, India.; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India., Yaladanda N; Department of Applied Biology, CSIR-Indian Institute of Chemical Technology, Hyderabad, India.; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India., Pillai AKB; Institute of Advanced Virology, Bio 360 Life Sciences Park, Thonnakkal, Trivandrum, Kerala, 695 317, India., Mutheneni SR; Department of Applied Biology, CSIR-Indian Institute of Chemical Technology, Hyderabad, India. msrinivas@iict.res.in.; Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India. msrinivas@iict.res.in. |
| Source: | Virus genes [Virus Genes] 2026 Feb; Vol. 62 (1), pp. 51-66. Date of Electronic Publication: 2025 Dec 02. |
| Publication Type: | Journal Article |
| Journal Info: | Publisher: Kluwer Academic Country of Publication: United States NLM ID: 8803967 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1572-994X (Electronic) Linking ISSN: 09208569 NLM ISO Abbreviation: Virus Genes Subsets: MEDLINE |
| Database: | MEDLINE Ultimate |
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