Machine Learning-Driven Nanopore Sensing for Quantitative, Label-Free miRNA Detection.

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Bibliographic Details
Title: Machine Learning-Driven Nanopore Sensing for Quantitative, Label-Free miRNA Detection.
Authors: Koch C; Department of Chemistry, Molecular Science Research Hub, Imperial College London, London, UK.; Department of Life Science, Sir Alexander Fleming Building, Imperial College London, London, UK., Sakthimani S; Department of Chemistry, Molecular Science Research Hub, Imperial College London, London, UK., Noakes VM; Department of Chemistry, Molecular Science Research Hub, Imperial College London, London, UK., Cretu M; Department of Chemistry, Molecular Science Research Hub, Imperial College London, London, UK., Newman D; Oxford Nanopore Technologies, Oxford, UK., Gutierrez R; Oxford Nanopore Technologies, Oxford, UK., Bruce M; Oxford Nanopore Technologies, Oxford, UK., Gorelik J; National Heart and Lung Institute, ICTEM, Imperial College London, London, UK., Guerra N; Department of Life Science, Sir Alexander Fleming Building, Imperial College London, London, UK., Edel JB; Department of Chemistry, Molecular Science Research Hub, Imperial College London, London, UK., Ivanov AP; Department of Chemistry, Molecular Science Research Hub, Imperial College London, London, UK.
Source: Small methods [Small Methods] 2026 Feb; Vol. 10 (4), pp. e02335. Date of Electronic Publication: 2026 Jan 19.
Publication Type: Journal Article
Journal Info: Publisher: WILEY-VCH Verlag GmbH & Co. KGaA Country of Publication: Germany NLM ID: 101724536 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 2366-9608 (Electronic) Linking ISSN: 23669608 NLM ISO Abbreviation: Small Methods Subsets: MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2366-9608
DOI:10.1002/smtd.202502335