Structure-enhanced Deep Learning Accelerates Aptamer Selection for Small Molecule Families like Steroids

High-throughput next generation sequencing (HT-NGS) data from manual designed library for hydrocortisone (CS) in the SELEX experiment. The dataset contains the round 7 and 8 sequencing data in fastq format with forward and reverse read, and can be further analyzed using conventional tools like AptaSuite or the article related web server AptaClux for post-analyzing the HT-NGS data.

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2024-12-04
DOI
https://doi.org/10.5281/zenodo.14272346
Primary Topic
Machine Learning in Materials Science
Type
article
Field-Weighted Citation Impact
0.00
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article

Structure-enhanced Deep Learning Accelerates Aptamer Selection for Small Molecule Families like Steroids

Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Materials Science
article

Structure-enhanced Deep Learning Accelerates Aptamer Selection for Small Molecule Families like Steroids

article en

Abstract

High-throughput next generation sequencing (HT-NGS) data from manual designed library for hydrocortisone (CS) in the SELEX experiment. The dataset contains the round 7 and 8 sequencing data in fastq format with forward and reverse read, and can be further analyzed using conventional tools like AptaSuite or the article related web server AptaClux for post-analyzing the HT-NGS data.

Zenodo (CERN European Organization for Nuclear Research)
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Machine Learning in Materials Science
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Structure-enhanced Deep Learning Accelerates Aptamer Selection for Small Molecule Families like Steroids · Zenodo (CERN European Organization for Nuclear Research) (2024) | TGRS Research Map | TGRS