Miss. Shriprabha Upadhyaya
Bio
I joined the Applied Bioinformatics Group in 2020 as part of my master’s dissertation. My project aimed to improve gene annotations by classifying potential low confidence gene models using machine learning. I started my PhD with the group in 2022, and my thesis focuses on improving trait association and prediction approaches using machine learning.
Publications
| Title | Year | Published by | Link |
|---|---|---|---|
| Dissection of local haplotype diversity at soybean rust loci reveals resistance-associated and context-dependent variation patterns in diverse germplasm | 2026 | Theoretical and Applied Genetics | DOI |
| Trait Association for Flowering Time in Lentil from Global Multi-Environment Data Using GWAS and Machine Learning | 2026 | Plants | DOI |
| Local Haplotyping Analysis for Flowering Time in Soybean Using Crosshap | 2026 | Legume Genomics | DOI |
| Understanding plant phenotypes in crop breeding through explainable AI | 2025 | Plant Biotechnology Journal | DOI |
| Plant disease epidemiology in the age of artificial intelligence and machine learning | 2025 | Agriculture Communications | DOI |
| Brassica Panache | 2025 | Plant Genome | DOI |
| Global genotype by environment prediction competition reveals that diverse modeling strategies can deliver satisfactory maize yield estimates | 2025 | Genetics | DOI |
| Image‐based crop disease detection using machine learning | 2025 | Plant Pathology | DOI |
| Genomics‐based plant disease resistance prediction using machine learning | 2024 | Plant Pathology | DOI |
| Local haplotyping reveals insights into the genetic control of flowering time variation in wild and domesticated soybean | 2024 | The Plant Genome | DOI |
| Focus on the Crop Not the Weed | 2024 | Remote Sensing | DOI |
| DNABERT-based explainable lncRNA identification in plant genome assemblies | 2023 | Computational and Structural Biotechnology Journal | DOI |
| Evaluating Plant Gene Models Using Machine Learning | 2022 | Plants | DOI |