Leopold Parts
researcher ORCID ID = 0000-0002-2618-670X
Facts
9 properties- Instance of
- human
- Occupation
- researcher
- Educated at
- University of Cambridge[2]
- Official website
- www.sanger.ac.uk[6]
- Doctoral advisor
- Richard M. Durbin[3]
- Participant in
- International Mathematical Olympiad[7]
🏷️ Also known as
en Leopold Parts
🔖 Identifiers · 8
- ORCID iD
- 0000-0002-2618-670X
- VIAF cluster ID
- 5161816243127721643
- ELNET ID
- a12190639
- Google Knowledge Graph ID
- /g/11fm381ky8
- Google Scholar author ID
- ktEf4ZUAAAAJ
- International Mathematical Olympiad participant ID
- 5425
- Semantic Scholar author ID
- 2827805
- X (Twitter) username
- LeopoldParts
🔗 Connections
Author of 9
Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning,
A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization,
Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets,
ArtSeg—Artifact segmentation and removal in brightfield cell microscopy images without manual pixel-level annotations,
Computational biology: deep learning,
Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images,
Practical segmentation of nuclei in brightfield cell images with neural networks trained on fluorescently labelled samples,
Predicting base editing outcomes using position-specific sequence determinants
+1 more
← Authored by 9
Accurate Classification of Protein Subcellular Localization from High-Throughput Microscopy Images Using Deep Learning,
A comprehensive clinically informed map of dependencies in cancer cells and framework for target prioritization,
Agreement between two large pan-cancer CRISPR-Cas9 gene dependency data sets,
ArtSeg—Artifact segmentation and removal in brightfield cell microscopy images without manual pixel-level annotations,
Computational biology: deep learning,
Evaluating Very Deep Convolutional Neural Networks for Nucleus Segmentation from Brightfield Cell Microscopy Images,
Practical segmentation of nuclei in brightfield cell images with neural networks trained on fluorescently labelled samples,
Predicting base editing outcomes using position-specific sequence determinants
+1 more
Affiliated with 3
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