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NanoPyx: super-fast bioimage analysis powered by adaptive machine learning

Saraiva, B. M.; Cunha, I. M.; Brito, A. D.; Follain, G.; Portela, R.; Haase, R.; Pereira, P. M.; Jacquemet, G.; Henriques, R.

2023-08-14 bioinformatics
10.1101/2023.08.13.553080 bioRxiv
Show abstract

To overcome the challenges posed by large and complex microscopy datasets, we have developed NanoPyx, an adaptive bioimage analysis framework designed for high-speed processing. At the core of NanoPyx is the Liquid Engine, an agent-based machine-learning system that predicts acceleration strategies for image analysis tasks. Unlike traditional single-algorithm methods, the Liquid Engine generates multiple CPU and GPU code variations using a meta-programming system, creating a competitive environment where different algorithms are benchmarked against each other to achieve optimal performance under the user"s computational environment. In initial experiments focusing on super-resolution analysis methods, the Liquid Engine demonstrated an over 10-fold computational speed improvement by accurately predicting the ideal scenarios to switch between algorithmic implementations. NanoPyx is accessible to users through a Python library, code-free Jupyter notebooks, and a napari plugin, making it suitable for individuals regardless of their coding proficiency. Furthermore, the optimisation principles embodied by the Liquid Engine have broader implications, extending their applicability to various high-performance computing fields.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Bioinformatics
◐ 1204 papers in training set
Top 0.9%
22.5%
Similar papers in this journal
2
Nature Methods
○ 385 papers in training set
Top 0.4%
19.0%
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3
Biological Imaging
◐ 15 papers in training set
Top 0.1%
6.9%
Similar papers in this journal
4
Nature Communications
○ 5641 papers in training set
Top 26%
5.6%
Similar papers in this journal
50% of probability mass above
5
Bioinformatics Advances
◐ 203 papers in training set
Top 1%
5.3%
Similar papers in this journal
6
PLOS Computational Biology
● 1863 papers in training set
Top 8%
4.1%
7
Communications Biology
○ 993 papers in training set
Top 12%
2.0%
8
Journal of Microscopy
○ 20 papers in training set
Top 0.1%
1.8%
9
Patterns
○ 78 papers in training set
Top 1%
1.7%
10
NAR Genomics and Bioinformatics
◐ 242 papers in training set
Top 2%
1.7%
11
Scientific Reports
○ 3612 papers in training set
Top 56%
1.5%
12
Biophysical Journal
○ 631 papers in training set
Top 3%
1.4%
13
GigaScience
◐ 212 papers in training set
Top 3%
1.4%
14
Journal of Molecular Biology
○ 232 papers in training set
Top 3%
1.2%
15
Nucleic Acids Research
◐ 1281 papers in training set
Top 10%
1.2%
16
Briefings in Bioinformatics
◐ 354 papers in training set
Top 5%
1.2%
17
SoftwareX
○ 15 papers in training set
Top 0.2%
0.9%
18
PLOS ONE
● 5266 papers in training set
Top 57%
0.9%
19
Advanced Science
○ 286 papers in training set
Top 9%
0.9%
20
Computational and Structural Biotechnology Journal
● 242 papers in training set
Top 6%
0.9%
21
Protein Science
○ 246 papers in training set
Top 3%
0.9%
22
BMC Bioinformatics
○ 457 papers in training set
Top 5%
0.9%
23
eLife
● 5828 papers in training set
Top 65%
0.9%
24
Cell Reports Methods
○ 165 papers in training set
Top 3%
0.9%
25
Proceedings of the National Academy of Sciences
● 2444 papers in training set
Top 43%
0.6%
26
Frontiers in Immunology
○ 638 papers in training set
Top 10%
0.6%

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.