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Found 5 papers, 1 papers with code
Comprehensive Overview of Bottom-up Proteomics using Mass Spectrometry
We expect this work to serve as a basic resource for new practitioners in the field of shotgun or bottom-up proteomics.
Deep metric learning improves lab of origin prediction of genetically engineered plasmids
We also demonstrate that we can perform few-shot-learning and obtain $76\%$ top-10 accuracy using only $10\%$ of the sequences.
Analysis of the first Genetic Engineering Attribution Challenge
The ability to identify the designer of engineered biological sequences -- termed genetic engineering attribution (GEA) -- would help ensure due credit for biotechnological innovation, while holding designers accountable to the communities they affect.
A Linear Transportation $\mathrm{L}^p$ Distance for Pattern Recognition
The transportation $\mathrm{L}^p$ distance, denoted $\mathrm{TL}^p$, has been proposed as a generalisation of Wasserstein $\mathrm{W}^p$ distances motivated by the property that it can be applied directly to colour or multi-channelled images, as well as multivariate time-series without normalisation or mass constraints.
PDE-Inspired Algorithms for Semi-Supervised Learning on Point Clouds
In this paper we extend the labels by minimising the constrained discrete $p$-Dirichlet energy.
