Title
Biology Inspired Growth in Meta-Learning
Document Type
Conference Proceeding
Publication Date
Spring 4-4-2019
Abstract
Visually detecting peaks in LC-MS chromatograms is a straightforward task for humans, but biases and pattern recognition skill variability cause problems with reproducibility. Algorithmic review of clinical raw data without human intervention solves this but is difficult. Existing algorithms model and fit peaks within a semi-automated workflow. To increase confidence in the results, we propose an independent algorithm that uses raw chromatograms as input and classifies peaks using a convolutional neural network, similar to those used for image-based diagnostics. It classifies chromatograms as either peak, no-peak, high-intensity-no-peak, or small-peak, obtaining accuracies of 88.8% overall, 97.1% on non-peaks, and 93.8% on peaks, on 2.3 million chromatograms.
External Access URL
https://www.msacl.org/view_abstract/view_abstract_in_program.php?id=722&event=2019%20US
Recommended Citation
LaKemper, C.A., Wang, C., & Yoder, J.A., (2022). Biology inspired growth in meta-learning [Conference presentation]. Genetic and Evolutionary Computation Conference Companion (GECCO '22). Association for Computing Machinery, New York, NY, USA, 63-64. https://doi.org/10.1145/3520304.3533945