Tree-Based Learning on Amperometric Time Series Data Demonstrates High
Accuracy for Classification
- URL: http://arxiv.org/abs/2302.02650v1
- Date: Mon, 6 Feb 2023 09:44:53 GMT
- Title: Tree-Based Learning on Amperometric Time Series Data Demonstrates High
Accuracy for Classification
- Authors: Jeyashree Krishnan, Zeyu Lian, Pieter E. Oomen, Xiulan He, Soodabeh
Majdi, Andreas Schuppert, Andrew Ewing
- Abstract summary: We present a universal method for the classification with respect to diverse amperometric datasets using data-driven approaches in computational science.
We demonstrate a very high prediction accuracy (greater than or equal to 95%)
This is one of the first studies that propose a scheme for machine learning, and in particular, supervised learning on full amperometry time series data.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Elucidating exocytosis processes provide insights into cellular
neurotransmission mechanisms, and may have potential in neurodegenerative
diseases research. Amperometry is an established electrochemical method for the
detection of neurotransmitters released from and stored inside cells. An
important aspect of the amperometry method is the sub-millisecond temporal
resolution of the current recordings which leads to several hundreds of
gigabytes of high-quality data. In this study, we present a universal method
for the classification with respect to diverse amperometric datasets using
data-driven approaches in computational science. We demonstrate a very high
prediction accuracy (greater than or equal to 95%). This includes an end-to-end
systematic machine learning workflow for amperometric time series datasets
consisting of pre-processing; feature extraction; model identification;
training and testing; followed by feature importance evaluation - all
implemented. We tested the method on heterogeneous amperometric time series
datasets generated using different experimental approaches, chemical
stimulations, electrode types, and varying recording times. We identified a
certain overarching set of common features across these datasets which enables
accurate predictions. Further, we showed that information relevant for the
classification of amperometric traces are neither in the spiky segments alone,
nor can it be retrieved from just the temporal structure of spikes. In fact,
the transients between spikes and the trace baselines carry essential
information for a successful classification, thereby strongly demonstrating
that an effective feature representation of amperometric time series requires
the full time series. To our knowledge, this is one of the first studies that
propose a scheme for machine learning, and in particular, supervised learning
on full amperometry time series data.
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