Unknown

Dataset Information

0

A versatile computational algorithm for time-series data analysis and machine-learning models

ABSTRACT: Here we introduce Local Topological Recurrence Analysis (LoTRA), a simple computational approach for analyzing time-series data. Its versatility is elucidated using simulated data, Parkinsonian gait, and in vivo brain dynamics. We also show that this algorithm can be used to build a remarkably simple machine-learning model capable of outperforming deep-learning models in detecting Parkinson’s disease from a single digital handwriting test.

SUBMITTER: Chomiak T 

PROVIDER: S-EPMC8578326 | biostudies-literature |

REPOSITORIES: biostudies-literature

Json Xml

Similar Datasets

Unknown A quantum machine learning algorithm based on generative models.
| S-EPMC6286170 | biostudies-other
Unknown A machine learning approach to predict metabolic pathway dynamics from time-series multiomics data.
| S-EPMC5974308 | biostudies-literature
Unknown Assessing Versatile Machine Learning Models for Glioma Radiogenomic Studies across Hospitals.
| S-EPMC8306149 | biostudies-literature
Unknown Learning Time Series Detection Models from Temporally Imprecise Labels.
| S-EPMC6241530 | biostudies-literature
Unknown Bioactuarial models of national mortality time series data.
| S-EPMC4191260 | biostudies-literature
Unknown Time Series Analysis and Forecasting with Automated Machine Learning on a National ICD-10 Database.
| S-EPMC7400312 | biostudies-literature
Unknown Human pol II promoter prediction: time series descriptors and machine learning.
| S-EPMC552959 | biostudies-literature
Unknown Human pol II promoter prediction: time series descriptors and machine learning
| S-EPMC1069632 | biostudies-literature
Unknown Precipitation isotope time series predictions from machine learning applied in Europe.
| S-EPMC8256050 | biostudies-literature
Unknown Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data.
| S-EPMC8280207 | biostudies-literature