33,273 research outputs found
A Comparative Study of Machine Learning Models for Tabular Data Through Challenge of Monitoring Parkinson's Disease Progression Using Voice Recordings
People with Parkinson's disease must be regularly monitored by their
physician to observe how the disease is progressing and potentially adjust
treatment plans to mitigate the symptoms. Monitoring the progression of the
disease through a voice recording captured by the patient at their own home can
make the process faster and less stressful. Using a dataset of voice recordings
of 42 people with early-stage Parkinson's disease over a time span of 6 months,
we applied multiple machine learning techniques to find a correlation between
the voice recording and the patient's motor UPDRS score. We approached this
problem using a multitude of both regression and classification techniques.
Much of this paper is dedicated to mapping the voice data to motor UPDRS scores
using regression techniques in order to obtain a more precise value for unknown
instances. Through this comparative study of variant machine learning methods,
we realized some old machine learning methods like trees outperform cutting
edge deep learning models on numerous tabular datasets.Comment: Accepted at "HIMS'20 - The 6th Int'l Conf on Health Informatics and
Medical Systems"; https://americancse.org/events/csce2020/conferences/hims2
ProtNN: Fast and Accurate Nearest Neighbor Protein Function Prediction based on Graph Embedding in Structural and Topological Space
Studying the function of proteins is important for understanding the
molecular mechanisms of life. The number of publicly available protein
structures has increasingly become extremely large. Still, the determination of
the function of a protein structure remains a difficult, costly, and time
consuming task. The difficulties are often due to the essential role of spatial
and topological structures in the determination of protein functions in living
cells. In this paper, we propose ProtNN, a novel approach for protein function
prediction. Given an unannotated protein structure and a set of annotated
proteins, ProtNN finds the nearest neighbor annotated structures based on
protein-graph pairwise similarities. Given a query protein, ProtNN finds the
nearest neighbor reference proteins based on a graph representation model and a
pairwise similarity between vector embedding of both query and reference
protein-graphs in structural and topological spaces. ProtNN assigns to the
query protein the function with the highest number of votes across the set of k
nearest neighbor reference proteins, where k is a user-defined parameter.
Experimental evaluation demonstrates that ProtNN is able to accurately classify
several datasets in an extremely fast runtime compared to state-of-the-art
approaches. We further show that ProtNN is able to scale up to a whole PDB
dataset in a single-process mode with no parallelization, with a gain of
thousands order of magnitude of runtime compared to state-of-the-art
approaches
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