Protein sequence representations yield only moderate Parkinson's disease classification performance (F1 0.704 max), with substantial class overlap and no significant differences between classical and embedding-based methods.
de la Fuente Garcia, S
2 Pith papers cite this work. Polarity classification is still indexing.
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q-bio.QM 2years
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Higher-quality automatic speech recognition transcripts enable simple lexical models to achieve better Alzheimer's disease detection performance on the ADReSSo dataset.
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Evaluating the Limitations of Protein Sequence Representations for Parkinson's Disease Classification
Protein sequence representations yield only moderate Parkinson's disease classification performance (F1 0.704 max), with substantial class overlap and no significant differences between classical and embedding-based methods.
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Impact of automatic speech recognition quality on Alzheimer's disease detection from spontaneous speech: a reproducible benchmark study with lexical modeling and statistical validation
Higher-quality automatic speech recognition transcripts enable simple lexical models to achieve better Alzheimer's disease detection performance on the ADReSSo dataset.