A controlled benchmark shows protein primary sequence representations achieve only moderate discriminative performance (best F1 0.704) for Parkinson's disease classification, with substantial class overlap and no significant differences across methods.
Mei, et al., Machine learning for Parkinson’s disease diagnosis
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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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Limitations of Sequence-Based Protein Representations for Parkinson's Disease Classification: A Leakage-Free Benchmark
A controlled benchmark shows protein primary sequence representations achieve only moderate discriminative performance (best F1 0.704) for Parkinson's disease classification, with substantial class overlap and no significant differences across 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.