Author

Carla Agurto

IBM (United States)ORCID 0000-0002-0617-4488Unclaimed ORCID profile · follow to get notified of new preprints

Biology Disciplines

Click to filter author papers

Signature Assays, Organisms & Methods

Click to filter · or open tag

Organism:human· 3 Approach:statistical model· 2 Research Focus:benchmarking· 1 Research Focus:differential expression· 1 Approach:machine learning (classical)· 1 Assay:proteomics· 1

Frequent Co-Authors

Guillermo CecchiiD· 3 shared Raquel NoreliD· 2 shared Bryan K. HoiD· 1 shared Elif EyigöziD· 1 shared John Jeremy RiceiD· 1 shared Mary PietrowicziD· 1 shared Muhammad A. ParvaziD· 1 shared Nelly Alia-klein AiD· 1 shared

Activity by Year

2018-2023 · peak 2/yr

Co-Author Collaboration Network

10 top collaborators · 23 mutual co-author links

Click any collaborator node to inspect or filter shared papers

Focal author (center)Selected co-authorORCID co-authorUnlinked name

Curved chords = mutual co-authorship

3 shared papersNeuroscience

Guillermo CecchiORCID

Mutual co-authors in cluster

Author profile →

3 papers

Recent·Most cited

NeurosciencebioRxivPreprintJul 18, 2023

Speak and you shall predict: speech at initial cocaine abstinence as a biomarker of long-term drug use behavior

Carla Agurto, Guillermo Cecchi, Sarah G. King…·Rita Z. Goldstein

This paper shows that speech analysis using natural language processing can predict long-term cocaine use outcomes better than traditional measures in initially abstinent individuals with cocaine use disorder.

3 citations

humanstatistical model

𝕏 PostBluesky

NeurosciencebioRxivPreprintSep 18, 2018

Speech-based identification of L-DOPA ON/OFF state in Parkinson’s Disease subjects

Raquel Norel, Carla Agurto, John Jeremy Rice…·Guillermo Cecchi

This study uses speech analysis to classify Parkinson's disease patients' L-DOPA medication states with high accuracy.

6 citations

humanstatistical model

𝕏 PostBluesky

NeuroscienceInterspeech (USB)Peer-reviewedAug 28, 2018

Detection of Amyotrophic Lateral Sclerosis (ALS) via Acoustic Analysis

Raquel Norel, Mary Pietrowicz, Carla Agurto…·Guillermo Cecchi

This paper uses acoustic speech analysis to detect and assess ALS progression, achieving 79% accuracy for males and 83% for females using a mobile app dataset.

60 citationstop 1%

humanproteomics

𝕏 PostBluesky