Digital Module 18: Automated Scoring https://ncme.elevate.commpartners.com

2020 ◽  
Vol 39 (3) ◽  
pp. 141-142
Author(s):  
Sue Lottridge ◽  
Amy Burkhardt ◽  
Michelle Boyer
Keyword(s):  
Automated Scoring ◽  
Digital Module

scholarly journals High-throughput behavioral screen in C. elegans reveals Parkinson’s disease drug candidates

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Salman Sohrabi ◽  
Danielle E. Mor ◽  
Rachel Kaletsky ◽  
William Keyes ◽  
Coleen T. Murphy
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Throughput ◽  
Potential Candidate ◽  
Automated Scoring ◽  
C Elegans ◽  
Drug Candidates ◽  
Branched Chain ◽  
Approved Drugs

AbstractWe recently linked branched-chain amino acid transferase 1 (BCAT1) dysfunction with the movement disorder Parkinson’s disease (PD), and found that RNAi-mediated knockdown of neuronal bcat-1 in C. elegans causes abnormal spasm-like ‘curling’ behavior with age. Here we report the development of a machine learning-based workflow and its application to the discovery of potentially new therapeutics for PD. In addition to simplifying quantification and maintaining a low data overhead, our simple segment-train-quantify platform enables fully automated scoring of image stills upon training of a convolutional neural network. We have trained a highly reliable neural network for the detection and classification of worm postures in order to carry out high-throughput curling analysis without the need for user intervention or post-inspection. In a proof-of-concept screen of 50 FDA-approved drugs, enasidenib, ethosuximide, metformin, and nitisinone were identified as candidates for potential late-in-life intervention in PD. These findings point to the utility of our high-throughput platform for automated scoring of worm postures and in particular, the discovery of potential candidate treatments for PD.

scholarly journals Automated scoring of fear-related behavior using EthoVision software

2009 ◽  
Vol 178 (2) ◽  
pp. 323-326 ◽  
Author(s):  
Jon Pham ◽  
Sara M. Cabrera ◽  
Carles Sanchis-Segura ◽  
Marcelo A. Wood
Keyword(s):  
Automated Scoring

A Review of Strategies for Validating Computer-Automated Scoring

2002 ◽  
Vol 15 (4) ◽  
pp. 391-412 ◽  
Author(s):  
Yongwei Yang ◽  
Chad W. Buckendahl ◽  
Piotr J. Juszkiewicz ◽  
Dennison S. Bhola
Keyword(s):  
Automated Scoring

Validation of automated scores of TOEFL iBT tasks against non-test indicators of writing ability

2010 ◽  
Vol 27 (3) ◽  
pp. 335-353 ◽  
Author(s):  
Sara Cushing Weigle
Keyword(s):  
Validity Evidence ◽  
Educational Testing ◽  
Automated Scoring ◽  
Self Assessment ◽  
Writing Ability ◽  
Testing Service ◽  
Independent Writing ◽  
Complex Skills ◽  
Toefl Ibt ◽  
Educational Testing Service

Automated scoring has the potential to dramatically reduce the time and costs associated with the assessment of complex skills such as writing, but its use must be validated against a variety of criteria for it to be accepted by test users and stakeholders. This study approaches validity by comparing human and automated scores on responses to TOEFL® iBT Independent writing tasks with several non-test indicators of writing ability: student self-assessment, instructor assessment, and independent ratings of non-test writing samples. Automated scores were produced using e-rater ®, developed by Educational Testing Service (ETS). Correlations between both human and e-rater scores and non-test indicators were moderate but consistent, providing criterion-related validity evidence for the use of e-rater along with human scores. The implications of the findings for the validity of automated scores are discussed.

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