Dataset Information

Shortcuts for faster image creation in PyMOL.

ABSTRACT: PyMOL is often used to generate images of biomolecular structures. Hundreds of parameters in PyMOL provide precise control over the appearance of structures. We developed 241 Python functions-called "shortcuts"-that extend and ease the use of PyMOL. A user runs a shortcut by entering its name at the PyMOL prompt. We clustered the shortcuts by functionality into 25 groups for faster look-up. One set of shortcuts generates new styles of molecular representation. Another group saves files with time stamps in the file names; the unique filenames avoid overwriting files that have already been developed. A third group submits search terms in the user's web browser. The help function prints the function's documentation to the command history window. This documentation includes the PyMOL commands that the user can reuse by copying and pasting onto the command line or into a script file. The shortcuts should save the average PyMOL user many hours per year searching for code fragments in their computer or on-line. STATEMENT FOR LAY PUBLIC: Computer-generated images of protein structures are vital to the interpretation of and communication about the molecular structure of proteins. PyMOL is a popular computer program for generating such images. We made a large collection of macros or shortcuts that save time by executing complex operations with a few keystrokes.

SUBMITTER: Mooers BHM

PROVIDER: S-EPMC6933860 | biostudies-literature | 2020 Jan

REPOSITORIES: biostudies-literature

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Shortcuts for faster image creation in PyMOL.

Mooers Blaine H M BHM

Protein science : a publication of the Protein Society 20191127 1

PyMOL is often used to generate images of biomolecular structures. Hundreds of parameters in PyMOL provide precise control over the appearance of structures. We developed 241 Python functions-called "shortcuts"-that extend and ease the use of PyMOL. A user runs a shortcut by entering its name at the PyMOL prompt. We clustered the shortcuts by functionality into 25 groups for faster look-up. One set of shortcuts generates new styles of molecular representation. Another group saves files with time ...[more]

PMID: 31710740

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Project description:BackgroundIt has been shown that AI models can learn race on medical images, leading to algorithmic bias. Our aim in this study was to enhance the fairness of medical image models by eliminating bias related to race, age, and sex. We hypothesise models may be learning demographics via shortcut learning and combat this using image augmentation.MethodsThis study included 44,953 patients who identified as Asian, Black, or White (mean age, 60.68 years ±18.21; 23,499 women) for a total of 194,359 chest X-rays (CXRs) from MIMIC-CXR database. The included CheXpert images comprised 45,095 patients (mean age 63.10 years ±18.14; 20,437 women) for a total of 134,300 CXRs were used for external validation. We also collected 1195 3D brain magnetic resonance imaging (MRI) data from the ADNI database, which included 273 participants with an average age of 76.97 years ±14.22, and 142 females. DL models were trained on either non-augmented or augmented images and assessed using disparity metrics. The features learned by the models were analysed using task transfer experiments and model visualisation techniques.FindingsIn the detection of radiological findings, training a model using augmented CXR images was shown to reduce disparities in error rate among racial groups (-5.45%), age groups (-13.94%), and sex (-22.22%). For AD detection, the model trained with augmented MRI images was shown 53.11% and 31.01% reduction of disparities in error rate among age and sex groups, respectively. Image augmentation led to a reduction in the model's ability to identify demographic attributes and resulted in the model trained for clinical purposes incorporating fewer demographic features.InterpretationThe model trained using the augmented images was less likely to be influenced by demographic information in detecting image labels. These results demonstrate that the proposed augmentation scheme could enhance the fairness of interpretations by DL models when dealing with data from patients with different demographic backgrounds.FundingNational Science and Technology Council (Taiwan), National Institutes of Health.

Dataset Information

Shortcuts for faster image creation in PyMOL.

Publications

Shortcuts for faster image creation in PyMOL.

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