Dataset Information

Using Deep Learning to Extrapolate Protein Expression Measurements.

ABSTRACT: Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ?20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing. Here, a novel method is proposed using deep learning to extrapolate the observed protein expression values in label-free MS experiments to all proteins, leveraging gene functional annotations and RNA measurements as key predictive attributes. This method is tested on four datasets, including human cell lines and human and mouse tissues. This method predicts the protein expression values with average R2 scores between 0.46 and 0.54, which is significantly better than predictions based on correlations using the RNA expression data alone. Moreover, it is demonstrated that the derived models can be "transferred" across experiments and species. For instance, the model derived from human tissues gave a R2=0.51 when applied to mouse tissue data. It is concluded that protein abundances generated in label-free MS experiments can be computationally predicted using functional annotated attributes and can be used to highlight aberrant protein abundance values.

SUBMITTER: Barzine MP

PROVIDER: S-EPMC7757209 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature

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Using Deep Learning to Extrapolate Protein Expression Measurements.

Barzine Mitra Parissa MP Freivalds Karlis K Wright James C JC Opmanis Mārtiņš M Rituma Darta D Ghavidel Fatemeh Zamanzad FZ Jarnuczak Andrew F AF Celms Edgars E Čerāns Kārlis K Jonassen Inge I Lace Lelde L Vizcaíno Juan Antonio JA Choudhary Jyoti Sharma JS Brazma Alvis A Viksna Juris J

Proteomics 20201016 21-22

Mass spectrometry (MS)-based quantitative proteomics experiments typically assay a subset of up to 60% of the ≈20 000 human protein coding genes. Computational methods for imputing the missing values using RNA expression data usually allow only for imputations of proteins measured in at least some of the samples. In silico methods for comprehensively estimating abundances across all proteins are still missing. Here, a novel method is proposed using deep learning to extrapolate the observed prote ...[more]

PMID: 32937025

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Project description:PurposeVisual acuity (VA) is a critical component of the eye examination but is often only documented in electronic health records (EHRs) as unstructured free-text notes, making it challenging to use in research. This study aimed to improve on existing rule-based algorithms by developing and evaluating deep learning models to perform named entity recognition of different types of VA measurements and their lateralities from free-text ophthalmology notes: VA for each of the right and left eyes, with and without glasses correction, and with and without pinhole.DesignCross-sectional study.SubjectsA total of 319 756 clinical notes with documented VA measurements from approximately 90 000 patients were included.MethodsThe notes were split into train, validation, and test sets. Bidirectional Encoder Representations from Transformers (BERT) models were fine-tuned to identify VA measurements from the progress notes and included BERT models pretrained on biomedical literature (BioBERT), critical care EHR notes (ClinicalBERT), both (BlueBERT), and a lighter version of BERT with 40% fewer parameters (DistilBERT). A baseline rule-based algorithm was created to recognize the same VA entities to compare against BERT models.Main outcome measuresModel performance was evaluated on a held-out test set using microaveraged precision, recall, and F1 score for all entities.ResultsOn the human-annotated subset, BlueBERT achieved the best microaveraged F1 score (F1 = 0.92), followed by ClinicalBERT (F1 = 0.91), DistilBERT (F1 = 0.90), BioBERT (F1 = 0.84), and the baseline model (F1 = 0.83). Common errors included labeling VA in sections outside of the examination portion of the note, difficulties labeling current VA alongside a series of past VAs, and missing nonnumeric VAs.ConclusionsThis study demonstrates that deep learning models are capable of identifying VA measurements from free-text ophthalmology notes with high precision and recall, achieving significant performance improvements over a rule-based algorithm. The ability to recognize VA from free-text notes would enable a more detailed characterization of ophthalmology patient cohorts and enhance the development of models to predict ophthalmology outcomes.Financial disclosuresProprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Dataset Information

Using Deep Learning to Extrapolate Protein Expression Measurements.

Publications

Using Deep Learning to Extrapolate Protein Expression Measurements.

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