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Improved protein structure prediction using potentials from deep learning.
Nature
Jan 15, 2020
Protein structure prediction can be used to determine the three-dimensional shape of a protein from its amino acid sequence. This problem is of fundamental importance as the structure of a protein largely determines its function; however, protein str...
Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India.
Scientific reports
Jan 14, 2020
In general, chest radiographs (CXR) have high sensitivity and moderate specificity for active pulmonary tuberculosis (PTB) screening when interpreted by human readers. However, they are challenging to scale due to hardware costs and the dearth of pro...
Transfer Learning in ECG Classification from Human to Horse Using a Novel Parallel Neural Network Architecture.
Scientific reports
Jan 13, 2020
Automatic or semi-automatic analysis of the equine electrocardiogram (eECG) is currently not possible because human or small animal ECG analysis software is unreliable due to a different ECG morphology in horses resulting from a different cardiac inn...
VAMPr: VAriant Mapping and Prediction of antibiotic resistance via explainable features and machine learning.
PLoS computational biology
Jan 13, 2020
Antimicrobial resistance (AMR) is an increasing threat to public health. Current methods of determining AMR rely on inefficient phenotypic approaches, and there remains incomplete understanding of AMR mechanisms for many pathogen-antimicrobial combin...
Automatic detection of perforators for microsurgical reconstruction.
Breast (Edinburgh, Scotland)
Jan 12, 2020
The deep inferior epigastric perforator (DIEP) is the most commonly used free flap in mastectomy reconstruction. Preoperative imaging techniques are routinely used to detect location, diameter and course of perforators, with direct intervention from ...
Automated screening of natural language in electronic health records for the diagnosis septic shock is feasible and outperforms an approach based on explicit administrative codes.
Journal of critical care
Jan 9, 2020
PURPOSE: Identification of patients for epidemiologic research through administrative coding has important limitations. We investigated the feasibility of a search based on natural language processing (NLP) on the text sections of electronic health r...
Shock, Septic
Predictive Value of Tests
Natural Language Processing
Epidemiologic Studies
Pattern Recognition, Automated
Databases, Factual
False Positive Reactions
Humans
Prospective Studies
International Classification of Diseases
Adult
Female
Software
Intensive Care Units
Sensitivity and Specificity
Electronic Health Records
Hospitalization
Middle Aged
Incidence
Young Adult
ProDCoNN: Protein design using a convolutional neural network.
Proteins
Jan 6, 2020
Designing protein sequences that fold to a given three-dimensional (3D) structure has long been a challenging problem in computational structural biology with significant theoretical and practical implications. In this study, we first formulated this...
pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens.
Cancer immunology research
Jan 6, 2020
Identification of neoantigens is a critical step in predicting response to checkpoint blockade therapy and design of personalized cancer vaccines. This is a cross-disciplinary challenge, involving genomics, proteomics, immunology, and computational a...
A personalized computational model predicts cancer risk level of oral potentially malignant disorders and its web application for promotion of non-invasive screening.
Journal of oral pathology & medicine : official publication of the International Association of Oral Pathologists and the American Academy of Oral Pathology
Jan 4, 2020
BACKGROUND: Despite their high accuracy to recognize oral potentially malignant disorders (OPMDs) with cancer risk, non-invasive oral assays are poor in discerning whether the risk is high or low. However, it is critical to identify the risk levels, ...
A lesson in implementation: A pre-post study of providers' experience with artificial intelligence-based clinical decision support.
International journal of medical informatics
Dec 30, 2019
BACKGROUND: To explore attitudes about artificial intelligence (AI) among staff who utilized AI-based clinical decision support (CDS).
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