Journal of the American Society for Mass Spectrometry
Apr 8, 2020
Top-down mass spectrometry (MS) is a powerful tool for the identification and comprehensive characterization of proteoforms arising from alternative splicing, sequence variation, and post-translational modifications. However, the complex data set gen...
Seasonal influenza viruses are constantly changing and produce a different set of circulating strains each season. Small genetic changes can accumulate over time and result in antigenically different viruses; this may prevent the body's immune system...
Journal of chemical theory and computation
Apr 7, 2020
Understanding the conformational characteristics of protein complexes in solution is crucial for a deeper insight in their biological function. Molecular dynamics simulations performed on high performance computing plants and with modern simulation t...
Human microbiota plays a key role in human health and growing evidence supports the potential use of microbiome as a predictor of various diseases. However, the high-dimensionality of microbiome data, often in the order of hundreds of thousands, yet ...
Predicting immunogenicity for biotherapies using patient and drug-related factors represents nowadays a challenging issue. With the growing ability to collect massive amount of data, machine learning algorithms can provide efficient predictive tools....
The aging process results in multiple traceable footprints, which can be quantified and used to estimate an organism's age. Examples of such aging biomarkers include epigenetic changes, telomere attrition, and alterations in gene expression and metab...
A means of quantifying continuous, free-living energy expenditure (EE) would advance the study of bioenergetics. The aim of this study was to apply a non-linear, machine learning algorithm (random forest) to predict minute level EE for a range of act...
Radiomics is an emerging field that involves extraction and quantification of features from medical images. These data can be mined through computational analysis and models to identify predictive image biomarkers that characterize intra-tumoral dyna...
Quasi-stable electrical fields in the EEG, called microstates carry information on the dynamics of large scale brain networks. Using machine learning techniques, we explored whether abnormalities in microstates can be used to classify patients with s...
Journal of chemical information and modeling
Apr 6, 2020
Computational high throughput screening (HTS) has emerged as a significant tool in material science to accelerate the discovery of new materials with target properties in recent years. However, despite many successful cases in which HTS led to the no...
Join thousands of healthcare professionals staying informed about the latest AI breakthroughs in medicine. Get curated insights delivered to your inbox.