Research Briefing

Filter By:

Article Type
Year
  • We developed a machine learning method that consistently and accurately identified dominant patterns of disease progression in amyotrophic later sclerosis (ALS), Alzheimer’s disease and Parkinson’s disease. Of note, the model was able to identify nonlinear progression trajectories in ALS, a finding that has clinical implications for patient stratification and clinical trial design.

    Research Briefing
  • A systematic procedure is reported for calculating effective carrier lifetimes in semiconductor crystals from first-principles calculations. Consideration of three major recombination mechanisms and the use of realistic carrier and defect densities is key in resolving the discrepancy between experimental measurements and lifetimes calculated from nonadiabatic molecular dynamics simulations.

    Research Briefing
  • A machine learning method is developed and used to predict the adsorption configurations and energies of complex molecules at the surfaces of transition metals and alloys. This method will be useful for investigating complex reaction networks at complex catalyst materials to understand and improve the performance of heterogeneous catalysts.

    Research Briefing
  • Inspired by active learning approaches, we have developed a computational method that selects minimal gene sets capable of reliably identifying cell-types and transcriptional states in large sets of single-cell RNA-sequencing data. As the procedure focuses computational resources on poorly classified cells, active support vector machine (ActiveSVM) scales to data sets with over one million cells.

    Research Briefing
  • A cell clustering model for spatial transcripts that uses cell embedding obtained by graph neural networks can be applied to datasets from multiple platforms for cell type or subpopulation identification and further analysis of the spatial microenvironment.

    Research Briefing
  • Determining the origin of engineered DNA can help to foster responsible innovation within the biotechnology community. A convolutional neural network approach that learns distances between engineered DNA sequences and various labs that could have created them is used to accurately predict the lab-of-origin.

    Research Briefing
  • Biomimetic nanoparticles can form complexes with proteins. Structural descriptors have been identified to predict nanoparticle–protein complex formation and their interaction sites. These descriptors include geometrical and graph-theoretical molecular features that are universally applicable to all nanoscale macromolecules of both organic and inorganic chemistries.

    Research Briefing
  • Stochastic modeling of antibody binding dynamics on patterned antigen substrates suggests the separation distance between adjacent antigens could be a control mechanism for the directed bipedal migration of bound antibodies.

    Research Briefing
  • A fully automated, high-throughput computational framework accurately predicts stable species in liquid solutions by computing the nuclear magnetic resonance chemical shifts. Data collected from the framework can provide fingerprints to guide the rational design of liquid solutions with optimal properties.

    Research Briefing
  • Networks offer a powerful visual representation of complex systems. Cartographs introduce a diverse set of network layouts for highlighting and visually inspecting chosen characteristics of a network. The resulting visualizations are interpretable and can be used to explore complex datasets, such as large-scale biological networks.

    Research Briefing