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Showing 1–10 of 10 results
Advanced filters: Author: Anthony Gitter Clear advanced filters
  • Mutational effect transfer learning (METL) is a protein language model framework that unites machine learning and biophysical modeling. Transformer-based neural networks are pretrained on biophysical simulation data to capture fundamental relationships between protein sequence, structure and energetics.

    • Sam Gelman
    • Bryce Johnson
    • Philip A. Romero
    ResearchOpen Access
    Nature Methods
    Volume: 22, P: 1868-1879
  • Shengchao Liu et al. present ProteinDT, a deep learning approach that can incorporate domain knowledge from textual descriptions into protein representation on a large scale.

    • Shengchao Liu
    • Yanjing Li
    • Anima Anandkumar
    Research
    Nature Machine Intelligence
    Volume: 7, P: 580-591
  • Tisza et al. carry out a sequencing-based analysis of wastewater samples from major cities, to detect and quantify hundreds of distinct pathogenic viruses, finding striking correlations between virus abundance and local clinical cases.

    • Michael Tisza
    • Sara Javornik Cregeen
    • Anthony W. Maresso
    ResearchOpen Access
    Nature Communications
    Volume: 14, P: 1-10
  • Biological processes are inherently dynamic and therefore capturing data about gene expression at multiple time points can provide valuable insights into biological systems. This Review discusses experimental and analytical considerations for studies of gene expression dynamics, and the possibilities for integration with other data sets.

    • Ziv Bar-Joseph
    • Anthony Gitter
    • Itamar Simon
    Reviews
    Nature Reviews Genetics
    Volume: 13, P: 552-564
  • In this DREAM challenge, 75 methods for the identification of disease-relevant modules from molecular networks are compared and validated with GWAS data. The authors provide practical guidelines for users and establish benchmarks for network analysis.

    • Sarvenaz Choobdar
    • Mehmet E. Ahsen
    • Daniel Marbach
    ResearchOpen Access
    Nature Methods
    Volume: 16, P: 843-852