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Showing 1–7 of 7 results
Advanced filters: Author: Namrata Anand Clear advanced filters

  • Rational protein design to achieve a given protein backbone conformation is needed to engineer specific functions. Here Anand et al. describe a machine learning method using a learned neural network potential for fixed-backbone protein design.

    • Namrata Anand
    • Raphael Eguchi
    • Po-Ssu Huang
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-11

  • Results for the final phase of the 1000 Genomes Project are presented including whole-genome sequencing, targeted exome sequencing, and genotyping on high-density SNP arrays for 2,504 individuals across 26 populations, providing a global reference data set to support biomedical genetics.

    • Adam Auton
    • Gonçalo R. Abecasis
    • Gonçalo R. Abecasis
    ResearchOpen Access
    Nature
    Volume: 526, P: 68-74

  • Predicting treatment response in cancer remains a highly complex task. Here, the authors develop Precily, a deep neural network framework to predict treatment response in cancer by considering gene expression, pathway activity estimates and drug features, and test this method in multiple datasets and preclinical models.

    • Smriti Chawla
    • Anja Rockstroh
    • Debarka Sengupta
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-15

  • In an inter-laboratory study, the authors compare the accuracy and performance of three optical density calibration protocols (colloidal silica, serial dilution of silica microspheres, and colony-forming unit (CFU) assay). They demonstrate that serial dilution of silica microspheres is the best of these tested protocols, allowing precise and robust calibration that is easily assessed for quality control and can also evaluate the effective linear range of an instrument.

    • Jacob Beal
    • Natalie G. Farny
    • Jiajie Zhou
    ResearchOpen Access
    Communications Biology
    Volume: 3, P: 1-29