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6 results for “Machine learning”

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  1. The YTHDF proteins modulate Alzheimer's disease-associated brain gene signatures.

    Molecular neurodegeneration · 2026 · 21(1)

    Gene signatures of Alzheimer's disease (AD) brains reflect the output of a complex interplay of genetic, epigenetic, epi-transcriptomic, and post-transcriptional regulations. To nominate candidate factors modulating these signatures, we developed a machine learning model to integrate cellular and molecular features …

    PMID 42811341 DOI ↗

  2. HydRA: Deep-learning models for predicting RNA-binding capacity from protein interaction association context and protein sequence.

    Molecular cell · 2023 · 83(14): 2595-2611.e11

    RNA-binding proteins (RBPs) control RNA metabolism to orchestrate gene expression and, when dysfunctional, underlie human diseases. Proteome-wide discovery efforts predict thousands of RBP candidates, many of which lack canonical RNA-binding domains (RBDs). Here, we present a hybrid ensemble RBP classifier …

    PMID 37421941 DOI ↗

  3. A multi-scale map of cell structure fusing protein images and interactions.

    Nature · 2021 · 600(7889): 536-542

    The cell is a multi-scale structure with modular organization across at least four orders of magnitude<sup>1</sup>. Two central approaches for mapping this structure-protein fluorescent imaging and protein biophysical association-each generate extensive datasets, but of distinct qualities and resolutions that are …

    PMID 34819669 DOI ↗

  4. Pooled CRISPR screens with imaging on microraft arrays reveals stress granule-regulatory factors.

    Nature methods · 2020 · 17(6): 636-642

    Genetic screens using pooled CRISPR-based approaches are scalable and inexpensive, but restricted to standard readouts, including survival, proliferation and sortable markers. However, many biologically relevant cell states involve cellular and subcellular changes that are only accessible by microscopic visualization, and …

    PMID 32393832 DOI ↗

  5. Predicting the functional states of human iPSC-derived neurons with single-cell RNA-seq and electrophysiology.

    Molecular psychiatry · 2016 · 21(11): 1573-1588

    Human neural progenitors derived from pluripotent stem cells develop into electrophysiologically active neurons at heterogeneous rates, which can confound disease-relevant discoveries in neurology and psychiatry. By combining patch clamping, morphological and transcriptome analysis on single-human neurons in vitro, we defined …

    PMID 27698428 DOI ↗

  6. Early specification of CD8+ T lymphocyte fates during adaptive immunity revealed by single-cell gene-expression analyses.

    Nature immunology · 2014 · 15(4): 365-372

    T lymphocytes responding to microbial infection give rise to effector cells that mediate acute host defense and memory cells that provide long-lived immunity, but the fundamental question of when and how these cells arise remains unresolved. Here we combined single-cell …

    PMID 24584088 DOI ↗

Datasets 3

GSE227455 GSE · GEO

RBP Footprint Grand Challenge: An evaluation of novel computational approaches to RNA-binding protein target prediction from structural data

Homo sapiens · GPL20301

GSE122649 GSE · GEO

Postmortem Cortex Samples Identify Distinct Molecular Subtypes of ALS: Retrotransposon Activation, Oxidative Stress, and Activated Glia [motor cortex]

Homo sapiens · GPL18573

GSE124439 GSE · GEO

Postmortem Cortex Samples Identify Distinct Molecular Subtypes of ALS: Retrotransposon Activation, Oxidative Stress, and Activated Glia

Homo sapiens · GPL16791

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