Dataset Information

Multi-layered maps of neuropil with segmentation-guided contrastive learning.

ABSTRACT: Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produces representations of cells directly from 3D imagery and segmentations. When applied to volumes of human and mouse cortex, SegCLR enables accurate classification of cellular subcompartments and achieves performance equivalent to a supervised approach while requiring 400-fold fewer labeled examples. SegCLR also enables inference of cell types from fragments as small as 10 μm, which enhances the utility of volumes in which many neurites are truncated at boundaries. Finally, SegCLR enables exploration of layer 5 pyramidal cell subtypes and automated large-scale analysis of synaptic partners in mouse visual cortex.

SUBMITTER: Dorkenwald S

PROVIDER: S-EPMC10703674 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

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Multi-layered maps of neuropil with segmentation-guided contrastive learning.

Dorkenwald Sven S Li Peter H PH Januszewski Michał M Berger Daniel R DR Maitin-Shepard Jeremy J Bodor Agnes L AL Collman Forrest F Schneider-Mizell Casey M CM da Costa Nuno Maçarico NM Lichtman Jeff W JW Jain Viren V

Nature methods 20231120 12

Maps of the nervous system that identify individual cells along with their type, subcellular components and connectivity have the potential to elucidate fundamental organizational principles of neural circuits. Nanometer-resolution imaging of brain tissue provides the necessary raw data, but inferring cellular and subcellular annotation layers is challenging. We present segmentation-guided contrastive learning of representations (SegCLR), a self-supervised machine learning technique that produce ...[more]

PMID: 37985712

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Project description:MotivationAccurately identifying and predicting molecular properties is a crucial task in molecular machine learning, and the key lies in how to extract effective molecular representations. Contrastive learning opens new avenues for representation learning, and a large amount of unlabeled data enables the model to generalize to the huge chemical space. However, existing contrastive learning-based models face two challenges: (i) existing methods destroy the original molecular environment and ignore chemical prior information, and (ii) there is a lack of a prior knowledge to guide the prediction of molecular properties.ResultsIn this work, we propose a molecular property prediction framework called MolFCL, which consists of fragment-based contrastive learning and functional group-based prompt learning. Specifically, we introduced fragment-fragment interactions for the first time in the contrastive learning framework and designed a fragment-based augmented molecular graph that integrates the original chemical environment and fragment reactions. Furthermore, we proposed a novel functional group-based prompt learning during fine-tuning, which first incorporates functional group knowledge and the corresponding atomic signals, to improve molecular representation and provide interpretable analyses. The results show that MolFCL outperforms state-of-the-art baseline models on 23 molecular property prediction datasets. Moreover, visualizations show that MolFCL can learn to embed molecules into representations that can distinguish chemical properties. MolFCL can give higher weight to functional groups consistent with chemical knowledge during the prediction of molecular properties, which offers an interpretable ability of the model. Overall, MolFCL is a practically useful tool for molecular property prediction and assists drug scientists in designing drugs more effectively.Availability and implementationMolFCL is available at https://github.com/tangxiangcsu/MolFCLSupplementary.

Dataset Information

Multi-layered maps of neuropil with segmentation-guided contrastive learning.

Publications

Multi-layered maps of neuropil with segmentation-guided contrastive learning.

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