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  1. Abstract Background

    Genotypes are strongly associated with disease phenotypes, particularly in brain disorders. However, the molecular and cellular mechanisms behind this association remain elusive. With emerging multimodal data for these mechanisms, machine learning methods can be applied for phenotype prediction at different scales, but due to the black-box nature of machine learning, integrating these modalities and interpreting biological mechanisms can be challenging. Additionally, the partial availability of these multimodal data presents a challenge in developing these predictive models.


    To address these challenges, we developed DeepGAMI, an interpretable neural network model to improve genotype–phenotype prediction from multimodal data. DeepGAMI leverages functional genomic information, such as eQTLs and gene regulation, to guide neural network connections. Additionally, it includes an auxiliary learning layer for cross-modal imputation allowing the imputation of latent features of missing modalities and thus predicting phenotypes from a single modality. Finally, DeepGAMI uses integrated gradient to prioritize multimodal features for various phenotypes.


    We applied DeepGAMI to several multimodal datasets including genotype and bulk and cell-type gene expression data in brain diseases, and gene expression and electrophysiology data of mouse neuronal cells. Using cross-validation and independent validation, DeepGAMI outperformed existing methods for classifying disease types, and cellular and clinical phenotypes, even using single modalities (e.g., AUC score of 0.79 for Schizophrenia and 0.73 for cognitive impairment in Alzheimer’s disease).


    We demonstrated that DeepGAMI improves phenotype prediction and prioritizes phenotypic features and networks in multiple multimodal datasets in complex brains and brain diseases. Also, it prioritized disease-associated variants, genes, and regulatory networks linked to different phenotypes, providing novel insights into the interpretation of gene regulatory mechanisms. DeepGAMI is open-source and available for general use.

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  2. Free, publicly-accessible full text available July 19, 2024
  3. Free, publicly-accessible full text available June 1, 2024
  4. Abstract

    Pressure‐sensitive‐adhesives (PSAs) are pervasive in electronic, automobile, packaging, and biomedical applications due to their ability to stick to numerous surfaces without undergoing chemical reactions. These materials are typically synthesized by the free radical copolymerization of alkyl acrylates and acrylic acid, leading to an ensemble of polymer chains with varying composition and molecular weight. Here, reversible addition−fragmentation chain‐transfer (RAFT) copolymerizations in a semi‐batch reactor are used to tailor the molecular architecture and bulk mechanical properties of acrylic copolymers. In the absence of cross‐links, the localization of acrylic acid toward the chain ends leads to microphase separation, creep resistance, and enhanced tack. However, in the presence of Al(acac)3crosslinker, the creep resistance remains unchanged and mostly the large‐strain mechanical properties are affected. This behavior is attributed to microphase separation, but also to a change in the energy required to break physical associations, and untangle and elongate associative polymers to large deformations.

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