Browsing by Author "Dannenfelser, Ruth"

Now showing 1 - 4 of 4
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    A best-match approach for gene set analyses in embedding spaces
    (Cold Spring Harbor Laboratory Press, 2024) Li, Lechuan; Dannenfelser, Ruth; Cruz, Charlie; Yao, Vicky
    Embedding methods have emerged as a valuable class of approaches for distilling essential information from complex high-dimensional data into more accessible lower-dimensional spaces. Applications of embedding methods to biological data have demonstrated that gene embeddings can effectively capture physical, structural, and functional relationships between genes. However, this utility has been primarily realized by using gene embeddings for downstream machine-learning tasks. Much less has been done to examine the embeddings directly, especially analyses of gene sets in embedding spaces. Here, we propose an Algorithm for Network Data Embedding and Similarity (ANDES), a novel best-match approach that can be used with existing gene embeddings to compare gene sets while reconciling gene set diversity. This intuitive method has important downstream implications for improving the utility of embedding spaces for various tasks. Specifically, we show how ANDES, when applied to different gene embeddings encoding protein–protein interactions, can be used as a novel overrepresentation- and rank-based gene set enrichment analysis method that achieves state-of-the-art performance. Additionally, ANDES can use multiorganism joint gene embeddings to facilitate functional knowledge transfer across organisms, allowing for phenotype mapping across model systems. Our flexible, straightforward best-match methodology can be extended to other embedding spaces with diverse community structures between set elements.
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    Current progress and open challenges for applying deep learning across the biosciences
    (Springer Nature, 2022) Sapoval, Nicolae; Aghazadeh, Amirali; Nute, Michael G.; Antunes, Dinler A.; Balaji, Advait; Baraniuk, Richard; Barberan, C.J.; Dannenfelser, Ruth; Dun, Chen; Edrisi, Mohammadamin; Elworth, R.A. Leo; Kille, Bryce; Kyrillidis, Anastasios; Nakhleh, Luay; Wolfe, Cameron R.; Yan, Zhi; Yao, Vicky; Treangen, Todd J.
    Deep Learning (DL) has recently enabled unprecedented advances in one of the grand challenges in computational biology: the half-century-old problem of protein structure prediction. In this paper we discuss recent advances, limitations, and future perspectives of DL on five broad areas: protein structure prediction, protein function prediction, genome engineering, systems biology and data integration, and phylogenetic inference. We discuss each application area and cover the main bottlenecks of DL approaches, such as training data, problem scope, and the ability to leverage existing DL architectures in new contexts. To conclude, we provide a summary of the subject-specific and general challenges for DL across the biosciences.
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    Joint embedding of biological networks for cross-species functional alignment
    (Oxford University Press, 2023) Li, Lechuan; Dannenfelser, Ruth; Zhu, Yu; Hejduk, Nathaniel; Segarra, Santiago; Yao, Vicky
    Model organisms are widely used to better understand the molecular causes of human disease. While sequence similarity greatly aids this cross-species transfer, sequence similarity does not imply functional similarity, and thus, several current approaches incorporate protein–protein interactions to help map findings between species. Existing transfer methods either formulate the alignment problem as a matching problem which pits network features against known orthology, or more recently, as a joint embedding problem.We propose a novel state-of-the-art joint embedding solution: Embeddings to Network Alignment (ETNA). ETNA generates individual network embeddings based on network topological structure and then uses a Natural Language Processing-inspired cross-training approach to align the two embeddings using sequence-based orthologs. The final embedding preserves both within and between species gene functional relationships, and we demonstrate that it captures both pairwise and group functional relevance. In addition, ETNA’s embeddings can be used to transfer genetic interactions across species and identify phenotypic alignments, laying the groundwork for potential opportunities for drug repurposing and translational studies.https://github.com/ylaboratory/ETNA
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    Selective Neuronal Vulnerability in Alzheimer's Disease: A Network-Based Analysis
    (Elsevier, 2020) Roussarie, Jean-Pierre; Yao, Vicky; Rodriguez-Rodriguez, Patricia; Oughtred, Rose; Rust, Jennifer; Plautz, Zakary; Kasturia, Shirin; Albornoz, Christian; Wang, Wei; Schmidt, Eric F.; Dannenfelser, Ruth; Tadych, Alicja; Brichta, Lars; Barnea-Cramer, Alona; Heintz, Nathaniel; Hof, Patrick R.; Heiman, Myriam; Dolinski, Kara; Flajolet, Marc; Troyanskaya, Olga G.; Greengard, Paul
    A major obstacle to treating Alzheimer's disease (AD) is our lack of understanding of the molecular mechanisms underlying selective neuronal vulnerability, a key characteristic of the disease. Here, we present a framework integrating high-quality neuron-type-specific molecular profiles across the lifetime of the healthy mouse, which we generated using bacTRAP, with postmortem human functional genomics and quantitative genetics data. We demonstrate human-mouse conservation of cellular taxonomy at the molecular level for neurons vulnerable and resistant in AD, identify specific genes and pathways associated with AD neuropathology, and pinpoint a specific functional gene module underlying selective vulnerability, enriched in processes associated with axonal remodeling, and affected by amyloid accumulation and aging. We have made all cell-type-specific profiles and functional networks available at http://alz.princeton.edu. Overall, our study provides a molecular framework for understanding the complex interplay between A?, aging, and neurodegeneration within the most vulnerable neurons in AD.