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Cell Systems
Scopus
ISSN 2405-4712 · Elsevier BV · 12 artikel di indeks
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Artikel Jurnal Teks lengkap
Hotspot identifies informative gene modules across modalities of single-cell genomics
Two fundamental aims that emerge when analyzing single-cell RNA-seq data are identifying which genes vary in an informative manner and determining how these genes organize into modules. Here, we propose a general approach to these problems, called "Hotspot," that operates directly on a given metric…
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Learning the protein language: Evolution, structure, and function
Language models have recently emerged as a powerful machine-learning approach for distilling information from massive protein sequence databases. From readily available sequence data alone, these models discover evolutionary, structural, and functional organization across protein space. Using…
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MHCflurry 2.0: Improved Pan-Allele Prediction of MHC Class I-Presented Peptides by Incorporating Antigen Processing
Computational prediction of the peptides presented on major histocompatibility complex (MHC) class I proteins is an important tool for studying T cell immunity. The data available to develop such predictors have expanded with the use of mass spectrometry to identify naturally presented MHC ligands…
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nucleAIzer: A Parameter-free Deep Learning Framework for Nucleus Segmentation Using Image Style Transfer
Single-cell segmentation is typically a crucial task of image-based cellular analysis. We present nucleAIzer, a deep-learning approach aiming toward a truly general method for localizing 2D cell nuclei across a diverse range of assays and light microscopy modalities. We outperform the 739 methods…
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MONN: A Multi-objective Neural Network for Predicting Compound-Protein Interactions and Affinities
Computational approaches for understanding compound-protein interactions (CPIs) can greatly facilitate drug development. Recently, a number of deep-learning-based methods have been proposed to predict binding affinities and attempt to capture local interaction sites in compounds and proteins…
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Solo: Doublet Identification in Single-Cell RNA-Seq via Semi-Supervised Deep Learning
Single-cell RNA sequencing (scRNA-seq) measurements of gene expression enable an unprecedented high-resolution view into cellular state. However, current methods often result in two or more cells that share the same cell-identifying barcode; these "doublets" violate the fundamental premise of…
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Large-Scale Multi-omic Analysis of COVID-19 Severity
We performed RNA-seq and high-resolution mass spectrometry on 128 blood samples from COVID-19-positive and COVID-19-negative patients with diverse disease severities and outcomes. Quantified transcripts, proteins, metabolites, and lipids were associated with clinical outcomes in a curated…
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Ultra-High-Throughput Clinical Proteomics Reveals Classifiers of COVID-19 Infection
The COVID-19 pandemic is an unprecedented global challenge, and point-of-care diagnostic classifiers are urgently required. Here, we present a platform for ultra-high-throughput serum and plasma proteomics that builds on ISO13485 standardization to facilitate simple implementation in regulated…
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Artikel Jurnal
Fast and Flexible Protein Design Using Deep Graph Neural Networks
Protein structure and function is determined by the arrangement of the linear sequence of amino acids in 3D space. We show that a deep graph neural network, ProteinSolver, can precisely design sequences that fold into a predetermined shape by phrasing this challenge as a constraint satisfaction…