AIMC Topic: Proteomics

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Barley Grain Proteome Assessment Using Multi-Environment Trial Data and Machine Learning.

Journal of agricultural and food chemistry
Proteomics can be used to assess individual protein abundances, which could reflect genotypic and environmental effects and potentially predict grain/malt quality. In this study, 79 barley grain samples (genotype-location-year combinations) from Cali...

A multi-species benchmark for training and validating mass spectrometry proteomics machine learning models.

Scientific data
Training machine learning models for tasks such as de novo sequencing or spectral clustering requires large collections of confidently identified spectra. Here we describe a dataset of 2.8 million high-confidence peptide-spectrum matches derived from...

Assessing polyomic risk to predict Alzheimer's disease using a machine learning model.

Alzheimer's & dementia : the journal of the Alzheimer's Association
INTRODUCTION: Alzheimer's disease (AD) is the most common form of dementia in the elderly. Given that AD neuropathology begins decades before symptoms, there is a dire need for effective screening tools for early detection of AD to facilitate early i...

A deep learning framework for hepatocellular carcinoma diagnosis using MS1 data.

Scientific reports
Clinical proteomics analysis is of great significance for analyzing pathological mechanisms and discovering disease-related biomarkers. Using computational methods to accurately predict disease types can effectively improve patient disease diagnosis ...

Screening biomarkers for autism spectrum disorder using plasma proteomics combined with machine learning methods.

Clinica chimica acta; international journal of clinical chemistry
BACKGROUND AND AIMS: Autism spectrum disorder (ASD) is a common neurodevelopmental disorder in children. Early intervention is effective. Investigation of novel blood biomarkers of ASD facilitates early detection and intervention.

Omics in IgG4-related disease.

Chinese medical journal
Research on IgG4-related disease (IgG4-RD), an autoimmune condition recognized to be a unique disease entity only two decades ago, has processed from describing patients' symptoms and signs to summarizing its critical pathological features, and furth...

Generative AI Models for the Protein Scaffold Filling Problem.

Journal of computational biology : a journal of computational molecular cell biology
De novo protein sequencing is an important problem in proteomics, playing a crucial role in understanding protein functions, drug discovery, design and evolutionary studies, etc. Top-down and bottom-up tandem mass spectrometry are popular approaches ...

Artificial intelligence and omics in malignant gliomas.

Physiological genomics
Glioblastoma multiforme (GBM) is one of the most common and aggressive type of malignant glioma with an average survival time of 12-18 mo. Despite the utilization of extensive surgical resections using cutting-edge neuroimaging, and advanced chemothe...

LineageFilter: Improved Proteotyping of Complex Samples Using Metaproteomics and Machine Learning.

Journal of proteome research
Metaproteomics is a powerful tool to characterize how microbiota function by analyzing their proteic content by tandem mass spectrometry. Given the complexity of these samples, accurately assessing their taxonomical composition without prior informat...

GraphPI: Efficient Protein Inference with Graph Neural Networks.

Journal of proteome research
The integration of deep learning approaches in biomedical research has been transformative, enabling breakthroughs in various applications. Despite these strides, its application in protein inference is impeded by the scarcity of extensively labeled ...