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Proteomics

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Predicting Outcomes of Preterm Neonates Post Intraventricular Hemorrhage.

International journal of molecular sciences
Intraventricular hemorrhage (IVH) in preterm neonates presents a high risk for developing posthemorrhagic ventricular dilatation (PHVD), a severe complication that can impact survival and long-term outcomes. Early detection of PHVD before clinical on...

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...

Machine learning-based clustering identifies obesity subgroups with differential multi-omics profiles and metabolic patterns.

Obesity (Silver Spring, Md.)
OBJECTIVE: Individuals living with obesity are differentially susceptible to cardiometabolic diseases. We hypothesized that an integrative multi-omics approach might improve identification of subgroups of individuals with obesity who have distinct ca...

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 ...

An improved cancer diagnosis algorithm for protein mass spectrometry based on PCA and a one-dimensional neural network combining ResNet and SENet.

The Analyst
Cancer is one of the most serious health problems worldwide. Because cancer has no specific symptoms in its early stages, it is often not diagnosed until it is in advanced stages, reducing the likelihood of successful treatment. Therefore, early diag...

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 ...

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...

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.

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 ...