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Proteomics

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In-silico generation of high-dimensional immune response data in patients using a deep neural network.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Technologies for single-cell profiling of the immune system have enabled researchers to extract rich interconnected networks of cellular abundance, phenotypical and functional cellular parameters. These studies can power machine learning approaches t...

Hepatoprotective Activity of Lignin-Derived Polyphenols Dereplicated Using High-Resolution Mass Spectrometry, In Vivo Experiments, and Deep Learning.

International journal of molecular sciences
Chronic liver diseases affect more than 1 billion people worldwide and represent one of the main public health issues. Nonalcoholic fatty liver disease (NAFLD) accounts for the majority of mortal cases, while there is no currently approved therapeuti...

Multi-dimensional feature recognition model based on capsule network for ubiquitination site prediction.

PeerJ
Ubiquitination is an important post-translational modification of proteins that regulates many cellular activities. Traditional experimental methods for identification are costly and time-consuming, so many researchers have proposed computational met...

A deep learning model based on whole slide images to predict disease-free survival in cutaneous melanoma patients.

Scientific reports
The application of deep learning on whole-slide histological images (WSIs) can reveal insights for clinical and basic tumor science investigations. Finding quantitative imaging biomarkers from WSIs directly for the prediction of disease-free survival...

AlphaPeptDeep: a modular deep learning framework to predict peptide properties for proteomics.

Nature communications
Machine learning and in particular deep learning (DL) are increasingly important in mass spectrometry (MS)-based proteomics. Recent DL models can predict the retention time, ion mobility and fragment intensities of a peptide just from the amino acid ...

GediNET for discovering gene associations across diseases using knowledge based machine learning approach.

Scientific reports
The most common approaches to discovering genes associated with specific diseases are based on machine learning and use a variety of feature selection techniques to identify significant genes that can serve as biomarkers for a given disease. More rec...

Current uses of artificial intelligence in the analysis of biofluid markers involved in corneal and ocular surface diseases: a systematic review.

Eye (London, England)
Corneal and ocular surface diseases (OSDs) carry significant psychosocial and economic burden worldwide. We set out to review the literature on the application of artificial intelligence (AI) and bioinformatics for analysis of biofluid biomarkers in ...

Analysis of the Human Protein Atlas Weakly Supervised Single-Cell Classification competition.

Nature methods
While spatial proteomics by fluorescence imaging has quickly become an essential discovery tool for researchers, fast and scalable methods to classify and embed single-cell protein distributions in such images are lacking. Here, we present the design...

How (Not) to Generate a Highly Predictive Biomarker Panel Using Machine Learning.

Journal of proteome research
This review "teaches" researchers how to make their lackluster proteomics data look really impressive, by applying an inappropriate but pervasive strategy that selects features in a biased manner. The strategy is demonstrated and used to build a clas...

Predicting genes associated with RNA methylation pathways using machine learning.

Communications biology
RNA methylation plays an important role in functional regulation of RNAs, and has thus attracted an increasing interest in biology and drug discovery. Here, we collected and collated transcriptomic, proteomic, structural and physical interaction data...