AIMC Topic: Proteomics

Clear Filters Showing 281 to 290 of 537 articles

MSSort-DIA: A deep learning classification tool of the peptide precursors quantified by OpenSWATH.

Journal of proteomics
OpenSWATH is an analysis toolkit commonly used for data independent acquisition (DIA). Although the output of OpenSWATH is controlled at 1% false discovery rate (FDR), the output report still contains many peptide precursors with low similarity fragm...

Machine Learning analysis of high-grade serous ovarian cancer proteomic dataset reveals novel candidate biomarkers.

Scientific reports
Ovarian cancer is one of the most common gynecological malignancies, ranking third after cervical and uterine cancer. High-grade serous ovarian cancer (HGSOC) is one of the most aggressive subtype, and the late onset of its symptoms leads in most cas...

Using deep learning to annotate the protein universe.

Nature biotechnology
Understanding the relationship between amino acid sequence and protein function is a long-standing challenge with far-reaching scientific and translational implications. State-of-the-art alignment-based techniques cannot predict function for one-thir...

Protein-DNA/RNA interactions: Machine intelligence tools and approaches in the era of artificial intelligence and big data.

Proteomics
With the development of artificial intelligence (AI) technologies and the availability of large amounts of biological data, computational methods for proteomics have undergone a developmental process from traditional machine learning to deep learning...

Inferring protein expression changes from mRNA in Alzheimer's dementia using deep neural networks.

Nature communications
Identifying the molecular systems and proteins that modify the progression of Alzheimer's disease and related dementias (ADRD) is central to drug target selection. However, discordance between mRNA and protein abundance, and the scarcity of proteomic...

A knowledge graph to interpret clinical proteomics data.

Nature biotechnology
Implementing precision medicine hinges on the integration of omics data, such as proteomics, into the clinical decision-making process, but the quantity and diversity of biomedical data, and the spread of clinically relevant knowledge across multiple...

Precipitate-Supported Thermal Proteome Profiling Coupled with Deep Learning for Comprehensive Screening of Drug Target Proteins.

ACS chemical biology
Although thermal proteome profiling (TPP) acts as a popular modification-free approach for drug target deconvolution, some key problems are still limiting screening sensitivity. In the prevailing TPP workflow, only the soluble fractions are analyzed ...

Deep learning approaches for data-independent acquisition proteomics.

Expert review of proteomics
INTRODUCTION: Data-independent acquisition (DIA) is an emerging technology for large-scale proteomic studies. DIA data analysis methods are evolving rapidly, and deep learning has cut a conspicuous figure in this field.

DeepPhospho accelerates DIA phosphoproteome profiling through in silico library generation.

Nature communications
Phosphoproteomics integrating data-independent acquisition (DIA) enables deep phosphoproteome profiling with improved quantification reproducibility and accuracy compared to data-dependent acquisition (DDA)-based phosphoproteomics. DIA data mining he...

pValid 2: A deep learning based validation method for peptide identification in shotgun proteomics with increased discriminating power.

Journal of proteomics
Tandem mass spectrometry has been the principal method in shotgun proteomics for peptide and protein identification. However, incorrect identifications reported by proteome search engines are still unknown, and further validation methods are needed. ...