AIMC Topic: Proteomics

Clear Filters Showing 391 to 400 of 537 articles

Sequence-Based Prediction of Cysteine Reactivity Using Machine Learning.

Biochemistry
As one of the most intrinsically reactive amino acids, cysteine carries a variety of important biochemical functions, including catalysis and redox regulation. Discovery and characterization of cysteines with heightened reactivity will help annotate ...

Feature selection method based on support vector machine and shape analysis for high-throughput medical data.

Computers in biology and medicine
Proteomics data analysis based on the mass-spectrometry technique can provide a powerful tool for early diagnosis of tumors and other diseases. It can be used for exploring the features that reflect the difference between samples from high-throughput...

Deep Learning-Based Multi-Omics Integration Robustly Predicts Survival in Liver Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research
Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict HCC survival from multiple patient cohorts is lacking. To fill ...

Protein complexes, big data, machine learning and integrative proteomics: lessons learned over a decade of systematic analysis of protein interaction networks.

Expert review of proteomics
Elucidation of the networks of physical (functional) interactions present in cells and tissues is fundamental for understanding the molecular organization of biological systems, the mechanistic basis of essential and disease-related processes, and fo...

Gene Ontology-Based Analysis of Zebrafish Omics Data Using the Web Tool Comparative Gene Ontology.

Zebrafish
Gene Ontology (GO) analysis is a powerful tool in systems biology, which uses a defined nomenclature to annotate genes/proteins within three categories: "Molecular Function," "Biological Process," and "Cellular Component." GO analysis can assist in r...

A knowledge-based T2-statistic to perform pathway analysis for quantitative proteomic data.

PLoS computational biology
Approaches to identify significant pathways from high-throughput quantitative data have been developed in recent years. Still, the analysis of proteomic data stays difficult because of limited sample size. This limitation also leads to the practice o...

Machine Learning of Global Phosphoproteomic Profiles Enables Discrimination of Direct versus Indirect Kinase Substrates.

Molecular & cellular proteomics : MCP
Mass spectrometry allows quantification of tens of thousands of phosphorylation sites from minute amounts of cellular material. Despite this wealth of information, our understanding of phosphorylation-based signaling is limited, in part because it is...

Nano Random Forests to mine protein complexes and their relationships in quantitative proteomics data.

Molecular biology of the cell
Ever-increasing numbers of quantitative proteomics data sets constitute an underexploited resource for investigating protein function. Multiprotein complexes often follow consistent trends in these experiments, which could provide insights about thei...

Identifying the missing proteins in human proteome by biological language model.

BMC systems biology
BACKGROUND: With the rapid development of high-throughput sequencing technology, the proteomics research becomes a trendy field in the post genomics era. It is necessary to identify all the native-encoding protein sequences for further function and p...

MUMAL2: Improving sensitivity in shotgun proteomics using cost sensitive artificial neural networks and a threshold selector algorithm.

BMC bioinformatics
BACKGROUND: This work presents a machine learning strategy to increase sensitivity in tandem mass spectrometry (MS/MS) data analysis for peptide/protein identification. MS/MS yields thousands of spectra in a single run which are then interpreted by s...