AIMC Topic: Proteomics

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The proteome landscape of the kingdoms of life.

Nature
Proteins carry out the vast majority of functions in all biological domains, but for technological reasons their large-scale investigation has lagged behind the study of genomes. Since the first essentially complete eukaryotic proteome was reported, ...

PB-Net: Automatic peak integration by sequential deep learning for multiple reaction monitoring.

Journal of proteomics
Mass spectrometry (MS) based proteomics has become an indispensable component of modern molecular and cellular biochemistry analysis. Multiple reaction monitoring (MRM) is one of the most well-established MS techniques for molecule detection and quan...

Enhancing Top-Down Proteomics Data Analysis by Combining Deconvolution Results through a Machine Learning Strategy.

Journal of the American Society for Mass Spectrometry
Top-down mass spectrometry (MS) is a powerful tool for the identification and comprehensive characterization of proteoforms arising from alternative splicing, sequence variation, and post-translational modifications. However, the complex data set gen...

Multiview learning for understanding functional multiomics.

PLoS computational biology
The molecular mechanisms and functions in complex biological systems currently remain elusive. Recent high-throughput techniques, such as next-generation sequencing, have generated a wide variety of multiomics datasets that enable the identification ...

Big data in IBD: big progress for clinical practice.

Gut
IBD is a complex multifactorial inflammatory disease of the gut driven by extrinsic and intrinsic factors, including host genetics, the immune system, environmental factors and the gut microbiome. Technological advancements such as next-generation se...

Combining feature selection and shape analysis uncovers precise rules for miRNA regulation in Huntington's disease mice.

BMC bioinformatics
BACKGROUND: MicroRNA (miRNA) regulation is associated with several diseases, including neurodegenerative diseases. Several approaches can be used for modeling miRNA regulation. However, their precision may be limited for analyzing multidimensional da...

A Multi-Omics Interpretable Machine Learning Model Reveals Modes of Action of Small Molecules.

Scientific reports
High-throughput screening and gene signature analyses frequently identify lead therapeutic compounds with unknown modes of action (MoAs), and the resulting uncertainties can lead to the failure of clinical trials. We developed an approach for uncover...

In silico spectral libraries by deep learning facilitate data-independent acquisition proteomics.

Nature communications
Data-independent acquisition (DIA) is an emerging technology for quantitative proteomic analysis of large cohorts of samples. However, sample-specific spectral libraries built by data-dependent acquisition (DDA) experiments are required prior to DIA ...

Joint learning improves protein abundance prediction in cancers.

BMC biology
BACKGROUND: The classic central dogma in biology is the information flow from DNA to mRNA to protein, yet complicated regulatory mechanisms underlying protein translation often lead to weak correlations between mRNA and protein abundances. This is pa...

Modeling aspects of the language of life through transfer-learning protein sequences.

BMC bioinformatics
BACKGROUND: Predicting protein function and structure from sequence is one important challenge for computational biology. For 26 years, most state-of-the-art approaches combined machine learning and evolutionary information. However, for some applica...