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How <i>Phytophthora</i> Effectors Disrupt Post-Translational Regulation in Plant Immunity: Canonical and Non-Canonical Mechanisms

Gilberto Muñoz-Pérez, Fátima Álvarez-Camarena, Julio Vega-Arreguin • Kinases and Phosphatases (2026)

Plant–pathogen interactions are shaped by dynamic regulatory processes that control immune signaling. Among these, post-translational modifications (PTMs) play central roles in modulating protein activity, stability, and interaction network...

DOAJ Open Access (DOAJ) DOI: 10.3390/kinasesphosphatases4020011

PRIMUS: Comprehensive proteomics of mouse intervertebral discs that inform novel biology and relevance to human disease modelling

Mateusz Kudelko, Peikai Chen, Vivian Tam, Ying Zhang et al. • Matrix Biology Plus (2021)

Mice are commonly used to study intervertebral disc (IVD) biology and related diseases such as IVD degeneration. Discs from both the lumbar and tail regions are used. However, little is known about compartmental characteristics in the diffe...

DOAJ Open Access (DOAJ) DOI: 10.1016/j.mbplus.2021.100082

Deep learning in omics: a survey and guideline

Zhiqiang Zhang, Yi Chen Zhao, Xiangke Liao, Wenqiang Shi et al. • Briefings in Functional Genomics (2018)

Omics, such as genomics, transcriptome and proteomics, has been affected by the era of big data. A huge amount of high dimensional and complex structured data has made it no longer applicable for conventional machine learning algorithms. Fo...

OPENALEX DOI: https://doi.org/10.1093/bfgp/ely030

Deep Learning in Proteomics

Bo Wen, Wen‐Feng Zeng, Yuxing Liao, Zhiao Shi et al. • PROTEOMICS (2020)

Proteomics, the study of all the proteins in biological systems, is becoming a data-rich science. Protein sequences and structures are comprehensively catalogued in online databases. With recent advancements in tandem mass spectrometry (MS)...

OPENALEX cc-by DOI: https://doi.org/10.1002/pmic.201900335 PMC: 7757195

Deep Learning and Its Applications in Biomedicine

Chensi Cao, Feng Liu, Hai Tan, Deshou Song et al. • Genomics Proteomics & Bioinformatics (2018)

Advances in biological and medical technologies have been providing us explosive volumes of biological and physiological data, such as medical images, electroencephalography, genomic and protein sequences. Learning from these data facilitat...

OPENALEX cc-by DOI: https://doi.org/10.1016/j.gpb.2017.07.003 PMC: 6000200

Machine learning in pain research

Jörn Lötsch, Alfred Ultsch • Pain (2017)

1. Introduction Pain and pain chronification are incompletely understood and unresolved medical problems that continue to have a high prevalence.14 It has been accepted that pain is a complex phenomenon.2,32,72 Contemporary methods of compu...

OPENALEX cc-by-nc-nd DOI: https://doi.org/10.1097/j.pain.0000000000001118 PMC: 5895117

A Protein‐Centric Strategy Coupled with Match‐Between‐Run Glycoproteomics Enables Discovery of Robust Site‐Specific Glycan Biomarkers for Hepatocellular Carcinoma

Lei Liu, Taiheng Ma, Qi Liu, He Zhu et al. • Advanced Science (2026)

ABSTRACT Dysregulated protein glycosylation is a hallmark of cancer, and systematic investigation of glycosylation patterns is crucial for identifying biomarkers. However, current glycoproteomic studies are constrained by the limited quanti...

DOAJ Open Access (DOAJ) DOI: 10.1002/advs.202516299

Machine learning in bioinformatics

Pedro Larrañaga, Borja Calvo, Roberto Santana, Concha Bielza et al. • Briefings in Bioinformatics (2006)

This article reviews machine learning methods for bioinformatics. It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochast...

OPENALEX Open Access DOI: https://doi.org/10.1093/bib/bbk007