Area of research
Computer Vision and Pattern Recognition · Ecology
Research interest
Research focused on Segmentation and Artificial intelligence, with related work in Breast cancer, SKP2, Closing (real estate). Notable publications include 'Combined Single‐Cell and Spatial Transcriptomics Reveal the Metabolic Evolvement of Breast Cancer during Early Dissemination', 'Automated vessel segmentation in lung CT and CTA images via deep neural networks', and 'Analysis of segmentation of lung parenchyma based on deep learning methods'.
METTL5 deficiency induces oligoasthenoteratozoospermia via impaired 18S rRNA m6A methylation in humans and mice
Deep Closing: Enhancing Topological Connectivity in Medical Tubular Segmentation
Enhancing Tandem Partial Hydrogenation–Hydrolysis of Diphenyl Ethers to Cyclohexanols with Surface-Oxidized MXene
Development and validation of a liquid chromatography-tandem mass spectrometry method for the determination of polymixin B1, B2, ile-B1, E1, and E2 in human plasma and its clinical pharmacokinetic application
Plasma bile acid profile analysis by liquid chromatography-tandem mass spectrometry and its application in healthy subjects and IBD patients
Combined Single‐Cell and Spatial Transcriptomics Reveal the Metabolic Evolvement of Breast Cancer during Early Dissemination
Connecting the Dots: N6‐Methyladenosine (m<sup>6</sup>A) Modification in Spermatogenesis
Segmentation of lung airways based on deep learning methods
Epididymis cell atlas in a patient with a sex development disorder and a novel NR5A1 gene mutation
Automated vessel segmentation in lung CT and CTA images via deep neural networks
Analysis of segmentation of lung parenchyma based on deep learning methods
Phytochemical library screening reveals betulinic acid as a novel Skp2‐SCF E3 ligase inhibitor in non–small cell lung cancer