Hong, Mun-Gwan

The human secretome

Uhlén, M., Karlsson, M. J., Hober, A., Svensson, A.-S., Scheffel, J., Kotol, D., Zhong, W., Tebani, A., Strandberg, L., Edfors, F., et al. (2019) · Science Signaling

Advanced Proteomics Techniques and ApplicationsBioinformatics and Genomic Networks

Cited by 495

Discovery of drug-omics associations in type 2 diabetes with generative deep-learning models

Allesøe, R. L., Lundgaard, A. T., Hernández Medina, R., Aguayo-Orozco, A., Johansen, J., Nissen, J. N., Brorsson, C., Mazzoni, G., Niu, L., Biel, J. H., et al. (2023) · Nature Biotechnology

Bioinformatics and Genomic NetworksComputational Drug Discovery Methods

Cited by 111

Predicting and elucidating the etiology of fatty liver disease: a machine learning modeling and validation study in the IMI DIRECT cohorts

Atabaki-Pasdar, N., Ohlsson, M., Viñuela, A., Frau, F., Pomares-Millan, H., Haid, M., Jones, A. G., Thomas, E. L., Koivula, R. W., Kurbasic, A., et al. (2020) · PLOS Medicine

Liver Disease Diagnosis and TreatmentAlcohol Consumption and Health Effects

Cited by 97

Analysis of lipid pathway genes indicates association of sequence variation near SREBF1/TOM1L2/ATPAF2 with dementia risk

Reynolds, C. A., Hong, M.-G., Eriksson, U. K., Blennow, K., Wiklund, F., Johansson, B., Malmberg, B., Berg, S., Alexeyenko, A., Grönberg, H., et al. (2010) · Human Molecular Genetics

Alzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

Cited by 57