Jensen, Lars Juhl

STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets

Szklarczyk, D., Gable, A. L., Lyon, D., Junge, A., Wyder, S., Huerta-Cepas, J., Simonovic, M., Doncheva, N. T., Morris, J. H., Bork, P., et al. (2019) · Nucleic Acids Research

Bioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

Cited by 17,749

STRING v10: protein–protein interaction networks, integrated over the tree of life

Szklarczyk, D., Franceschini, A., Wyder, S., Forslund, K., Heller, D., Huerta-Cepas, J., Simonovic, M., Roth, A., Santos, A., Tsafou, K. P., et al. (2015) · Nucleic Acids Research

Bioinformatics and Genomic NetworksGenomics and Phylogenetic Studies

Cited by 10,436

The STRING database in 2023: protein–protein association networks and functional enrichment analyses for any sequenced genome of interest

Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., Gable, A. L., Fang, T., Doncheva, N. T., Pyysalo, S., et al. (2023) · Nucleic Acids Research

Bioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

Cited by 9,726

The STRING database in 2021: customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets

Szklarczyk, D., Gable, A. L., Nastou, K. C., Lyon, D., Kirsch, R., Pyysalo, S., Doncheva, N. T., Legeay, M., Fang, T., Bork, P., et al. (2021) · Nucleic Acids Research

Bioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

Cited by 8,056

The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible

Szklarczyk, D., Morris, J. H., Cook, H., Kuhn, M., Wyder, S., Simonovic, M., Santos, A., Doncheva, N. T., Roth, A., Bork, P., et al. (2017) · Nucleic Acids Research

Bioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

Cited by 6,980

STRING v9.1: protein-protein interaction networks, with increased coverage and integration

Franceschini, A., Szklarczyk, D., Frankild, S., Kuhn, M., Simonovic, M., Roth, A., Lin, J., Minguez, P., Bork, P., von Mering, C., et al. (2012) · Nucleic Acids Research

Bioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

Cited by 4,398

Fast Genome-Wide Functional Annotation through Orthology Assignment by eggNOG-Mapper

Huerta-Cepas, J., Forslund, K., Coelho, L. P., Szklarczyk, D., Jensen, L. J., von Mering, C., & Bork, P. (2017) · Molecular Biology and Evolution

Genomics and Phylogenetic StudiesBioinformatics and Genomic Networks

Cited by 2,709

Mining electronic health records: towards better research applications and clinical care

Jensen, P. B., Jensen, L. J., & Brunak, S. (2012) · Nature Reviews Genetics

Electronic Health Records SystemsMachine Learning in Healthcare

Cited by 1,680

The SIDER database of drugs and side effects

Kuhn, M., Letunic, I., Jensen, L. J., & Bork, P. (2016) · Nucleic Acids Research

Pharmacovigilance and Adverse Drug ReactionsBiomedical Text Mining and Ontologies

Cited by 1,451

Drug target identification using side-effect similarity

Campillos, M., Kuhn, M., Gavin, A.-C., Jensen, L. J., & Bork, P. (2008) · Science

Computational Drug Discovery MethodsBioinformatics and Genomic Networks

Cited by 1,248

A side effect resource to capture phenotypic effects of drugs

Kuhn, M., Campillos, M., Letunic, I., Jensen, L. J., & Bork, P. (2010) · Molecular Systems Biology

Pharmacovigilance and Adverse Drug ReactionsBiomedical Text Mining and Ontologies

Cited by 899

Recalibrating equus evolution using the genome sequence of an early middle pleistocene horse

Orlando, L., Ginolhac, A., Zhang, G., Froese, D., Albrechtsen, A., Stiller, M., Schubert, M., Cappellini, E., Petersen, B., Moltke, I., et al. (2013) · Nature

Genomics and Phylogenetic StudiesVeterinary Equine Medical Research

Cited by 841

eggNOG v4.0: nested orthology inference across 3686 organisms

Powell, S., Forslund, K., Szklarczyk, D., Trachana, K., Roth, A., Huerta-Cepas, J., Gabaldón, T., Rattei, T., Creevey, C., Kuhn, M., et al. (2014) · Nucleic Acids Research

Genomics and Phylogenetic StudiesBioinformatics and Genomic Networks

Cited by 652

SuperTarget and Matador: resources for exploring drug-target relationships

Gunther, S., Kuhn, M., Dunkel, M., Campillos, M., Senger, C., Petsalaki, E., Ahmed, J., Urdiales, E. G., Gewiess, A., Jensen, L. J., et al. (2007) · Nucleic Acids Research

Computational Drug Discovery MethodsBiomedical Text Mining and Ontologies

Cited by 647

DISEASES: Text mining and data integration of disease–gene associations

Pletscher-Frankild, S., Pallejà, A., Tsafou, K., Binder, J. X., & Jensen, L. J. (2015) · Methods

Biomedical Text Mining and OntologiesBioinformatics and Genomic Networks

Cited by 591

eggNOG: automated construction and annotation of orthologous groups of genes

Jensen, L. J., Julien, P., Kuhn, M., von Mering, C., Muller, J., Doerks, T., & Bork, P. (2007) · Nucleic Acids Research

Genomics and Phylogenetic StudiesBioinformatics and Genomic Networks

Cited by 547

Unexplored therapeutic opportunities in the human genome

Oprea, T. I., Bologa, C. G., Brunak, S., Campbell, A., Gan, G. N., Gaulton, A., Gomez, S. M., Guha, R., Hersey, A., Holmes, J., et al. (2018) · Nature Reviews Drug Discovery

Computational Drug Discovery MethodsBioinformatics and Genomic Networks

Cited by 378

Pharos: collating protein information to shed light on the druggable genome

Nguyen, D.-T., Mathias, S., Bologa, C., Brunak, S., Fernandez, N., Gaulton, A., Hersey, A., Holmes, J., Jensen, L. J., Karlsson, A., et al. (2017) · Nucleic Acids Research

Computational Drug Discovery MethodsBioinformatics and Genomic Networks

Cited by 279

A Nondegenerate Code of Deleterious Variants in Mendelian Loci Contributes to Complex Disease Risk

Blair, D. R., Lyttle, C. S., Mortensen, J. M., Bearden, C. F., Jensen, A. B., Khiabanian, H., Melamed, R., Rabadan, R., Bernstam, E. V., Brunak, S., et al. (2013) · Cell

Genetic Associations and EpidemiologyGenomics and Rare Diseases

Cited by 215

Standardized benchmarking in the quest for orthologs

Quest for Orthologs consortium, Altenhoff, A. M., Boeckmann, B., Capella-Gutierrez, S., Dalquen, D. A., DeLuca, T., Forslund, K., Huerta-Cepas, J., Linard, B., Pereira, C., et al. (2016) · Nature Methods

Genomics and Phylogenetic StudiesBioinformatics and Genomic Networks

Cited by 214

The SPECIES and ORGANISMS Resources for Fast and Accurate Identification of Taxonomic Names in Text

Pafilis, E., Frankild, S. P., Fanini, L., Faulwetter, S., Pavloudi, C., Vasileiadou, A., Arvanitidis, C., & Jensen, L. J. (2013) · PLoS ONE

Biomedical Text Mining and OntologiesAdvanced Text Analysis Techniques

Cited by 206

Co-evolution of transcriptional and post-translational cell-cycle regulation

Jensen, L. J., Jensen, T. S., de Lichtenberg, U., Brunak, S., & Bork, P. (2006) · Nature

Gene Regulatory Network AnalysisGenomics and Chromatin Dynamics

Cited by 184

TISSUES 2.0: an integrative web resource on mammalian tissue expression

Palasca, O., Santos, A., Stolte, C., Gorodkin, J., & Jensen, L. J. (2018) · Database

Bioinformatics and Genomic NetworksBiomedical Text Mining and Ontologies

Cited by 182

TCRD and pharos 2021: mining the human proteome for disease biology

Sheils, T. K., Mathias, S. L., Kelleher, K. J., Siramshetty, V. B., Nguyen, D.-T., Bologa, C. G., Jensen, L. J., Vidović, D., Koleti, A., Schürer, S. C., et al. (2021) · Nucleic Acids Research

Computational Drug Discovery MethodsBioinformatics and Genomic Networks

Cited by 160