Too many roads not taken
Open accessAbstract
Most protein research focuses on those known before the human genome was mapped. Work on the slew discovered since, urge Aled M. Edwards and his colleagues.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The bias toward well-described proteins is confirmed across publications, patents, NIH funding, GWAS, mouse phenotype data, molecular probes, and STRING queries.
“The bias towards well-described proteins2 is confirmed not only with respect to publications but also with respect to patents, NIH funding patterns, GWAS and mouse phenotype data (data not shown), availability of molecular probes such as antibodies and small molecules, and even queries in the STRING18database”
From Unexplored therapeutic opportunities in the human genome (2018) · cites this paper as [2]
“Too many roads not taken” (page not supplied) documents a persistent publication bias toward a small group of proteins, and “biased approach” says antibodies and small molecules are available for only a handful of well-studied proteins. The paper discusses GWAS links and a knockout phenotype, but does not establish the claimed bias across GWAS or mouse phenotype data. Our copy also does not report the claimed patterns in patents, NIH funding, or STRING queries. (LLM, conf=0.87)
Experimental — accuracy not yet measuredResearch efforts continue to focus on genes and proteins of known function, with similar trends in gene and protein annotation databases.
“Analysis of publication trends has revealed that research efforts continue to focus on genes and proteins of known function, with similar trends seen in gene and protein annotation databases [2,4,5].”
From Functional unknomics: Systematic screening of conserved genes of unknown function (2023) · cites this paper as [4]
In “Too many roads not taken” (page not available), the paper reports that most protein research remains concentrated on proteins known before the human genome was mapped, supporting the publication-trends portion of the claim. Our copy does not report a corresponding trend in gene or protein annotation databases, or establish the same pattern for genes generally. (LLM, conf=0.86)
Experimental — accuracy not yet measuredScientific factors proposed for bias include a lack of specific reagents and a tendency to focus on proteins that are abundant and widely expressed.
“In addition, scientific factors have been proposed, including a lack of specific reagents like antibodies or small molecule inhibitors, and a tendency to focus on proteins that are abundant and widely expressed and so likely to be present in cell lines and model organisms [4,7,9].”
From Functional unknomics: Systematic screening of conserved genes of unknown function (2023) · cites this paper as [4]
The “biased approach” section says antibodies and chemical inhibitors are available for only a handful of well-studied proteins, supporting the lack-of-reagents explanation. The passages do not say that researchers favor proteins because they are abundant, widely expressed, or likely to occur in cell lines and model organisms. Page numbers are not provided in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredA large share of biomedical research and therapeutic development concentrates on a small portion of the human genome.
“A large proportion of biomedical research and the development of therapeutics is focused on a small fraction of the human genome.”
From Unexplored therapeutic opportunities in the human genome (2018) · cites this paper as [2]
Biological research largely ignores less well known, but potentially important, genes.
“that biological research largely ignores less well known, but potentially important, genes [2,4,6,7].”
From Functional unknomics: Systematic screening of conserved genes of unknown function (2023) · cites this paper as [4]
Scientific research tends to focus on well-studied proteins, leading to neglect of poorly understood genes.
“It has become clear that scientific research tends to focus on well-studied proteins, leading to a concern that poorly understood genes are unjustifiably neglected.”
From Functional unknomics: Systematic screening of conserved genes of unknown function (2023) · cites this paper as [4]
Research activity tends to focus on what has already been studied in depth.
“These observations provide further support to the notion that research activity tends to focus on what has already been studied in depth [2,4,27].”
From Functional unknomics: Systematic screening of conserved genes of unknown function (2023) · cites this paper as [4]
The first draft of the human genome did not broaden research focus to increase the types and numbers of human genes subjected to experimentation.
“Although the first draft of the human genome (24,25) was expected to increase the types and numbers of human genes that were subjected to experimentation, this broadening of research focus did not occur (Figure 3) (13,15–17,20,23).”
From Protection of the human gene research literature from contract cheating organizations known as research paper mills (2022) · cites this paper as [13]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Mutations in the hepatocyte nuclear factor-1α gene in maturity-onset diabetes of the young (MODY3)
Yamagata, K., Oda, N., Kaisaki, P. J., Menzel, S., Furuta, H., Vaxillaire, M., Southam, L., Cox, R., Lathrop, M. G., Boriraj, V. V., et al. (1996) · Nature · doi:10.1038/384455a0 · Cited by 1,610 · see citing works
Mutations in the hepatocyte nuclear factor-4α gene in maturity-onset diabetes of the young (MODY1)
Yamagata, K., Furuta, H., Oda, N., Kaisaki, P. J., Menzel, S., Cox, N. J., Fajans, S. S., Signorini, S. G., Stoffel, M., & Bell, G. I. (1996) · Nature · doi:10.1038/384458a0 · Cited by 975 · see citing works
Muscatelli, F., Strom, T. M., Walker, A. P., Zanaria, E., Récan, D., Meindl, A., Bardoni, B., Guioli, S., Zehetner, G., Rabl, W., et al. (1994) · Nature · doi:10.1038/372672a0 · Cited by 753 · see citing works
Achermann, J. C., Ito, M., Ito, M., Hindmarsh, P. C., & Jameson, J. L. (1999) · Nature Genetics · doi:10.1038/9629 · Cited by 709 · see citing works
Lee, S., Sadovsky, Y., Swirnoff, A. H., Polish, J. A., Goda, P., Gavrilina, G. B., & Milbrandt, J. (1996) · Science · doi:10.1126/science.273.5279.1219 · Cited by 480 · see citing works
Frye, S. V. (2010) · Nature Chemical Biology · doi:10.1038/nchembio.296 · Cited by 375 · see citing works
Self-citationThe (un)targeted cancer kinome
Fedorov, O., Müller, S., & Knapp, S. (2010) · Nature Chemical Biology · doi:10.1038/nchembio.297 · Cited by 293 · see citing works
Kinase requirements in human cells: I. Comparing kinase requirements across various cell types
Grueneberg, D. A., Degot, S., Pearlberg, J., Li, W., Davies, J. E., Baldwin, A. E., Endege, W. O., Doench, J. G., Sawyer, J., Hu, Y., et al. (2008) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0808019105 · Cited by 88 · see citing works
Minireview: Genomics Versus Orphan Nuclear Receptors--A Half-Time Report
Willson, T. M., & Moore, J. T. (2002) · Molecular Endocrinology · doi:10.1210/mend.16.6.0849 · Cited by 82 · see citing works
Self-citation
Flagged first, then by citations
Cited by
Sorted by citations
Mass-spectrometric exploration of proteome structure and function
Aebersold, R., & Mann, M. (2016) · Nature · doi:10.1038/nature19949 · Cited by 2,334 · see citing works
Borst, O., Walker, B., Münzer, P., Russo, A., Schmid, E., Faggio, C., Bigalke, B., Laufer, S., Gawaz, M., & Lang, F. J. W. (2013) · Cellular Physiology and Biochemistry · doi:10.1159/000350110 · Cited by 1,867 · see citing works
DeepDTA: deep drug–target binding affinity prediction
Öztürk, H., Özgür, A., & Özkırımlı, E. (2018) · Bioinformatics · doi:10.1093/bioinformatics/bty593 · Cited by 1,735 · see citing works
Rouillard, A. D., Gundersen, G. W., Fernandez, N. F., Wang, Z., Monteiro, C. D., McDermott, M. G., & Ma’ayan, A. (2016) · Database · doi:10.1093/database/baw100 · Cited by 1,515 · see citing works
High-throughput discovery of novel developmental phenotypes
The International Mouse Phenotyping Consortium, Dickinson, M. E., Flenniken, A. M., Ji, X., Teboul, L., Wong, M. D., White, J. K., Meehan, T. F., Weninger, W. J., Westerberg, H., et al. (2016) · Nature · doi:10.1038/nature19356 · Cited by 1,196 · see citing works
Evidence for Network Evolution in an Arabidopsis Interactome Map
Dreze, M., Carvunis, A., Charloteaux, B., Galli, M., Pevzner, S. J., Tasan, M., Ahn, Y., Balumuri, P., Barabási, A.-Ĺ., Bautista, V., et al. (2011) · Science · doi:10.1126/science.1203877 · Cited by 906 · see citing works
Structure and dynamics of molecular networks: A novel paradigm of drug discovery
Csermely, P., Korcsmáros, T., Kiss, H., London, G., & Nussinov, R. (2013) · Pharmacology & Therapeutics · doi:10.1016/j.pharmthera.2013.01.016 · Cited by 889 · see citing works
DGIdb 3.0: a redesign and expansion of the drug–gene interaction database
Cotto, K. C., Wagner, A. H., Feng, Y.-Y., Kiwala, S., Coffman, A. C., Spies, G., Wollam, A., Spies, N. C., Griffith, O. L., & Griffith, M. (2017) · Nucleic Acids Research · doi:10.1093/nar/gkx1143 · Cited by 866 · see citing works
Animal models of obesity and diabetes mellitus
Kleinert, M., Clemmensen, C., Hofmann, S. M., Moore, M. C., Renner, S., Woods, S. C., Huypens, P., Beckers, J., de Angelis, M. H., Schürmann, A., et al. (2018) · Nature Reviews Endocrinology · doi:10.1038/nrendo.2017.161 · Cited by 853 · see citing works
The promise and peril of chemical probes
Arrowsmith, C. H., Audia, J. E., Austin, C., Baell, J., Bennett, J., Blagg, J., Bountra, C., Brennan, P. E., Brown, P. J., Bunnage, M. E., et al. (2015) · Nature Chemical Biology · doi:10.1038/nchembio.1867 · Cited by 839 · see citing works
Self-citationPRESTO-Tango as an open-source resource for interrogation of the druggable human GPCRome
Kroeze, W. K., Sassano, M. F., Huang, X.-P., Lansu, K., McCorvy, J. D., Giguère, P. M., Sciaky, N., & Roth, B. L. (2015) · Nature Structural & Molecular Biology · doi:10.1038/nsmb.3014 · Cited by 755 · see citing works
An atlas of substrate specificities for the human serine/threonine kinome
Johnson, J. L., Yaron, T. M., Huntsman, E. M., Kerelsky, A., Song, J., Regev, A., Lin, T.-Y., Liberatore, K., Cizin, D. M., Cohen, B. M., et al. (2023) · Nature · doi:10.1038/s41586-022-05575-3 · Cited by 728 · see citing works
Multiple knockout mouse models reveal lincRNAs are required for life and brain development
Sauvageau, M., Goff, L. A., Lodato, S., Bonev, B., Groff, A. F., Gerhardinger, C., Sanchez-Gomez, D. B., Hacisuleyman, E., Li, E. V., Spence, M. C., et al. (2013) · eLife · doi:10.7554/elife.01749 · Cited by 675 · see citing works
A Call for Systematic Research on Solute Carriers
César‐Razquin, A., Snijder, B., Frappier‐Brinton, T., Isserlin, R., Gyimesi, G., Bai, X., Reithmeier, R. A., Hepworth, D., Hediger, M. A., Edwards, A. M., et al. (2015) · Cell · doi:10.1016/j.cell.2015.07.022 · Cited by 578 · see citing works
Self-citationIntegrative approaches for finding modular structure in biological networks
Mitra, K., Carvunis, A.-R., Ramesh, S. K., & Ideker, T. (2013) · Nature Reviews Genetics · doi:10.1038/nrg3552 · Cited by 535 · see citing works
Genome-wide generation and systematic phenotyping of knockout mice reveals new roles for many genes.
White, J. K., Gerdin, A.-K., Karp, N. A., Ryder, E., Buljan, M., Bussell, J. N., Salisbury, J., Clare, S., Ingham, N. J., Podrini, C., et al. (2013) · Cell · doi:10.1016/j.cell.2013.06.022 · Cited by 509 · see citing works
Family-wide chemical profiling and structural analysis of PARP and tankyrase inhibitors
Wahlberg, E., Karlberg, T., Kouznetsova, E., Markova, N., Macchiarulo, A., Thorsell, A. G., Pol, E., Frostell, Å., Ekblad, T., Öncü, D., et al. (2012) · Nature Biotechnology · doi:10.1038/nbt.2121 · Cited by 460 · see citing works
A repository of assays to quantify 10,000 human proteins by SWATH-MS
Rosenberger, G., Koh, C. C., Guo, T., Röst, H. L., Kouvonen, P., Collins, B. C., Heusel, M., Liu, Y., Caron, E., Vichalkovski, A., et al. (2014) · Scientific Data · doi:10.1038/sdata.2014.31 · Cited by 413 · see citing works
DGIdb 2.0: mining clinically relevant drug–gene interactions
Wagner, A. H., Coffman, A. C., Ainscough, B. J., Spies, N. C., Skidmore, Z. L., Campbell, K. M., Krysiak, K., Pan, D., McMichael, J. F., Eldred, J. M., et al. (2016) · Nucleic Acids Research · doi:10.1093/nar/gkv1165 · Cited by 413 · see citing works
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 · doi:10.1038/nrd.2018.14 · Cited by 378 · see citing works
Illuminating the dark phosphoproteome
Needham, E. J., Parker, B. L., Burykin, T., James, D. E., & Humphrey, S. J. (2019) · Science Signaling · doi:10.1126/scisignal.aau8645 · Cited by 322 · see citing works
Human SRMAtlas: A Resource of Targeted Assays to Quantify the Complete Human Proteome
Kusebauch, U., Campbell, D. S., Deutsch, E. W., Chu, C. S., Spicer, D. A., Brusniak, M.-Y., Slagel, J., Sun, Z., Stevens, J., Grimes, B., et al. (2016) · Cell · doi:10.1016/j.cell.2016.06.041 · Cited by 310 · see citing works
The pharmacological landscape and therapeutic potential of serine hydrolases
Bachovchin, D. A., & Cravatt, B. F. (2012) · Nature Reviews Drug Discovery · doi:10.1038/nrd3620 · Cited by 307 · see citing works
Erratum: Unexplored therapeutic opportunities in the human genome
Oprea, T. I., Bologa, C., 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 · doi:10.1038/nrd.2018.52 · Cited by 304 · see citing works
Fundamentals of protein interaction network mapping
Snider, J. D., Kotlyar, M., Saraon, P., Yao, Z., Jurišica, I., & Štagljar, I. (2015) · Molecular Systems Biology · doi:10.15252/msb.20156351 · Cited by 298 · see citing works
Modulators of 14-3-3 Protein–Protein Interactions
Stevers, L. M., Sijbesma, E., Botta, M., MacKintosh, C., Obšil, T., Landrieu, I., Cau, Y., Wilson, A. J., Karawajczyk, A., Eickhoff, J., et al. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b00574 · Cited by 289 · see citing works
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 · doi:10.1093/nar/gkw1072 · Cited by 279 · see citing works
Quantitative proteomics: challenges and opportunities in basic and applied research
Schubert, O. T., Röst, H. L., Collins, B. C., Rosenberger, G., & Aebersold, R. H. (2017) · Nature Protocols · doi:10.1038/nprot.2017.040 · Cited by 276 · see citing works
Large-scale investigation of the reasons why potentially important genes are ignored
Stoeger, T., Gerlach, M., Morimoto, R. I., & Nunes Amaral, L. A. (2018) · PLOS Biology · doi:10.1371/journal.pbio.2006643 · Cited by 261 · see citing works
Getting pharmaceutical R&D back on target
Bunnage, M. E. (2011) · Nature Chemical Biology · doi:10.1038/nchembio.581 · Cited by 259 · see citing works
Reproducible Quantification of Cancer-Associated Proteins in Body Fluids Using Targeted Proteomics
Hüttenhain, R., Soste, M., Selevsek, N., Röst, H. L., Sethi, A., Carapito, C., Farrah, T., Deutsch, E. W., Kusebauch, U., Moritz, R. L., et al. (2012) · Science Translational Medicine · doi:10.1126/scitranslmed.3003989 · Cited by 245 · see citing works
Personalized Cancer Medicine: Molecular Diagnostics, Predictive biomarkers, and Drug Resistance
de Castro, D. G., Clarke, P. A., Al‐Lazikani, B., & Workman, P. (2012) · Clinical Pharmacology & Therapeutics · doi:10.1038/clpt.2012.237 · Cited by 234 · see citing works
Bartha, I., di Iulio, J., Venter, J. C., & Telenti, A. (2017) · Nature Reviews Genetics · doi:10.1038/nrg.2017.75 · Cited by 233 · see citing works
Trimmer, J. S. (2015) · Neuron · doi:10.1016/j.neuron.2014.12.042 · Cited by 227 · see citing works
The Protein Interaction Landscape of the Human CMGC Kinase Group
Varjosalo, M. T., Keskitalo, S., van Drogen, A., Nurkkala, H., Vichalkovski, A., Aebersold, R. H., & Gstaiger, M. (2013) · Cell Reports · doi:10.1016/j.celrep.2013.03.027 · Cited by 221 · see citing works
Improving and Accelerating Drug Development for Nervous System Disorders
Pankevich, D. E., Altevogt, B. M., Dunlop, J., Gage, F. H., & Hyman, S. (2014) · Neuron · doi:10.1016/j.neuron.2014.10.007 · Cited by 205 · see citing works
Chang, J. W., Niphakis, M. J., Lum, K. M., Cognetta, A. B., Wang, C., Matthews, M. L., Niessen, S., Buczynski, M. W., Parsons, L. H., & Cravatt, B. F. (2012) · Chemistry & Biology · doi:10.1016/j.chembiol.2012.03.009 · Cited by 193 · see citing works
Understudied proteins: opportunities and challenges for functional proteomics
Kustatscher, G., Collins, T., Gingras, A., Guo, T., Hermjakob, H., Ideker, T., Lilley, K. S., Lundberg, E. K., Marcotte, E. M., Ralser, M., et al. (2022) · Nature Methods · doi:10.1038/s41592-022-01454-x · Cited by 191 · see citing works
Applications of chemogenomic library screening in drug discovery
Jones, L. H., & Bunnage, M. E. (2017) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2016.244 · Cited by 186 · see citing works
How Chemoproteomics Can Enable Drug Discovery and Development
Moellering, R. E., & Cravatt, B. F. (2012) · Chemistry & Biology · doi:10.1016/j.chembiol.2012.01.001 · Cited by 182 · see citing works
Targeted and Untargeted Proteomics Approaches in Biomarker Development
Sobsey, C. A., Ibrahim, S., Richard, V. R., Gaspar, V. P., Mitsa, G., Lacasse, V., Zahedi, R. P., Batist, G., & Borchers, C. H. (2019) · PROTEOMICS · doi:10.1002/pmic.201900029 · Cited by 174 · see citing works
Schubert, O. T., Mouritsen, J., Ludwig, C., Röst, H. L., Rosenberger, G., Arthur, P. K., Claassen, M., Campbell, D. S., Sun, Z., Farrah, T., et al. (2013) · Cell Host & Microbe · doi:10.1016/j.chom.2013.04.008 · Cited by 173 · see citing works
Advancing Biological Understanding and Therapeutics Discovery with Small-Molecule Probes
Schreiber, S. L., Kotz, J., Li, M., Aubé, J., Austin, C. P., Reed, J. C., Rosen, H., White, E. L., Sklar, L. A., Lindsley, C. W., et al. (2015) · Cell · doi:10.1016/j.cell.2015.05.023 · Cited by 168 · see citing works
Target 2035: probing the human proteome
Carter, A. J., Kraemer, O., Zwick, M., Mueller‐Fahrnow, A., Arrowsmith, C. H., & Edwards, A. M. (2019) · Drug Discovery Today · doi:10.1016/j.drudis.2019.06.020 · Cited by 164 · see citing works
Self-citationThe Roles of Jumonji-Type Oxygenases in Human Disease
Johansson, C., Tumber, A., Che, K., Cain, P., Nowak, R. P., Gileadi, C., & Oppermann, U. C. T. (2014) · Epigenomics · doi:10.2217/epi.13.79 · Cited by 163 · see citing works
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 · doi:10.1093/nar/gkaa993 · Cited by 160 · see citing works
In silico prediction of physical protein interactions and characterization of interactome orphans
Kotlyar, M., Pastrello, C., Pivetta, F., Sardo, A. L., Cumbaa, C. A., Li, H., Naranian, T., Niu, Y., Ding, Z., Vafaee, F., et al. (2014) · Nature Methods · doi:10.1038/nmeth.3178 · Cited by 160 · see citing works
Will Artificial Intelligence for Drug Discovery Impact Clinical Pharmacology?
Zhavoronkov, A., Vanhaelen, Q., & Oprea, T. I. (2020) · Clinical Pharmacology & Therapeutics · doi:10.1002/cpt.1795 · Cited by 159 · see citing works
Protein kinase biochemistry and drug discovery
Schwartz, P. A., & Murray, B. W. (2011) · Bioorganic Chemistry · doi:10.1016/j.bioorg.2011.07.004 · Cited by 157 · see citing works
Coordinating Knowledge Creation in Multidisciplinary Teams: Evidence from Early-Stage Drug Discovery
Ben‐Menahem, S. M., von Krogh, G., Erden, Z., & Schneider, A. (2015) · Academy of Management Journal · doi:10.5465/amj.2013.1214 · Cited by 157 · see citing works
Pseudo Natural Products—Chemical Evolution of Natural Product Structure
Karageorgis, G., Foley, D. J., Laraia, L., Brakmann, S., & Waldmann, H. (2021) · Angewandte Chemie International Edition · doi:10.1002/anie.202016575 · Cited by 157 · see citing works
Progress towards a public chemogenomic set for protein kinases and a call for contributions
Drewry, D. H., Wells, C. I., Andrews, D. M., Angell, R. M., Al‐Ali, H., Axtman, A. D., Capuzzi, S. J., Elkins, J. M., Ettmayer, P., Frederiksen, M. U., et al. (2017) · PLoS ONE · doi:10.1371/journal.pone.0181585 · Cited by 154 · see citing works
Self-citationProteomics meets the scientific method
Picotti, P., Bodenmiller, B., & Aebersold, R. H. (2012) · Nature Methods · doi:10.1038/nmeth.2291 · Cited by 149 · see citing works
A public-private partnership to unlock the untargeted kinome
Knapp, S., Arruda, P., Blagg, J., Burley, S. K., Drewry, D. H., Edwards, A. M., Fabbro, D., Gillespie, P., Gray, N. S., Küster, B., et al. (2012) · Nature Chemical Biology · doi:10.1038/nchembio.1113 · Cited by 139 · see citing works
Self-citationPharos 2023: an integrated resource for the understudied human proteome
Kelleher, K. J., Sheils, T. K., Mathias, S. L., Yang, J. J., Metzger, V. T., Siramshetty, V. B., Nguyen, D.-T., Jensen, L. J., Vidović, D., Schürer, S. C., et al. (2023) · Nucleic Acids Research · doi:10.1093/nar/gkac1033 · Cited by 137 · see citing works
Hidden in plain sight: what remains to be discovered in the eukaryotic proteome?
Wood, V., Lock, A., Harris, M. A., Rutherford, K., Bähler, J., & Oliver, S. G. (2019) · Open Biology · doi:10.1098/rsob.180241 · Cited by 132 · see citing works
Dissecting Antibodies with Regards to Linear and Conformational Epitopes
Forsström, B., Axnäs, B. B., Rockberg, J., Danielsson, H., Anna, B., & Uhlén, M. (2015) · PLoS ONE · doi:10.1371/journal.pone.0121673 · Cited by 132 · see citing works
Glimmers in illuminating the druggable genome
Rodgers, G., Austin, C., Anderson, J., Pawlyk, A., Colvis, C., Margolis, R., & Baker, J. (2018) · Nature Reviews Drug Discovery · doi:10.1038/nrd.2017.252 · Cited by 126 · see citing works
Mass spectrometric protein maps for biomarker discovery and clinical research
Liu, Y., Hüttenhain, R., Collins, B. C., & Aebersold, R. H. (2013) · Expert Review of Molecular Diagnostics · doi:10.1586/14737159.2013.845089 · Cited by 123 · see citing works
Inference and quantification of peptidoforms in large sample cohorts by SWATH-MS
Rosenberger, G., Liu, Y., Röst, H. L., Ludwig, C., Buil, A., Bensimon, A., Soste, M., Spector, T. D., Dermitzakis, E. T., Collins, B. C., et al. (2017) · Nature Biotechnology · doi:10.1038/nbt.3908 · Cited by 121 · see citing works
The why and how of phenotypic small-molecule screens
Eggert, U. (2013) · Nature Chemical Biology · doi:10.1038/nchembio.1206 · Cited by 120 · see citing works
Kinase Interaction Network Expands Functional and Disease Roles of Human Kinases
Buljan, M., Ciuffa, R., van Drogen, A., Vichalkovski, A., Mehnert, M., Rosenberger, G., Lee, S., Varjosalo, M. T., Pernas, L. E., Spegg, V., et al. (2020) · Molecular Cell · doi:10.1016/j.molcel.2020.07.001 · Cited by 115 · see citing works
Chemical Biology Probes from Advanced DNA-encoded Libraries
Salamon, H., Škopić, M. K., Jung, K., Bugain, O., & Brunschweiger, A. (2016) · ACS Chemical Biology · doi:10.1021/acschembio.5b00981 · Cited by 111 · see citing works
Murakami, Y., Tripathi, L. P., Prathipati, P., & Mizuguchi, K. (2017) · Current Opinion in Structural Biology · doi:10.1016/j.sbi.2017.02.005 · Cited by 110 · see citing works
Roberts, A. M., Ward, C. C., & Nomura, D. K. (2017) · Current Opinion in Biotechnology · doi:10.1016/j.copbio.2016.08.003 · Cited by 108 · see citing works
Seeding Collaborations to Advance Kinase Science with the GSK Published Kinase Inhibitor Set (PKIS)
Drewry, D. H., Willson, T. M., & Zuercher, W. J. (2014) · Current Topics in Medicinal Chemistry · doi:10.2174/1568026613666131127160819 · Cited by 106 · see citing works
Self-citationKnowledge graphs and their applications in drug discovery
MacLean, F. (2021) · Expert Opinion on Drug Discovery · doi:10.1080/17460441.2021.1910673 · Cited by 105 · see citing works
Enabling Large-Scale Design, Synthesis and Validation of Small Molecule Protein-Protein Antagonists
Koes, D. R., Khoury, K., Huang, Y., Wang, W., Biśta, M., Popowicz, G. M., Wolf, S., Holak, T. A., Dömling, A. S. S., & Camacho, C. J. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032839 · Cited by 104 · see citing works
The Kinase Chemogenomic Set (KCGS): An Open Science Resource for Kinase Vulnerability Identification
Wells, C. I., Al‐Ali, H., Andrews, D. M., Asquith, C. R. M., Axtman, A. D., Chung, M., Đikić, I., Ebner, D. V., Elkins, J. M., Ettmayer, P., et al. (2021) · International Journal of Molecular Sciences · doi:10.3390/ijms22020566 · Cited by 104 · see citing works
Self-citationAebersold, R. H., Bader, G. D., Edwards, A. M., Eyk, J. E. V., Kussmann, M., Qin, J., & Omenn, G. S. (2012) · Journal of Proteome Research · doi:10.1021/pr301151m · Cited by 102 · see citing works
Self-citationThe G protein-coupled receptors deorphanization landscape
Laschet, C., Dupuis, N., & Hanson, J. (2018) · Biochemical Pharmacology · doi:10.1016/j.bcp.2018.02.016 · Cited by 101 · see citing works
EPMA position paper in cancer: current overview and future perspectives
Grech, G., Zhan, X., Yoo, B. C., Bubnov, R. V., Hagan, S., Danesi, R., Vittadini, G., & Desiderio, D. M. (2015) · The EPMA Journal · doi:10.1186/s13167-015-0030-6 · Cited by 101 · see citing works
A wealth of discovery built on the Human Genome Project — by the numbers
Gates, A. J., Gysi, D. M., Kellis, M., & Barabási, A.-L. (2021) · Nature · doi:10.1038/d41586-021-00314-6 · Cited by 98 · see citing works
Chemical proteomics reveals the target landscape of 1,000 kinase inhibitors
Reinecke, M., Brear, P., Vornholz, L., Berger, B., Seefried, F., Wilhelm, S., Samaras, P., Gyenis, L., Litchfield, D. W., Médard, G., et al. (2023) · Nature Chemical Biology · doi:10.1038/s41589-023-01459-3 · Cited by 93 · see citing works
Network-based characterization of drug-regulated genes, drug targets, and toxicity
Kotlyar, M., Fortney, K., & Jurisica, I. (2012) · Methods · doi:10.1016/j.ymeth.2012.06.003 · Cited by 92 · see citing works
The fall of the innovation empire and its possible rise through open science
Gold, E. R. (2021) · Research Policy · doi:10.1016/j.respol.2021.104226 · Cited by 90 · see citing works
Schubert, K. O., Föcking, M., Prehn, J. H. M., & Cotter, D. (2011) · Molecular Psychiatry · doi:10.1038/mp.2011.123 · Cited by 90 · see citing works
Roth, B. L., & Kroeze, W. K. (2015) · Journal of Biological Chemistry · doi:10.1074/jbc.r115.654764 · Cited by 88 · see citing works
Lean Big Data integration in systems biology and systems pharmacology
Ma’ayan, A., Rouillard, A. D., Clark, N. R., Wang, Z., Duan, Q., & Kou, Y. (2014) · Trends in Pharmacological Sciences · doi:10.1016/j.tips.2014.07.001 · Cited by 88 · see citing works
Cowan‐Jacob, S. W., Jahnke, W., & Knapp, S. (2014) · Future Medicinal Chemistry · doi:10.4155/fmc.13.216 · Cited by 88 · see citing works
The proteomic landscape of genome-wide genetic perturbations
Messner, C. B., Demichev, V., Muenzner, J., Aulakh, S. K., Barthel, N., Röhl, A., Herrera-Domínguez, L., Egger, A.-S., Kamrad, S., Hou, J., et al. (2023) · Cell · doi:10.1016/j.cell.2023.03.026 · Cited by 84 · see citing works
Breast cancer in the era of integrating “Omics” approaches
Rossi, C., Cicalini, I., Cufaro, M. C., Consalvo, A., Upadhyaya, P., Sala, G., Antonucci, I., Boccio, P. D., Stuppia, L., & Laurenzi, V. D. (2022) · Oncogenesis · doi:10.1038/s41389-022-00393-8 · Cited by 82 · see citing works
Objective, Quantitative, Data-Driven Assessment of Chemical Probes
Antolín, A. A., Tym, J. E., Komianou, A., Collins, I., Workman, P., & Al‐Lazikani, B. (2017) · Cell chemical biology · doi:10.1016/j.chembiol.2017.11.004 · Cited by 81 · see citing works
Hüttenhain, R., Choi, M., de la Fuente, L. M., Oehl, K., Chang, C.-Y., Zimmermann, A., Malander, S., Olsson, H. L., Šurinová, S., Clough, T., et al. (2019) · Molecular & Cellular Proteomics · doi:10.1074/mcp.ra118.001221 · Cited by 80 · see citing works
Highley, M., Landuyt, B., Prenen, H., Harper, P., & de Bruijn, E. A. (2022) · Pharmacological Reviews · doi:10.1124/pharmrev.120.000121 · Cited by 75 · see citing works
Kveler, K., Starosvetsky, E., Ziv-Kenet, A., Kalugny, Y., Gorelik, Y., Shalev-Malul, G., Aizenbud-Reshef, N., Dubovik, T., Briller, M., Campbell, J. P., et al. (2018) · Nature Biotechnology · doi:10.1038/nbt.4152 · Cited by 74 · see citing works
Literature‐based discovery: Beyond the ABCs
Smalheiser, N. R. (2011) · Journal of the American Society for Information Science and Technology · doi:10.1002/asi.21599 · Cited by 74 · see citing works
A Novel Protein Kinase-Like Domain in a Selenoprotein, Widespread in the Tree of Life
Dudkiewicz, M., Szczepińska, T., Grynberg, M., & Pawłowski, K. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032138 · Cited by 73 · see citing works
Tumor protein D52 (TPD52) and cancer—oncogene understudy or understudied oncogene?
Byrne, J. A., Frost, S., Chen, Y., & Bright, R. K. (2014) · Tumor Biology · doi:10.1007/s13277-014-2006-x · Cited by 71 · see citing works
Functional unknomics: Systematic screening of conserved genes of unknown function
Rocha, J. J., Jayaram, S. A., Stevens, T. J., Muschalik, N., Shah, R. D., Emran, S., Robles, C., Freeman, M., & Munro, S. (2023) · PLOS Biology · doi:10.1371/journal.pbio.3002222 · Cited by 70 · see citing works
Prioritizing multiple therapeutic targets in parallel using automated DNA-encoded library screening
Machutta, C. A., Kollmann, C. S., Lind, K. E., Bai, X., Chan, P. F., Huang, J., Ballell, L., Belyanskaya, S. L., Besra, G. S., Barros-Aguirre, D., et al. (2017) · Nature Communications · doi:10.1038/ncomms16081 · Cited by 70 · see citing works
Predicting protein targets for drug-like compounds using transcriptomics
Pabon, N. A., Xia, Y., Estabrooks, S. K., Ye, Z., Herbrand, A. K., Süß, E., Biondi, R. M., Assimon, V. A., Gestwicki, J. E., Brodsky, J. L., et al. (2018) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1006651 · Cited by 70 · see citing works
Mari, T., Mösbauer, K., Wyler, E., Landthaler, M., Drosten, C., & Selbach, M. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00198 · Cited by 70 · see citing works
Integrating knowledge and omics to decipher mechanisms via large‐scale models of signaling networks
Garrido‐Rodríguez, M., Zirngibl, K., Ivanova, O., Lobentanzer, S., & Sáez-Rodríguez, J. (2022) · Molecular Systems Biology · doi:10.15252/msb.202211036 · Cited by 64 · see citing works
Review: High-performance computing to detect epistasis in genome scale data sets
Upton, A., Trelles, O., Cornejo-García, J. A., & Perkins, J. R. (2015) · Briefings in Bioinformatics · doi:10.1093/bib/bbv058 · Cited by 64 · see citing works
Identification and pharmacological characterization of succinate receptor agonists
Geubelle, P., Gilissen, J., Dilly, S., Poma, L., Dupuis, N., Laschet, C., Abboud, D., Inoue, A., Jouret, F., Pirotte, B., et al. (2017) · British Journal of Pharmacology · doi:10.1111/bph.13738 · Cited by 63 · see citing works
The Rise and Fall of a Scaffold: A Trend Analysis of Scaffolds in the Medicinal Chemistry Literature
Zdrazil, B., & Guha, R. (2017) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.7b00954 · Cited by 61 · see citing works
Targeted proteome investigation via selected reaction monitoring mass spectrometry
Maiolica, A., Jünger, M. A., Ezkurdia, I., & Aebersold, R. H. (2012) · Journal of Proteomics · doi:10.1016/j.jprot.2012.04.048 · Cited by 60 · see citing works
The 2022 Report on the Human Proteome from the HUPO Human Proteome Project
Omenn, G. S., Lane, L., Overall, C. M., Pineau, C., Packer, N. H., Cristea, I. M., Lindskog, C., Weintraub, S. T., Orchard, S. E., Roehrl, M. H. A., et al. (2022) · Journal of Proteome Research · doi:10.1021/acs.jproteome.2c00498 · Cited by 60 · see citing works
The era of high-quality chemical probes
Licciardello, M. P., & Workman, P. (2022) · RSC Medicinal Chemistry · doi:10.1039/d2md00291d · Cited by 60 · see citing works
- Authors
- Edwards, A. M., Isserlin, R., Bader, G. D., Frye, S. V., Willson, T. M., & Yu, F. H.
- Affiliations
- University of Toronto (CA)
- University of North Carolina at Chapel Hill (US)
- GlaxoSmithKline (United States) (US)
- Seoul National University (KR)
- Corresponding author
- Edwards, Aled M.
- Venue
- Nature
- Published
- 2011-02-09
- Volume
- 470
- Issue
- 7333
- Pages
- 163-165
- ISSN
- 0028-0836 (ISSN-L)
- 1476-4687
- OpenAlex
- W2012750155
- Cited by
- 411
- References
- 9
- Citations by year
- 2026: 15 · 2025: 23 · 2024: 22 · 2023: 39 · 2022: 29 · 2021: 26 · 2020: 21 · 2019: 20 · 2018: 29 · 2017: 24 · 2016: 27 · 2015: 34 · 2014: 28 · 2013: 32 · 2012: 27
- FWCI
- 13.49
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Nature (publishedVersion, public-domain)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Funders
- GlaxoSmithKline
- Seoul National University
- University of Toronto
- University of North Carolina at Chapel Hill
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Keywords
- protein research, human genome, proteomics, protein discovery, research priorities
- MeSH terms
- Humans, Ion Channels, Protein Kinases, Bibliometrics, Human Genome Project, Receptors, Cytoplasmic and Nuclear, Biomedical Research
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
