Industry Context — Common BS Fingerprints in Science, Research & Laboratories
HumanCyc
(https://humancyc.org) 📸 Data Snapshot: June 20, 2026Analyze the raw signals below. How would a machine score this business’s credibility?
Here are the exact signals captured from up to six pages of the site — the same raw inputs the evaluation engine analyzed. They are grouped by signal type so you can weigh each the way the machine does.
🏗️ Semantic Structure — heading hierarchy & page identity (Info Density · Commodity Fingerprint)
HOMEPAGE HumanCyc: Encyclopedia of Human Genes and Metabolism (https://humancyc.org)
HumanCyc: Encyclopedia of Human Genes and Metabolism
HumanCyc is a curated database of human metabolic pathways, enzymes, metabolites, and reactions. Analyze omics data on its zoomable metabolic map.
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NAV_HEADER_HEADING_REPEATED_BODY_FOOTER BioCyc and Pathway Tools Publications (https://humancyc.org/publications.shtml)
BioCyc and Pathway Tools Publications
Publications from the BioCyc and Pathway Tools projects, featuring database enhancements and software advances.
HEADER_HEADING_REPEATED Not Found (https://humancyc.org/[ORGID]/class-tree/)
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📝 The Narrative — clean text per page (Info Density · Semantic Coherence)
HOMEPAGE (https://humancyc.org) HumanCyc: Encyclopedia of Human Genes and Metabolism
[IMG: HumanCyc] [H3] Heme, Not Just the Red Pigment in Blood. [H3] Heme, an iron-containing porphyrin, functions as a prosthetic group in numerous proteins. These range from components of the electron transport chain to many enzymes. No wonder the biosynthesis of heme is highly conserved in evolution! Learn More [IMG: Scanning Electron Microscope Image of Blood Cells] Scanning Electron Microscope Image of Blood Cells, put in the public domain by NCI/Donald BlissBruce Wetzel and Harry Schaefer. [H3] Navigate Metabolic Map for Pathways [H3] The Cellular Overview enables you to browse through a zoomable metabolic map diagram that displays pathways according to groups based on biological function, or classes of metabolites produced/consumed. Learn More [IMG: a portion of a Cellular Overview diagram, showing several pathway classes] Cellular overview image generated by Pathway Tools [H3] Important Mammalian Pathways: The Urea Cycle [H3] The urea cycle is responsible for mediating excretion of nitrogenous waste. It was the first metabolic pathway to be discovered, five years before the discovery of the TCA cycle. The cycle occurs across both the cytosol and the mitchondrion. Learn More [IMG: colorized electron micrograph of mitochondria in a mammalian lung cell] colorized selection from Mitochondria, mammalian lung - TEMput in the Public Domain by author, Louisa Howard. [H3] RouteSearch: Search for Paths through the Metabolic Network [H3] Search for lowest-cost paths through the metabolic network of the selected organism. Or, design lowest-cost pathways to novel compounds by adding reactions from MetaCyc. Learn More [IMG: a schematic showing 3 hypothetical routes from one compound to another, one that is very long, one that is short but most of the atoms from the starting compound do not end up in the target compound, and the best route, which is intermediate in length but conserves the atoms in the starting compound.] . [H3] A Pathway Linked to Central Nervous System Disorders [H3] The GABA shunt forms a closed loop in the CNS for conserving the supply of the excitatory neurotransmitter GABA. One of the enzymes of this pathway, 4-aminobutyrate aminotransferase is a target for the anti-epileptic drug vigabatrin. Learn More [IMG: Brain MRI] selection from Brain MRI Source: Dr. Leon Kaufman. UCSF. [H3] Metabolomics Data Analysis [H3] Multiple tools are available in this website for metabolomics data analysis. Learn More [IMG: Enrichment dialog displayed over SmartTable] SmartTable display generated by Pathway Tools. [H3] The Truth about the Thanksgiving Turkey... [H3] Of the 20 amino acids, at least 9 are essential. Essential L-tryptophan is required for serotonin, melatonin and niacin synthesis. Its requirement for NAD biosynthesis was discovered when humans stricken with pellagra recovered after the addition of tryptophan or niacin to their diets. Learn More [IMG: photo of a roast turkey] Some rights reserved by QUOI Media. [H3] Gene Expression Data Analysis [H3] Multiple tools are available in this website for analysis of gene expression data. Learn More [IMG: a portion of a cellular overview diagram, overlaid with a gene expression time series dataset. Detail popups are shown for three reactions in three different styles: a bar chart, a heatmap, and a line graph.] Cellular Overview Omics Viewer image generated by Pathway Tools. [H1] HumanCyc: Encyclopedia of Human Genes and Metabolism HumanCyc provides an encyclopedic reference on human metabolic pathways, the human genome, and human metabolites. HumanCyc is part of the larger BioCyc collection of thousands of Pathway/Genome Databases for sequenced genomes. Click the "Change Current Database" button above to explore the available databases. For more information on HumanCyc, see our article "Computational prediction of human metabolic pathways from the complete human genome" in Genome Biology. User Guide Try Free Request Free Classroom Use [H2] What people are saying... "BsubCyc is a tool of the utmost value." [IMG: Penn State] Paul BabitzkeProf. of Biochemistry& Molecular Biology "My lab uses these resources on a daily basis." [IMG: University of Wisconsin] Patricia Kiley,Professor and Chair,Dep't. of Biomolecular Chemistry "We rely on BioCyc's Gene Pages and Overview Diagrams almost daily." [IMG: University of Minnesota] Arkady KhodurskyAssoc. Prof. Biochemistry "We use BioCyc and MetaCyc extensively to investigate the metabolic and regulatory processes of organisms we study." [IMG: Pacific Northwest National Lab] William Cannon, Team LeadComputational Biology "BioCyc is the go-to resource of knowledge and tools for Ginkgo scientists." [IMG: Ginkgo Bioworks] "BioCyc is a tremendous resource for pathway analysis in metabolomics." [IMG: University of Georgia] Art Edison, Dept of Genetics "We make extensive use of the BioCyc full metabolic network diagram for omics data analysis." [IMG: Great Lakes Bioenergy] Timothy J. Donohue, Director "I have not found another database that has a better interface than BioCyc." [IMG: University of Michigan] Gary B. Huffnagle, ProfessorMicrobiology and Immunology See more BioCyc testimonials [H2] Learning Library [H3] Tutorial Videos Tutorial #1: Introduction to BioCyc Quick Introduction to BioCyc (4:30) Searching in BioCyc (17:00) Genes (15:00) Genome Browser (11:45) Pathways (13:19) Reactions (3:37) Compounds (6:42) Tutorial #2: Introduction to SmartTables The following Tutorial will guide you through SmartTables, which enable you to create, upload, share, and analyze sets of genes, metabolites, pathways, and sequence sites. The Tutorial is broken up into parts, ranging from basic operations to more advanced uses such as gene expression analysis and metabolomics. SmartTables Overview (4:30) SmartTables Basics (12:43) SmartTables Transformations (8:08) SmartTables Import and Export (9:00) SmartTables Gene Expression Analysis (7:40) Metabolomics Analysis with SmartTables (6:45) Tutorial #3: Zoomable Metabolic Map, Comparative Tools, Regulatory Network This tutorial introduces users to many of the advanced tools available on the BioCyc.org website for navigating cellular networks, analyzing large-scale datasets, and comparing organisms. The Cellular Overview: Navigating metabolic networks (17:15) Comparative Genomics (21:06) Ortholog Viewing (10:02) The Regulatory Overview -- exploring transcriptional regulatory networks (15:21) Tutorial #4: Omics Data Analysis This tutorial will show you how to use BioCyc's tools for omics data analysis, including the cellular omics viewer, the omics dashboard, and other tools. Transcriptomics Analysis Tools [brief tour (2:40) Metabolomics Analysis Tools (3:26) [click here for a detailed tutorial] Omics Dashboard (16:35) Omics Dashboard Part (24:29) Tutorial #5: Pathway Collages Pathway collages are multi-pathway diagrams that you can customize by, for example, overlaying omics data, altering the relative positions of pathways, and modifying connections among pathways. Learn how to generate, customize and export high-quality pathway-collage diagrams showing collections of user-specified pathways. Pathway Collages (22:39) Tutorial #6: Creating a Pathway/Genome Database Learn the entire process of building a BioCyc-like Pathway/Genome Database (PGDB) for an organism with a sequenced and annotated genome. Build a PGDB for your own lab or for the whole scientific community. Part 1A: Introduction to Database Building and Pathologic (14:04) Part 1B: Building a Database: Detailed Pathologic Example (23:53) Part 2A: General Editing Strategies (8:00) Part 2B: Creating and Editing Reactions and Compounds (17:32) Part 2C: Updating Proteins, Citations, GO Terms, and Enzymatic Reactions (26:10) Part 2D: Making and Editing Pathways (9:42) Tutorial #7: Using the Structured Advanced Query Page An introduction to the Structured Advanced Query Page, which allows complex queries and queries across one or more databases in the BioCyc collection. You'll learn about:The basic steps of setting up an advanced query; Four examples of increasingly complex queries, including how to query across multiple databases; Where to learn more about the structure of BioCyc databases. Structured Advanced Query Page Quick Introduction (6:27) Structured Advanced Query Page Full Tutorial (42:15)
SUB-PAGE · THIN (https://humancyc.org/[ORGID]/select-gen-el/) Not Found
Not FoundThe server doesn't know how to respond to http://humancyc.org/%5BORGID%5D/select-gen-el/. Please make sure you have the correct URL.Host: biocyc18Report Errors or Provide Feedback Page generated by Pathway Tools version 29.5 (software by SRI International) on Sat Jun 20, 2026, BIOCYC18.EcoCyc version 29.6.
SUB-PAGE (https://humancyc.org/publications.shtml) BioCyc and Pathway Tools Publications
[H1] BioCyc and Pathway Tools Publications If you use BioCyc databases or the Pathway Tools software in your research, we ask that you cite relevant publications from the following list. Like to learn more? View one or more BioCyc Web seminars. [H2] Publications on the Full BioCyc Database Collection [BioCyc19] Karp, P.D., et al., The BioCyc collection of microbial genomes and metabolic pathways Briefings in Bioinformatics (2019). [H2] Publications on the EcoCyc Database [EcoCyc25] Karp, P.D., Paley, S., Caspi, R., Kothari, A., Krummenacker, M., Midford, P., Moore, L.R., Subhraveti, P., Gama-Castro, S., Tierrafria, V., Paloma, L., Muñiz-Rascado, L., Bonavides-Martinez, C., Santos-Zavaleta, A., Mackie, A., Sun, G., Ahn-Horst, T.A., Choi, H., Covert, M.W., Collado-Vides, J. and Paulsen, I., The EcoCyc database (2025) EcoSal Plus doi:10.1128/ecosalplus.ESP-0019-2024 PMID:40304522 [EcoCyc24] Moore, L.R.,Caspi, R., Boyd, D., Berkem, M., Mackie, A., Paley, S., Karp, P.D. Revisiting the y-ome of Escherichia coli Nucleic Acids Research doi::10.1093/nar/gkae857 (2024) [EcoCyc21] Keseler, I.M., Gama-Castro, S., Mackie, A., Billington, R., Billington, R., Caspi, R., Kothari, A., Krummenacker, M., Midford, P., Muñiz-Rascado, L., Ong, W., Paley, S., Santos-Zavaleta, A., Subhraveti, P., Tierrafria, V., Wolfe, A., Collado-Vides, J., Paulsen, I., and Karp, P.D. The EcoCyc database in 2021 Front Microbiol 2021; 12: 711077. See also: The EcoCyc publications page. [H2] Publications on the MetaCyc Database [MetaCyc20] R.Caspi, R.Billington, I.M. Keseler, A.Kothari, M.Krummenacker, P.E.Midford, W.K. Ong, S.Paley, P.Subhraveti, P.D. Karp The MetaCyc database of metabolic pathways and enzymes - a 2019 update Nucleic Acids Res48(D1):D445-D453.doi: 10.1093/nar/gkz862.(2020) [MetaCyc13] Caspi R., Dreher K., and Karp P.D. The challenge of constructing, classifying, and representing metabolic pathways. FEMS Microbiol Lett. doi: 10.1111/1574-6968.12194. (2013) [MetaCyc11] Karp P.D., and Caspi R., A survey of metabolic databases emphasizing the MetaCyc family Archives of Toxicology 85:1015-33 (2011) [H2] Publications on the HumanCyc Database [HumanCyc04] P.Romero., J.Wagg., M.L.Green., D.Kaiser., M.Krummenacker., and P.D.KarpComputational prediction of human metabolic pathways from the complete human genome,Genome Biology 6:R2 R2.1-17 (2004) [H2] Publications on the CyanoCyc Portal [CyanoCyc04] Moore, L.R., Caspi, R., Campbell, D.A., Casey, J.R., Crevecoeur, S., Lea-Smith, D.J., Long, B., Omar, N.M., Paley, S.M., Schmelling, N.M., Torrado, A., Zehr, J.P., and P.D.KarpCyanoCyc cyanobacterial web portal,Front. Microbiol.,doi: 10.3389/fmicb.2024.1340413 (2024) [H2] Publications on the Pathway Tools Software Behind BioCyc Latest Comprehensive Description of Pathway Tools: [PTools24]P. D. Karp, S.M. Paley, M. Krummenacker, A. Kothari, P.E. Midford, P. Subhraveti, A. Swart, L. Moore, and R. Caspi. Pathway Tools version 28.0: Integrated software for Pathway/Genome informatics and Systems Biology, arXiv:1510.03964:1–107, 2024. doi: 10.48550/arXiv.1510.03964. [PTools19Survey] Karp, P.D. and Midford, P.E. and Paley, S.M. and Krummenacker, M. and Billington, R. and Kothari, A. and Ong, W.K. and Subhraveti, P. and Keseler, I.M. and Caspi, R., Pathway Tools version 23.0: Integrated Software for Pathway/Genome Informatics and Systems Biology, arXiv (2020). Overviews of Pathway Tools: [PTools19] Karp P.D., Midford, P.E., Billington, R., Kothari A., Krummenacker M., Latendresse, M., Ong, W.K., Subhraveti P., Caspi R., Fulcher C.A., Keseler I.M., Paley S.M. Pathway Tools version 23.0 update: software for pathway/genome informatics and systems biologyBriefings in Bioinformatics doi: 10.1093/bib/bbz104. (2019) [PTools15] Karp P.D., Latendresse M., Paley S.M., Krummenacker M., Ong Q., Billington R., Kothari A., Weaver D., Lee T., Subhraveti P., Spaulding A., Fulcher C.A., Keseler I.M., Caspi R. Pathway Tools version 19.0: Integrated Software for Pathway/Genome Informatics and Systems BiologyBriefings in Bioinformatics doi: 10.1093/bib/bbv079. (2015) [PTools10] P.D.Karp., S.M.Paley., M. Krummenacker. et al Pathway Tools version 13.0: Integrated Software for Pathway/Genome Informatics and Systems BiologyBriefings in Bioinformatics 11:40-79 (2010) Genome Browser: [PTools24] Herson, J. , Krummenacker, M. , Spaulding, A. , O’Maille, P. and Karp,P.D.The Genome Explorer genome browser,mSystems doi:10.1128/msystems.00267-24 (2024) Comparative Genome Dashboard and Omics Dashboard: [PTools24] Paley Suzanne , Caspi Ron , O'Maille Paul , Karp Peter D., .The Comparative Genome DashboardFrontiers in Microbiology. doi:10.3389/fmicb.2024.1447632 (2024) [PTools24] Paley Suzanne , Karp Peter D., .The Omics Dashboard for Interactive Exploration of Metabolomics and Multi-Omics DataMetabolites. doi:10.3390/metabo14010065 (2024) Transcriptomics Data Analysis: [PTools17] Austin Swart, Ron Caspi, Suzanne Paley and P.D. Karp.Visual analysis of multi-omics data.Frontiers in bioinformatics doi: 10.3389/fbinf.2024.1395981 (2024). [PTools17] Suzanne Paley, Karen Parker, Aaron Spaulding, Jean-Francois Tomb, Paul O'Maille, and Peter Karp.The Omics Dashboard for interactive exploration of gene-expression data,Nucleic Acids Research 45:12113-24 doi:10.1093/nar/gkx910 (2017) [PTools06] S.M. Paley and P.D. Karp.The Pathway Tools Cellular Overview Diagram and Omics Viewer,Nucleic Acids Research 34:3771-8 (2006) Metabolomics Data Analysis: [PTools15] P.D. Karp., Billington. R., Holland. T.A., Kothari. A., Krummenacker. M., Weaver. D., M. Latendresse., Paley. S. Computational Metabolomics Operations at BioCyc.org,DATABASE - Metabolites — Open Access Metabolism & Metabolomics Journal , doi: 10.3390/metabo5020291, (2015) SmartTables(Groups):[PTools13] Mike Travers., S.M Paley., J.Shrager., T.A Holland., and Peter KarpGroups:knowledge spreadsheets for symbolic biocomputingDatabase, doi:10.1093/database/bat061 (2013) Flux Balance Analysis (FBA) and Multiple Gap-Filling: [PTools22] M. Latendresse, W.K. Ong, P.D. KarpMetabolic Modeling with MetaFluxMethods Mol Biol doi: 10.1007/978-1-0716-1585-0_12 (2022) [PTools20] W.K. Ong, P.E. Midford, P.D. KarpTaxonomic weighting improves the accuracy of a gap-filling algorithm for metabolic modelsBioinformatics36(6):1823-1830 doi: 10.1093/bioinformatics/btz813 (2020) [PTool18]P.D. Karp, D. Weaver, M.LatendresseHow accurate is automated gap filling of metabolic modelsBMC Syst Biol12(1):73 doi: 10.1186/s12918-018-0593-7 (2018) [PTools14] Mario Latendresse, Efficiently Gap-Filling Reaction Networks, BMC Bioinformatics, 15:225, doi:10.1186/1471-2105-15-225, (June 2014) [PTools12] Mario Latendresse, Markus Krummenacker, Miles Trupp, and Peter KarpConstruction and Completion of Flux-Balance Models from Pathway DatabasesBioinformatics, doi: 10.1093/bioinformatics/btr681, (2012) Metabolic and Regulatory Network Visualization: [PTools21] Suzanne Paley and Peter D. KarpThe BioCyc Metabolic Network Explorer,BMC Bioinformatics 22:208, (2021) [PTools21] S. Paley, R. Billington, J. Herson, M. Krummenacker and P. D. KarpPathway Tools Visualization of Organism-Scale Metabolic Networks,Metabolites 11:64, (2021) [PTools11] Suzanne M Paley, Mario Latendresse and Peter D. KarpRegulatory network operations in the Pathway Tools software,BMC Bioinformatics 13:243, (2012) [PTools11]Mario Latendresse and Peter D. KarpWeb-based metabolic network visualization with a zooming user interface,BMC Bioinformatics 12:176, (2011) Metabolic Route Search: [PTools19] M. Krummenacker, M. Latendresse, P.D. Karp Metabolic route computation in organism communities Microbiome7(1):89. doi: 10.1186/s40168-019-0706-6. (2019) [PTools14] M. Latendresse, M.Krummenacker, P.D. Karp Optimal metabolic route search based on atom mappings Bioinformatics30(14) doi: 10.1093/bioinformatics/btu150.(2014)Atom Mapping:[PTools12] Mario Latendresse, Jeremiah P. Malerich, Mike Travers, and Peter D. KarpAccurate Atom-Mapping Computation for Biochemical ReactionsDATABASE - The Journal of Chemical Information and Modelling(ACS)doi:10.1021/ci3002217, (2012) Biological Database Querying:[PTools10b] Mario Latendresse and Peter D. KarpAn advanced web query interface for biological databasesDATABASE - The Journal of Biological Databases and Curation doi: 10.1093/database/baq006, (2010) Pathway Tools APIs:[PTools05] M. Krummenacker., S. Paley., L. Mueller., T. Yan., and P.D. Karp.Querying and Computing with BioCyc Databases,Bioinformatics 21:3454-5 (2005) Pathway Drawing Algorithms: [PTools16] S.Paley, P.E.O'Maille, D.Weaver and P.D. Karp Pathway collages: personalized multi-pathway diagrams BMC Bioinformatics17(1):529 doi: 10.1186/s12859-016-1382-1.(2016) The Pathway Tools Cellular Overview Diagram and Omics Viewer,Nucleic Acids Research 34:3771-8 2006. [PTools94] P.D. Karp and S.M. Paley Automated drawing of metabolic pathways [PDF] [PS]Proceedings of the Third International Conference on Bioinformatics and Genome Research, pp225-38 (1994) Pathway Prediction/Metabolic Reconstruction: [PTools14]N.W. Hanson, K.M. Konwar, A.K. Hawley, T. Altman, P.D. Karp, S.J. Hallam.Metabolic pathways for the whole community BMC Genomics15(1) :619. doi: 10.1186/1471-2164-15-619. (2014) [PTools11] P.D. Karp., M. Latendresse., R. Caspi.The Pathway Tools pathway prediction algorithm.Standards in Genomic Sciences5(3): 424–429. doi: 10.4056/sigs.1794338 (2011) [PTools10a] J.M Dale., L. Popescu., and P.D. KarpMachine learning methods for metabolic pathway prediction.BMC Bioinformatics 8:15 (2010) [PTools02a] S.M.Paley and P.D.KarpEvaluation of computational metabolic-pathway predictions for H. pyloriBioinformatics 18:715-24 (2002) Pathway Hole Filling Algorithm:[PTools04] M.Green and P.D.KarpA Bayesian method for identifying missing enzymes in predicted metabolic pathway databases.BMC Bioinformatics 5:76 (2004) Operon Predictor: [PTools04a] P.Romero and P.D.KarpUsing functional and organizational information to improve genome-wide computational prediction of transcription units on pathway/genome databases. Bioinformatics (2004) Predictor of Transport Reactions:[PTools08] T.J.Lee., I. Paulsen., and P.D.KarpAnnotation-based inference of transporter function.Bioinformatics 24:i259-67 (2008) [H2] Publications on the Pathway Tools Ontology [PTools04b] P.D.Karp., S. Paley., C.J. Krieger., and Zhang P.An Evidence Ontology for use in Pathway/Genome Databases.Pacific Symposium on Biocomputing 9:190-201 (2004) [PTools00] P.D.KarpAn ontology for biological function based on molecular interactions.Bioinformatics 16:269-85 (2000) [PTools94a] P. Karp and S. PaleyRepresentations of metabolic knowledge: Pathwaysin Proceedings of the Second International Conference on Intelligent Systems for Molecular Biology. (Altman, R. and Brutlag, D. and Karp, P. and Lathrop, R. and Searls, D. eds.), (Menlo Park, CA), AAAI Press, (1994) [PTools93] P. Karp and M. RileyRepresentations of metabolic knowledgein Proceedings of the First International Conference on Intelligent Systems for Molecular Biology. (L. Hunter, D. Searls, and J. Shavlik, eds.), (Menlo Park, CA), pp. 207-215, AAAI Press, (1993) [H2] Publications on the Ocelot Object-Oriented DBMS Underlying Pathway Tools [Ocelot99] P.D.Karp, V.K.Chaudhri, and S.M PaleyA collaborative environment for authoring large knowledge basesJ Intelligent Information Systems. 13:155-94 (1999) [H2] Other Pathway Bioinformatics Publications [Pathway05] M.L.Green and P.D. KarpGenome Annotation Errors in Pathway Databases Due to Semantic Ambiguity in Partial EC Numbers.Nucleic Acids Research 33:4035-9 (2005) [Pathway06] M.L Green and P.D. KarpThe outcomes of pathway database computations depend on pathway ontology.Nucleic Acids Research 34:3687-97 (2006)
SUB-PAGE · THIN (https://humancyc.org/[ORGID]/class-tree/) Not Found
Not FoundThe server doesn't know how to respond to http://humancyc.org/%5BORGID%5D/class-tree/. Please make sure you have the correct URL.Host: biocyc18Report Errors or Provide Feedback Page generated by Pathway Tools version 29.5 (software by SRI International) on Sat Jun 20, 2026, BIOCYC18.EcoCyc version 29.6.
🛡️ Trust Signals — reviews, proof links, trust-theatre flag (Trust & Proof)
| Page | Reviews | Proof links |
|---|---|---|
| / (home) | 7 | 1 |
| /[ORGID]/select-gen-el/ | 3 | 1 |
| /publications.shtml | 3 | 1 |
| /[ORGID]/class-tree/ | 3 | 1 |
🔗 Identity & Technical Layer — schema JSON-LD: identity chains, entity gaps (Identity & Authority)
Your Diagnosis
Before revealing the machine’s verdict, predict the BS score for each signal. Higher = more BS (more fluff, less verifiable substance). Drag each slider, then submit to compare your judgment against the engine.
Stuck? Reveal the heuristic lens — how the deterministic page-auditor reads each signal (no AI, pure pattern rules)
These are the structural rules a local, deterministic auditor applies — the same lens you can use to judge each signal. They describe what to look for, not this company’s result.
Classify each sentence as substantive or hollow. Grounding markers — numbers, currencies, dates, technical units, named entities — outweigh marketing adjectives. When fluff sits right next to hard evidence, the fluff is forgiven.
Pull the main entities out of the H1, then check whether they actually recur through the body. A page that announces one thing and then talks about another drifts. Headings with no real sentences underneath read as pseudo-substance.
Count trust words (review, testimonial, rating, verified) against real outbound proof links (Google, Trustpilot, Clutch, G2, Yelp). Lots of trust language with zero verification links is trust theatre. Unlinked logo galleries count against it.
Look at how much sentence length varies. Natural writing varies its rhythm; templated or mass-produced copy is statistically uniform. Very low variation reads as commodity content — unless unique named entities break the pattern.
Inspect the JSON-LD. Is there an Organization or Person schema, and does it carry sameAs links to real external profiles (LinkedIn, socials)? Missing schema or no identity declaration signals an anonymous entity.
Want to apply this lens yourself? The free BS Indicator Chrome extension runs these heuristic checks live on any page. Bear in mind it is a single-page, deterministic tool — it relies only on pattern rules for the page in front of it and does not perform the cross-page semantic correlation this audit uses, so its readout is a starting lens, not the full verdict.
Based on 126 businesses audited.
HumanCyc has 30.3 points less BS than the average for Science, Research & Laboratories.
Science, Research & Laboratories BS: HumanCyc (humancyc.org)
This is a high-authority scientific resource with virtually no bullshit. It prioritizes technical documentation and peer-reviewed validation over marketing aesthetics. It is a rare example of a site where substance exceeds the signals provided in the hero section.
Implement Organization and Dataset schema to improve machine readability of the database’s authority. Fix the dynamic URL routing errors ([ORGID]) to ensure all sub-pages resolve for users. Link the named testimonials on the homepage directly to their respective university faculty pages. Add Person schema for the key researchers listed in the Credits and Publications sections.
HumanCyc perfectly matches the Science, Research & Laboratories category. The content is exclusively focused on bioinformatics, genomic databases, and metabolic pathway analysis, featuring high-level technical discourse and peer-reviewed citations.
“The score of 4 is driven primarily by minor technical gaps (missing schema) and the presence of unlinked testimonials on the homepage. Information density and semantic coherence are nearly perfect, placing this site in the 'Minimal BS' category.”
This training module utilizes a snapshot of public data from HumanCyc, captured on June 20, 2026, to demonstrate how machine logic evaluates different types of business narratives.
Purpose: This data is presented under “Fair Use” / “Educational Exception” for the purpose of forensic semantic analysis, allowing users to compare human intuition against machine-generated evaluations.
Notice to HumanCyc: This analysis is part of a non-adversarial audit conducted by 1 Euro SEO. The results provided by 1EuroSEO are intended as professional feedback to help improve any website’s machine-readability and authority signals. The 1EuroSEO BS Detection Tool is a free tool, and anyone can test any company to see how their content is interpreted by AI models.
Any company can use the insights for free and improve its voice by comparing it to industry clichés or competitors. When a company has updated its content, it can always submit a new audit request, which will be reflected in a new current score.
To all users: You are encouraged to visit the live site at https://humancyc.org to view the most current version of its content and learn from the source what this company is about and what it offers.