Decoding the Foodome: Molecular Networks Connecting Diet and Health

Literature Cited

1. 1.

   Adjibade M, Julia C, Allès B, Touvier M, Lemogne C, et al. 2019.. Prospective association between ultra-processed food consumption and incident depressive symptoms in the French NutriNet-Santé cohort. . BMC Med. 17:(1):78
   [Crossref] [Google Scholar]
2. 2.

   Afendi FM, Okada T, Yamazaki M, Hirai-Morita A, Nakamura Y, et al. 2012.. KNApSAcK family databases: integrated metabolite–plant species databases for multifaceted plant research. . Plant Cell Physiol. 53:(2):e1
   [Crossref] [Google Scholar]
3. 3.

   Aguilera JM. 2019.. The food matrix: implications in processing, nutrition and health. . Crit. Rev. Food Sci. Nutr. 59:(22):3612–29
   [Crossref] [Google Scholar]
4. 4.

   Alonso-Pedrero L, Ojeda-Rodríguez A, Martínez-González MA, Zalba G, Bes-Rastrollo M, Marti A. 2020.. Ultra-processed food consumption and the risk of short telomeres in an elderly population of the Seguimiento Universidad de Navarra (SUN) Project. . Am. J. Clin. Nutr. 111:(6):1259–66
   [Crossref] [Google Scholar]
5. 5.

   AOAC Int. 2023.. Official Methods of Analysis of AOAC International, Vols . 1–2. Washington, DC:: Oxford Univ. Press. , 22nd ed..
   [Google Scholar]
6. 6.

   Ara T, Sakurai N, Takahashi S, Waki N, Suganuma H, et al. 2021.. TOMATOMET: A metabolome database consists of 7118 accurate mass values detected in mature fruits of 25 tomato cultivars. . Plant Direct 5:(4):e00318
   [Crossref] [Google Scholar]
7. 7.

   Aronica L, Levine AJ, Brennan K, Mi J, Gardner C, et al. 2017.. A systematic review of studies of DNA methylation in the context of a weight loss intervention. . Epigenomics 9:(5):769–87
   [Crossref] [Google Scholar]
8. 8.

   Baldridge AS, Huffman MD, Taylor F, Xavier D, Bright B, et al. 2019.. The healthfulness of the US packaged food and beverage supply: a cross-sectional study. . Nutrients 11:(8):1704
   [Crossref] [Google Scholar]
9. 9.

   Banerjee P, Erehman J, Gohlke B-O, Wilhelm T, Preissner R, Dunkel M. 2015.. Super Natural II: a database of natural products. . Nucleic Acids Res. 43:(D1):D935–39
   [Crossref] [Google Scholar]
10. 10.

    Bar-Even A, Noor E, Flamholz A, Buescher JM, Milo R. 2011.. Hydrophobicity and charge shape cellular metabolite concentrations. . PLOS Comput. Biol. 7:(10):e1002166
    [Crossref] [Google Scholar]
11. 11.

    Barabási A-L. 2016.. Network Science. Cambridge, UK:: Cambridge Univ. Press
    [Google Scholar]
12. 12.

    Barabási A-L, Gulbahce N, Loscalzo J. 2011.. An integrative systems medicine approach to mapping human metabolic diseases. . Nat. Rev. Genet. 12:(1):56–68
    [Crossref] [Google Scholar]
13. 13.

    Barabási A-L, Menichetti G, Loscalzo J. 2020.. The unmapped chemical complexity of our diet. . Nat. Food 1:(1):33–37
    [Crossref] [Google Scholar]
14. 14.

    Barabási AL, Gulbahce N, Loscalzo J. 2011.. Network medicine: a network-based approach to human disease. . Nat. Rev. Genet. 12:(1):56–68
    [Crossref] [Google Scholar]
15. 15.

    Belanger D, Colwell LJ. 2023.. Hallucinating functional protein sequences. . Nat. Biotechnol. 41:(8):1073–74
    [Crossref] [Google Scholar]
16. 16.

    Berry SEE, Tydeman EA, Lewis HB, Phalora R, Rosborough J, et al. 2008.. Manipulation of lipid bioaccessibility of almond seeds influences postprandial lipemia in healthy human subjects. . Am. J. Clin. Nutr. 88:(4):922–29
    [Crossref] [Google Scholar]
17. 17.

    Bertelli A, Biagi M, Corsini M, Baini G, Cappellucci G, Miraldi E. 2021.. Polyphenols: from theory to practice. . Foods 10:(11):2595
    [Crossref] [Google Scholar]
18. 18.

    Beslay M, Srour B, Méjean C, Allès B, Fiolet T, et al. 2020.. Ultra-processed food intake in association with BMI change and risk of overweight and obesity: a prospective analysis of the French NutriNet-Santé cohort. . PLOS Med. 17:(8):e1003256
    [Crossref] [Google Scholar]
19. 19.

    Birhane A, Kasirzadeh A, Leslie D, Wachter S. 2023.. Science in the age of large language models. . Nat. Rev. Phys. 5:(5):277–80
    [Crossref] [Google Scholar]
20. 20.

    Blasco T, Pérez-Burillo S, Balzerani F, Hinojosa-Nogueira D, Lerma-Aguilera A, et al. 2021.. An extended reconstruction of human gut microbiota metabolism of dietary compounds. . Nat. Commun. 12::4728
    [Crossref] [Google Scholar]
21. 21.

    Bose C, Alves I, Singh P, Palade PT, Carvalho E, et al. 2020.. Sulforaphane prevents age-associated cardiac and muscular dysfunction through Nrf2 signaling. . Aging Cell 19:(11):e13261
    [Crossref] [Google Scholar]
22. 22.

    Braesco V, Souchon I, Sauvant P, Haurogné T, Maillot M, et al. 2022.. Ultra-processed foods: How functional is the NOVA system?. Eur. J. Clin. Nutr. 76::1245–53
    [Crossref] [Google Scholar]
23. 23.

    Callaway E. 2022.. AlphaFold's new rival? Meta AI predicts shape of 600 million proteins. . Nature 611:(7935):211–12
    [Crossref] [Google Scholar]
24. 24.

    Capuano E, Oliviero T, van Boekel MAJS. 2018.. Modeling food matrix effects on chemical reactivity: challenges and perspectives. . Crit. Rev. Food Sci. Nutr. 58:(16):2814–28
    [Crossref] [Google Scholar]
25. 25.

    Cenikj G, Popovski G, Stojanov R, Seljak BK, Eftimov T. 2020.. BuTTER: bidirectional LSTM for food named-entity recognition. . In 2020 IEEE International Conference on Big Data (Big Data), pp. 3550–56. Piscataway, NJ:: IEEE
    [Google Scholar]
26. 26.

    Chareonrungrueangchai K, Wongkawinwoot K, Anothaisintawee T, Reutrakul S. 2020.. Dietary factors and risks of cardiovascular diseases: an umbrella review. . Nutrients 12:(4):1088
    [Crossref] [Google Scholar]
27. 27.

    Chatterjee A, Walters R, Shafi Z, Ahmed OS, Sebek M, et al. 2023.. Improving the generalizability of protein-ligand binding predictions with AI-Bind. . Nat. Commun. 14::1989
    [Crossref] [Google Scholar]
28. 28.

    Chen L, Lu W, Wang L, Xing X, Chen Z, et al. 2021.. Metabolite discovery through global annotation of untargeted metabolomics data. . Nat. Methods 18:(11):1377–85
    [Crossref] [Google Scholar]
29. 29.

    Chen X, Zhang Z, Yang H, Qiu P, Wang H, et al. 2020.. Consumption of ultra-processed foods and health outcomes: a systematic review of epidemiological studies. . Nutr. J. 19:(1):86
    [Crossref] [Google Scholar]
30. 30.

    Cheng F, Desai RJ, Handy DE, Wang R, Schneeweiss S, et al. 2018.. Network-based approach to prediction and population-based validation of in silico drug repurposing. . Nat. Commun. 9::2691
    [Crossref] [Google Scholar]
31. 31.

    Cheng F, Kovács IA, Barabási AL. 2019.. Network-based prediction of drug combinations. . Nat. Commun. 10::1197
    [Crossref] [Google Scholar]
32. 32.

    Corbin KD, Carnero EA, Dirks B, Igudesman D, Yi F, et al. 2023.. Host-diet-gut microbiome interactions influence human energy balance: a randomized clinical trial. . Nat. Commun. 14::3161
    [Crossref] [Google Scholar]
33. 33.

    Cordain L, Eaton SB, Sebastian A, Mann N, Lindeberg S, et al. 2005.. Origins and evolution of the Western diet: health implications for the 21st century. . Am. J. Clin. Nutr. 81:(2):341–54
    [Crossref] [Google Scholar]
34. 34.

    D'Archivio M, Filesi C, Varì R, Scazzocchio B, Masella R. 2010.. Bioavailability of the polyphenols: status and controversies. . Int. J. Mol. Sci. 11:(4):1321–42
    [Crossref] [Google Scholar]
35. 35.

    Da Silva Oliveira MS, Silva-Amparo L. 2018.. Food-based dietary guidelines: a comparative analysis between the Dietary Guidelines for the Brazilian Population 2006 and 2014. . Public Health Nutr. 21:(1):210–17
    [Crossref] [Google Scholar]
36. 36.

    Davis AP, Wiegers TC, Johnson RJ, Sciaky D, Wiegers J, Mattingly CJ. 2023.. Comparative Toxicogenomics Database (CTD): update 2023. . Nucleic Acids Res. 51:(D1):D1257–62
    [Crossref] [Google Scholar]
37. 37.

    Debras C, Chazelas E, Sellem L, Porcher R, Druesne-Pecollo N, et al. 2022.. Artificial sweeteners and risk of cardiovascular diseases: results from the prospective NutriNet-Santé cohort. . BMJ 378::e071204
    [Crossref] [Google Scholar]
38. 38.

    do Valle IF, Roweth HG, Malloy MW, Moco S, Barron D, et al. 2021.. Network medicine framework shows that proximity of polyphenol targets and disease proteins predicts therapeutic effects of polyphenols. . Nat. Food 2:(3):143–55
    [Crossref] [Google Scholar]
39. 39.

    Dooley DM, Griffiths EJ, Gosal GS, Buttigieg PL, Hoehndorf R, et al. 2018.. FoodOn: a harmonized food ontology to increase global food traceability, quality control and data integration. . npj Sci. Food 2::23
    [Crossref] [Google Scholar]
40. 40.

    Drew Bennett WF, He S, Bilodeau CL, Jones D, Sun D, et al. 2020.. Predicting small molecule transfer free energies by combining molecular dynamics simulations and deep learning. . J. Chem. Inf. Model. 60:(11):5375–81
    [Crossref] [Google Scholar]
41. 41.

    Dührkop K, Fleischauer M, Ludwig M, Aksenov AA, Melnik AV, et al. 2019.. SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information. . Nat. Methods 16:(4):299–302
    [Crossref] [Google Scholar]
42. 42.

    Eaton SB. 2006.. The ancestral human diet: What was it and should it be a paradigm for contemporary nutrition?. Proc. Nutr. Soc. 65:(1):1–6
    [Crossref] [Google Scholar]
43. 43.

    EFSA (Eur. Food Saf. Auth.). 2015.. The Food Classification and Description System FoodEx2 (revision 2). Tech. Rep., Support. Publ. 2015-804E , EFSA, Parma, Italy:. https://efsa.onlinelibrary.wiley.com/doi/epdf/10.2903/sp.efsa.2015.EN-804
    [Google Scholar]
44. 44.

    EFSA (Eur. Food Saf. Auth.). 2020.. Food classification standardization—the FoodEx2 system. Fact Sheet , EFSA, Parma, Italy:. https://www.efsa.europa.eu/en/data/data-standardisation
    [Google Scholar]
45. 45.

    Elgart V, Loscalzo J. 2023.. Local generation and efficient evaluation of numerous drug combinations in a single sample. . eLife 12::e85439
    [Crossref] [Google Scholar]
46. 46.

    Evans PC. 2011.. The influence of sulforaphane on vascular health and its relevance to nutritional approaches to prevent cardiovascular disease. . EPMA J. 2:(1):9–14
    [Crossref] [Google Scholar]
47. 47.

    Fardet A, Rock E, Bassama J, Bohuon P, Prabhasankar P, et al. 2015.. Current food classifications in epidemiological studies do not enable solid nutritional recommendations for preventing diet-related chronic diseases: the impact of food processing. . Adv. Nutr. 6:(6):629–38
    [Crossref] [Google Scholar]
48. 48.

    Fernandes AC, Rieger DK, Proença RPC. 2019.. Public health nutrition policies should focus on healthy eating, not on calorie counting, even to decrease obesity. . Adv. Nutr. 10:(4):549–56
    [Crossref] [Google Scholar]
49. 49.

    Fiolet T, Srour B, Sellem L, Kesse-Guyot E, Allès B, et al. 2018.. Consumption of ultra-processed foods and cancer risk: results from NutriNet-Santé prospective cohort. . BMJ 360::k322
    [Crossref] [Google Scholar]
50. 50.

    Fukagawa NK, McKillop K, Pehrsson PR, Moshfegh A, Harnly J, Finley J. 2022.. USDA's FoodData Central: What is it and why is it needed today?. Am. J. Clin. Nutr. 115:(3):619–24
    [Crossref] [Google Scholar]
51. 51.

    Fung TT, Willett WC, Stampfer MJ, Manson JAE, Hu FB. 2001.. Dietary patterns and the risk of coronary heart disease in women. . Arch. Intern. Med. 161:(15):1857–62
    [Crossref] [Google Scholar]
52. 52.

    Ghiassian SD, Menche J, Barabási A-L. 2015.. A DIseAse MOdule Detection (DIAMOnD) algorithm derived from a systematic analysis of connectivity patterns of disease proteins in the human interactome. . PLOS Comput. Biol. 11:(4):e1004120
    [Crossref] [Google Scholar]
53. 53.

    Ghiassian SD, Menche J, Chasman DI, Giulianini F, Wang R, et al. 2016.. Endophenotype network models: common core of complex diseases. . Sci. Rep. 6::27414
    [Crossref] [Google Scholar]
54. 54.

    Gibney MJ, Forde CG. 2022.. Nutrition research challenges for processed food and health. . Nat. Food 3::104–9
    [Crossref] [Google Scholar]
55. 55.

    Grassby T, Mandalari G, Grundy MML, Edwards CH, Bisignano C, et al. 2017.. In vitro and in vivo modeling of lipid bioaccessibility and digestion from almond muffins: the importance of the cell-wall barrier mechanism. . J. Funct. Foods 37::263–71
    [Crossref] [Google Scholar]
56. 56.

    Guney E, Menche J, Vidal M, Barábasi AL. 2016.. Network-based in silico drug efficacy screening. . Nat. Commun. 7::10331
    [Crossref] [Google Scholar]
57. 57.

    Halgren TA, Murphy RB, Friesner RA, Beard HS, Frye LL, et al. 2004.. Glide: a new approach for rapid, accurate docking and scoring. 2. Enrichment factors in database screening. . J. Med. Chem. 47:(7):1750–59
    [Crossref] [Google Scholar]
58. 58.

    Haut Cons. Santé Publique. 2018.. Avis relatif aux objectifs de santé publique quantifiés pour la politique nutritionnelle de santé publique (PNNS) 2018–2022. Rep. , HCSP, Paris, France:. https://www.hcsp.fr/Explore.cgi/avisrapportsdomaine?clefr=648
    [Google Scholar]
59. 59.

    Hooton F, Menichetti G, Barabási A-L. 2020.. Exploring food contents in scientific literature with FoodMine. . Sci. Rep. 10::16191
    [Crossref] [Google Scholar]
60. 60.

    Hu FB. 2002.. Dietary pattern analysis: a new direction in nutritional epidemiology. . Curr. Opin. Lipidol. 13:(1):3–9
    [Crossref] [Google Scholar]
61. 61.

    Huang K, Fu T, Glass LM, Zitnik M, Xiao C, Sun J. 2021.. DeepPurpose: a deep learning library for drug-target interaction prediction. . Bioinformatics 36:(22/23):5545–47
    [Crossref] [Google Scholar]
62. 62.

    Huang K, Xiao C, Glass LM, Sun J. 2021.. MolTrans: molecular interaction transformer for drug–target interaction prediction. . Bioinformatics 37:(6):830–36
    [Crossref] [Google Scholar]
63. 63.

    Huttlin EL, Bruckner RJ, Navarrete-Perea J, Cannon JR, Baltier K, et al. 2021.. Dual proteome-scale networks reveal cell-specific remodeling of the human interactome. . Cell 184:(11):3022–40.e28
    [Crossref] [Google Scholar]
64. 64.

    Ioannidis JPA. 2005.. Why most published research findings are false. . PLOS Med. 2:(8):e124
    [Crossref] [Google Scholar]
65. 65.

    Ioannidis JPA. 2018.. The challenge of reforming nutritional epidemiologic research. . JAMA 320:(10):969–70
    [Crossref] [Google Scholar]
66. 66.

    Irwin JJ, Tang KG, Young J, Dandarchuluun C, Wong BR, et al. 2020.. ZINC20—a free ultralarge-scale chemical database for ligand discovery. . J. Chem. Inf. Model. 60:(12):6065–73
    [Crossref] [Google Scholar]
67. 67.

    Janda M, Seah BKB, Jakob D, Beckmann J, Geier B, Liebeke M. 2021.. Determination of abundant metabolite matrix adducts illuminates the dark metabolome of MALDI–mass spectrometry imaging datasets. . Anal. Chem. 93:(24):8399–407
    [Crossref] [Google Scholar]
68. 68.

    Jeong H, Tombor B, Albert R, Oltvai ZN, Barabási AL. 2000.. The large-scale organization of metabolic networks. . Nature 407:(6804):651–54
    [Crossref] [Google Scholar]
69. 69.

    Jew S, Abumweis SS, Jones PJH. 2009.. Evolution of the human diet: linking our ancestral diet to modern functional foods as a means of chronic disease prevention. . J. Med. Food 12:(5):925–34
    [Crossref] [Google Scholar]
70. 70.

    Johnston N. 2004.. Sulforaphane halts breast cancer cell growth. . Drug Discov. Today 9:(21):908
    [Crossref] [Google Scholar]
71. 71.

    Kalakoti Y, Yadav S, Sundar D. 2022.. TransDTI: transformer-based language models for estimating DTIs and building a drug recommendation workflow. . ACS Omega 7:(3):2706–17
    [Crossref] [Google Scholar]
72. 72.

    Katidi A, Vlassopoulos A, Noutsos S, Kapsokefalou M. 2023.. Ultra-processed foods in the Mediterranean diet according to the NOVA classification system; a food level analysis of branded foods in Greece. . Foods 12:(7):1520
    [Crossref] [Google Scholar]
73. 73.

    Kaufman-Szymczyk A, Majewski G, Lubecka-Pietruszewska K, Fabianowska-Majewska K. 2015.. The role of sulforaphane in epigenetic mechanisms, including interdependence between histone modification and DNA methylation. . Int. J. Mol. Sci. 16:(12):29732–43
    [Crossref] [Google Scholar]
74. 74.

    Kelli HM, Kim JH, Tahhan AS, Liu C, Ko YA, et al. 2019.. Living in food deserts and adverse cardiovascular outcomes in patients with cardiovascular disease. . J. Am. Heart Assoc. 8:(4):e010694
    [Crossref] [Google Scholar]
75. 75.

    Khera AV, Emdin CA, Drake I, Natarajan P, Bick AG, et al. 2016.. Genetic risk, adherence to a healthy lifestyle, and coronary disease. . N. Engl. J. Med. 375:(24):2349–58
    [Crossref] [Google Scholar]
76. 76.

    Kiely M, Black LJ, Plumb J, Kroon PA, Hollman PC, et al. 2010.. EuroFIR eBASIS: application for health claims submissions and evaluations. . Eur. J. Clin. Nutr. 64:(Suppl. 3):S101–7
    [Crossref] [Google Scholar]
77. 77.

    Kolonel LN, Yoshizawa CN, Hankin JH. 1988.. Diet and prostatic cancer: a case-control study in Hawaii. . Am. J. Epidemiol. 127:(5):999–1012
    [Crossref] [Google Scholar]
78. 78.

    Koscielny G, An P, Carvalho-Silva D, Cham JA, Fumis L, et al. 2017.. OpenTargets: a platform for therapeutic target identification and validation. . Nucleic Acids Res. 45:(D1):D985–94
    [Crossref] [Google Scholar]
79. 79.

    Kubo R, Ichimura H, Usui T, Hashitsume N. 1990.. Statistical Mechanics. Amsterdam:: North-Holland
    [Google Scholar]
80. 80.

    Kuhn M, Szklarczyk D, Pletscher-Frankild S, Blicher TH, Von Mering C, et al. 2014.. STITCH 4: integration of protein–chemical interactions with user data. . Nucleic Acids Res. 42:(D1):D401–7
    [Crossref] [Google Scholar]
81. 81.

    Lacroix S, Klicic Badoux J, Scott-Boyer MP, Parolo S, Matone A, et al. 2018.. A computationally driven analysis of the polyphenol–protein interactome. . Sci. Rep. 8::2232
    [Crossref] [Google Scholar]
82. 82.

    Lane MM, Davis JA, Beattie S, Gómez-Donoso C, Loughman A, et al. 2020.. Ultraprocessed food and chronic noncommunicable diseases: a systematic review and meta-analysis of 43 observational studies. . Obes. Rev. 22:(3):e13146
    [Crossref] [Google Scholar]
83. 83.

    Le Marchand L, Hankin JH, Kolonel LN, Wilkens LR. 1991.. Vegetable and fruit consumption in relation to prostate cancer risk in Hawaii: a reevaluation of the effect of dietary β-carotene. . Am. J. Epidemiol. 133:(3):215–19
    [Crossref] [Google Scholar]
84. 84.

    Li Y, Zhang T, Korkaya H, Liu S, Lee H-F, et al. 2010.. Sulforaphane, a dietary component of broccoli/broccoli sprouts, inhibits breast cancer stem cells. . Clin. Cancer Res. 16:(9):2580–90
    [Crossref] [Google Scholar]
85. 85.

    Liu T, Lin Y, Wen X, Jorissen RN, Gilson MK. 2007.. BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. . Nucleic Acids Res. 35:(Suppl. 1):D198–201
    [Crossref] [Google Scholar]
86. 86.

    Loscalzo J, Barabási A-L, Silverman EK. 2017.. Network Medicine: Complex Systems in Human Disease and Therapeutics. Cambridge, MA:: Harvard Univ. Press
    [Google Scholar]
87. 87.

    Luck K, Kim DK, Lambourne L, Spirohn K, Begg BE, et al. 2020.. A reference map of the human binary protein interactome. . Nature 580:(7803):402–8
    [Crossref] [Google Scholar]
88. 88.

    Madani A, Krause B, Greene ER, Subramanian S, Mohr BP, et al. 2023.. Large language models generate functional protein sequences across diverse families. . Nat. Biotechnol. 41:(8):1099–106
    [Crossref] [Google Scholar]
89. 89.

    Mallmann LP, O Rios A, Rodrigues E. 2023.. MS-FINDER and SIRIUS for phenolic compound identification from high-resolution mass spectrometry data. . Food Res. Int. 163::112315
    [Crossref] [Google Scholar]
90. 90.

    Mandalari G, Parker ML, Grundy MML, Grassby T, Smeriglio A, et al. 2018.. Understanding the effect of particle size and processing on almond lipid bioaccessibility through microstructural analysis: from mastication to faecal collection. . Nutrients 10:(2):213
    [Crossref] [Google Scholar]
91. 91.

    Maron BA, Wang RS, Shevtsov S, Drakos SG, Arons E, et al. 2021.. Individualized interactomes for network-based precision medicine in hypertrophic cardiomyopathy with implications for other clinical pathophenotypes. . Nat. Commun. 12::873
    [Crossref] [Google Scholar]
92. 92.

    Mazzio EA, Soliman KFA. 2014.. Epigenetics and nutritional environmental signals. . Integr. Comp. Biol. 54:(1):21
    [Crossref] [Google Scholar]
93. 93.

    Medvedeva YA, Lennartsson A, Ehsani R, Kulakovskiy IV, Vorontsov IE, et al. 2015.. EpiFactors: a comprehensive database of human epigenetic factors and complexes. . Database 2015::bav067
    [Crossref] [Google Scholar]
94. 94.

    Menche J, Sharma A, Kitsak M, Ghiassian SD, Vidal M, et al. 2015.. Uncovering disease-disease relationships through the incomplete interactome. . Science 347:(6224):1257601
    [Crossref] [Google Scholar]
95. 95.

    Mendez D, Gaulton A, Bento AP, Chambers J, De Veij M, et al. 2019.. ChEMBL: towards direct deposition of bioassay data. . Nucleic Acids Res. 47:(D1):D930–40
    [Crossref] [Google Scholar]
96. 96.

    Menichetti G, Barabási AL. 2022.. Nutrient concentrations in food display universal behaviour. . Nat. Food 3:(5):375–82
    [Crossref] [Google Scholar]
97. 97.

    Menichetti G, Ravandi B, Mozaffarian D, Barabási A-L. 2023.. Machine learning prediction of the degree of food processing. . Nat. Commun. 14::2312
    [Crossref] [Google Scholar]
98. 98.

    Mensink RP, Zock PL, Kester ADM, Katan MB. 2003.. Effects of dietary fatty acids and carbohydrates on the ratio of serum total to HDL cholesterol and on serum lipids and apolipoproteins: a meta-analysis of 60 controlled trials. . Am. J. Clin. Nutr. 77:(5):1146–55
    [Crossref] [Google Scholar]
99. 99.

    Micha R, Wallace SK, Mozaffarian D. 2010.. Red and processed meat consumption and risk of incident coronary heart disease, stroke, and diabetes mellitus: a systematic review and meta-analysis. . Circulation 121:(21):2271–83
    [Crossref] [Google Scholar]
100. 100.

     Milanlouei S, Menichetti G, Li Y, Loscalzo J, Willett WC, Barabási AL. 2020.. A systematic comprehensive longitudinal evaluation of dietary factors associated with acute myocardial infarction and fatal coronary heart disease. . Nat. Commun. 11::6074
     [Crossref] [Google Scholar]
101. 101.

     Monteiro CA, Cannon G, Lawrence M, Louzada Costa ML, Pereira Machado P. 2019.. Ultra-processed foods, diet quality, and health using the NOVA classification system. Rep. , Food Agric. Organ., Rome:. https://www.fao.org/3/ca5644en/ca5644en.pdf
     [Google Scholar]
102. 102.

     Monteiro CA, Cannon G, Moubarac JC, Levy RB, Louzada Costa ML, Jaime PC. 2018.. The UN Decade of Nutrition, the NOVA food classification and the trouble with ultra-processing. . Public Health Nutr. 21:(1):5–17
     [Crossref] [Google Scholar]
103. 103.

     Moubarac J-C, Parra DC, Cannon G, Monteiro CA. 2014.. Food classification systems based on food processing. Significance and implications for policies and actions: a systematic literature review and assessment. . Curr. Obes. Rep. 3:(2):256–72
     [Crossref] [Google Scholar]
104. 104.

     Mozaffarian D, Clarke R. 2009.. Quantitative effects on cardiovascular risk factors and coronary heart disease risk of replacing partially hydrogenated vegetable oils with other fats and oils. . Eur. J. Clin. Nutr. 63:(Suppl. 2):22–33
     [Crossref] [Google Scholar]
105. 105.

     Mozaffarian D, Fleischhacker S, Andrés JR. 2021.. Prioritizing nutrition security in the US. . JAMA 325:(16):1605–6
     [Crossref] [Google Scholar]
106. 106.

     Mozaffarian D, Rosenberg I, Uauy R. 2018.. History of modern nutrition science—implications for current research, dietary guidelines, and food policy. . BMJ 361::k2392
     [Crossref] [Google Scholar]
107. 107.

     Naimi S, Viennois E, Gewirtz AT, Chassaing B. 2021.. Direct impact of commonly used dietary emulsifiers on human gut microbiota. . Microbiome 9::66
     [Crossref] [Google Scholar]
108. 108.

     Nasirian F, Menichetti G. 2023.. Molecular interaction networks and cardiovascular disease risk: the role of food bioactive small molecules. . Arterioscler. Thromb. Vasc. Biol. 43::813–23
     [Crossref] [Google Scholar]
109. 109.

     Natl. Food Inst. 2016.. Frida, version 2, Tech. Univ. Denmark, Lyngby:, updated Nov. 30, 2023. https://frida.fooddata.dk/
     [Google Scholar]
110. 110.

     Novotny JA, Gebauer SK, Baer DJ. 2012.. Discrepancy between the Atwater factor predicted and empirically measured energy values of almonds in human diets. . Am. J. Clin. Nutr. 96:(2):296–301
     [Crossref] [Google Scholar]
111. 111.

     Ofaim S, Menichetti G, Sebek M, Barabási A-L. 2022.. Genomics-based annotations help unveil the molecular composition of edible plants. . bioRxiv 2022.01.24.477528. https://doi.org/10.1101/2022.01.24.477528
112. 112.

     Paci P, Fiscon G, Conte F, Wang RS, Handy DE, et al. 2022.. Comprehensive network medicine-based drug repositioning via integration of therapeutic efficacy and side effects. . npj Syst. Biol. Appl. 8:(1):12
     [Crossref] [Google Scholar]
113. 113.

     Pagliai G, Dinu M, Madarena MP, Bonaccio M, Iacoviello L, Sofi F. 2021.. Consumption of ultra-processed foods and health status: a systematic review and meta-analysis. . Br. J. Nutr. 125:(3):308–18
     [Crossref] [Google Scholar]
114. 114.

     PAHO (Pan Am. Health Organ.), WHO (World Health Organ.). 2019.. Ultra-processed food and drink products in Latin America: trends, impact on obesity, policy implications. Rep. , PAHO, Washington, DC:. https://iris.paho.org/handle/10665.2/51094
     [Google Scholar]
115. 115.

     Patel CJ, Ioannidis JPA. 2014.. Studying the elusive environment in large scale. . JAMA 311:(21):2173–74
     [Crossref] [Google Scholar]
116. 116.

     Patel CJ, Manrai AK. 2015.. Development of exposome correlation globes to map out environment-wide associations. . Pac. Symp. Biocomput. 20::231–42
     [Google Scholar]
117. 117.

     Patten JJ, Keiser PT, Morselli-Gysi D, Menichetti G, Mori H, et al. 2022.. Identification of potent inhibitors of SARS-CoV-2 infection by combined pharmacological evaluation and cellular network prioritization. . iScience 25:(9):104925
     [Crossref] [Google Scholar]
118. 118.

     Pence HE, Williams A. 2010.. ChemSpider: an online chemical information resource. . J. Chem. Educ. 87:(11):1123–24
     [Crossref] [Google Scholar]
119. 119.

     Placzek S, Schomburg I, Chang A, Jeske L, Ulbrich M, et al. 2017.. BRENDA in 2017: new perspectives and new tools in BRENDA. . Nucleic Acids Res. 45:(D1):D380–88
     [Crossref] [Google Scholar]
120. 120.

     Popovski G, Seljak BK, Eftimov T. 2019.. FoodBase corpus: a new resource of annotated food entities. . Database 2019::baz121
     [Crossref] [Google Scholar]
121. 121.

     Raikos V. 2017.. Food matrix: natural barrier or vehicle for effective delivery of carotenoids from processed foods?. Insights Nutr. Metab. 1:(1):3–8
     [Google Scholar]
122. 122.

     Ravandi B, Mehler P, Barabási A-L, Menichetti G. 2022.. GroceryDB: prevalence of processed food in grocery stores. . medRxiv 2022.04.23.22274217. https://doi.org/10.1101/2022.04.23.22274217
123. 123.

     Rees JS, Castellano S, Andrés AM. 2020.. The genomics of human local adaptation. . Trends Genet. 36:(6):415–28
     [Crossref] [Google Scholar]
124. 124.

     Rothwell JA, Perez-Jimenez J, Neveu V, Medina-Remón A, M'Hiri N, et al. 2013.. Phenol-Explorer 3.0: a major update of the Phenol-Explorer database to incorporate data on the effects of food processing on polyphenol content. . Database 2013::bat070
     [Crossref] [Google Scholar]
125. 125.

     Ruiz-Carmona S, Alvarez-Garcia D, Foloppe N, Garmendia-Doval AB, Juhos S, et al. 2014.. rDock: a fast, versatile and open source program for docking ligands to proteins and nucleic acids. . PLOS Comput. Biol. 10:(4):e1003571
     [Crossref] [Google Scholar]
126. 126.

     Sadler CR, Grassby T, Hart K, Raats M, Sokolović M, Timotijevic L. 2021.. Processed food classification: conceptualisation and challenges. . Trends Food Sci. Technol. 112::149–62
     [Crossref] [Google Scholar]
127. 127.

     Sebek M, Menichetti G. 2024.. Network science and machine learning for precision nutrition. . In Precision Nutrition, ed. D Heber, Z Li, J Ordovas , pp. 367–402. Cambridge, MA:: Academic Press
     [Google Scholar]
128. 128.

     Sebek ML, Menichetti G, Barabási A-L. 2022.. Estimating nutrient concentration in food using untargeted metabolomics. . bioRxiv 2022.12.02.518912. https://doi.org/10.1101/2022.12.02.518912
129. 129.

     Sellem L, Srour B, Javaux G, Chazelas E, Chassaing B, et al. 2023.. Food additive emulsifiers and risk of cardiovascular disease in the NutriNet-Santé cohort: prospective cohort study. . BMJ 382::e076058
     [Crossref] [Google Scholar]
130. 130.

     Sensoy I. 2014.. A review on the relationship between food structure, processing, and bioavailability. . Crit. Rev. Food Sci. Nutr. 54:(7):902–9
     [Crossref] [Google Scholar]
131. 131.

     Sharma A, Menche J, Huang CC, Ort T, Zhou X, et al. 2014.. A disease module in the interactome explains disease heterogeneity, drug response and captures novel pathways and genes in asthma. . Hum. Mol. Genet. 24:(11):3005–20
     [Crossref] [Google Scholar]
132. 132.

     Shrivastava AD, Swainston N, Samanta S, Roberts I, Muelas MW, Kell DB. 2021.. MassGenie: a transformer-based deep learning method for identifying small molecules from their mass spectra. . Biomolecules 11:(12):1793
     [Crossref] [Google Scholar]
133. 133.

     Slimani N, Deharveng G, Southgate DAT, Biessy C, Chajès V, et al. 2009.. Contribution of highly industrially processed foods to the nutrient intakes and patterns of middle-aged populations in the European Prospective Investigation into Cancer and Nutrition study. . Eur. J. Clin. Nutr. 63:(Suppl. 4):206–25
     [Crossref] [Google Scholar]
134. 134.

     Smith CA, O'Maille G, Want EJ, Qin C, Trauger SA, et al. 2005.. METLIN: a metabolite mass spectral database. . Ther. Drug Monit. 27:(6):747–51
     [Crossref] [Google Scholar]
135. 135.

     Sorokina M, Merseburger P, Rajan K, Yirik MA, Steinbeck C. 2021.. COCONUT online: Collection of Open Natural Products database. . J. Cheminform. 13::2
     [Crossref] [Google Scholar]
136. 136.

     Souza AL, Patti GJ. 2021.. A protocol for untargeted metabolomic analysis: from sample preparation to data processing. . Methods Mol. Biol. 2276::357–82
     [Crossref] [Google Scholar]
137. 137.

     Srour B, Fezeu LK, Kesse-Guyot E, Allès B, Debras C, et al. 2020.. Ultraprocessed food consumption and risk of type 2 diabetes among participants of the NutriNet-Santé prospective cohort. . JAMA Intern. Med. 180:(2):283–91
     [Crossref] [Google Scholar]
138. 138.

     Srour B, Fezeu LK, Kesse-Guyot E, Allès B, Méjean C, et al. 2019.. Ultra-processed food intake and risk of cardiovascular disease: prospective cohort study (NutriNet-Santé). . BMJ 365::l1451
     [Crossref] [Google Scholar]
139. 139.

     Stojanov R, Popovski G, Cenikj G, Seljak BK, Eftimov T. 2021.. A fine-tuned bidirectional encoder representations from transformers model for food named-entity recognition: algorithm development and validation. . J. Med. Internet Res. 23:(8):e28229
     [Crossref] [Google Scholar]
140. 140.

     Stricker T, Bonner R, Lisacek F, Hopfgartner G. 2021.. Adduct annotation in liquid chromatography/high-resolution mass spectrometry to enhance compound identification. . Anal. Bioanal. Chem. 413::503–17
     [Crossref] [Google Scholar]
141. 141.

     Suez J, Cohen Y, Valdés-Mas R, Mor U, Dori-Bachash M, et al. 2022.. Personalized microbiome-driven effects of non-nutritive sweeteners on human glucose tolerance. . Cell 185:(18):3307–28.e19
     [Crossref] [Google Scholar]
142. 142.

     Swain MC, Cole JM. 2016.. ChemDataExtractor: a toolkit for automated extraction of chemical information from the scientific literature. . J. Chem. Inf. Model. 56:(10):1894–904
     [Crossref] [Google Scholar]
143. 143.

     Tang J, Tanoli ZUR, Ravikumar B, Alam Z, Rebane A, et al. 2018.. Drug Target Commons: a community effort to build a consensus knowledge base for drug–target interactions. . Cell Chem. Biol. 25:(2):224–29.e2
     [Crossref] [Google Scholar]
144. 144.

     Theodoris CV, Xiao L, Chopra A, Chaffin MD, Al Sayed ZR, et al. 2023.. Transfer learning enables predictions in network biology. . Nature 618:(7965):616–24
     [Crossref] [Google Scholar]
145. 145.

     Tobler M. 2023.. Evolutionary medicine I: aging and diseases of civilization. . In A Primer of Evolution, chapter 12. https://michitobler.github.io/primer-of-evolution/evolutionary-medicine-i-aging-and-diseases-of-civilization.html
     [Google Scholar]
146. 146.

     Toews R. 2023.. The next frontier for large language models is biology. . Forbes, July 16. https://www.forbes.com/sites/robtoews/2023/07/16/the-next-frontier-for-large-language-models-is-biology/
     [Google Scholar]
147. 147.

     Trott O, Olson AJ. 2010.. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. . J. Comput. Chem. 31:(2):455–61
     [Crossref] [Google Scholar]
148. 148.

     Tsugawa H, Kind T, Nakabayashi R, Yukihira D, Saito K, et al. 2015.. MS-FINDER: strategy for structure elucidation on LC-MS/MS based metabolomics by using chemo- and bioinformatics resources. . In 63rd Annual Conference on Mass Spectrometry. Tsukuba, Japan:: Mass Spectrom. Soc. Japan
     [Google Scholar]
149. 149.

     Ungar PS. 2007.. Evolution of the Human Diet: The Known, the Unknown, and the Unknowable. New York:: Oxford Univ. Press
     [Google Scholar]
150. 150.

     UNICEF. 2018.. The state of food security and nutrition in the world 2018. Rep. , UNICEF, New York:. https://www.unicef.org/reports/state-food-security-and-nutrition-world-2018
     [Google Scholar]
151. 151.

     Wang D, Barabási A-L. 2021.. The Science of Science. Cambridge, UK:: Cambridge Univ. Press
     [Google Scholar]
152. 152.

     Wang M, Carver JJ, Phelan VV, Sanchez LM, Garg N, et al. 2016.. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking. . Nat. Biotechnol. 34:(8):828–37
     [Crossref] [Google Scholar]
153. 153.

     Willett W. 2013.. Nutritional Epidemiology. New York:: Oxford Univ. Press
     [Google Scholar]
154. 154.

     Willett W, Rockström J, Loken B, Springmann M, Lang T, et al. 2019.. Food in the Anthropocene: the EAT-Lancet Commission on healthy diets from sustainable food systems. . Lancet 393:(10170):447–92
     [Crossref] [Google Scholar]
155. 155.

     Willett WC, Stampfer MJ, Manson JE, Colditz GA, Speizer FE, et al. 1993.. Intake of trans fatty acids and risk of coronary heart disease among women. . Lancet 341:(8845):581–85
     [Crossref] [Google Scholar]
156. 156.

     Wyatt P, Berry SE, Finlayson G, O'Driscoll R, Hadjigeorgiou G, et al. 2021.. Postprandial glycaemic dips predict appetite and energy intake in healthy individuals. . Nat. Metab. 3:(4):523–29
     [Crossref] [Google Scholar]
157. 157.

     Yannai S. 2013.. Dictionary of Food Compounds. Boca Raton, FL:: CRC
     [Google Scholar]
158. 158.

     Yu W, Clyne M, Khoury MJ, Gwinn M. 2009.. Phenopedia and genopedia: disease-centered and gene-centered views of the evolving knowledge of human genetic associations. . Bioinformatics 26:(1):145–46
     [Crossref] [Google Scholar]