[en] The transition toward new approach methodologies for toxicity testing has accelerated the development of computational models that utilize transcriptomic data to predict chemical-induced adverse effects. Here, we applied supervised machine learning to gene expression data derived from primary human hepatocytes and rat liver models (in vitro and in vivo) to predict drug-induced hepatic steatosis. We evaluated five machine learning classifiers using microarray data from the Open TG-GATEs database. Among these, support vector machine (SVM) consistently achieved the highest performance, with area under the receiver operating characteristic curve (ROC-AUC) of 0.820 in primary human hepatocytes, 0.975 in the rat in vitro model, and 0.966 in the rat in vivo model. To gain mechanistic insights, we functionally profiled the top-ranked predictive genes. Enrichment analyses revealed strong associations with lipid metabolism, mitochondrial function, insulin signalling, oxidative stress, all biological processes central to steatosis pathogenesis. Key predictive genes such as CYP1A1, PLIN2, and GCK mapped to lipid metabolism networks and liver disease annotations, while others highlighted novel transcriptomics signals. Integration with differentially expressed genes and known steatosis markers highlighted both overlapping and distinct molecular features, suggesting that machine learning models capture biologically relevant signals. These findings demonstrate the potential of machine learning models guided by transcriptomic data to identify early molecular signatures of drug-induced hepatic steatosis. The support vector machine model's strong predictive accuracy across species highlights its promise as a scalable and interpretable tool for chemical risk assessment. As data limitations in human toxicology persist, expanding high-quality transcriptomic resources will be critical to further advance non-animal approaches in regulatory toxicology.
Disciplines :
Life sciences: Multidisciplinary, general & others
Author, co-author :
Bwanya, Brian ; Department of Translational Genomics, GROW Research Institute for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, the Netherlands
Lodhi, Saad ; Department of Translational Genomics, GROW Research Institute for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, the Netherlands
de Kok, Theo M; Department of Translational Genomics, GROW Research Institute for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, the Netherlands
Maia Ladeira, Luiz Carlos ; Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique
Verheijen, Marcha Ct; Department of Translational Genomics, GROW Research Institute for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, the Netherlands
Jennen, Danyel Gj ; Department of Translational Genomics, GROW Research Institute for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, the Netherlands
Caiment, Florian ; Université de Liège - ULiège > Département de gestion vétérinaire des Ressources Animales (DRA) > GIGA-R : Génomique animale ; Department of Translational Genomics, GROW Research Institute for Oncology and Developmental Biology, Maastricht University, 6229 ER Maastricht, the Netherlands. Electronic address: florian.caiment@maastrichtuniversity.nl
Language :
English
Title :
Machine learning classification of steatogenic compounds using toxicogenomics profiles.
Aguayo, A., Brunak, S., Taboureau, O., Extrapolation of drug induced liver injury responses from cancer cell lines using machine learning approaches. Comput. Toxicol., 17, 2021, 100147, 10.1016/j.comtox.2020.100147.
Aigner, E., Strasser, M., Haufe, H., Sonnweber, T., Hohla, F., Stadlmayr, A., Solioz, M., Tilg, H., Patsch, W., Weiss, G., Stickel, F., Datz, C., A role for low hepatic copper concentrations in nonalcoholic Fatty liver disease. Am. J. Gastroenterol. 105:9 (2010), 1978–1985, 10.1038/ajg.2010.170.
Alexander-Dann, B., Pruteanu, L.L., Oerton, E., Sharma, N., Berindan-Neagoe, I., Modos, D., Bender, A., Developments in toxicogenomics: understanding and predicting compound-induced toxicity from gene expression data. Mol. Omics 14:4 (2018), 218–236, 10.1039/c8mo00042e.
Ancuceanu, R., Hovanet, M.V., Anghel, A.I., Furtunescu, F., Neagu, M., Constantin, C., Dinu, M., Computational Models Using Multiple Machine Learning Algorithms for Predicting Drug Hepatotoxicity with the DILIrank Dataset. Int J. Mol. Sci., 21(6), 2020, 10.3390/ijms21062114.
Atkins, J.T., George, G.C., Hess, K., Marcelo-Lewis, K.L., Yuan, Y., Borthakur, G., Khozin, S., LoRusso, P., Hong, D.S., Pre-clinical animal models are poor predictors of human toxicities in phase 1 oncology clinical trials. Br. J. Cancer 123:10 (2020), 1496–1501, 10.1038/s41416-020-01033-x.
Bajt, M.L., Farhood, A., Lemasters, J.J., Jaeschke, H., Mitochondrial bax translocation accelerates DNA fragmentation and cell necrosis in a murine model of acetaminophen hepatotoxicity. J. Pharm. Exp. Ther. 324:1 (2008), 8–14, 10.1124/jpet.107.129445.
Bastian, F.B., Cammarata, A.B., Carsanaro, S., Detering, H., Huang, W.T., Joye, S., Niknejad, A., Nyamari, M., Mendes de Farias, T., Moretti, S., Tzivanopoulou, M., Wollbrett, J., Robinson-Rechavi, M., Bgee in 2024: focus on curated single-cell RNA-seq datasets, and query tools. Nucleic Acids Res 53:D1 (2025), D878–D885, 10.1093/nar/gkae1118.
Biau, G. r, Devroye, L., Lectures on the Nearest Neighbor Method, 1st ed., 2015, Springer International Publishing: Imprint: Springer, 10.1007/978-3-319-25388-6.
Blades, B., Ayton, S., Hung, Y.H., Bush, A.I., La Fontaine, S., Copper and lipid metabolism: A reciprocal relationship. Biochim Biophys. Acta Gen. Subj., 1865(11), 2021, 129979, 10.1016/j.bbagen.2021.129979.
Browne, P., Paul Friedman, K., Boekelheide, K., Thomas, R.S., Adverse effects in traditional and alternative toxicity tests. Regul. Toxicol. Pharm., 148, 2024, 105579, 10.1016/j.yrtph.2024.105579.
Cai, F.F., Song, Y.N., Lu, Y.Y., Zhang, Y., Hu, Y.Y., Su, S.B., Analysis of plasma metabolic profile, characteristics and enzymes in the progression from chronic hepatitis B to hepatocellular carcinoma. Aging (Albany NY) 12:14 (2020), 14949–14965, 10.18632/aging.103554.
Caloni, F., De Angelis, I., Hartung, T., Replacement of animal testing by integrated approaches to testing and assessment (IATA): a call for in vivitrosi. Arch. Toxicol. 96:7 (2022), 1935–1950, 10.1007/s00204-022-03299-x.
Cataldi, M., Citro, V., Resnati, C., Manco, F., Tarantino, G., New Avenues for Treatment and Prevention of Drug-Induced Steatosis and Steatohepatitis: Much More Than Antioxidants. Adv. Ther. 38:5 (2021), 2094–2113, 10.1007/s12325-021-01669-y.
Chalasani, N., Li, Y.J., Dellinger, A., Navarro, V., Bonkovsky, H., Fontana, R.J., Gu, J., Barnhart, H., Phillips, E., Lammert, C., Schwantes-An, T.H., Nicoletti, P., Kleiner, D.E., Hoofnagle, J.H., Drug Induced Liver Injury, N., Clinical features, outcomes, and HLA risk factors associated with nitrofurantoin-induced liver injury. J. Hepatol. 78:2 (2023), 293–300, 10.1016/j.jhep.2022.09.010.
Cristianini, N., Shawe-Taylor, J., An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. 2000, Cambridge University Press.
Dirven, H., Vist, G.E., Bandhakavi, S., Mehta, J., Fitch, S.E., Pound, P., Ram, R., Kincaid, B., Leenaars, C.H.C., Chen, M., Wright, R.A., Tsaioun, K., Performance of preclinical models in predicting drug-induced liver injury in humans: a systematic review. Sci. Rep., 11(1), 2021, 6403, 10.1038/s41598-021-85708-2.
Dong, S., Chen, Q.L., Song, Y.N., Sun, Y., Wei, B., Li, X.Y., Hu, Y.Y., Liu, P., Su, S.B., Mechanisms of CCl4-induced liver fibrosis with combined transcriptomic and proteomic analysis. J. Toxicol. Sci. 41:4 (2016), 561–572, 10.2131/jts.41.561.
Dong, Y., Yu, Q., Chen, Y., Xu, N., Zhao, Q., Jia, C., Zhang, B., Zhang, K., Zhang, B., Xing, L., Li, M., The Ccz1 mediates the autophagic clearance of damaged mitochondria in response to oxidative stress in Candida albicans. Int J. Biochem Cell Biol. 69 (2015), 41–51, 10.1016/j.biocel.2015.10.002.
Durinck, S., Spellman, P.T., Birney, E., Huber, W., Mapping identifiers for the integration of genomic datasets with the R/Bioconductor package biomaRt. Nat. Protoc. 4:8 (2009), 1184–1191, 10.1038/nprot.2009.97.
Evangelista, J.E., Xie, Z., Marino, G.B., Nguyen, N., Clarke, D.J.B., Ma'ayan, A., Enrichr-KG: bridging enrichment analysis across multiple libraries. Nucleic Acids Res 51:W1 (2023), W168–W179, 10.1093/nar/gkad393.
Ganter, B., Tugendreich, S., Pearson, C.I., Ayanoglu, E., Baumhueter, S., Bostian, K.A., Brady, L., Browne, L.J., Calvin, J.T., Day, G.J., Breckenridge, N., Dunlea, S., Eynon, B.P., Furness, L.M., Ferng, J., Fielden, M.R., Fujimoto, S.Y., Gong, L., Hu, C., Jarnagin, K., Development of a large-scale chemogenomics database to improve drug candidate selection and to understand mechanisms of chemical toxicity and action. J. Biotechnol. 119:3 (2005), 219–244, 10.1016/j.jbiotec.2005.03.022.
Gautier, L., Cope, L., Bolstad, B.M., Irizarry, R.A., affy-analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20:3 (2004), 307–315, 10.1093/bioinformatics/btg405.
Gawron, P., Smula, E., Schneider, R., Ostaszewski, M., Exploration and comparison of molecular mechanisms across diseases using MINERVA Net. Protein Sci., 32(2), 2023, e4565, 10.1002/pro.4565.
Gluchowski, N.L., Becuwe, M., Walther, T.C., Farese, R.V. Jr., Lipid droplets and liver disease: from basic biology to clinical implications. Nat. Rev. Gastroenterol. Hepatol. 14:6 (2017), 343–355, 10.1038/nrgastro.2017.32.
Godoy, P., Hewitt, N.J., Albrecht, U., Andersen, M.E., Ansari, N., Bhattacharya, S., Bode, J.G., Bolleyn, J., Borner, C., Bottger, J., Braeuning, A., Budinsky, R.A., Burkhardt, B., Cameron, N.R., Camussi, G., Cho, C.S., Choi, Y.J., Craig Rowlands, J., Dahmen, U., Hengstler, J.G., Recent advances in 2D and 3D in vitro systems using primary hepatocytes, alternative hepatocyte sources and non-parenchymal liver cells and their use in investigating mechanisms of hepatotoxicity, cell signaling and ADME. Arch. Toxicol. 87:8 (2013), 1315–1530, 10.1007/s00204-013-1078-5.
Gu, J.J., Wang, Z., Reeves, R., Magnuson, N.S., PIM1 phosphorylates and negatively regulates ASK1-mediated apoptosis. Oncogene 28:48 (2009), 4261–4271, 10.1038/onc.2009.276.
Guo, W., Liu, J., Dong, F., Song, M., Li, Z., Khan, M.K.H., Patterson, T.A., Hong, H., Review of machine learning and deep learning models for toxicity prediction. Exp. Biol. Med (Maywood) 248:21 (2023), 1952–1973, 10.1177/15353702231209421.
Guyon, I., Weston, J., Barnhill, S., Vapnik, V., Gene Selection for Cancer Classification using Support Vector Machines. Mach. Learn. 46:1 (2002), 389–422, 10.1023/A:1012487302797.
Hammer, H., Schmidt, F., Marx-Stoelting, P., Potz, O., Braeuning, A., Cross-species analysis of hepatic cytochrome P450 and transport protein expression. Arch. Toxicol. 95:1 (2021), 117–133, 10.1007/s00204-020-02939-4.
Hastie, T., Tibshirani, R., Friedman, J.H., The elements of statistical learning: data mining, inference, and prediction, 2nd ed.)., 2009, Springer.
Hosack, T., Damry, D., Biswas, S., Drug-induced liver injury: a comprehensive review. Ther. Adv. Gastroenterol., 16, 2023, 17562848231163410, 10.1177/17562848231163410.
Idilman, I.S., Ozdeniz, I., Karcaaltincaba, M., Hepatic Steatosis: Etiology, Patterns, and Quantification. Semin Ultrasound CT MR 37:6 (2016), 501–510, 10.1053/j.sult.2016.08.003.
Igarashi, Y., Nakatsu, N., Yamashita, T., Ono, A., Ohno, Y., Urushidani, T., Yamada, H., Open TG-GATEs: a large-scale toxicogenomics database. (Database issue) Nucleic Acids Res 43 (2015), D921–D927, 10.1093/nar/gku955.
Imai, Y., Varela, G.M., Jackson, M.B., Graham, M.J., Crooke, R.M., Ahima, R.S., Reduction of hepatosteatosis and lipid levels by an adipose differentiation-related protein antisense oligonucleotide. Gastroenterology 132:5 (2007), 1947–1954, 10.1053/j.gastro.2007.02.046.
Jiang, J., van Ertvelde, J., Ertaylan, G., Peeters, R., Jennen, D., de Kok, T.M., Vinken, M., Unraveling the mechanisms underlying drug-induced cholestatic liver injury: identifying key genes using machine learning techniques on human in vitro data sets. Arch. Toxicol. 97:11 (2023), 2969–2981, 10.1007/s00204-023-03583-4.
Kolaric, T.O., Nincevic, V., Kuna, L., Duspara, K., Bojanic, K., Vukadin, S., Raguz-Lucic, N., Wu, G.Y., Smolic, M., Drug-induced Fatty Liver Disease: Pathogenesis and Treatment. J. Clin. Transl. Hepatol. 9:5 (2021), 731–737, 10.14218/JCTH.2020.00091.
Kuhn, M., & Wickham, H. (2020). Tidymodels: a collection of packages for modeling and machine learning using tidyverse principles. Retrieved 12/03/2024 from 〈https://www.tidymodels.org〉.
Ladeira, L., Verhoeven, A., Ertvelde, J. v, Jiang, J., Sanz-Serrano, J., Gamba, A., Vanhaecke, T., Heusinkveld, H.J., Jover, R., Vinken, M., Geris, L., Staumont, B., Unlocking liver physiology. Compr. Pathw. maps Mech. Underst. Creat., 2024, 10.5281/zenodo.14515238.
Ladeira, L., Verhoeven, A., van Ertvelde, J., Jiang, J., Gamba, A., Sanz-Serrano, J., Vanhaecke, T., Heusinkveld, H.J., Jover, R., Vinken, M., Geris, L., Staumont, B., Unlocking liver physiology: comprehensive pathway maps for mechanistic understanding [Brief Research Report]. Front. Toxicol., 7, 2025, 2025, 10.3389/ftox.2025.1619651.
Li, H., Toth, E., Cherrington, N.J., Alcohol Metabolism in the Progression of Human Nonalcoholic Steatohepatitis. Toxicol. Sci. 164:2 (2018), 428–438, 10.1093/toxsci/kfy106.
Lin, Z., Chou, W.C., Machine Learning and Artificial Intelligence in Toxicological Sciences. Toxicol. Sci. 189:1 (2022), 7–19, 10.1093/toxsci/kfac075.
Liu, Y., Jing, R., Wen, Z., Li, M., Narrowing the Gap Between In Vitro and In Vivo Genetic Profiles by Deconvoluting Toxicogenomic Data In Silico. Front Pharm., 10, 2019, 1489, 10.3389/fphar.2019.01489.
Ma, Y., Lee, G., Heo, S.Y., Roh, Y.S., Oxidative Stress Is a Key Modulator in the Development of Nonalcoholic Fatty Liver Disease. Antioxid. (Basel), 11(1), 2021, 10.3390/antiox11010091.
Maddirevula, S., Shagrani, M., Ji, A.R., Horne, C.R., Young, S.N., Mather, L.J., Alqahtani, M., McKerlie, C., Wood, G., Potter, P.K., Abdulwahab, F., AlSheddi, T., van der Woerd, W.L., van Gassen, K.L.I., AlBogami, D., Kumar, K., Muhammad Akhtar, A.S., Binomar, H., Almanea, H., Alkuraya, F.S., Large-scale genomic investigation of pediatric cholestasis reveals a novel hepatorenal ciliopathy caused by PSKH1 mutations. Genet Med, 26(11), 2024, 101231, 10.1016/j.gim.2024.101231.
Martignoni, M., Groothuis, G.M., de Kanter, R., Species differences between mouse, rat, dog, monkey and human CYP-mediated drug metabolism, inhibition and induction. Expert Opin. Drug Metab. Toxicol. 2:6 (2006), 875–894, 10.1517/17425255.2.6.875.
Masubuchi, Y., Ihara, A., Protection of mice against carbon tetrachloride-induced acute liver injury by endogenous and exogenous estrogens. Drug Metab. Pharm., 46, 2022, 100460, 10.1016/j.dmpk.2022.100460.
McIntosh, A.L., Senthivinayagam, S., Moon, K.C., Gupta, S., Lwande, J.S., Murphy, C.C., Storey, S.M., Atshaves, B.P., Direct interaction of Plin2 with lipids on the surface of lipid droplets: a live cell FRET analysis. Am. J. Physiol. Cell Physiol. 303:7 (2012), C728–C742, 10.1152/ajpcell.00448.2011.
Mihajlovic, M., Vinken, M., Mitochondria as the Target of Hepatotoxicity and Drug-Induced Liver Injury: Molecular Mechanisms and Detection Methods. Int J. Mol. Sci., 23(6), 2022, 10.3390/ijms23063315.
Morrell, A., Tallino, S., Yu, L., Burkhead, J.L., The role of insufficient copper in lipid synthesis and fatty-liver disease. IUBMB Life 69:4 (2017), 263–270, 10.1002/iub.1613.
O'Donovan, S.D., Cavill, R., Wimmenauer, F., Lukas, A., Stumm, T., Smirnov, E., Lenz, M., Ertaylan, G., Jennen, D.G.J., van Riel, N.A.W., Driessens, K., Peeters, R.L.M., de Kok, T., Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data. PLoS One, 18(11), 2023, e0292030, 10.1371/journal.pone.0292030.
Palma, F.R., He, C., Danes, J.M., Paviani, V., Coelho, D.R., Gantner, B.N., Bonini, M.G., Mitochondrial Superoxide Dismutase: What the Established, the Intriguing, and the Novel Reveal About a Key Cellular Redox Switch. Antioxid. Redox Signal 32:10 (2020), 701–714, 10.1089/ars.2019.7962.
Peter, A., Stefan, N., Cegan, A., Walenta, M., Wagner, S., Konigsrainer, A., Konigsrainer, I., Machicao, F., Schick, F., Haring, H.U., Schleicher, E., Hepatic glucokinase expression is associated with lipogenesis and fatty liver in humans. J. Clin. Endocrinol. Metab. 96:7 (2011), E1126–E1130, 10.1210/jc.2010-2017.
Petrosillo, G., Portincasa, P., Grattagliano, I., Casanova, G., Matera, M., Ruggiero, F.M., Ferri, D., Paradies, G., Mitochondrial dysfunction in rat with nonalcoholic fatty liver. Involv. Complex I React. Oxyg. Species cardiolipin. Biochim Biophys. Acta 1767:10 (2007), 1260–1267, 10.1016/j.bbabio.2007.07.011.
Pilling, D., Martinez, T.C., Gomer, R.H., Inhibition of CCl4-induced liver inflammation and fibrosis by a NEU3 inhibitor. PLoS One, 19(11), 2024, e0308060, 10.1371/journal.pone.0308060.
Pu, J., Wang, J., Qin, Z., Wang, A., Zhang, Y., Wu, X., Wu, Y., Li, W., Xu, Z., Lu, Y., Tang, Q., Wei, H., IGF2BP2 Promotes Liver Cancer Growth Through an m6A-FEN1-Dependent Mechanism. Front Oncol., 10, 2020, 578816, 10.3389/fonc.2020.578816.
Pyo, J.H., Kim, T.J., Lee, H., Choi, S.C., Cho, S.J., Choi, Y.H., Min, Y.W., Min, B.H., Lee, J.H., Kang, M., Lee, Y.C., Kim, J.J., Proton pump inhibitors use and the risk of fatty liver disease: A nationwide cohort study. J. Gastroenterol. Hepatol. 36:5 (2021), 1235–1243, 10.1111/jgh.15236.
Satapathy, S.K., Kuwajima, V., Nadelson, J., Atiq, O., Sanyal, A.J., Drug-induced fatty liver disease: An overview of pathogenesis and management. Ann. Hepatol. 14:6 (2015), 789–806, 10.5604/16652681.1171749.
Schmeisser, S., Miccoli, A., von Bergen, M., Berggren, E., Braeuning, A., Busch, W., Desaintes, C., Gourmelon, A., Grafstrom, R., Harrill, J., Hartung, T., Herzler, M., Kass, G.E.N., Kleinstreuer, N., Leist, M., Luijten, M., Marx-Stoelting, P., Poetz, O., van Ravenzwaay, B., Tralau, T., New approach methodologies in human regulatory toxicology - Not if, but how and when!. Environ. Int, 178, 2023, 108082, 10.1016/j.envint.2023.108082.
Statnikov, A., Aliferis, C.F., Tsamardinos, I., Hardin, D., Levy, S., A comprehensive evaluation of multicategory classification methods for microarray gene expression cancer diagnosis. Bioinformatics 21:5 (2005), 631–643, 10.1093/bioinformatics/bti033.
Sudhahar, V., Urao, N., Oshikawa, J., McKinney, R.D., Llanos, R.M., Mercer, J.F., Ushio-Fukai, M., Fukai, T., Copper transporter ATP7A protects against endothelial dysfunction in type 1 diabetic mice by regulating extracellular superoxide dismutase. Diabetes 62:11 (2013), 3839–3850, 10.2337/db12-1228.
Tahri-Joutey, M., Andreoletti, P., Surapureddi, S., Nasser, B., Cherkaoui-Malki, M., Latruffe, N., Mechanisms Mediating the Regulation of Peroxisomal Fatty Acid Beta-Oxidation by PPARalpha. Int J. Mol. Sci., 22(16), 2021, 10.3390/ijms22168969.
Tardelli, M., Bruschi, F.V., Claudel, T., Fuchs, C.D., Auer, N., Kunczer, V., Stojakovic, T., Scharnagl, H., Habib, A., Grabner, G.F., Zimmermann, R., Lotersztajn, S., Trauner, M., Lack of monoacylglycerol lipase prevents hepatic steatosis by favoring lipid storage in adipose tissue and intestinal malabsorption. J. Lipid Res 60:7 (2019), 1284–1292, 10.1194/jlr.M093369.
Tsai, T.H., Chen, E., Li, L., Saha, P., Lee, H.J., Huang, L.S., Shelness, G.S., Chan, L., Chang, B.H., The constitutive lipid droplet protein PLIN2 regulates autophagy in liver. Autophagy 13:7 (2017), 1130–1144, 10.1080/15548627.2017.1319544.
Vedi, M., Smith, J.R., Thomas Hayman, G., Tutaj, M., Brodie, K.C., De Pons, J.L., Demos, W.M., Gibson, A.C., Kaldunski, M.L., Lamers, L., Laulederkind, S.J.F., Thota, J., Thorat, K., Tutaj, M.A., Wang, S.J., Zacher, S., Dwinell, M.R., Kwitek, A.E., 2022 updates to the Rat Genome Database: a Findable, Accessible, Interoperable, and Reusable (FAIR) resource. Genetics, 224(1), 2023, 10.1093/genetics/iyad042.
Verheijen, M., Sarkans, U., Wolski, W., Jennen, D., Caiment, F., Kleinjans, J., HeCaTo, S.C., Multi-omics HeCaToS dataset of repeated dose toxicity for cardiotoxic & hepatotoxic compounds. Sci. Data, 9(1), 2022, 699, 10.1038/s41597-022-01825-1.
Verheijen, M.C., Meier, M.J., Asensio, J.O., Gant, T.W., Tong, W., Yauk, C.L., Caiment, F., R-ODAF: Omics data analysis framework for regulatory application. Regul. Toxicol. Pharm., 131, 2022, 105143, 10.1016/j.yrtph.2022.105143.
Verhoeven, A., van Ertvelde, J., Boeckmans, J., Gatzios, A., Jover, R., Lindeman, B., Lopez-Soop, G., Rodrigues, R.M., Rapisarda, A., Sanz-Serrano, J., Stinckens, M., Sepehri, S., Teunis, M., Vinken, M., Jiang, J., Vanhaecke, T., A quantitative weight-of-evidence method for confidence assessment of adverse outcome pathway networks: A case study on chemical-induced liver steatosis. Toxicology, 505, 2024, 153814, 10.1016/j.tox.2024.153814.
Wang, F., Man, C., Wang, X., Odle, J., Maltecca, C., Lin, X., MicroRNA and mRNA sequencing analyses reveal key hepatic metabolic and signaling pathways responsive to maternal undernutrition in full-term fetal pigs. J. Nutr. Biochem, 116, 2023, 109312, 10.1016/j.jnutbio.2023.109312.
Wang, J., Chen, L., Qiang, P., The role of IGF2BP2, an m6A reader gene, in human metabolic diseases and cancers. Cancer Cell Int, 21(1), 2021, 99, 10.1186/s12935-021-01799-x.
Weiler, S., Merz, M., Kullak-Ublick, G.A., Drug-induced liver injury: the dawn of biomarkers?. F1000Prime Rep., 7, 2015, 34, 10.12703/P7-34.
Williams, D.P., Shipley, R., Ellis, M.J., Webb, S., Ward, J., Gardner, I., Creton, S., Novel in vitro and mathematical models for the prediction of chemical toxicity. Toxicol. Res (Camb. 2:1 (2013), 40–59, 10.1039/c2tx20031g.
Winter, D.J., rentrez: An R package for the NCBI eUtils API. R. J. 9:2 (2017), 520–526 〈https://journal.r-project.org/archive/2017/RJ-2017-058/index.html〉.
Xu, Z., He, B., Jiang, Y., Zhang, M., Tian, Y., Zhou, N., Zhou, Y., Chen, M., Tang, M., Gao, J., Peng, F., Igf2bp2 knockdown improves CCl(4)-induced liver fibrosis and TGF-beta-activated mouse hepatic stellate cells by regulating Tgfbr1. Int Immunopharmacol., 110, 2022, 108987, 10.1016/j.intimp.2022.108987.
Yang, M., Wang, D., Wang, X., Mei, J., Gong, Q., Role of Folate in Liver Diseases. Nutrients, 16(12), 2024, 10.3390/nu16121872.
Zou, H., Hastie, T., Regularization and Variable Selection Via the Elastic Net. J. R. Stat. Soc. Ser. B Stat. Methodol. 67:2 (2005), 301–320, 10.1111/j.1467-9868.2005.00503.x.
