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Bioinformatics Publications

2023

Immediate Office

  1. Exploring the Knowledge Gaps in Infant Drug Exposure from Human Milk: A Clinical Pharmacology Perspective.
    Guinn D., Pressly M.A., Liu Z., Ceresa C., Samuels S., Wang Y-M, Madabushi R., Schmidt S., and Fletcher E.P.
    The Journal of Clinical Pharmacology. Mar 2023; 63(3):273-276. 10.1002/jcph.2177
  2. DICTrank: The largest reference list of 1318 human drugs ranked by risk of drug-induced cardiotoxicity using FDA labeling.
    Qu Y., Li T., Liu Z., and Tong W.
    Drug Discovery Today. November 2023; 28(11):103770. 10.1016/j.drudis.2023.103770
  3. A generative adversarial network model alternative to animal studies for clinical pathology assessment.
    Chen X., Roberts R., Liu Z., and Tong W.
    Nature Communications. November 2023; 14(1):7141. 10.1038/s41467-023-42933-9
  4. PLM-ARG: antibiotic resistance gene identification using a pretrained protein language model.
    Wu J., Ouyang J., Qin H., Zhou J., Roberts R., Siam R., Wang L., Tong W., Liu Z., and Shi T.
    Bioinformatics. November 2023; 39(11):btad690. 10.1093/bioinformatics/btad690
  5. DeepAmes: A deep learning-powered Ames test predictive model with potential for regulatory application.
    Li T., Liu Z., Thakkar S., Roberts R., and Tong W.
    Regulatory Toxicology and Pharmacology. October 2023; 144:105486. 10.1016/j.yrtph.2023.105486
  6. Predicting drug-induced liver injury with artificial intelligence—a mini-review.
    Li T., Kusko R., Thakkar S., Liu Z., and Tong W.
    In: Artificial Intelligence, Machine Learning, and Deep Learning in Precision Medicine in Liver Diseases (Chapter 12). Eds. Su T-H, Kao J-H. Academic Press, London. 2023:233-251. 10.1016/B978-0-323-99136-0.00012-X
  7. TransOrGAN: An Artificial Intelligence Mapping of Rat Transcriptomic Profiles Between Organs, Ages, and Sexes.
    Li T., Roberts R., Liu Z., and Tong W.
    Chemical Research in Toxicology. June 2023; 36(6):916-925. 10.1021/acs.chemrestox.3c00037
  8. Artificial intelligence and real-world data for drug and food safety - A regulatory science perspective.
    Thakkar S., Slikker Jr. W., Yiannas F., Silva P., Blais B., Chng R.K., Liu Z., Adholeya A., Pappalardo F., Soares M.D.L.C., Beeler P.E., Whelan M., Roberts R., Borlak J., Hugas M., Torrecilla-Salinas C., Girard P., Diamond M.C., Verloo D., Panda B., Rose M.C., Jornet J.B., Furuhama A., Fang H., Kwegyir-Afful E., Heintz K., Arvidson K., Burgos J.G., Horst A., and Tong W.
    Regulatory Toxicology and Pharmacology. May 2023; 140:105388. 10.1016/j.yrtph.2023.105388
  9. The Quartet Data Portal: integration of community-wide resources for multiomics quality control.
    Yang J., Liu Y., Shang J., Chen Q., Chen Q., Ren L., Zhang N., Yu Y., Li Z., Song Y., Yang S., Scherer A., Tong W., Hong H., Xiao W., Shi L., and Zheng Y.
    Genome Biology. October 2023; 24:245. 10.1186/s13059-023-03091-9
  10. Evaluation of QSAR models for predicting mutagenicity: outcome of the Second Ames/QSAR international challenge project.
    Furuhama A., Kitazawa A., Yao J., Matos Dos Santos C.E., Rathman J., Yang C., Ribeiro J.V., Cross K., Myatt G., Raitano G., Benfenati E., Jeliazkova N., Saiakhov R., Chakravarti S., Foster R.S., Bossa C., Battistelli C.L., Benigni R., Sawada T., Wasada H., Hashimoto T., Wu M., Barzilay R., Daga P.R., Clark R.D., Mestres J., Montero A., Gregori-Puigjané E., Petkov P., Ivanova H., Mekenyan O., Matthews S., Guan D., Spicer J., Lui R., Uesawa Y., Kurosaki K., Matsuzaka Y., Sasaki S., Cronin M.T.D., Belfield S.J., Firman J.W., Spînu N., Qiu M., Keca J.M., Gini G., Li T., Tong W., Hong H., Liu Z., Igarashi Y., Yamada H., Sugiyama K.I., and Honma M.
    SAR QSAR Environ Res. 2023 Oct-Dec;34(12):983-1001. doi: 10.1080/1062936X.2023.2284902. Epub 2023 Dec 4. PMID: 38047445.
  11. Applying Genomics in Regulatory Toxicology: a report of the ECETOC workshop on omics threshold on non-adversity.
    Gant T.W., Auerbach S.S., Von Bergen M., Bouhifd M., Botham P.A., Caiment F., Currie R.A., Harrill J., Johnson K., Li D., Rouquie D., van Ravenzwaay B., Sistare F., Tralau T., Viant M.R., van de Laan J.W., and Yauk C.
    Archives of Toxicology. Aug 2023; 97:2291-2302. 10.1007/s00204-023-03522-3

Bioinformatics Branch 

  1. Distinct Conformations of SARS-CoV-2 Omicron Spike Protein and Its Interaction with ACE2 and Antibody.
    Lee M., Major M., and Hong H.
    Int J Mol Sci. 2023 Feb 14;24(4):3774. doi: 10.3390/ijms24043774. PMID: 36835186; PMCID: PMC9967551.
  2. Preface. In: Machine Learning and Deep Learning in Computational Toxicology. 
    Kusko R. and Hong H.
    Ed. Hong H.
    Springer, Cham. 2023: v-vii. 10.1007/978-3-031-20730-3.
  3. Machine Learning and Deep Learning Promotes Computational Toxicology for Risk Assessment of Chemicals.
    Kusko R. and Hong H.
    In: Machine Learning and Deep Learning in Computational Toxicology (Chapter 1). Ed. Hong H.
    Springer, Cham. 2023:1-17. 10.1007/978-3-031-20730-3_1.
  4. ED Profiler: Machine Learning Tool for Screening Potential Endocrine Disrupting Chemicals.
    Yang X., Liu H., Kusko R., and Hong H.
    In: Machine Learning and Deep Learning in Computational Toxicology (Chapter 10). Ed. Hong H.
    Springer, Cham. 2023: 243-262. 10.1007/978-3-031-20730-3_10.
  5. Machine Learning for Predicting Organ Toxicity.
    Liu J., Guo W., Dong F., Patterson T.A., and Hong H.
    In: Machine Learning and Deep Learning in Computational Toxicology (Chapter 22). Ed. Hong H.
    Springer, Cham. 2023: 519-537. 10.1007/978-3-031-20730-3_22.
  6. Quantitative Target-specific Toxicity Prediction Modeling (QTTPM): Coupling Machine Learning with Dynamic Protein-Ligand Interaction Descriptors (dyPLIDs) to Predict Androgen Receptor-mediated Toxicity.
    Thangapandian S., Idakwo G., Luttrell J., Hong H., Zhang C., and Gong P.
    In: Machine Learning and Deep Learning in Computational Toxicology (Chapter 11). Ed. Hong H.
    Springer, Cham. 2023: 263-295. 10.1007/978-3-031-20730-3_11.
  7. Machine Learning for Predicting Gas Adsorption Capacities of Metal Organic Framework.
    Guo W., Liu J., Dong F., Patterson T.A., and Hong H.
    In: Machine Learning and Deep Learning in Computational Toxicology (Chapter 28). Ed. Hong H.
    Springer, Cham. 2023: 629-654. 10.1007/978-3-031-20730-3_28.
  8. Mold2 Descriptors Facilitate Development of Machine Learning and Deep Learning Models for Predicting Toxicity of Chemicals.
    Hong H., Liu J., Ge W., Sakkiah S., Guo W., Yavas G., Zhang C., Gong P., Tong W., and Patterson T.A.
    In: Machine Learning and Deep Learning in Computational Toxicology (Chapter 12). Ed. Hong H.
    Springer, Cham. 2023: 297-321. 10.1007/978-3-031-20730-3_12.
  9. Computational Modeling for the Prediction of Hepatotoxicity Caused by Drugs and Chemicals.
    Chen M., Liu J., Liao T-J, Ashby K., Wu Y., Wu L., Tong W., and Hong H.
    In: Machine Learning and Deep Learning in Computational Toxicology (Chapter 23). Ed. Hong H.
    Springer, Cham. 2023: 541-561. 10.1007/978-3-031-20730-3_23.
  10. Deep Learning Methods for Omics Data Imputation.
    Huang L., Song M., Shen H., Hong H., Gong P., Deng H-W, and Zhang C.
    Biology. October 2023; 12(10):1313. 10.3390/biology12101313.
  11. Analyzing 3D structures of the SARS-CoV-2 main protease reveals structural features of ligand binding for COVID-19 drug discovery.
    Xu L., Chen R., Liu J., Patterson T.A., and Hong H.
    Drug Discovery Today. October 2023; 28(10):103727. 10.1016/j.drudis.2023.103727.
  12. In silico modeling-based new approach methods to predict drug and herb-induced liver injury: A review.
    Shin H.K., Huang R., and Chen M.
    Food and Chemical Toxicology. September 2023; 179:113948. 10.1016/j.fct.2023.113948.
  13. In: QSAR in Safety Evaluation and Risk Assessment (Preface).  
    Hong H.
    Ed. Hong H.
    Academic Press, London. 2023: xix-xx. 10.1016/B978-0-443-15339-6.00002-3.
  14. Deploying QSAR to discriminate excess toxicity and identify the toxic mode of action of organic pollutants to aquatic organisms.
    Su L., He M., Qu J., Gui B., Li J., Kusko R., Hong H., and Zhao Y.
    In: QSAR in Safety Evaluation and Risk Assessment (Chapter 31). Ed. Hong H.
    Academic Press, London. 2023: 427-445. 10.1016/B978-0-443-15339-6.00017-5.
  15. QSAR models for predicting in vivo reproductive toxicity.
    Liu J., Dong F., Guo W., Li Z., Xu L., Song M., Patterson T.A., and Hong H.
    In: QSAR in Safety Evaluation and Risk Assessment (Chapter 23). Ed. Hong H.
    Academic Press, London. 2023: 315-327. 10.1016/B978-0-443-15339-6.00013-8.
  16. EADB—A database providing curated data for developing QSAR models to facilitate the assessment of endocrine activity.
    Dong F., Guo W., Liu J., Xu L., Lee M., Song M., Li Z., Patterson T.A., and Hong H.
    In: QSAR in Safety Evaluation and Risk Assessment (Chapter 19). Ed. Hong H.
    Academic Press, London. 2023: 259-272. 10.1016/B978-0-443-15339-6.00015-1.
  17. Decision Forest—a machine learning algorithm for QSAR modeling.
    Hong H., Liu J., Guo W., Dong F., Lee M., Xu L., Li Z., Song M., Chen M., Zou W., Tong W., and Patterson T.A.
    In: QSAR in Safety Evaluation and Risk Assessment (Chapter 4). Ed. Hong H.
    Academic Press, London. 2023: 35-48. 10.1016/B978-0-443-15339-6.00029-1.
  18. QSAR facilitating safety evaluation and risk assessment.
    Kusko R. and Hong H.
    In: QSAR in Safety Evaluation and Risk Assessment (Chapter 1). Ed. Hong H.
    Academic Press, London. 2023: 1-10. 10.1016/B978-0-443-15339-6.00036-9.
  19. Three-Dimensional Structural Insights Have Revealed the Distinct Binding Interactions of Agonists, Partial Agonists, and Antagonists with the μ Opioid Receptor.
    Li Z., Liu J., Chang N., Huang R., Xia M., Patterson T.A., and Hong H.
    International Journal of Molecular Sciences. 2023; 24(8):7042. 10.3390/ijms24087042.
  20. Review of machine learning and deep learning models for toxicity prediction.
    Guo W., Liu J., Dong F., Song M., Li Z., Khan M.K.H., Patterson T.A., and Hong H.
    Exp Biol Med (Maywood). 2023 Dec 6:15353702231209421. doi: 10.1177/15353702231209421. Epub ahead of print. PMID: 38057999.
  21. Developing a SARS-CoV-2 main protease binding prediction random forest model for drug repurposing for COVID-19 treatment.
    Liu J., Xu L., Guo W., Li Z., Khan M.K.H., Ge W., Patterson T.A., and Hong H.
    Exp Biol Med (Maywood). 2023 Nov 24:15353702231209413. doi: 10.1177/15353702231209413. Epub ahead of print. PMID: 37997891.
  22. Machine learning and deep learning for brain tumor MRI image segmentation.
    Khan M.K.H., Guo W., Liu J., Dong F., Li Z., Patterson T.A., and Hong H.
    Exp Biol Med (Maywood). 2023 Dec 16:15353702231214259. doi: 10.1177/15353702231214259. Epub ahead of print. PMID: 38102956.
  23. Correcting batch effects in large-scale multiomics studies using a reference-material-based ratio method.
    Yu Y., Zhang N., Mai Y., Ren L., Chen Q., Cao Z., Chen Q., Liu Y., Hou W., Yang J., Hong H., Xu J., Tong W., Dong L., Shi L., Fang X., and Zheng Y.
    Genome Biol. 2023 Sep 7; 24(1):201. doi: 10.1186/s13059-023-03047-z. PMID: 37674217; PMCID: PMC10483871.
  24. Quartet RNA reference materials improve the quality of transcriptomic data through ratio-based profiling.
    Yu Y., Hou W., Liu Y., Wang H., Dong L., Mai Y., Chen Q., Li Z., Sun S., Yang J., Cao Z., Zhang P., Zi Y., Liu R., Gao J., Zhang N., Li J., Ren L., Jiang H., Shang J., Zhu S., Wang X., Qing T., Bao D., Li B., Li B., Suo C., Pi Y., Wang X., Dai F., Scherer A., Mattila P., Han J., Zhang L., Jiang H., Thierry-Mieg D., Thierry-Mieg J., Xiao W., Hong H., Tong W., Wang J., Li J., Fang X., Jin L., Xu J., Qian F., Zhang R., Shi L., and Zheng Y.
    Nat Biotechnol. 2023 Sep 7. doi: 10.1038/s41587-023-01867-9. Epub ahead of print. Erratum in: Nat Biotechnol. 2023 Oct 2; PMID: 37679545.
  25. A systematic analysis and data mining of opioid-related adverse events submitted to the FAERS database
    Le H., Hong H., Ge W., Francis H., Lyn-Cook B., Hwang Y.T., Rogers P., Tong W., and Zou W. 
    Exp Biol Med (Maywood). 2023 Dec 30:15353702231211860. doi: 10.1177/15353702231211860. Epub ahead of print. PMID: 38158803.
  26. Towards a Light-mediated Gene Therapy for the Eye using Caged Ethinylestradiol and the Inducible Cre/lox System.
    Kiy Z., Chaud J., Xu L., Brandhorst E., Kamali T., Vargas C., Keller S., Hong H., Specht A., and Cambridge S.
    Angew Chem Int Ed Engl. 2023 Dec 21:e202317675. doi: 10.1002/anie.202317675. Epub ahead of print. PMID: 38127455.

Biostatistics Branch

  1. Statistical methods for exploring spontaneous adverse event reporting databases for drug-host factor interactions.
    Lu Z, Suzuki A, Wang D.
    BMC Medical Research Methodology. Mar 2023; 23:71. 10.1186/s12874-023-01885-w
  2. The impact of misclassification errors on the performance of biomarkers based on next-generation sequencing, a simulation study
    Dong Wang, Sue-Jane Wang & Samir Lababidi
    Journal of Biopharmaceutical Statistics, DOI: 10.1080/10543406.2023.2269251
  3. Controlling for Confounding in Complex Survey Machine Learning Models to Assess Drug Safety and Risk.
    Rogers P.
    In: Machine Learning and Deep Learning in Computational Toxicology (Chapter 14). Ed. Hong H.
    Springer, Cham. 2023:355-374. 10.1007/978-3-031-20730-3_14
  4. Optimize and Strengthen Machine Learning Models Based on in vitro Assays with Mechanistic Knowledge and Real-World Data.
    Mahanama RV, Biswas A, Wang, D.
    In: Machine Learning and Deep Learning in Computational Toxicology (Chapter 7). Ed. Hong H.
    Springer, Cham. 2023:183-198. 10.1007/978-3-031-20730-3_7

R2R Branch   

  1. Development of Benchmark Datasets for Text Mining and Sentiment Analysis to Accelerate Regulatory Literature Review.
    Wu L., Chen S., Shpyleva S., Harris K., Fahmi T., Flanigan T., Tong W., Xu J., and Ren Z.
    Regulatory Toxicology and Pharmacology. 2023 January; 137:105287. 10.1016/j.yrtph.2022.105287.
  2. A Weakly Supervised Deep Learning Framework for Whole Slide Classification to Facilitate Digital Pathology in Animal Study.
    Bussola N, Xu J, Wu L, Gorini L, Zhang Y, Furlanello C, Tong W.
    Chemical Research in Toxicology. Aug 2023; 36:1321-1331. 10.1021/acs.chemrestox.3c00058
  3. Classifying Free Texts into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents.
    Gray M, Xu J, Tong W, Wu L.
    Chemical Research in Toxicology. Aug 2023; 36: 1290-1299. 10.1021/acs.chemrestox.3c00028
  4. Single-cell RNA-sequencing and subcellular spatial transcriptomics facilitate the translation of liver microphysiological systems for regulatory application.
    Li D, Fang Z, Shi Q, Zhang N, Gong B, Tong W, Coskun AF, Xu J.
    Journal of Pharmaceutical Analysis. 2023; 13(7);691-693. 10.1016/j.jpha.2023.06.013
  5. Assessments of tumor mutational burden estimation by targeted panel sequencing: A comprehensive simulation analysis.
    Li D, Wang D, Johann DJ Jr, Hong H, Xu J.
    Exp Biol Med (Maywood). 2023 Dec 8:15353702231211882. doi: 10.1177/15353702231211882. Epub ahead of print. PMID: 38062992.
  6. Multi-omics data integration using ratio-based quantitative profiling with Quartet reference materials.
    Zheng Y, Liu Y, Yang J, Dong L, Zhang R, Tian S, Yu Y, Ren L, Hou W, Zhu F, Mai Y, Han J, Zhang L, Jiang H, Lin L, Lou J, Li R, Lin J, Liu H, Kong Z, Wang D, Dai F, Bao D, Cao Z, Chen Q, Chen Q, Chen X, Gao Y, Jiang H, Li B, Li B, Li J, Liu R, Qing T, Shang E, Shang J, Sun S, Wang H, Wang X, Zhang N, Zhang P, Zhang R, Zhu S, Scherer A, Wang J, Wang J, Huo Y, Liu G, Cao C, Shao L, Xu J, Hong H, Xiao W, Liang X, Lu D, Jin L, Tong W, Ding C, Li J, Fang X, Shi L.
    Nat Biotechnol. 2023 Sep 7. doi: 10.1038/s41587-023-01934-1. Epub ahead of print. PMID: 37679543.
  7. Quartet DNA reference materials and datasets for comprehensively evaluating germline variant calling performance.
    Ren L, Duan X, Dong L, Zhang R, Yang J, Gao Y, Peng R, Hou W, Liu Y, Li J, Yu Y, Zhang N, Shang J, Liang F, Wang D, Chen H, Sun L, Hao L; Quartet Project Team; Scherer A, Nordlund J, Xiao W, Xu J, Tong W, Hu X, Jia P, Ye K, Li J, Jin L, Hong H, Wang J, Fan S, Fang X, Zheng Y, Shi L.
    Genome Biol. 2023 Nov 27;24(1):270. doi: 10.1186/s13059-023-03109-2. PMID: 38012772; PMCID: PMC10680274.
  8. 2022 White Paper on Recent Issues in Bioanalysis: FDA Draft Guidance on Immunogenicity Information in Prescription Drug Labeling, LNP & Viral Vectors Therapeutics/Vaccines Immunogenicity, Prolongation Effect, ADA Affinity, Risk-based Approaches, NGS, qPCR, ddPCR Assays (Part 3 – Recommendations on Gene Therapy, Cell Therapy, Vaccines Immunogenicity & Technologies; Immunogenicity & Risk Assessment of Biotherapeutics and Novel Modalities; NAb Assays Integrated Approach).
    Pan L, Mora J, Walravens K, Wagner L, Hopper S, Loo L, Bettoun D, Bond S, Dessy F, Downing S, Garafolo F, Gupta S, Henderson N, Irwin C, Ishii-Watabi A, Kar S, Jawa V, Joseph J, Malvaux L, Marshall J-C, McDevitt J, Mohapatra S, Seitzer J, Smith J, Solstad T, Sugimoto H, Tounekti O, Wu B, Wu Y, Xu Y, Xu J,  Yamamoto T,. Yang L, Torri A, Kirshner S, Maxfield K, Vasconcelos JP, Abhari MR, Verthelyi D, Brodsky E, Carrasco-Triguero M, Kamerud J, Andisik M, Baltrukonis D, Bivi N, Cludts I, Coble K, Gorovits B, Gunn GR, Gupta S, Millner AH, Joyce A, Kubiak RJ, Kumar S,  Liao K, Manangeeswaran M, Partridge M, Pine S, Poetzel J, Rajadhyaksha M, Rasamoelisolo M, Richards S, Song Y, Swanson S, Thacker S, Wadhwa M, Wolf A, Zhang L, Zhou L.
    Bioanalysis. July 2023; 15(14):773-859 . 10.4155/bio-2023-0135

 

2022

         Immediate Office

  1. DeepCausality: A General AI-Powered Causal Inference Framework for Free Text: A Case Study of Liver Tox.
    Wang X., Xu X., Tong W., Liu Q., and Liu Z.
    Frontiers in Artificial Intelligence. 2022 December; 5:999289. 10.3389/frai.2022.999289/full.
  2. Prediction of Drug-Induced Liver Injury and Cardiotoxicity Using Chemical Structure and In Vitro Assay Data.
    Ye L., Ngan D.K., Xu T., Liu Z., Zhao J., Sakamuru S., Xhang L., Zhao T., Xia M., Simeonov A., and Huang R.
    Toxicology and Applied Pharmacology. 2022 November; 454:116250. 10.1016/j.taap.2022.116250.
  3. Best Practice and Reproducible Science are Required to Advance Artificial Intelligence in Real-World Applications.
    Liu Z., Li T., Connor S., Thakkar S., Roberts R., and Tong W.
    Briefings in Bioinformatics. July 2022; 23(4): bbac237. 10.1093/bib/bbac237
  4. R-ODAF: Omics Data Analysis Framework for Regulatory Application.
    Verheijen M.C.T., Meier M.J., Sensio J.O., Gant T.W., Tong W., Yauk C.L., and Caiment F.
    Regulatory Toxicology and Pharmacology. June 2022; 131: 105143. 10.1016/j.yrtph.2022.105143. 
  5. Editorial: Emerging Technologies Powering Rare and Neglected Disease Diagnosis and Therapy Development.
    Liu Z., Hatim Q., Thakkar S., Roberts R., and Shi T.
    Frontiers in Pharmacology. 2022 Apr; 13:877401. 10.3389/fphar.2022.877401.
  6. Delivery of Oligonucleotides: Efficiency with Lipid Conjugation and Clinical Outcome. 
    Tran P., Weldemichael T., Liu Z., and Li H.Y. 
    Pharmaceutics. 2022 Feb 1; 14(2):342. 10.3390/pharmaceutics14020342.
  7. Towards Accurate and Reliable Resolution of Structural Variants for Clinical Diagnosis.
    Liu Z., Roberts R., Mercer T.R., Xu J., Sedlazeck F.J., and Tong W.
    Genome Biology. 2022 Mar 3; 23:68. 10.1186/s13059-022-02636-8.
  8. Tox-GAN: An Artificial Intelligence Approach Alternative to Animal Studies – A Case Study with Toxicogenomics.
    Chen X., Roberts R., Tong W., and Liu Z. 
    Toxicological Sciences. 2022 Apr; 186(2):242-259. 10.1093/toxsci/kfab157.
  9. AI-powered Drug Repurposing for Developing COVID-19 Treatments.
    Liu Z., Chen X., Carter W., Morui A., Komatsu T.E., Pahwa S., Chan-Tack K., Snyder K., Petrick N., Cha K., Lai-Nag M., Hatim Q., Thakkar S., Lin Y., Huang R., Wang D., Patterson T.A., and Tong W.
    Comprehensive Precision Medicine. 2022 Jan 1. 10.1016/B978-0-12-824010-6.000058.
     

    Bioinformatics Branch
  10. Editorial: Cell Signaling Status Alteration in Development and Disease.
    Wu J., Liu H., Zhao X., Hong H., and Werner J.
    Frontiers in Cell and Developmental Biology. 2022 December; 10:1068887. 10.3389/fcell.2022.1068887.
  11. An Autoencoder-Based Deep Learning Method for Genotype Imputation.
    Song M., Greenbaum J., Luttrell J., Zhou W., Wu C., Luo Z., Qiu C., Zhao L.J., Su K.-J, Tian Q., Shen H., Hong H., Gong P., Shi X., Deng H.-W., and Zhang C.
    Frontiers in Artificial Intelligence. 2022 November; 5:1028978. 10.3389/frai.2022.1028978.
  12. Deep Learning Models for Predicting Gas Adsorption Capacity of Nanomaterials.
    Guo W., Liu J., Dong F., Chen R., Das J., Ge W., Xu X., and Hong H.
    Nanomaterials. 2022 October; 12(19):3376. 10.3390/ nano12193376.
  13. W hole Exome Sequencing Reveals Genetic Variants in HLA Class II Genes Associated with Transplant-Free Survival of Indeterminate Acute Liver Failure.
    Liao T.-J., Pan B., Hong H., Hayashi P., Rule J.A., Ganger D., Lee W.M., Rakela J., and Chen M.
    Clinical and Translational Gastroenterology. July 2022; 13(7): e00502. 10.14309/ctg.0000000000000502.
  14. Machine Learning Models on Chemical Inhibitors of Mitochondrial Electron Transport Chain.
    Tang W., Liu W., Wang Z., Hong H., and Chen J. 
    Journal of Hazardous Materials. 2022 Mar 15; 426:128067. 10.1016/j.jhazmat.2021.128067.
  15. Machine Learning Models for Predicting Cytotoxicity of Nanomaterials.
    Zouwei J., Guo W., Wood E.L., Liu J., Sakkiah S., Xu X., Patterson T.A., and Hong H.H. 
    Chemical Research in Toxicology. 2022 Feb 21; 35(2):125-139. 10.1021/acs.chemrestox.1c00310.  
  16. Unleashing Innovation on Precision Public Health - Highlights from the MCBIOS and MAQC 2021 Joint Conference.
    Homayouni R., Hong H., Manda P., Nanduri B., and Toby I.T.
    Frontiers in Artificial Intelligence. 2022; 5:859700. 10.3389/frai.2022.859700.
  17. Epigenetics in Drug Disposition & Drug Therapy: Symposium Report of the 24th North American Meeting of the International Society for the Study of Xenobiotics (ISSX).
    Maldonato B.J., Vergara A.G., Yadav J., et al.
    Drug Metab Rev. 2022 Aug;54(3):318-330.
  18. Machine Learning Models for Predicting Liver Toxicity.
    Liu J., Guo W., Sakkiah S., Ji Z., Yavas G., Zou W., Chen M., Tong W., Patterson T.A., and Hong H
    Methods Mol Biol. 2022;2425:393-415. doi: 10.1007/978-1-0716-1960-5_15. PMID: 35188640.
  19. Machine Learning Models for Rat Multigeneration Reproductive Toxicity Prediction.
    Liu J., Guo W., Dong F., Aungst J., Fitzpatrick S., Patterson T.A., and Hong H.
    Front Pharmacol. 2022 Sep 27;13:1018226. doi: 10.3389/fphar.2022.1018226. PMID: 36238576; PMCID: PMC9552001.
  20. Machine Learning Models for Predicting Cytotoxicity of Nanomaterials.
    Ji Z., Guo W., Wood E.L., Liu J., Sakkiah S., Xu X., Patterson T.A., and Hong H.
    Chem Res Toxicol. 2022 Feb 21;35(2):125-139. doi: 10.1021/acs.chemrestox.1c00310. Epub 2022 Jan 14. PMID: 35029374.
  21. Assessing Reproducibility of Inherited Variants Detected with Short-Read Whole Genome Sequencing.
    Pan B., Ren L., Onuchic V., Guan M., Kusko R., Bruinsma S., Trigg L., Scherer A., Ning B., Zhang C., Glidewell-Kenney C., Xiao C., Donaldson E., Sedlazeck F.J., Schroth G., Yavas G., Grunenwald H., Chen H., Meinholz H., Meehan J., Wang J., Yang J., Foox J., Shang J., Miclaus K., Dong L., Shi L., Mohiyuddin M., Pirooznia M., Gong P., Golshani R., Wolfinger R., Lababidi S., Sahraeian S.M.E., Sherry S., Han T., Chen T., Shi T., Hou W., Ge W., Zou W., Guo W., Bao W., Xiao W., Fan X., Gondo Y., Yu Y., Zhao Y., Su Z., Liu Z., Tong W., Xiao W., Zook J.M., Zheng Y., and Hong H.
    Genome Biol. 2022 Jan 3;23(1):2. doi: 10.1186/s13059-021-02569-8. PMID: 34980216; PMCID: PMC8722114.
  22. Achieving Robust Somatic Mutation Detection with Deep Learning Models Derived from Reference Data Sets of a Cancer Sample.
    Sahraeian S.M.E., Fang L.T., Karagiannis K., Moos M., Smith S., Santana-Quintero L., Xiao C., Colgan M., Hong H., Mohiyuddin M., and Xiao W.
    Genome Biol. 2022 Jan 7;23(1):12. doi: 10.1186/s13059-021-02592-9. PMID: 34996510; PMCID: PMC8740374.

    Biostatistics Branch
  23. A Targeted Simulation-Extrapolation Method for Evaluating Biomarkers Based on New Technologies in Precision Medicine.
    Wang D., Wang S.J., and Lababidi S.
    Pharmaceutical Statistics. 2022 May-Jun, 21(3), 584-598.  10.1002/pst.2187.
  24. Variational Bayesian Inference for Association Over Phylogenetic Trees for Microorganisms.
    Hao X., Eskridge K.M., and Wang D.
    Journal of Applied Statistics. 2022 Mar; 49(5):1140-1153. 10.1080/02664763.2020.1854200.
  25. A Robust Biostatistical Method Leverages Informative but Uncertainly Determined qPCR Data for Biomarker Detection, Early Diagnosis, and Treatment.          
    Zhuang W., Camacho L., Silva C.S., Thomson M., and Snyder K.
    PLoS ONE. 2022, 17(1): e0263070. https://doi.org/10.1371/ journal.pone.0263070. 
  26. Epigenetics in Drug Disposition & Drug Therapy: Symposium Report of the 24(th) North American Meeting of the International Society for the Study of Xenobiotics (ISSX).
    Maldonato B.J., Vergara A.G., Yadav J., et al.
    Drug Metab Rev. 2022 Aug;54(3):318-330.
  27. Integrative Approaches for Studying the Role of Noncoding RNAs in Influencing Drug Efficacy and Toxicity.
    Li D., Chen M., Hong H., Tong W., and Ning B. 
    Expert Opinion on Drug Metabolism & Toxicology. 2022, 18(2), 151-163. 10.1080/17425255.2022.2054802.

    R2R Branch
  28. Ultra-Deep Multi-Oncopanel Sequencing of Benchmarking Samples with a Wide Range of Variant Allele Frequencies.
    Gong B., Kusko R., Jones W., Tong W., and Xu J.
    Scientific Data. 2022; 9: 288. 10.1038/s41597-022-01359-6. 
  29. NeuroCORD: A Language Model to Facilitate COVID-19-Associated Neurological Disorder Studies.
    Wu L., Ali S., Ali H., Brock T., Xu J., and Tong W.
    International Journal of Environmental Research and Public Health. 2022, 19 (16), 9974.
  30. Ultra-Deep Sequencing Data from a Liquid Biopsy Proficiency Study Demonstrating Analytic Validity.
    Gong B., Deveson I.W., Mercer T., Johann Jr. D.J., Jones W., Tong W., and Xu J.
    Scientific Data. 2022, 9 (1), 170.
  31. Accurate Species Identification of Food-Contaminating Beetles with Quality-Improved Elytral Images and Deep Learning.
    Bisgin H., Bera T., Wu L., Ding H., Bisgin N., Liu Z., Pava-Ripoll M., Barnes A., Campbell J.F., Vyas H., Furlanello C., Tong W., and Xu J.
    Frontiers in Artificial Intelligence. 2022.
  32. Deep Oncopanel Sequencing Reveals Within Block Position-Dependent Quality Degradation in FFPE Processed Samples.
    Zhang Y., Blomquist T.M., Kusko R., Stetson D., Zhang Z., Yin L., Sebra R., Gong B., Lococo J.S., Mittal V.K., Novoradovskaya N., Yeo J.-Y., Dominiak N., Hipp J., Raymond A., Qui F., Arib H., Smith M.L., Brock J.E., Farkas D.H., Craig D.J., Crawford E.L., Li D., Morrison T., Tom N., Xiao W., Yang M., Mason C.E., Richmond T.A., Jones W., Johann Jr. D.J., Shi L., Tong W., Willey J.C., and Xu J.
    Genome Biology. June 2022; 23: 141. 10.1186/s13059-022-02709-8.
  33. Using Synthetic Chromosome Controls to Evaluate the Sequencing of Difficult Regions Within the Human Genome.
    Reis A.L.M., Deveson I.W., Madala B.S., Wong T., Barker C., Xu J., Lennon N., Tong W., and Mercer T.R.
    SEQC2 Consortium. Genome Biol. 2022 Jan 12; 23(1):19. 10.1186/s13059-021-02579-6.

 

 

 
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