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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 MA, Liu Z, Ceresa C, Samuels S, Wang Y-M, Madabushi R, Schmidt S, Fletcher EP.
    The Journal of Clinical Pharmacology. Mar 2023; 63(3):273-276. 10.1002/jcph.2177

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. Kusko R, Hong H. In: Machine Learning and Deep Learning in Computational Toxicology.
    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, 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, 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 TA, 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, 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 TA, 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. 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
  9. 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, Patterson TA. 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
  10. 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, 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
  11. 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

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

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.

 

 

 

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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