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Bioinformatics Publications

NCTR Bioinformatics Tools Publications, 2022-Present

2024 | 2023 | 2022

2024

Immediate Office

  1. Context is Everything in Regulatory Application of Large Language Models (LLMs).
    Tong W., Renaudin M.; GCRSR Interagency LLMs Taskforce.
    Drug Discovery Today. 2024, 29(4):103916. 10.1016/j.drudis.2024.103916.
  2. Generation of a Drug-Induced Renal Injury List to Facilitate the Development of New Approach Methodologies for Nephrotoxicity.
    Connor S., Li T., Qu Y., Roberts R.A., and Tong W.
    Drug Discovery Today. 2024, 29(4):103938. 10.1016/j.drudis.2024.10393.
  3. Text Summarization with ChatGPT for Drug Labeling Documents.
    Ying L., Liu Z., Fang H., Kusko R., Wu L., Harris S., and Tong W.
    Drug Discovery Today. 2024, 29(6):104018. 10.1016/j.drudis.2024.104018.

Bioinformatics Branch 

  1. Automatic Text Classification of Drug-Induced Liver Injury Using Document-Term Matrix and XGBoost.
    Chen M., Wu Y., Wingerd B., Liu Z., Xu J., Thakkar S., Pedersen T.J., Donnelly T., Mann N., Tong W., Wolfinger R.D., and Bao W.
    Frontiers in Artificial Intelligence. 2024, 7:1401810. 10.3389/frai.2024.1401810.
  2. BERT-Based Language Model for Accurate Drug Adverse Event Extraction from Social Media: Implementation, Evaluation, and Contributions to Pharmacovigilance Practices.
    Dong F., Guo W., Liu J., Patterson T.A., and Hong, H.
    Frontiers in Public Health. 2024, 12:1392180. 10.3389/fpubh.2024.1392180.  
  3. Computational Models for Predicting Liver Toxicity in the Deep Learning Era.
    Mostafa F. and Chen M.
    Frontiers in Toxicology. 2024, 5:1340860. 10.3389/ftox.2023.1340860.  
  4. Computational Nanotoxicology Models for Environmental Risk Assessment of Engineered NanomaterialsExternal Link Disclaimer.
    Tang W., Zhang X., Hong H., Chen J., Zhao Q., and Wu F.
    Nanomaterials. 2024, 14(2):155. 10.3390/nano14020155.  
  5. Decoding the κ Opioid Receptor (KOR): Advancements in Structural Understanding and Implications for Opioid Analgesic Development.
    Li Z., Huang R., Xia M., Chang N., Guo W., Liu J., Dong F., Liu B., Varghese A., Aslam A., Patterson T.A., and Hong H.
    Molecules. 2024, 29(11):2635. 10.3390/molecules29112635.  
  6. Fingerprinting Interactions between Proteins and Ligands for Facilitating Machine Learning in Drug Discovery.
    Li Z., Huang R., Xia M., Patterson T.A., and Hong H.
    Biomolecules. 2024, 14(1):72. 10.3390/biom14010072.  
  7. Machine Learning to Predict Drug-Induced Liver Injury and Its Validation on Failed Drug Candidates in Development.
    Mostafa F., Howle V., and Chen M.
    Toxics. 2024, 12(6):385. 10.3390/toxics12060385.  
  8. Medical Device Report Analyses from MAUDE: Device and Patient Outcomes, Adverse Events, and Sex-Based Differential Effects.
    Liao T.-J., Crosby L., Cross K., Chen M., and Elespuru R.
    Regulatory Toxicology and Pharmacology. 2024, 149:105591. 10.1016/j.yrtph.2024.105591.
  9. RxNorm for Drug Name Normalization: A Case Study of Prescription Opioids in the FDA Adverse Events Reporting System.
    Le H., Chen R., Harris S., Fang H., Lyn-Cook B., Hong H., Ge W., Rogers P., Tong W., and Zou W.
    Frontiers in Bioinformatics. 2024, 3:1328613. 10.3389/fbinf.2023.1328613.
  10. 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.
    Angewandte Chemie International Edition. 2024, 63(9): e202317675. 10.1002/anie.202317675.

Biostatistics Branch

 

R2R Branch 

  1. 2023 White Paper on Recent Issues in Bioanalysis: Deuterated Drugs; LNP; Tumor/FFPE Biopsy; Targeted Proteomics; Small Molecule Covalent Inhibitors; Chiral Bioanalysis; Remote Regulatory Assessments; Sample Reconciliation/Chain of Custody (Part 1A - Recommendations on Mass Spectrometry, Chromatography, Sample Preparation Latest Developments, Challenges, and Solutions and BMV/Regulated Bioanalysis Part 1B - Regulatory Agencies’ Inputs on Regulated Bioanalysis/BMV, Biomarkers/IVD/CDx/BAV, Immunogenicity, Gene & Cell Therapy and Vaccine).
    Baratta M., Jian W., Hengel S., et al.
    Bioanalysis. 2024, 16(9):307-364. 10.1080/17576180.2024.2347153.
  2. 2023 White Paper on Recent Issues in Bioanalysis: ISR for ADA Assays, the Rise of dPCR vs qPCR, International Reference Standards for Vaccine Assays, Anti-AAV TAb Post-Dose Assessment, NanoString Validation, ELISpot as Gold Standard (Part 3 – Recommendations on Gene Therapy, Cell Therapy, Vaccines Immunogenicity & Technologies; Biotherapeutics Immunogenicity & Risk Assessment; ADA/NAb Assay/Reporting Harmonization).
    Mora J., Palmer R., Wagner L., et al.
    Bioanalysis. 2024, 16(7):77-119. 10.4155/bio-2024-0024.
  3. A Framework Enabling LLMs into Regulatory Environment for Transparency and Trustworthiness and its Application to Drug Labeling Document.
    Wu L., Xu J., Thakkar S., Gray M., Qu Y., Li D., and Tong W.
    Regulatory Toxicology and Pharmacology. 2024, 149:105613. 10.1016/j.yrtph.2024.105613.
  4. Extend the Benchmarking Indel Set by Manual Review Using the Individual Cell Line Sequencing Data from the Sequencing Quality Control 2 (SEQC2) Project.
    Gong B., Li D., Zhang Y., et al.
    Scientific Reports. 2024, 14:7028. 10.1038/s41598-024-57439-7.
  5. In Pursuit of Precision Medicine for Brain Injury and Neurotoxicity and Improved Protection of Human Health.
    Slikker W., Wu L., Xu J., and Shafer T.J.
    In: Comprehensive Precision Medicine (7.11). Ed. Ramos K.S.
    Elsevier. 2024, 2:495-510. 10.1016/B978-0-12-824010-6.00048-4.
  6. Measurement and Mitigation of Bias in Artificial Intelligence: A Narrative Literature Review for Regulatory Science.
    Gray M., Samala R., Liu Q., Skiles D., Xu J., Tong J., and Wu L.
    Clinical Pharmacology & Therapeutics. 2024, 115(4):687-697. 10.1002/cpt.3117.
  7. Towards Accurate Indel Calling for Oncopanel Sequencing Through an International Pipeline Competition at PrecisionFDA.
    Gong B., Lababidi S., Kusko R., Bouri K., Prezek S., Thovarai V., Prasanna A., Maier E.J., Golkaram M., Sun X., Kyriakidis K., Kitajima J.P., Sahraeian S.M.E., Guo Y., Johnson E., Jones W., Tong W., and Xu J.
    Scientific Reports. 2024, 14(1):8165. 10.1038/s41598-024-58573-y.

2023

Immediate Office

  1. A Generative Adversarial Network Model Alternative to Animal Studies for Clinical Pathology Assessment.
    Chen X., Roberts R., Liu Z., and Tong W.
    Nature Communications. 2023, 14(1):7141. 10.1038/s41467-023-42933-9.
  2. 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. 2023, 97:2291-2302. 10.1007/s00204-023-03522-3.
  3. 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. 2023, 140:105388. 10.1016/j.yrtph.2023.105388.
  4. Bidirectional Encoder Representations from Transformers-Like Large Language Models in Patient Safety and Pharmacovigilance: A Comprehensive Assessment of Causal Inference Implications.
    Wang X., Xu X., Liu Z., and Tong W.
    Experimental Biology and Medicine. 2023, 248(21):1908-1917. 10.1177/15353702231215895.
  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. 2023, 144:105486. 10.1016/j.yrtph.2023.105486.
  6. 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. 2023, 28(11):103770. 10.1016/j.drudis.2023.103770.
  7. 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, 34(12):983-1001. doi: 10.1080/1062936X.2023.2284902. Epub 2023 Dec 4. PMID: 38047445.
  8. 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. 2023, 63(3):273-276. 10.1002/jcph.2177.
  9. 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. 2023, 39(11):btad690. 10.1093/bioinformatics/btad690.
  10. 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.
  11. 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. 2023, 24:245. 10.1186/s13059-023-03091-9.
  12. 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. 2023, 36(6):916-925. 10.1021/acs.chemrestox.3c00037.

Bioinformatics Branch 

  1. 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. 2023; 28(10):103727. 10.1016/j.drudis.2023.103727.
  2. 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, 15353702231211860. doi: 10.1177/15353702231211860. Epub ahead of print. PMID: 38158803.
  3. 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.
  4. 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, 24(1):201. doi: 10.1186/s13059-023-03047-z. PMID: 37674217; PMCID: PMC10483871.
  5. 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.
  6. Deep Learning Methods for Omics Data Imputation.
    Huang L., Song M., Shen H., Hong H., Gong P., Deng H-W, and Zhang C.
    Biology. 2023, 12(10):1313. 10.3390/biology12101313.
  7. 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.
  8. 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, 15353702231209413. doi: 10.1177/15353702231209413. Epub ahead of print. PMID: 37997891.
  9. 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, 24(4):3774. doi: 10.3390/ijms24043774. PMID: 36835186; PMCID: PMC9967551.
  10. 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.
  11. 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.
  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. 2023, 179:113948. 10.1016/j.fct.2023.113948.
  13. Integrating Artificial Intelligence with Bioinformatics Promotes the Public Health.
    Hong H. and Slikker W.
    Experimental Biology and Medicine. 2023, 248(21):1905-1907. 10.1177/15353702231223575.
  14. 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, 15353702231214259. doi: 10.1177/15353702231214259. Epub ahead of print. PMID: 38102956.
  15. 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.
  16. 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.
  17. 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.
  18. 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.
  19. Preface. In: Machine Learning and Deep Learning in Computational Toxicology. 
    Kusko R. and Hong H.
    In: Machine Learning and Deep Learning in Computational Toxicology (Preface). Ed. Hong H.
    Springer, Cham. 2023, v-vii. 10.1007/978-3-031-20730-3.
  20. Preface. In: QSAR in Safety Evaluation and Risk Assessment.
    Hong H.
    In: QSAR in Safety Evaluation and Risk Assessment (Preface). Ed. Hong H.
    Academic Press, London. 2023, xix-xx. 10.1016/B978-0-443-15339-6.00002-3.
  21. 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.
  22. 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, doi: 10.1038/s41587-023-01867-9. Epub ahead of print. Erratum in: Nat Biotechnol. 2023 Oct 2; PMID: 37679545.
  23. 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.
  24. 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.
  25. 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). 2023,15353702231209421. doi: 10.1177/15353702231209421. Epub ahead of print. PMID: 38057999.
  26. 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.
  27. 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, e202317675. doi: 10.1002/anie.202317675. Epub ahead of print. PMID: 38127455.

Biostatistics Branch

  1. 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.
  2. The Impact of Misclassification Errors on the Performance of Biomarkers Based on Next-Generation Sequencing, a Simulation Study.
    Wang D., Wang S.-J., and Lababidi S.
    Journal of Biopharmaceutical Statistics. 2023, DOI: 10.1080/10543406.2023.2269251.
  3. Optimize and Strengthen Machine Learning Models Based on in vitro Assays with Mechanistic Knowledge and Real-World Data.
    Mahanama R.V., Biswas A., and 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.
  4. Statistical Methods for Exploring Spontaneous Adverse Event Reporting Databases for Drug-Host Factor Interactions.
    Lu Z., Suzuki A., and Wang D.
    BMC Medical Research Methodology. 2023, 23:71. 10.1186/s12874-023-01885-w.

R2R Branch   

  1. 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., et al. 
    Bioanalysis. 2023, 15(14):773-859. 10.4155/bio-2023-0135.
  2. Assessments of Tumor Mutational Burden Estimation by Targeted Panel Sequencing: A Comprehensive Simulation Analysis.
    Li D., Wang D., Johann D.J., Jr., Hong H., and Xu J.
    Exp Biol Med (Maywood). 2023, 15353702231211882. doi: 10.1177/15353702231211882. Epub ahead of print. PMID: 38062992.
  3. 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., and Tong W.
    Chemical Research in Toxicology. 2023, 36:1321-1331. 10.1021/acs.chemrestox.3c00058.
  4. Classifying Free Texts into Predefined Sections Using AI in Regulatory Documents: A Case Study with Drug Labeling Documents.
    Gray M., Xu J., Tong W., and Wu L.
    Chemical Research in Toxicology. 2023, 36: 1290-1299. 10.1021/acs.chemrestox.3c00028.
  5. 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, 137:105287. 10.1016/j.yrtph.2022.105287.
  6. Multi-Omics Data Integration Using Ratio-Based Quantitative Profiling with Quartet Reference Materials.
    Zheng Y., Liu Y., Yang J., et al. 
    Nat Biotechnol. 2023. 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., et al.
    Genome Biol. 2023, 24(1):270. doi: 10.1186/s13059-023-03109-2. PMID: 38012772; PMCID: PMC10680274.
  8. RxBERT: Enhancing Drug Labeling Text Mining and Analysis with AI Language Modeling.
    Wu L., Gray M., Dang O., Xu J., Fang H., and Tong W.
    Experimental Biology and Medicine. 2023, 248(21):1937-1943. 10.1177/15353702231220669.
  9. 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 A.F., and Xu J.
    Journal of Pharmaceutical Analysis. 2023, 13(7);691-693. 10.1016/j.jpha.2023.06.013.

2022

Immediate Office

  1. 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. 10.1016/B978-0-12-824010-6.000058.
  2. 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. 2022, 23(4): bbac237. 10.1093/bib/bbac237.
  3. 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, 5:999289. 10.3389/frai.2022.999289/full.
  4. Delivery of Oligonucleotides: Efficiency with Lipid Conjugation and Clinical Outcome. 
    Tran P., Weldemichael T., Liu Z., and Li H.Y. 
    Pharmaceutics. 2022, 14(2):342. 10.3390/pharmaceutics14020342.
  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, 13:877401. 10.3389/fphar.2022.877401.
  6. 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, 454:116250. 10.1016/j.taap.2022.116250.
  7. 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. 2022, 131: 105143. 10.1016/j.yrtph.2022.105143. 
  8. 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, 23:68. 10.1186/s13059-022-02636-8.
  9. 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, 186(2):242-259. 10.1093/toxsci/kfab157.

Bioinformatics Branch

  1. 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, 23(1):12. doi: 10.1186/s13059-021-02592-9. PMID: 34996510; PMCID: PMC8740374.
  2. 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, 5:1028978. 10.3389/frai.2022.1028978.
  3. 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, 23(1):2. doi: 10.1186/s13059-021-02569-8. PMID: 34980216; PMCID: PMC8722114.
  4. 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, 12(19):3376. 10.3390/ nano12193376.
  5. 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, 10:1068887. 10.3389/fcell.2022.1068887.
  6. 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, 54(3):318-330.
  7. 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, 426:128067. 10.1016/j.jhazmat.2021.128067.
  8. 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, 35(2):125-139. 10.1021/acs.chemrestox.1c00310.  
  9. 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.
  10. 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, 13:1018226. doi: 10.3389/fphar.2022.1018226. PMID: 36238576; PMCID: PMC9552001.
  11. 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.
  12. Whole 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. 2022, 13(7): e00502. 10.14309/ctg.0000000000000502.

Biostatistics Branch

  1. 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. 
  2. 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, 21(3), 584-598.  10.1002/pst.2187.
  3. 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, 54(3):318-330.
  4. 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.
  5. Variational Bayesian Inference for Association Over Phylogenetic Trees for Microorganisms.
    Hao X., Eskridge K.M., and Wang D.
    Journal of Applied Statistics. 2022, 49(5):1140-1153. 10.1080/02664763.2020.1854200.

R2R Branch

  1. 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.
  2. 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. 2022, 23: 141. 10.1186/s13059-022-02709-8.
  3. 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.
  4. 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. 
  5. 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.
  6. 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, 23(1):19. 10.1186/s13059-021-02579-6.
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