Virus-like Particles Identify an HIV V1V2 Apex-Binding Neutralizing Antibody that Lacks a Protruding Loop von Cale, Evan M., Gorman, Jason, Radakovich, Nathan A., Crooks, Ema T., Osawa, Keiko, Tong, Tommy, Li, Jiaqi, Nagarajan, Raju, Ozorowski, Gabriel, Ambrozak, David R., Asokan, Mangai, Bailer, Robert T., Bennici, Anthony K., Chen, Xuejun, Doria-Rose, Nicole A., Druz, Aliaksandr, Feng, Yu, Joyce, M. Gordon, Louder, Mark K., O’Dell, Sijy, Oliver, Courtney, Pancera, Marie, Connors, Mark, Hope, Thomas J., Kepler, Thomas B., Wyatt, Richard T., Ward, Andrew B., Georgiev, Ivelin S., Kwong, Peter D., Mascola, John R., Binley, James M.

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Virus-like Particles Identify an HIV V1V2 Apex-Binding Neutralizing Antibody that Lacks a Protruding Loop von Cale, Evan M., Gorman, Jason, Radakovich, Nathan A., Crooks, Ema T., Osawa, Keiko, Tong, Tommy, Li, Jiaqi, Nagarajan, Raju, Ozorowski, Gabriel, Ambrozak, David R., Asokan, Mangai, Bailer, Robert T., Bennici, Anthony K., Chen, Xuejun, Doria-Rose, Nicole A., Druz, Aliaksandr, Feng, Yu, Joyce, M. Gordon, Louder, Mark K., O’Dell, Sijy, Oliver, Courtney, Pancera, Marie, Connors, Mark, Hope, Thomas J., Kepler, Thomas B., Wyatt, Richard T., Ward, Andrew B., Georgiev, Ivelin S., Kwong, Peter D., Mascola, John R., Binley, James M.

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Machine learning in haematological malignancies von Radakovich, Nathan, Nagy, Matthew, Nazha, Aziz

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Acute myeloid leukemia and artificial intelligence, algorithms and new scores von Radakovich, Nathan, Cortese, Matthew, Nazha, Aziz

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Lessons Learned in Using Laser Interstitial Thermal Therapy for Treatment of Brain Tumors: A Case Series of 238 Patients from a Single Institution von Shao, Jianning, Radakovich, Nathan R., Grabowski, Matthew, Borghei-Razavi, Hamid, Knusel, Konrad, Joshi, Krishna C., Muhsen, Baha'eddin A., Hwang, Lee, Barnett, Gene H., Mohammadi, Alireza M.

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Artificial Intelligence in Hematology: Current Challenges and Opportunities von Radakovich, Nathan, Nagy, Matthew, Nazha, Aziz

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Personalized Prediction Model to Risk Stratify Patients With Myelodysplastic Syndromes von Nazha, Aziz, Komrokji, Rami, Meggendorfer, Manja, Jia, Xuefei, Radakovich, Nathan, Shreve, Jacob, Hilton, C. Beau, Nagata, Yasunubo, Hamilton, Betty K., Mukherjee, Sudipto, Al Ali, Najla, Walter, Wencke, Hutter, Stephan, Padron, Eric, Sallman, David, Kuzmanovic, Teodora, Kerr, Cassandra, Adema, Vera, Steensma, David P., Dezern, Amy, Roboz, Gail, Garcia-Manero, Guillermo, Erba, Harry, Haferlach, Claudia, Maciejewski, Jaroslaw P., Haferlach, Torsten, Sekeres, Mikkael A.

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Why Machine Learning Should Be Taught in Medical Schools von Nagy, Matthew, Radakovich, Nathan, Nazha, Aziz

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The Impact of Clinical Decision Support Alerts on Clostridioides difficile Testing: A Systematic Review von Dunn, Aaron N., Radakovich, Nathan, Ancker, Jessica S., Donskey, Curtis J., Deshpande, Abhishek

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The effect of antibiotic use on immune-checkpoint inhibitor efficacy in patients with advanced urothelial carcinoma von Khan, Mohammad, Radakovich, Nathan, Rini, Brian I., Mian, Omar Y., Ornstein, Moshe Chaim, Gupta, Shilpa

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Impact of primary tumor location, histology, and host factors on objective response to immune checkpoint inhibitors in metastatic urothelial carcinoma von Dawsey, Scott, Khan, Mohammad Siddique, Radakovich, Nathan, Wei, Wei, Mian, Omar Y., Ornstein, Moshe Chaim, Gilligan, Timothy D., Gupta, Shilpa

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The effect of antibiotic use within 30 days of initiation of immune checkpoint inhibitor (ICI) efficacy in patients with metastatic urothelial carcinoma (mUC) in real-world setting von Gupta, Shilpa, Khan, Mohammad Siddique, Radakovich, Nathan, Wei, Wei, Ornstein, Moshe Chaim, Gilligan, Timothy D., Mian, Omar Y., Dawsey, Scott

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Personalized Transcriptomic Analyses Identify Unique Signatures That Correlate with Genomic Subtypes in Acute Myeloid Leukemia (AML) Using Explainable Artificial Intelligence von Rouphail, Yazan, Radakovich, Nathan, Shreve, Jacob, Mukherjee, Sudipto, Jha, Babal K., Maciejewski, Jaroslaw P., Sekeres, Mikkael A., Nazha, Aziz

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A personalized prediction model for hospital readmission risk for cancer patients von Shreve, Jacob Tyler, Lee, Sarah, Felix, Christina, Benish Shirley, Rachel, Hilton, Cameron Beau, Radakovich, Nathan, Stevenson, James, Montero, Alberto J., Nazha, Aziz

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A machine learning model of response to hypomethylating agents in myelodysplastic syndromes von Radakovich, Nathan, Sallman, David A., Buckstein, Rena, Brunner, Andrew, Dezern, Amy, Mukerjee, Sudipto, Komrokji, Rami, Al-Ali, Najla, Shreve, Jacob, Rouphail, Yazan, Parmentier, Anne, Mamedov, Alexandre, Siddiqui, Mohammed, Guan, Yihong, Kuzmanovic, Teodora, Hasipek, Metis, Jha, Babal, Maciejewski, Jaroslaw P., Sekeres, Mikkael A., Nazha, Aziz

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Machine Learning Models to Predict Major Adverse Cardiovascular Events After Orthotopic Liver Transplantation: A Cohort Study von Jain, Vardhmaan, Bansal, Agam, Radakovich, Nathan, Sharma, Vikram, Khan, Muhammad Zarrar, Harris, Kevin, Bachour, Salam, Kleb, Cerise, Cywinski, Jacek, Argalious, Maged, Quintini, Cristiano, Menon, K.V. Narayanan, Nair, Ravi, Tong, Michael, Kapadia, Samir, Fares, Maan

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In Vivo validation of novel p53-independent therapeutic modalities for small cell lung cancer (SCLC) von Kang, Kai, Radakovich, Nathan, Khunger, Arjun, Schrump, David S., MacPherson, David, Rubinstein, Mark P., Wrangle, John M., Saunthararajah, Yogen, Velcheti, Vamsidhar

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A geno-clinical decision model for the diagnosis of myelodysplastic syndromes von Radakovich, Nathan, Meggendorfer, Manja, Malcovati, Luca, Hilton, C. Beau, Sekeres, Mikkael A., Shreve, Jacob, Rouphail, Yazan, Walter, Wencke, Hutter, Stephan, Galli, Anna, Pozzi, Sara, Elena, Chiara, Padron, Eric, Savona, Michael R., Gerds, Aaron T., Mukherjee, Sudipto, Nagata, Yasunobu, Komrokji, Rami S., Jha, Babal K., Haferlach, Claudia, Maciejewski, Jaroslaw P., Haferlach, Torsten, Nazha, Aziz

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A Personalized Clinical-Decision Tool to Improve the Diagnostic Accuracy of Myelodysplastic Syndromes von Radakovich, Nathan, Meggendorfer, Manja, Malcovati, Luca, Sekeres, Mikkael A., Shreve, Jacob, Hilton, Cameron Beau, Rouphail, Yazan, Walter, Wencke, Hutter, Stephan, Mukherjee, Sudipto, Kerr, Cassandra M., Jha, Babal K., Gallì, Anna, Pozzi, Sarah, Gerds, Aaron T., Haferlach, Claudia, Maciejewski, Jaroslaw P., Haferlach, Torsten, Nazha, Aziz

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Genotype-Phenotype Correlations in Patients with Myeloid Malignancies Using Explainable Artificial Intelligence von Radakovich, Nathan, Malcovati, Luca, Meggendorfer, Manja, Sekeres, Mikkael A., Shreve, Jacob, Hilton, Cameron Beau, Rouphail, Yazan, Walter, Wencke, Hutter, Stephan, Gallì, Anna, Pozzi, Sarah, Gerds, Aaron T., Mukherjee, Sudipto, Kerr, Cassandra M, Jha, Babal K., Haferlach, Claudia, Maciejewski, Jaroslaw P., Haferlach, Torsten, Nazha, Aziz

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