Machine Learning Approaches for Cancer Prediction

• 4206 · PDF The ancestry-transcriptome link: Machine learning predicts chemotherapy response in breast cancer Michelle Guevara-Nieto, María J. López-Munevar, Carlos Orozco-Castaño, Rafael Parra-Medina, Laura Fejerman, Valentina Zavala, Jone Garai, Jovanny Zabaleta, Alba L. Combita-Rojas, Liliana López-Kleine
• 4207 Omics-aware patch aggregation via multimodal co-training with a scalable multi-omics encoder for slide-level prediction across an oncology biomarker panel Hwanil Choi, Tae Hyun Hwang, Soonyoung Lee, Jongseong Jang
• 4208 · PDF Machine learning models for predicting prostate cancer risk in Hispanic men Ricardo J. Rodríguez-Colón, Amaia V. Varela-Parrilla, Zinned C. Medina-Nieves, María M. Sánchez-Vázquez, Magaly Martínez-Ferrer
• 4209 A multi-variable machine learning model integrating stage, histology, grade, and treatment to predict mortality in salivary gland cancer: A SEER 2010-2021 population-based study Chiugo Okoye, Chinemerem Martlin Emeasoba, Chidi Obialo-Ibeawuchi, Olanipekun Ntukidem, Oboseh John Ogedegbe, Uchenna Amaechi
• 4210 · PDF Transformer and pretraining on external ehr cohort boosts infection risk prediction in hematologic malignancies Banafshe Felfeliyan, Natasha Markuzon
• 4211 · PDF AI-driven epigenomic profiling reveals early predictors of cutaneous squamous cell carcinoma risk in hidradenitis suppurativa Murali R. Kuracha, Sree Naga V. Kuracha, Aaren Vedangi, Radhakrishna Uppala, Lavanya Uppala, Venkata Duvvuri
• 4212 Baseline peripheral blood scRNA-seq AI estimator framework predicts solid-tumor response and adverse events via molecular foundation models and cell-to-patient learning Pablo Moreno, Marta Milo, Ricardo Miragaia, Alex Proutski, Virginia Savova, Ikbel Achour
• 4213 · PDF Generative genomics accurately predicts cancer gene expression Gregory Koytiger, Alice M. Walsh, Vaishali Marar, Kayla A. Johnson, Max Highsmith, Alexander R. Abbas, Andrew Stirn, Ariel Brumbaugh, Alex David, Darren Hui, Jeffrey Kahn, Sheng-Yong Niu, Liza J. Ray, Candace Savonen, Stein Setvik, Jeffrey T. Leek, Robert K. Bradley
• 4215 · PDF A comparative assessment of machine learning models for predicting prostate cancer using PVT1 biomarkers and PSA Pragyan Kadel, Rachel E. Bonacci, Emmanuel Owusu Asante-Asamani, Olorunseun O. Ogunwobi
• 4216 · PDF Interpreting PLMs for cancer discovery: High attention hotspots predict pathogenic mutation positions and novel drug binding sites Sophia J. Pribus, Gowri Nayar, Russ Altman
• 4217 · PDF Multi-modal modeling of genomic, histopathologic, and lab data predicts survival after hepatectomy in oligometastatic colorectal cancer Divya Koyyalagunta, Stefanie Gerstberger, Marion Liu, Chenlian Fu, Simran Chhabria, Madison Darmofal, Kevin Michael Boehm, Justin Jee, Michele Waters, Vinod P. Balachandran, Kevin Soares, Alice C. Wei, Jeffrey A. Drebin, T. Peter Kingham, William R. Jarnagin, Jinru Shia, Francisco Sanchez-Vega, Michael I. D’Angelica, Karuna Ganesh, Quaid Morris
• 4218 · PDF ProteoBridge: Bridging skipped sections via histology-based protein prediction Minji Kim, Sunho Park, Seock-Jin Chung, Inyeop Jang, Jean R. Clemenceau, Soyoung Im, Eric Sha, Hwanil Choi, Soonyoung Lee, Jongseong Jang, Sam C. Wang, Tae Hyun Hwang
• 4219 · PDF Predicting immunotherapy response in patients with hepatocellular carcinoma from clinical and textual features using AI techniques Anwaar Saeed, Meghana Singh, Yuming Shi, Alireza Tojjari, Vaishnavi Balaji, Lakshya Sharma, Azhar Saeed, Thant Hoe, Yuxi Zhang, Sola Adeleke
• 4220 · PDF Predicting high-risk colorectal polyps using pre-colonoscopy features: Machine learning model development and validation Basheer Qolomany, Mrinalini Deverapall, Adeyinka O. Laiyemo, Zaki A. Sherif, Hassan Brim, Hassan Ashktorab
• 4221 CAPTYN, a six-variable machine-learning model predicting clinical benefit of atezolizumab-bevacizumab in hepatocellular carcinoma: Development and external validation in IMbrave150 Gae Hoon Jo, Sohyun Hwang, Bernhard Scheiner, Won Suk Lee, Beodeul Kang, Jung Sun Kim, Ho Yeong Lim, Chansik An, Dong Yun Kim, Inyoung Kim, Dong-hyuk Heo, Matthias Pinter, Beom Kyung Kim, Chan Kim, Hong Jae Chon
• 4222 · PDF Early prediction of engraftment outcomes in hematopoietic cell transplantation using neural ordinary differential equations Parham Habibzadeh
• 4223 · PDF AI-based prescreening of clonal hematopoiesis in patients with liver disease using Sysmex XN hematology analyzer data Jeongmin Park, Dahyun Kim, Ja Min Byun, Hyunsoo Cho, Eun Ju Cho, Youngil Koh
• 4224 Artificial intelligence-derived analysis of wearables-derived biometrics to characterize physiologic response to chemotherapy in solid organ cancers Alexander Zhu, Lizi Zhang, Yuhang Zhang, Alexandra Potter, Bryan Rettner, Alisha Keshwani, Alina Keshwani, Nicole Hu, Anton Melki, Zhengyu Fang, Meghan McCarthy, Jacob Baird, Aubrey Pope, Samuel Schwartz, Quiana Guo, Michael Lanuti, Xiao Li, Chi-Fu Jeffrey Yang
• 4225 · PDF AI-driven de novo design of humanized nanobodies targeting KLK2 for prostate cancer immunotherapy Fengze Jin, Puja Singh, Hanyong Chen, Christopher Warlick, Yibin Deng
• 4226 · PDF Machine learning based classification of breast cancer using lifestyle and clinical risk factors in Puerto Rican women Jorge E. Martínez-Jiménez, Sol V. Pérez-Mártir, Doralis de León-Vázquez, Nelly A. Arroyo, Julie Dutil
• 4227 · PDF Independent validation of the immunotherapy response model using real-world Moffitt Cancer Center cohort Isis Yanina Narvaez-Bandera, Alyssa Pybus, Tosin Jolaogun, Paulo C. Morais Lyra, Jeremy Goecks
• 4228 · PDF Validation of the CLL treatment infection model (CLL-TIM) in patients with newly diagnosed chronic lymphocytic leukemia (CLL) Raphael Mwangi, Tait D. Shanafelt, Soren Basnet, Emily L. West, Owen Keegan, Timothy G. Call, Yao Yuan, Bryan Alexis Vallejo, Paul J. Hampel, Lindsey E. Roeker, Yucai Wang, Saad J. Kenderian, Sara J. Achenbach, Aaron D. Norman, Kari G. Rabe, Neil E. Kay, James R. Cerhan, Curtis A. Hanson, Susan L. Slager, Sameer A. Parikh
• 4229 · PDF AI based explainable survival modeling for advanced non small cell lung cancer Kang Qin, An Qin, John V. Heymach

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