Publications by authors named "J E Latz"
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner.
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- - Partial differential equations (PDEs) are crucial for modeling various scientific processes, and numerical methods like the finite element method are commonly used to approximate their solutions
- - Recent advancements in deep neural networks have led to the development of physics-informed neural networks, which are designed to solve PDEs more effectively, although they have mostly been studied separately from traditional methods
- - In a comparative study of both approaches, it was found that while physics-informed neural networks may evaluate PDEs faster in some cases, they did not surpass the finite element method in overall solution time and accuracy for a variety of linear and nonlinear PDEs.
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. However, the inverse problem-solving aspect of these models presents a substantial challenge, either due to the high computational requirements of model-based approaches or the limited robustness of deep learning (DL) methods. We propose a novel framework that leverages the unique strengths of both approaches in a synergistic manner.
View Article and Find Full Text PDFIntroduction: Regardless of epidermal growth factor receptor (EGFR) mutation status, erlotinib improves survival for patients with advanced non-small cell lung cancer (NSCLC) after one or more chemotherapy regimens. Enzastaurin is an oral serine/threonine kinase inhibitor. This phase II study was designed to evaluate the efficacy and safety of erlotinib and enzastaurin in NSCLC, a combination with promise to overcome EGFR resistance based on preclinical models.
View Article and Find Full Text PDFPurpose: Enzastaurin, an oral serine/threonine kinase inhibitor, targets the protein kinase C and AKT pathways with anti-tumor and anti-angiogenic effects. Erlotinib, an oral epidermal growth factor receptor (EGFR) inhibitor, has activity in solid tumors. Based on the promising combination of EGFR inhibitors and anti-angiogenic agents, this phase I trial was initiated.
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