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Micrornas In Pancreatic Cancer: Advances In Biomarker Discovery And Therapeutic Implications, Roland Madadjim, Thuy An, Juan Cui
Micrornas In Pancreatic Cancer: Advances In Biomarker Discovery And Therapeutic Implications, Roland Madadjim, Thuy An, Juan Cui
School of Computing: Faculty Publications
Pancreatic cancer remains a formidable malignancy characterized by high mortality rates, primarily attributable to late-stage diagnosis and a dearth of effective therapeutic interventions. The identification of reliable biomarkers holds paramount importance in enhancing early detection, prognostic evaluation, and targeted treatment modalities. Small non-coding RNAs, particularly microRNAs, have emerged as promising candidates for pancreatic cancer biomarkers in recent years. In this review, we delve into the evolving role of cellular and circulating miRNAs, including exosomal miRNAs, in the diagnosis, prognosis, and therapeutic targeting of pancreatic cancer. Drawing upon the latest research advancements in omics data-driven biomarker discovery, we also perform a …
3dgaunet: 3d Generative Adversarial Networks With A 3d U-Net Based Generator To Achieve The Accurate And Effective Synthesis Of Clinical Tumor Image Data For Pancreatic Cancer, Yu Shi, Hannah Tang, Michael J. Baine, Michael A. Hollingsworth, Huijing Du, Dandan Zheng, Chi Zhang, Hongfeng Yu
3dgaunet: 3d Generative Adversarial Networks With A 3d U-Net Based Generator To Achieve The Accurate And Effective Synthesis Of Clinical Tumor Image Data For Pancreatic Cancer, Yu Shi, Hannah Tang, Michael J. Baine, Michael A. Hollingsworth, Huijing Du, Dandan Zheng, Chi Zhang, Hongfeng Yu
School of Computing: Faculty Publications
Pancreatic ductal adenocarcinoma (PDAC) presents a critical global health challenge, and early detection is crucial for improving the 5-year survival rate. Recent medical imaging and computational algorithm advances offer potential solutions for early diagnosis. Deep learning, particularly in the form of convolutional neural networks (CNNs), has demonstrated success in medical image analysis tasks, including classification and segmentation. However, the limited availability of clinical data for training purposes continues to represent a significant obstacle. Data augmentation, generative adversarial networks (GANs), and cross-validation are potential techniques to address this limitation and improve model performance, but effective solutions are still rare for 3D …
Data Mining The Functional Characterizations Of Proteins To Predict Their Cancer-Relatedness, Peter Revesz, Christopher Assi
Data Mining The Functional Characterizations Of Proteins To Predict Their Cancer-Relatedness, Peter Revesz, Christopher Assi
School of Computing: Faculty Publications
This paper considers two types of protein data. First, data about protein function described in a number of ways, such as, GO terms and PFAM families. Second, data about whether individual proteins are experimentally associated with cancer by an anomalous elevation or lowering of their expressions within cancerous cells. We combine these two types of protein data and test whether the first type of data, that is, the functional descriptors, can predict the second type of data, that is, cancer-relatedness. By using data mining and machine learning, we derive a classifier algorithm that using only GO term and PFAM family …