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Titlebook: Mathematical and Computational Oncology; Second International George Bebis,Max Alekseyev,Maria Rodriguez Martine Conference proceedings 202

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ch detective tales consciously created their own social milieu, half fabulous, half mythical — a milieu so powerful that later generations would comprehend the era in which these tales were written . the medium of the tales themselves. In so doing later generations of readers would understand the wo
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Fine-Tuning Deep Learning Architectures for Early Detection of Oral Cancerited access to specialist diagnosis. The use of artificial intelligence for decision making on oral cavity images has the potential to improve cancer management and survival rates. This study forms part of the MeMoSA. (.obil.uth .creening .nywhere) project. In this paper, we extended on our previous
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Discriminative Localized Sparse Representations for Breast Cancer Screeninguce its mortality and improve the quality of life. Computer-aided detection (CADx) and computer-aided diagnosis (CAD) techniques have shown promise for reducing the burden of human expert reading and improve the accuracy and reproducibility of results. Sparse analysis techniques have produced releva
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Activation vs. Organization: Prognostic Implications of T and B Cell Features of the PDAC Microenvirremains below 10%. Recent studies have revealed that CD4. T, CD8. T, and/or B cells in specific spatial arrangements relative to intratumoral regions correlate with clinical outcome for patients, but the complex functional states of those immune cell types remain to be incorporated into prognostic b
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tugHall: A Tool to Reproduce Darwinian Evolution of Cancer Cells for Simulation-Based Personalized Mel connected to the well-known cancer hallmarks with the specific mutational states of tumor-related genes. The hallmark variables depend linearly on the mutational states of tumor-related genes with specific weights. The cell behavior phenotypes are stochastically determined and the phenotypic prob
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The Potential of Single Cell RNA-Sequencing Data for the Prediction of Gastric Cancer Serum Biomarkegical monitoring can significantly improve survival rates. Previous studies have revealed several serum biomarkers that are elevated in GC patients, including CEA, CA19-9, and CA72-4. However, sensitivity of these biomarkers is below 30%. Identification of more sensitive and specific to GC markers i
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Theoretical Foundation of the Performance of Phylogeny-Based Somatic Variant DetectionFirst, we show the property of tumor phylogenetic tree: the total patterns of mutations are restricted if a multi-regional mutation profile follows a corresponding tumor phylogenetic tree, where a multi-regional mutation profile is a matrix in which predictions of somatic mutations at the correspond
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