UNNT: A novel Utility for comparing Neural Net and Tree-based models.

The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep learning in various domains of cancer research such as drug response problems. In our study, we explored tree-based models to improve the accuracy of a single drug response model and demonstrate that tree-based models such as XGBoost (eXtreme Gradient Boosting) have advantages over deep learning models, such as a convolutional neural network (CNN), for single drug response problems.

cTULIP: application of a human-based RNA-seq primary tumor classification tool for cross-species primary tumor classification in canine.

Introduction: The domestic dog, , is quickly gaining traction as an advantageous model for use in the study of cancer, one of the leading causes of death worldwide. Naturally occurring canine cancers share clinical, histological, and molecular characteristics with the corresponding human diseases.

Methods: In this study, we take a deep-learning approach to test how similar the gene expression profile of canine glioma and bladder cancer (BLCA) tumors are to the corresponding human tumors.

TULIP: An RNA-seq-based Primary Tumor Type Prediction Tool Using Convolutional Neural Networks.

Background: With cancer as one of the leading causes of death worldwide, accurate primary tumor type prediction is critical in identifying genetic factors that can inhibit or slow tumor progression. There have been efforts to categorize primary tumor types with gene expression data using machine learning, and more recently with deep learning, in the last several years.

Methods: In this paper, we developed four 1-dimensional (1D) Convolutional Neural Network (CNN) models to classify RNA-seq count data as one of 17 highly represented primary tumor types or 32 primary tumor types regardless of imbalanced representation.

Current and Future Management of Malignant Mesothelioma: A Consensus Report from the National Cancer Institute Thoracic Malignancy Steering Committee, International Association for the Study of Lung Cancer, and Mesothelioma Applied Research Foundation.

On March 28- 29, 2017, the National Cancer Institute (NCI) Thoracic Malignacy Steering Committee, International Association for the Study of Lung Cancer, and Mesothelioma Applied Research Foundation convened the NCI-International Association for the Study of Lung Cancer- Mesothelioma Applied Research Foundation Mesothelioma Clinical Trials Planning Meeting in Bethesda, Maryland. The goal of the meeting was to bring together lead academicians, clinicians, scientists, and the U.S.