Glioma Survival Analysis Empowered with Data Engineering—A Survey

Survival Analysis of Glioblastoma Patients: An Overview Empowered by Data Engineering Introduction Glioblastoma is a type of tumor that occurs in glial cells and accounts for 26.7% of all primary brain and central nervous system tumors. Survival analysis of glioblastoma patients is a key task in clinical management due to the heterogeneity of the t...

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients

Deep Learning of Imaging Phenotype and Genotype for Predicting Overall Survival Time of Glioblastoma Patients

Globally, the most common and deadly malignant brain tumor is glioblastoma (Glioblastoma, GBM). In recent years, research has continuously attempted to predict the overall survival time (OS) of GBM patients using machine learning techniques based on preoperative single-modality or multi-modality imaging phenotypes. Although these machine learning m...

St. Jude Survivorship Portal: Sharing and Analyzing Large Clinical and Genomic Datasets from Pediatric Cancer Survivors

St. Jude Survivorship Portal: Sharing and Analyzing Large Clinical and Genomic Datasets from Pediatric Cancer Survivors

St. Jude Survivorship Portal: Analysis and Sharing of Large-Scale Clinical and Genomic Data of Pediatric Cancer Survivors Research Background In the United States, the five-year survival rate for childhood cancer has increased from about 60% in the 1970s to over 85% today. Despite the significant improvement in survival rates, these childhood cance...

Identification of Clonal Hematopoiesis Driver Mutations Through In Silico Saturation Mutagenesis

Introduction In the process of healthy hematopoiesis, a group of hematopoietic stem cells (HSC) contribute to all blood-related lineages. However, as we age, this process often leads to clonal hematopoiesis (CH), meaning the expansion of clones originating from a particular HSC, which then occupies a significant portion of blood cells and platelets...

brainlife.io: a decentralized and open-source cloud platform to support neuroscience research

Academic report: brainlife.io: A decentralized and open-source cloud platform supporting neuroscience research Background and Motivation Neuroscience research is rapidly developing, and the advancement of data standardization, management, and processing tools has made research more rigorous and transparent. However, this also brings about complex d...

Prediction error processing and sharpening of expected information

Prediction error processing and sharpening of expected information

Scientific Report Background Introduction Perception and neuronal processing of sensory information are largely influenced by prior expectations. Perception is not merely passive reception, but an active inferential process by combining existing sensory information with prior information based on past experience and current context. The combination...