PREDICTION OF HEREDITARY CANCERS USING NEURAL NETWORKS.

ABSTRACT: Family history is a major risk factor for many types of cancer. Mendelian risk prediction models translate family histories into cancer risk predictions, based on knowledge of cancer susceptibility genes. These models are widely used in clinical practice to help identify high-risk individuals. Mendelian models leverage the entire family history, but they rely on many assumptions about cancer susceptibility genes that are either unrealistic or challenging to validate, due to low mutation prevalence. Training more flexible models, such as neural networks, on large databases of pedigrees can potentially lead to accuracy gains. In this paper we develop a framework to apply neural networks to family history data and investigate their ability to learn inherited susceptibility to cancer. While th

SUBMITTER: Guan BZ 

PROVIDER: S-EPMC10593124 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature