Deep Neural Network for Somatic Mutation Classification

Abstract

The detection and characterization of somatic mutations have become the important means to analyze the occurrence and development of cancer and, ultimately, will help to select effective and precise treatment for specific cancer patients. It is very difficult to detect somatic mutations accurately from the massive sequencing data. In this paper, a forest-graph-embedded deep feed-forward network (forgeNet) is utilized to detect somatic mutations from the sequencing data. In forgeNet, the random forest (RF) or Gradient Boosting Machine (GBM) and graph-embedded deep feed-forward network (GEDFN) are utilized to extract features and implement classification, respectively. Three real somatic mutation datasets collected from 48 triple-negative breast cancers are utilized to test the somatic mutation detection performances of forgeNet. The detection results show that forgeNet could make the 0.05%–0.424% improvements in terms of area under the curve (AUC) compared with support vector machines and random forest.

Effects of Sports Functional Food on Physical Function of Athletes under Ultrasound Observation

“…Mindray Resona ultrasound equipment was used, and the ultrasound probe frequency was 7-15 MHz, the relevant data of the subjects were measured before and after the experiment, including the thickness and elastic modulus of the transversus abdominis in the resting and contracting positions, thickness and elastic modulus of multifidus muscles in resting and contracted positions. The color ultrasound room was kept at 23-27°C and properly ventilated, before and after the experiment, the data of all subjects were measured by the same professional ultrasound doctor, avoid errors caused by different personnel experience [ 12 ].…”