3 results about "Generalized linear model" patented technology

The present disclosure relates to an application of gene markers in multi-cancer early detection, a method for constructing an early detection model, and a detection device. In the present disclosure, low-coverage whole-genome sequencing is conducted on cell-free DNAs (cfDNAs) from a plasma sample, and according to high-throughput sequencing results, six differential features of the cfDNA fragments are analyzed for each cancer. Then the training and modeling are conducted with a convolutional neural network to allow the early detection of a plurality of cancers at a low sequencing depth. Then the training and modeling are conducted with a generalized linear model (GLM), a gradient boosting machine, a random forest model, a deep learning model, and an extreme gradient boosting model, and staking is conducted with a GLM to construct a multi-feature algorithm, to allow the tissue-of-origin-based detection of cancers.

The application discloses a monochrome sensor mixed coding illumination and image reconstruction method and an imaging system, relates to the technical field of computational imaging, and comprises the following steps: first, at least three RGB mixed illumination modes are designed, and a coding matrix is constructed; then, a plurality of frames of gray scale images under the same target state are collected through a global shutter monochrome sensor according to sequence exposure; subsequently, a generalized linear model is constructed based on the coding matrix, an equation set is solved, and a color image is reconstructed; finally, a standard format image is output after post-processing. The application can eliminate dynamic color smearing in principle, has frame-level self-calibration capability, is compatible in algorithm and strong in robustness, keeps the system small and low in cost, and is suitable for dynamic imaging high requirement scenes such as medical and industrial scenes.

The application provides a personalized book recommendation method based on a Wide&Deep model. Previous studies mainly rely on manual analysis of readers' reading history, book classification and feedback to provide recommendation services for users, but there are problems of insufficient personalization and low efficiency. In order to solve this problem, a recommendation system combining generalized linear models and deep neural networks is proposed. The Wide part helps the model capture common behavior patterns of users by processing handcrafted features, while the Deep part is used to process high-dimensional sparse features and capture complex relationships between user interests. After preprocessing the user data, the Wide&Deep model is input to generate personalized recommendation results.