The application of deep learning techniques to predict the container rehandling probability can assist yards in generating container storage strategies to reduce the rehandling rate of container within the yard. However, since the container storage dataset collected from the yard suffers from multiple sources and data drift, it is difficult to guarantee that the container rehandling probability prediction model directly trained on container storage dataset performs consistently well on different container blocks. To address above issue, we take each container block as an independent data domain and propose a novel multi-domain merging adaptation (MDMA) approach, which includes the three stages of domain merging, domain adaptation and aggregation. In domain merging stage, we merge data domains into several domain cluster for limiting the number of the source cluster and boosting the knowledge transfer. In domain adaptation stage, the prediction model learns the domain-invariant representation for each pair of the source and target clusters by distribution matching manner. In aggregation stage, the domain- invariant features are adaptively aggregates together by feed-forward network. Extensive experiments are conducted on the container rehandling dataset, and the results demonstrate the performance and practical values of the proposed MDMA approach.

Due to the strong uncertainty in the retrieval order of containers and the complex coupling relationship between container yard production equipment, it is challenging for yards to formulate an appropriate slot allocation strategy to control the proportion of rehandling containers. Meanwhile, the unpredictable performance of the slot allocation strategy results in the yard lacking the means to adjust the allocation strategy. To address these issues, an efficient container rehandling probability prediction model based on deep learning has been proposed to assist yards in formulating and adjusting slot allocation strategy. Moreover, we design a container slot allocation strategy driven by the predictive container rehandling probability for reducing the proportion of rehandling container at yards. Extensive experiments on the container storage dataset demonstrate that: 1) the prediction model based on deep learning enables to efficiently and precisely predict the container rehandling, 2) taking Seq2Seq network as the prediction layer of model outperforms other deep sequence models on MSE, MAE, and accuracy, and 3) the slot allocation strategy based on the predictive container rehandling probability can effectively reduce the probability of the rehandling container.

SUMMARY: The next-generation sequencing brought opportunities for the diagnosis of genetic disorders due to its high-throughput capabilities. However, the majority of existing methods were limited to only sequencing candidate variants, and the process of linking these variants to a diagnosis of genetic disorders still required medical professionals to consult databases. Therefore, we introduce diseaseGPS, an integrated platform for the diagnosis of genetic disorders that combines both phenotype and genotype data for analysis. It offers not only a user-friendly GUI web application for those without a programming background but also scripts that can be executed in batch mode for bioinformatics professionals. The genetic and phenotypic data are integrated using the ACMG-Bayes method and a novel phenotypic similarity method, to prioritize the results of genetic disorders. diseaseGPS was evaluated on 6085 cases from Deciphering Developmental Disorders project and 187 cases from Shanghai Children's hospital. The results demonstrated that diseaseGPS performed better than other commonly used methods. AVAILABILITY AND IMPLEMENTATION: diseaseGPS is available to freely accessed at https://diseasegps.sjtu.edu.cn with source code at https://github.com/BioHuangDY/diseaseGPS.

Most Chinese EMRs, text mining over which enable compilation of specific disease databases, are stored as unstructured text, natural language in the field of medicine is distinct and differs from general Chinese. word segmentation of EMRs, pattern matching and feature mining methods were used to find the quantity of disease characteristics in case histories. a full-scale, comprehensive medical database was compiled. After construction of a semantic analysis model based on clinical natural language processing, this study perform pattern matching and disease feature mining and complete mining and analysis of diseases, realizing a semantic parsing and analysis system for diseases.