Inferring drug-disease associations is critical in unveiling disease mechanisms, as well as discovering novel functions of available drugs, or drug repositioning. Previous work is primarily based on drug-gene-disease relationship, which throws away many important information since genes execute their functions through interacting others. To overcome this issue, we propose a novel methodology that discover the drug-disease association based on protein complexes. Firstly, the integrated heterogeneous network consisting of drugs, protein complexes, and disease are constructed, where we assign weights to the drug-disease association by using probability. Then, from the tripartite network, we get the indirect weighted relationships between drugs and diseases. The larger the weight, the higher the reliability of the correlation. We apply our method to mental disorders and hypertension, and validate the result by using comparative toxicogenomics database. Our ranked results can be directly reinforced by existing biomedical literature, suggesting that our proposed method obtains higher specificity and sensitivity. The proposed method offers new insight into drug-disease discovery. Our method is publicly available at http://1.complexdrug.sinaapp.com/Drug_Complex_Disease/Data_Download.html.

In existing sophisticated text-to-SQL models, schema linking is often considered as a simple, minor component, belying its importance. By providing a schema linking corpus based on the Spider text-to-SQL dataset, we systematically study the role of schema linking. We also build a simple BERT-based baseline, called Schema-Linking SQL (SLSQL) to perform a data-driven study. We find when schema linking is done well, SLSQL demonstrates good performance on Spider despite its structural simplicity. Many remaining errors are attributable to corpus noise. This suggests schema linking is the crux for the current textto-SQL task. Our analytic studies provide insights on the characteristics of schema linking for future developments of text-to-SQL tasks. 1 * Equal contribution.

The multi-layer computing model is developed to calculate wide-angle neutron spectra, in the range from 0° to 180° with a 5° step, produced by bombarding a thick beryllium target with deuterons. The double-differential cross-sections (DDCSs) for the 9Be(d, xn) reaction are calculated using the TALYS-1.8 code. They are in agreement with the experimental data, and are much better than the PHITS-JQMD/GEM results at 15° , 30° , 45° and 60° neutron emission angles for deuteron energy of 10.0 MeV. In the TALYS-1.8 code, neutron contributions from direct reactions (break-up, stripping and knock-out reactions) are controlled by adjustable parameters, which describe the basic characteristics of typical direct reactions and control the relative intensity and the position of the ridgy hillock at the tail of DDCSs. It is found that the typical calculated wide-angle neutron spectra for different neutron emission angles and neutron angular distributions agree quite well with the experimental data for 13.5 MeV deuterons. The multi-layer computing model can reproduce the experimental data reasonably well by optimizing the adjustable parameters in the TALYS-1.8 code. Given the good agreement with the experimental data, the multi-layer computing model could provide better predictions of wide-angle neutron energy spectra, neutron angular distributions and neutron yields for the 9Be(d, xn) reaction neutron source.

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