Computational analysis can accurately detect drug-gene interactions (DGIs) cost-effectively. However, transductive learning models are the hotspot to reveal the promising performance for unknown DGIs (both drugs and genes are present in the training model), without special attention to the unseen DGIs (both drugs and genes are absent in the training model). In view of this, this study, for the first time, proposed an inductive learning-based model for the precise identification of unseen DGIs. In our study, by integrating disease nodes to avoid data sparsity, a multi-relational drug-disease-gene (DDG) graph was constructed to achieve effective fusion of data on DDG intro-relationships and inter-actions. Following the extraction of graph features by utilizing graph embedding algorithms, our next step was the retrieval of the attributes of individual gene and drug nodes. In this way, a hybrid feature characterization was represented by integrating graph features and node attributes. Machine learning (ML) models were built, enabling the fulfillment of transductive predictions of unknown DGIs. To realize inductive learning, this study generated an innovative idea of transforming known node vectors derived from the DDG graph into representations of unseen nodes using node similarities as weights, enabling inductive predictions for the unseen DGIs. Consequently, the final model was superior to existing models, with significant improvement in predicting both external unknown and unseen DGIs. The practical feasibility of our model was further confirmed through case study and molecular docking. In summary, this study establishes an efficient data-driven approach through the proposed modeling, suggesting its value as a promising tool for accelerating drug discovery and repurposing.

Objective:To preliminarily study the characteristics of ventricular synchronicity in patients of idiopathic pulmonary arterial hypertension (IPAH) with different heart function.
Methods:Our research included in 2 groups:IPAH group, n=40 patients intermittently collected in our hospital from 2010-10 to 2014-12 and Control group, n=23 normal subjects from the same period of time. Tissue Doppler echocardiography was conducted to compare interventricular systolic and diastolic time to peak (Ts and Te), their differences (Te-Ts) and (Te-Ts) of left ventricle (LV), right ventricle (RV), interventricular septum (IVS) between 2 groups.
Results:Ts and Te of RV were longer than Ts and Te of LV and IVS in both groups, P<0.05. Compared with Control group, by heart function reducing, IPAH group showed different Ts and Te of LV and IVS, P<0.05 and Ts was gradually prolonged, Te was gradually shorter;while Te of RV became longer firs tand then became shorter thereafter. In IPAH group, the patients with heart function grade IV had the longer Ts of LV and IVS than those with heart functionII, III;the patients with heart function IV had the shorter Te of LV, RV and IVS than those with heart function II, III, all P<0.05. In IPAH group with different heart function, Te between RV-IVS and RV-LV were different from Control group, all P<0.05;Te between LV-IVS in heart function IV patients had the largest difference from Control group, P<0.05. Compared with Control group, IPAH group had gradually decreased Te-Ts of LV and IVS by heart function reducing asgrade II>III>IV, all differences had statistic meaning.
Conclusion:IPAH patients with different right heart function may have interventricular systolic and diastolic de-synchronicity;by reduced heart function, interventricular Te-Ts could be specifically shortened.