BACKGROUND: Drug combination therapy often outperforms monotherapy in cancer treatment, but the vast number of available drugs makes manual screening for synergistic combinations costly. Computational methods, especially deep learning, can reduce the search space by predicting likely synergistic drug combinations. Recent studies have improved drug synergy prediction by modeling associations among different biological entities, but drug-drug interactions have not been fully leveraged in this scenario, which motivated the work presented in this paper. METHODS: This paper proposes a deep learning method named HGTSynergy to predict synergistic drug combinations, which employs a heterogeneous graph attention network and a tailored task to capture complex latent patterns in the drug network as prior knowledge. The learned knowledge is then transferred through a transfer learning framework to the downstream task of predicting drug synergy scores, effectively enhancing predictive performance. RESULTS: A five-fold nested cross-validation is employed to train HGTSynergy. In the synergy regression task, HGTSynergy outperforms seven deep learning methods, achieving a mean squared error of 222.83, root mean squared error of 14.91, and Pearson correlation coefficient of 0.75. For the synergy classification task, it also surpasses other methods with an area under the receiver operating characteristic curve of 0.90, area under the precision-recall curve of 0.63, accuracy of 0.94, precision of 0.72, and Cohen's Kappa of 0.52. The ablation study verifies that the heterogeneous graph attention network and the transfer learning framework both have a positive effect on prediction performance. Moreover, a series of analyses demonstrates that the proposed method exhibits strong generalization performance and interpretability. The case study further validates its consistency with prior research. CONCLUSIONS: This study suggests that drug synergy prediction can be improved by comprehensively modeling diverse drug-drug interaction types and leveraging transfer learning to extract prior knowledge from them. The ability of HGTSynergy to discover new anticancer synergistic drug combinations outperforms other state-of-the-art methods. HGTSynergy promises to be a powerful tool to pre-screen anticancer synergistic drug combinations.

Identifying synergistic anticancer drug combinations is a key challenge due to the vast number of potential drug pairs and the high cost of experimental screening. Recent advances leverage deep learning and graph neural networks (GNNs) to learn molecular representations directly from drug structures, but the limited number of unique drugs in available datasets restricts the effectiveness of such models. In this paper, we propose a framework that addresses this issue through a DDI-enhanced Deep Graph InfoMax pretraining strategy. Our approach captures structural and relational drug features by jointly maximizing local–global mutual information and incorporating drug–drug interaction prediction. A multi-level interaction model with semantic attention further enhances feature fusion, leading to improved synergy prediction performance. We compared our method with five advanced methods on two public datasets. The results demonstrate that the proposed method exhibits superior generalization ability.

Improving the quality of the oblique sounding ionogram under the low transmit power and miniaturized antenna array structure has always been a meaningful research content. Here we try to use the adaptive beamforming technology combined with Wuhan Multi-channel Ionospheric Sounding System (WMISS) to improve the situation effectively. First, an L-shaped antenna array is employed to receive the oblique sounding signal; subsequently, the precise angle of the arrival signal is estimated by the multiple signal classification (MUSIC) algorithm. Then three typical adaptive beamforming algorithms are employed to synthesize the multi-channel signals. The experimental results show that for the ionospheric signals of oblique incidence sounding (OIS), SNR can be improved to tens of dBs, and the interference can be significantly suppressed. Especially for the swept-frequency ionogram of OIS, the trace gets clearer and more continuous, which will provide great convenience for further interpretation and ionospheric parameter inversion. In addition, by comparing the application effects of the three typical beamforming algorithms, this letter provides a reference for selecting the synthesized methods of the ionospheric signals of OIS received by the miniaturized array.

The rotation invariance of the classical disc-based moments, such as Zernike moments (ZMs), pseudo-ZMs (PZMs), and orthogonal Fourier-Mellin moments (OFMMs), makes them attractive as descriptors for the purpose of recognition tasks. However, less work has been performed for the generalization of these moment functions. In this paper, four general forms are developed to obtain a class of disc-based generalized radial polynomials that are orthogonal over the unit circle. These radial polynomials are scaled to ensure numerical stability, and some useful properties are discussed for potential applications they could be used in. Then, these scaled radial polynomials are used as kernel functions to construct a series of unit discbased generalized orthogonal moments (DGMs). The variation of parameters in DGMs can form various types of orthogonal moments: 1) generalized ZMs; 2) generalized PZMs; and 3) generalized OFMMs. The classical ZMs, PZMs, and OFMMs correspond to a special case of these three generalized moments for which the free parameter α = 0. Each member of this family will share some excellent properties for image representation and recognition tasks, such as orthogonality and rotation invariance. In addition, we have also developed two algorithms, the so-called m-recursive and n-recursive methods for the computation of these proposed radial polynomials to improve the numerical stability. Experimental results show that the proposed methods are superior to the classical disc-based moments in terms of image representation capability and classification accuracy.