In the treatment of complex diseases, single drug therapy is limited by resistance and tolerance, making the exploration of efficient drug combinations crucial in cancer research. When evaluating drug efficacy, traditional deep learning methods analyze single-drug sensitivity and drug combination synergy in isolation, which cannot capture the complex internal relationship between them, resulting in prediction deviation. To overcome these limitations, this article proposes a novel drug combination synergy prediction model called MTDSN (Multi-Task Deep Synergy Network), which integrates multi-task learning and deep neural networks to simultaneously predict single-drug sensitivity and drug combination synergy. During the model construction, the Autoencoder integrated with Convolutional Block Attention Module (CBAM) is used to reduce the dimension of input features, and then the drug features and cell line features are connected and input into the shared module embedded with cross-stitch mechanism to exchange information. Finally, the features of each task are input into different task-specific branches to obtain the synergy score of the drug combination, the sensitivity score of the single drug and their corresponding classification results. Evaluated on the O'Neil dataset, MTDSN achieves the lowest mean squared error (MSE) and highest Pearson correlation coefficient (PCC) in drug synergy prediction, with an ROC-AUC of 0.92 and accuracy of 0.95 in the classification task, demonstrating substantial improvements in predictive efficacy.
We proposed a practical non-recursive parallel list ranking algorithm, NR-Ranking. NR-Ranking adopts an independent set to avoid potential operation contention between neighbor nodes. In each communication round, every node in an independent set bridges its left neighbor and right neighbor by adding edges with new distance; then all nodes in this independent set are excluded from previous linked lists. The probability of one node being selected into the independent set is about 1/3. According to the stop criterion of selecting an independent set, the number of communication rounds of NR-Ranking is different. It is bounded by log(p) if the selection step stops when the number of nodes in the reminder lists is less than n/p, where n is the number of nodes in the linked lists and p is the number of processors, or O(logw) if all nodes in the remaining lists are end nodes, where w is the length of the longest linked list. The complexity of computation and communication on both stop criteria is bounded by O(n). Experimental results confirm the above complexity analysis, and the implementation of NR-Ranking in GPS has achieved a speed increase of 4X when the number of workers increases from 8 to 48.
The rapid development of joint profiling methods makes it possible to simultaneously measure multi-modal data from the same cell. It enables the more comprehensive insight of cellular heterogeneity. However, due to the heterogeneity of features across different modalities and the complexity of each modal data, effectively integrating multi-modal data to achieve more accurate cell heterogeneity analysis remains challenging. Here, we propose a novel contrastive learning-based approach for single-cell multi-omics data clustering, named scCLC. Taking contrastive learning as backbone, scCLC integrates scRNA-seq data and scATAC-seq data for cell presentation learning with dedicated data augmentation strategy and self-supervised learning model. scCLC leverages the topological structure derived from single-cell multi-omics data to determine positive pairs, which not only increases the training sample size but also provides basic label priors for the model to learn more cluster-friendly cell representations. The experimental results on single-cell multi-omics datasets show the superior clustering performance of scCLC. When applied to visualization, scCLC can effectively separate the cell subtypes.
Depression, a widespread mental health condition, presents significant challenges due to stigma, concealed symptoms, and high treatment costs, underscoring the need for effective Automatic Depression Detection (ADD) systems. However, existing methods often face difficulties in modeling long-term dependencies and directional information. To address these limitations, we propose DepMambaformer, a novel multimodal depression detection framework. Our model integrates CNNs for local feature extraction, the designed DepBiMamba2 module to capture global dependencies and directional information, and multi-head attention for multimodal fusion of audio and video features. The combination of state space duality module with attention mechanisms demonstrates superior performance. Extensive experiments validate the effectiveness and scalability of DepMambaformer, outperforming state-of-the-art methods. Furthermore, ablation studies and visualizations provide deeper insights into the model's functionality. This work highlights the potential of advanced global feature modeling in enhancing ADD systems for early and efficient depression screening.
Neoantigens, defined as tumor-specific peptides triggering immune responses, have emerged as a promising therapeutic target. Recently, proteogenomic analysis employing liquid chromatography-tandem mass spectrometry (LC-MS/MS) has become a standard approach for neoantigen identification. However, the accuracy of this method is limited by inconsistent false discovery rate (FDR) estimation and inherent biases in the target-decoy method. Retention time (RT), a key physicochemical property of peptides, provides an effective metric for evaluating identified neoantigens' quality. Therefore, we propose PepHiFuse, a deep learning framework incorporating global biochemical semantics and local physical interactions to predict peptide RT. Moreover, it employs an adaptive weighting mechanism to balance these complementary feature domains dynamically. Evaluated on three large public datasets, PepHiFuse outperforms three recently published methods (AutoRT, AlphaPeptDeep, PepMNet). The significant improvement in RT prediction accuracy demonstrates the efficacy of PepHiFuse as a prediction tool. PepHiFuse also enhances neoantigen identification by selectively filtering low-confidence candidates while retaining high-quality targets across multiple FDR estimation methods when applied to clinical proteogenomic datasets. This ability emphasizes the practical utility of the PepHiFuse method, offering a critical step toward reliable immunotherapeutic discovery by incorporating accurate RT prediction into neoantigen validation. The code is available at https://github.com/lyotvincent/PepHiFuse.
