Predicting cancer drug response is crucial for personalized cancer therapy. While numerous deep learning methods have been developed for predicting cancer drug response, many focus solely on omics data, neglecting the valuable information contained within gene-gene interactions. Even though some studies have employed gene relationship networks, they still have not effectively extracted cell line features. Furthermore, drug feature extraction often overlooks substructure-based learning. In this study, we propose EGSDRP, a novel model designed to address these limitations. EGSDRP incorporates a new graph attention network based on Lipschitz normalization to extract features from three types of cancer cell line omics data: gene expression, copy number, and mutation. Additionally, it employs a substructure feature extraction module that integrates a gated message passing network and a Transformer encoder to dynamically capture drug substructure features. Experimental results demonstrate that EGSDRP achieves a Pearson Correlation Coefficient (PCC) of 0.944 on the GDSC2 dataset, outperforming other benchmark models. Significantly, our model successfully prioritized clinically validated anti-cancer agents, including Docetaxel and Epothilone B, underscoring its potential for effective drug discovery.

Predicting drug-target affinity (DTA) is a crucial component of drug repositioning and computer-aided drug development. In current deep learning-based methods, various features of drugs and targets are used to help improve the accuracy of DTA prediction. However, various drug fingerprints used in current methods ignore the effective drug information used in traditional drug development, and the use of protein features ignores the relatively mature research related to protein-protein interactions. To address these limitations, we propose PPDTA, a novel DTA prediction model based on the pharmacophore fingerprint and PPI networks. We introduce pharmacophore fingerprint containing pharmacophore information as a supplementary feature of drugs. The interaction between drugs and targets is a complex biological process in the human body, and learning from the PPI network helps to obtain richer protein features from neighboring proteins. We tested our approach on two public datasets and one new dataset and compared the proposed method with state-of-the-art methods. The results show that the performance of PPDTA is superior to other baseline methods. The source code and datasets of PPDTA are available at https://github.com/shangjun-Yang/PPDTA.

The accurate prediction of genedisease associations is essential for understanding pathogenic mechanisms and identifying therapeutic targets. While existing computational methods have integrated diverse biological information, they struggle with the sparsity of known associations and the lack of reliable negative samples. To address these issues, we propose NCVGAE, a novel framework that extends the Variational Graph Auto-Encoder (VGAE) by incorporating a hybrid negative sampling strategy and contrastive learning to enhance representation learning under sparse supervision. We devise a Hybrid Negative Sampling strategy that combines similarity-guided selection-using topological and biological similarity to identify high-confidence negatives-with random sampling to ensure diversity. This helps reduce noise from unreliable negatives and improves the quality of supervision. The encoder module employs a Graph Neural Network (GNN) to effectively learn robust embeddings for genes and diseases. During the training phase, a contrastive learning objective refines embeddings by pulling together true associations and pushing apart negatives. A multilayer perceptron then predicts association scores. Experiments on three public datasets show that NCVGAE consistently outperforms state-of-the-art methods in terms of AUC and AUPR, highlighting its effectiveness and generalizability.

This paper presents a hardware/software co-design solution to efficiently implement block-layer deduplication over SSD arrays. By introducing complex and varying dependency over the entire storage space, deduplication is infamously subject to high storage management overheads in terms of CPU/memory resource usage and I/O performance degradation. To fundamentally address this problem, one intuitive idea is to offload deduplication storage management from host into SSDs, which is motivated by the redundant dual address mapping in host-side deduplication layer and intra-SSD flash translation layer (FTL). The practical implementation of this idea is nevertheless challenging because of the array-wide deduplication vs. per-SSD FTL management scope mismatch. Aiming to tackle this challenge, this paper presents a solution, called ARM-Dedup, that makes SSD FTL deduplication-oriented and array-aware and accordingly re-architects deduplication software to achieve lightweight and high-performance deduplication over an SSD array. We implemented an ARM-Dedup prototype based on the Linux Dmdedup engine and mdraid software RAID over FEMU SSD emulators. Experimental results show that ARM-Dedup has good scalability and can improve system performance significantly, such as by up to 272% and 127% higher IOPS in synthetic and real-world workloads, respectively.