Coherent optical communication technology stands as a thriving research domain in modern communication. In pursuit of higher spectral efficiency, extensive research has been conducted on high-order modulation and broadband transmission technologies. However, these advancements are highly sensitive to transceiver in-phase/quadrature (IQ) skew impairments, posing a significant task of estimation and compensation for transceiver IQ skew. In this paper, an effective approach based on a specially designed training sequence (TS) to simultaneously estimate both transmitter-side and receiver-side IQ skew (Tx/Rx-skew) for coherent orthogonal frequency division multiplexing (OFDM) systems is proposed, in which the TS is highly reusable and holds the potential for reducing the system overhead. Furthermore, the designed TS is also robust to the sampling frequency offset (SFO) and has the capacity to estimate the SFO. The effectiveness of the proposed joint transceiver IQ skew and SFO estimation scheme is verified by simulation in a dual-polarization coherent OFDM system. The results indicate that the proposed scheme can offer the joint estimation for SFO and transceiver IQ skew with the estimation errors below 2ppm and 0.2ps, for the range of [-500, 500] ppm and [-1.8T, 1.8T] accordingly.

Gene regulatory network (GRN) inference is essential for understanding gene interactions that control biological functions and disease progression. Its goal is to uncover regulatory relationships among genes using gene expression data, such as single-cell RNA sequencing (scRNA-seq). With the rise of deep learning, numerous approaches-particularly those using Graph Neural Networks (GNNs)-have shown promising progress in GRN inference. However, scRNA-seq datasets exhibit high sparsity, noise, dropout events, and cellular heterogeneity. These characteristics make it difficult for fixed GNN architectures to generalize across scRNA-seq datasets, often leading to performance degradation. To address this challenge, we propose AutoGRN, an automated graph neural network framework for gene regulatory network inference tasks. AutoGRN incorporates a range of components that potentially influence GRN inference performance into its search space. Through a genetic search algorithm constrained by information entropy, AutoGRN identifies optimal GNN architectures by adapting to the characteristics of individual datasets. Experimental results show that AutoGRN outperforms existing methods in prediction accuracy across multiple public datasets.

Drug repositioning is critical in accelerating drug discovery, which identifies new indications for existing drugs by modeling drug-disease associations. Compared to traditional methods, graph neural networks (GNNs) have recently gained widespread attention due to their ability to effectively aggregate information from neighboring nodes in drug-disease heterogeneous graphs. The GNN-based methods need effective node embeddings for information aggregation. However, they generate the node embeddings by random initialization, rather than incorporating the high-quality expert knowledge involving biological mechanisms in the databases. This limits their capacity to generate interpretable node embeddings aligned with expert knowledge. To bridge this gap, we develop a novel framework dubbed DReKGNN (Drug Repositioning based on expert Knowledge augmented Graph Neural Network). To be specific, DReKGNN will first adopt large language models (LLMs) as a semantic bridge between expert knowledge and GNNs. To ensure the accuracy of expert knowledge, DReKGNN does not rely on prompt templates in LLMs to generate knowledge descriptions for drugs and diseases. Instead, it extracts expert knowledge directly from the DrugBank and OMIM databases. The effective node embeddings with interpretable semantic information will be generated from expert knowledge descriptions involving biological mechanisms by LLMs. Then, we demonstrate that there is a need to mitigate noise when LLM node embeddings serving drug repositioning prediction tasks. Considering this design need, we integrate GNNs with LLM node embeddings by a mean aggregation strategy. The experiment results of performance comparison and case study show the effectiveness of DReKGNN in predicting drug-disease associations. The code is available at https://github.com/csubigdata-Organization/DReKGNN.

Graph neural networks (GNNs) have shown promising performance in the gene regulatory network (G RN) inference task. As mainstream GNNs are developed based on link prediction paradigm, they are susceptible to the impact of noise and imbalanced samples. Recent advances challenge this paradigm by representing gene pairs as nodes in a new graph, thereby recasting GRN inference as graph anomaly detection paradigm and demonstrating superior performance. However, we reveal that diverse GRN datasets require varied model architectures to achieve accurate prediction, and using only a fixed model architecture limits the full potential of graph anomaly detection paradigm, thus leading to suboptimal performance. In this study, we propose AutoGRAD, an automated gene regulatory network inference framework, to systematically explore the benefits of graph anomaly detection paradigm under different model architectures. Through deconstructing graph anomaly detection paradigm for GRN inference, AutoGRAD presents a novel search space consisting of various components, such as similarity computation, graph sparsification, and aggregation function. To this end, AutoGRAD adopts a reinforcement learning search strategy with a proxy training mechanism, which significantly reduces search costs while dynamically identifying the optimal model architecture specific to the dataset. Experimental results on multiple benchmark GRN datasets confirm that AutoGRAD consistently outperforms state-of-the-art baseline models, demonstrating the effectiveness of automatic model architecture adaptation for G RN inference. The code is available at https://github.com/csubigdata-Oraanization/AutoGRAD.

Multi-modal multi-view graph learning models have achieved significant success in medical outcome prediction, combining various modalities to enhance the performance of various medical tasks. However, current architectures for multi-modal multi-view graph learning (M3GL) models heavily depend on manual design, demanding significant effort and expert experience. Meanwhile, significant advancements have been achieved in the field of graph neural architecture search (GNAS), contributing to the automated design of learning architectures based on graphs. However, GNAS faces challenges in automating multimodal multi-view graph learning (M3GL) models, as existing frameworks cannot handle M3GL architecture topology, and current search spaces do not consider M3GL models. To address the above challenges, we propose, for the first time, a multi-modal multi-view graph neural architecture search (M3GNAS) framework that automates the construction of the optimal M3GL models, enabling the integration of multi-modal features from different views. We also design an effective multi-modal multi-view learning (M3L) search space to develop inner-view and outer-view graph representation learning in the context of graph learning, obtaining a latent graph representation tailored to the specific requirements of downstream tasks. To examine the effectiveness of M3GNAS, it is evaluated on medical outcome prediction tasks. The experimental findings demonstrate our proposed framework’s superior performance compared to state-of-the-art models.

Drug repositioning has become attractive because it can significantly accelerate drug discovery and reduce development costs by identifying new indications for existing drugs. Based on graph neural networks (GNNs), the recent work utilizes inter-domain information (drug-disease association network) and intra-domain information (drug-drug similarity network and disease-disease similarity network) to learn the effective representation of drugs and diseases. However, they overlook the significant impact of the adaptive inter-domain and intra-domain information fusion operation on model performance under different data-splitting strategies. Moreover, manually designing GNN architectures for specific drug repositioning datasets is time-consuming and expert-dependent. To address the above problem, we propose an adaptive drug repositioning prediction method called AdaDR, which can adaptively fuse inter-domain and intra-domain information under different data-splitting strategies and automatically design the optimal GNN architecture for each drug repositioning dataset. Specifically, we first design a unified drug repositioning search space with different information fusion operations and various handcrafted GNN architectures. Then, a drug repositioning model search will be adopted to enable an efficient search. Empirical studies on three benchmark datasets demonstrate that the optimal drug repositioning model identified by our proposed AdaDR achieves the best performance among competitive baselines. Through the analysis of the case study, the applicability of AdaDR in practical scenarios is further validated. The code is available at: https://github.com/csubigdata-Organization/AdaDR.

Drug-drug interaction (DDI) has attracted widespread attention because when incompatible drugs are taken together, DDI will lead to adverse effects on the body, such as drug poisoning or reduced drug efficacy. The adverse effects of DDI are closely determined by the molecular structures of the drugs involved. To represent drug data effectively, researchers usually treat the molecular structure of drugs as a molecule graph. Then, previous studies can use the handcrafted graph neural network (GNN) model to learn the molecular graph representations of drugs for DDI prediction. However, in the field of bioinformatics, manually designing GNN architectures for specific molecular structure datasets is time-consuming and depends on expert experience. To address this problem, we propose an automatic drug-drug interaction prediction method named AutoDDI that can efficiently and automatically design the GNN architecture for drug-drug interaction prediction without manual intervention. To this end, we first design an effective search space for drug-drug interaction prediction by revisiting various handcrafted GNN architectures. Then, to efficiently and automatically design the optimal GNN architecture for each drug dataset from the search space, a reinforcement learning search algorithm is adopted. The experiment results show that AutoDDI can achieve the best performance on two real-world datasets. Moreover, the visual interpretation results of the case study show that AutoDDI can effectively capture drug substructure for drug-drug interaction prediction.

MOTIVATION: Understanding drug-response differences in cancer treatments is one of the most challenging aspects of personalized medicine. Recently, graph neural networks (GNNs) have become state-of-the-art methods in many graph representation learning scenarios in bioinformatics. However, building an optimal handcrafted GNN model for a particular drug sensitivity dataset requires manual design and fine-tuning of the hyperparameters for the GNN model, which is time-consuming and requires expert knowledge. RESULTS: In this work, we propose AutoCDRP, a novel framework for automated cancer drug-response predictor using GNNs. Our approach leverages surrogate modeling to efficiently search for the most effective GNN architecture. AutoCDRP uses a surrogate model to predict the performance of GNN architectures sampled from a search space, allowing it to select the optimal architecture based on evaluation performance. Hence, AutoCDRP can efficiently identify the optimal GNN architecture by exploring the performance of all GNN architectures in the search space. Through comprehensive experiments on two benchmark datasets, we demonstrate that the GNN architecture generated by AutoCDRP surpasses state-of-the-art designs. Notably, the optimal GNN architecture identified by AutoCDRP consistently outperforms the best baseline architecture from the first epoch, providing further evidence of its effectiveness. AVAILABILITY AND IMPLEMENTATION: https://github.com/BeObm/AutoCDRP.

Glycans play an indispensable role in various bio-logical processes, such as cancer and autoimmune diseases. The function of glycan is closely determined by its structure. Due to the branch and nonlinear properties of glycans, previous research treats the glycans graph structure as a topological graph to represent glycans data effectively. Graph neural networks (GNNs) are an efficient graph mining method and have many applications in bioinformatics. Therefore, researchers have successfully used handcrafted GNNs to predict glycan immunogenicity. However, a GNN architecture contains many different components, and designing GNN architectures for specific graphs in the bioinformatics field is time-consuming and expert-dependent. To address this challenge, we propose an efficient automatic graph neural network method called EAGNN that can efficiently and automatically construct GNN architecture for glycan immunogenicity prediction. We design an effective graph attention pooling (GAP) search space. We use differential architecture search to efficiently create the optimal GNN architecture in the search space to build the GNN model for glycan immunogenicity prediction. We test EAGNN on the data set SugarBase based on the glycan immunogenicity prediction task. The experiment results show that EAGNN can work more superiorly than the baseline model and achieve the best performance.

Differential analysis of gene-level expression is a commonly used approach for identifying disease-associated genes. Recently, intron retention (IR) has been shown to be associated with complex diseases such as cancers. IR provides value that is complementary to traditional gene-level expression. However, a systematic method to exploit IR for identifying disease-associated genes remains largely unexplored. We developed a pipeline to identify the disease-associated gene based on differential intron retention (IRDAG), which integrates IR events detected by two methods, IRFinder and iREAD. We applied it to Alzheimer's disease (AD). We found that many of the differential genes detected based on IR were not able to be discovered by the traditional gene-level differential expression method, suggesting that our method is complementary to traditional methods. We showed that the differential genes identified with IRDAG were functionally related to AD based on the analysis of protein-protein interaction networks and brain-specific functional gene networks. Being complementary to the existing method, IRDAG provides a new and generic approach for identifying the disease-associated gene.