Drug repositioning based on weighted local information augmented graph neural network.

Drug repositioning, the strategy of redirecting existing drugs to new therapeutic purposes, is pivotal in accelerating drug discovery. While many studies have engaged in modeling complex drug-disease associations, they often overlook the relevance between different node embeddings. Consequently, we propose a novel weighted local information augmented graph neural network model, termed DRAGNN, for drug repositioning. Specifically, DRAGNN firstly incorporates a graph attention mechanism to dynamically allocate attention coefficients to drug and disease heterogeneous nodes, enhancing the effectiveness of target node information collection. To prevent excessive embedding of information in a limited vector space, we omit self-node information aggregation, thereby emphasizing valuable heterogeneous and homogeneous information. Additionally, average pooling in neighbor information aggregation is introduced to enhance local information while maintaining simplicity. A multi-layer perceptron is then employed to generate the final association predictions. The model's effectiveness for drug repositioning is supported by a 10-times 10-fold cross-validation on three benchmark datasets. Further validation is provided through analysis of the predicted associations using multiple authoritative data sources, molecular docking experiments and drug-disease network analysis, laying a solid foundation for future drug discovery.

A weighted bilinear neural collaborative filtering approach for drug repositioning.

Drug repositioning is an efficient and promising strategy for traditional drug discovery and development. Many research efforts are focused on utilizing deep-learning approaches based on a heterogeneous network for modeling complex drug-disease associations. Similar to traditional latent factor models, which directly factorize drug-disease associations, they assume the neighbors are independent of each other in the network and thus tend to be ineffective to capture localized information. In this study, we propose a novel neighborhood and neighborhood interaction-based neural collaborative filtering approach (called DRWBNCF) to infer novel potential drugs for diseases. Specifically, we first construct three networks, including the known drug-disease association network, the drug-drug similarity and disease-disease similarity networks (using the nearest neighbors). To take the advantage of localized information in the three networks, we then design an integration component by proposing a new weighted bilinear graph convolution operation to integrate the information of the known drug-disease association, the drug's and disease's neighborhood and neighborhood interactions into a unified representation. Lastly, we introduce a prediction component, which utilizes the multi-layer perceptron optimized by the α-balanced focal loss function and graph regularization to model the complex drug-disease associations. Benchmarking comparisons on three datasets verified the effectiveness of DRWBNCF for drug repositioning. Importantly, the unknown drug-disease associations predicted by DRWBNCF were validated against clinical trials and three authoritative databases and we listed several new DRWBNCF-predicted potential drugs for breast cancer (e.g. valrubicin and teniposide) and small cell lung cancer (e.g. valrubicin and cytarabine).

DGHNE: network enhancement-based method in identifying disease-causing genes through a heterogeneous biomedical network.

The identification of disease-causing genes is critical for mechanistic understanding of disease etiology and clinical manipulation in disease prevention and treatment. Yet the existing approaches in tackling this question are inadequate in accuracy and efficiency, demanding computational methods with higher identification power. Here, we proposed a new method called DGHNE to identify disease-causing genes through a heterogeneous biomedical network empowered by network enhancement. First, a disease-disease association network was constructed by the cosine similarity scores between phenotype annotation vectors of diseases, and a new heterogeneous biomedical network was constructed by using disease-gene associations to connect the disease-disease network and gene-gene network. Then, the heterogeneous biomedical network was further enhanced by using network embedding based on the Gaussian random projection. Finally, network propagation was used to identify candidate genes in the enhanced network. We applied DGHNE together with five other methods into the most updated disease-gene association database termed DisGeNet. Compared with all other methods, DGHNE displayed the highest area under the receiver operating characteristic curve and the precision-recall curve, as well as the highest precision and recall, in both the global 5-fold cross-validation and predicting new disease-gene associations. We further performed DGHNE in identifying the candidate causal genes of Parkinson's disease and diabetes mellitus, and the genes connecting hyperglycemia and diabetes mellitus. In all cases, the predicted causing genes were enriched in disease-associated gene ontology terms and Kyoto Encyclopedia of Genes and Genomes pathways, and the gene-disease associations were highly evidenced by independent experimental studies.

ICSDA: a multi-modal deep learning model to predict breast cancer recurrence and metastasis risk by integrating pathological, clinical and gene expression data.

Breast cancer patients often have recurrence and metastasis after surgery. Predicting the risk of recurrence and metastasis for a breast cancer patient is essential for the development of precision treatment. In this study, we proposed a novel multi-modal deep learning prediction model by integrating hematoxylin & eosin (H&E)-stained histopathological images, clinical information and gene expression data. Specifically, we segmented tumor regions in H&E into image blocks (256 × 256 pixels) and encoded each image block into a 1D feature vector using a deep neural network. Then, the attention module scored each area of the H&E-stained images and combined image features with clinical and gene expression data to predict the risk of recurrence and metastasis for each patient. To test the model, we downloaded all 196 breast cancer samples from the Cancer Genome Atlas with clinical, gene expression and H&E information simultaneously available. The samples were then divided into the training and testing sets with a ratio of 7: 3, in which the distributions of the samples were kept between the two datasets by hierarchical sampling. The multi-modal model achieved an area-under-the-curve value of 0.75 on the testing set better than those based solely on H&E image, sequencing data and clinical data, respectively. This study might have clinical significance in identifying high-risk breast cancer patients, who may benefit from postoperative adjuvant treatment.

Virtual Water Embodied in Interregional Energy Trade in China: A City-Level Analysis.

Large volumes of water are used in energy production for both primary (e.g., fuel extraction) and secondary energy (e.g., electricity). In countries such as China, with a large internal trade in fuels and long-distance transmission grids, this can result in considerable water inequalities. Previous research focused on the water impacts of energy production at the national and provincial levels, which is too coarse to identify the spatial differences and make specific case studies. Here, we take the next step toward a spatially explicit economically integrated water-use for energy assessment by combining a bottom-up assessment approach with a city-level multiregional input-output model. Specifically, we examine the water consumption of energy production in China, distinguishing between water for primary and secondary energy at the level of coal mines, oil and gas fields, and power plants for the first time. Of the total energy-related freshwater consumption of 4.9 Gm3 in 2017, primary energy accounted for 19% (940 Mm3) and secondary energy accounted for 81% (3955 Mm3). Coal was the largest water consumer for both primary and secondary energy (540 and 3880 Mm3, respectively), with both oil (361, and 0.5 Mm3, respectively) and gas (7 and 69 Mm3, respectively) also consuming large amounts. Intercity virtual water, that is, water embodied in energy trade across cities, reached 54% (2.6 Gm3) of energy-related freshwater consumption. Across China, 32% of cities see a bilateral trade in secondary- and primary-energy-related virtual water (e.g., Daqing city exports virtual water embodied in primary fuel to other cities that is then used to produce electricity in those cities, part of which is used back in Daqing via transmission). For these 32% of cities, 73% export more virtual water than import and 27% import more virtual water than export. This study reveals significant differences in city-level virtual water patterns (e.g., scale and direction) between primary and secondary energy to provide information for cities about their virtual water inflow and outflow and the potential collaboration partners for water management.

Predicting colorectal cancer tumor mutational burden from histopathological images and clinical information using multi-modal deep learning.

MOTIVATION: Tumor mutational burden (TMB) is an indicator of the efficacy and prognosis of immune checkpoint therapy in colorectal cancer (CRC). In general, patients with higher TMB values are more likely to benefit from immunotherapy. Though whole-exome sequencing is considered the gold standard for determining TMB, it is difficult to be applied in clinical practice due to its high cost. There are also a few DNA panel-based methods to estimate TMB; however, their detection cost is also high, and the associated wet-lab experiments usually take days, which emphasize the need for faster and cheaper alternatives. RESULTS: In this study, we propose a multi-modal deep learning model based on a residual network (ResNet) and multi-modal compact bilinear pooling to predict TMB status (i.e. TMB high (TMB_H) or TMB low(TMB_L)) directly from histopathological images and clinical data. We applied the model to CRC data from The Cancer Genome Atlas and compared it with four other popular methods, namely, ResNet18, ResNet50, VGG19 and AlexNet. We tested different TMB thresholds, namely, percentiles of 10%, 14.3%, 15%, 16.3%, 20%, 30% and 50%, to differentiate TMB_H and TMB_L.For the percentile of 14.3% (i.e. TMB value 20) and ResNet18, our model achieved an area under the receiver operating characteristic curve of 0.817 after 5-fold cross-validation, which was better than that of other compared models. In addition, we also found that TMB values were significantly associated with the tumor stage and N and M stages. Our study shows that deep learning models can predict TMB status from histopathological images and clinical information only, which is worth clinical application.

Application of oncolytic virus in tumor therapy.

Oncolytic viruses (OVs) can selectively kill tumor cells without affecting normal cells, as well as activate the innate and adaptive immune systems in patients. Thus, they have been considered as a promising measure for safe and effective cancer treatment. Recently, a few genetically engineered OVs have been developed to further improve the effect of tumor elimination by expressing specific immune regulatory factors and thus enhance the body's antitumor immunity. In addition, the combined therapies of OVs and other immunotherapies have been applied in clinical. Although there are many studies on this hot topic, a comprehensive review is missing on illustrating the mechanisms of tumor clearance by OVs and how to modify engineered OVs to further enhance their antitumor effects. In this study, we provided a review on the mechanisms of immune regulatory factors in OVs. In addition, we reviewed the combined therapies of OVs with other therapies including radiotherapy and CAR-T or TCR-T cell therapy. The review is useful in further generalize the usage of OV in cancer treatment.

Visible Near-Infrared Photodetection Based on Ta2NiSe5/WSe2 van der Waals Heterostructures.

The increasing interest in two-dimensional materials with unique crystal structures and novel band characteristics has provided numerous new strategies and paradigms in the field of photodetection. However, as the demand for wide-spectrum detection increases, the size of integrated systems and the limitations of mission modules pose significant challenges to existing devices. In this paper, we present a van der Waals heterostructure photodetector based on Ta2NiSe5/WSe2, leveraging the inherent characteristics of heterostructures. Our results demonstrate that this detector exhibits excellent broad-spectrum detection ability from the visible to the infrared bands at room temperature, achieving an extremely high on/off ratio, without the need for an external bias voltage. Furthermore, compared to a pure material detector, it exhibits a fast response and low dark currents (~3.6 pA), with rise and fall times of 278 µs and 283 µs for the response rate, respectively. Our findings provide a promising method for wide-spectrum detection and enrich the diversity of room-temperature photoelectric detection.

Computational drug repositioning using similarity constrained weight regularization matrix factorization: A case of COVID-19.

Amid the COVID-19 crisis, we put sizeable efforts to collect a high number of experimentally validated drug-virus association entries from literature by text mining and built a human drug-virus association database. To the best of our knowledge, it is the largest publicly available drug-virus database so far. Next, we develop a novel weight regularization matrix factorization approach, termed WRMF, for in silico drug repurposing by integrating three networks: the known drug-virus association network, the drug-drug chemical structure similarity network, and the virus-virus genomic sequencing similarity network. Specifically, WRMF adds a weight to each training sample for reducing the influence of negative samples (i.e. the drug-virus association is unassociated). A comparison on the curated drug-virus database shows that WRMF performs better than a few state-of-the-art methods. In addition, we selected the other two different public datasets (i.e. Cdataset and HMDD V2.0) to assess WRMF's performance. The case study also demonstrated the accuracy and reliability of WRMF to infer potential drugs for the novel virus. In summary, we offer a useful tool including a novel drug-virus association database and a powerful method WRMF to repurpose potential drugs for new viruses.

CMF-Impute: an accurate imputation tool for single-cell RNA-seq data.

MOTIVATION: Single-cell RNA-sequencing (scRNA-seq) technology provides a powerful tool for investigating cell heterogeneity and cell subpopulations by allowing the quantification of gene expression at single-cell level. However, scRNA-seq data analysis remains challenging because of various technical noises such as dropout events (i.e. excessive zero counts in the expression matrix). RESULTS: By taking consideration of the association among cells and genes, we propose a novel collaborative matrix factorization-based method called CMF-Impute to impute the dropout entries of a given scRNA-seq expression matrix. We test CMF-Impute and compare it with the other five state-of-the-art methods on six popular real scRNA-seq datasets of various sizes and three simulated datasets. For simulated datasets, CMF-Impute outperforms other methods in imputing the closest dropouts to the original expression values as evaluated by both the sum of squared error and Pearson correlation coefficient. For real datasets, CMF-Impute achieves the most accurate cell classification results in spite of the choice of different clustering methods like SC3 or T-SNE followed by K-means as evaluated by both adjusted rand index and normalized mutual information. Finally, we demonstrate that CMF-Impute is powerful in reconstructing cell-to-cell and gene-to-gene correlation, and in inferring cell lineage trajectories. AVAILABILITY AND IMPLEMENTATION: CMF-Impute is written as a Matlab package which is available at https://github.com/xujunlin123/CMFImpute.git. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Enabling Oxygen-Sulfur Exchange Reaction to Produce Semicrystalline Copolymers from Carbon Disulfide and Ethylene Oxide.

This work describes the first example of semicrystalline poly(thiocarbonate)s from carbon disulfide (CS2 ) and ethylene oxide (EO), two mass producible low-cost monomers. Lewis acid/base pairs (LPs) exhibit high activity (EO conversion up to >99%, 8 h) in catalyzing the copolymerization under low Lewis pair/monomer ratio of 1:1500. Oxygen-sulfur exchange reaction (O-S ER) during the copolymerization of CS2 and EO, the generation and mutual copolymerization with COS, CO2 , and episulfide, is harnessed to introduce crystallizable segments [SC(O)O and SC(S)S] in the copolymer. The type of Lewis base is found to have a great impact on the chain microstructure and the crystalline properties. The formed copolymers with melting point from 117.7 to 245.3 °C are obtained. The maximum crystallinity is estimated to be 78% based on the powder wide-angle X-ray diffraction pattern. This work provides a general method to prepare semicrystalline sulfur-containing polymers.

Probing antiviral drugs against SARS-CoV-2 through virus-drug association prediction based on the KATZ method.

It is urgent to find an effective antiviral drug against SARS-CoV-2. In this study, 96 virus-drug associations (VDAs) from 12 viruses including SARS-CoV-2 and similar viruses and 78 small molecules are selected. Complete genomic sequence similarity of viruses and chemical structure similarity of drugs are then computed. A KATZ-based VDA prediction method (VDA-KATZ) is developed to infer possible drugs associated with SARS-CoV-2. VDA-KATZ obtained the best AUCs of 0.8803 when the walking length is 2. The predicted top 3 antiviral drugs against SARS-CoV-2 are remdesivir, oseltamivir, and zanamivir. Molecular docking is conducted between the predicted top 10 drugs and the virus spike protein/human ACE2. The results showed that the above 3 chemical agents have higher molecular binding energies with ACE2. For the first time, we found that zidovudine may be effective clues of treatment of COVID-19. We hope that our predicted drugs could help to prevent the spreading of COVID.

Single-cell RNA-seq clustering: datasets, models, and algorithms.

Single-cell RNA sequencing (scRNA-seq) technologies allow numerous opportunities for revealing novel and potentially unexpected biological discoveries. scRNA-seq clustering helps elucidate cell-to-cell heterogeneity and uncover cell subgroups and cell dynamics at the group level. Two important aspects of scRNA-seq data analysis were introduced and discussed in the present review: relevant datasets and analytical tools. In particular, we reviewed popular scRNA-seq datasets and discussed scRNA-seq clustering models including K-means clustering, hierarchical clustering, consensus clustering, and so on. Seven state-of-the-art scRNA clustering methods were compared on five public available datasets. Two primary evaluation metrics, the Adjusted Rand Index (ARI) and the Normalized Mutual Information (NMI), were used to evaluate these methods. Although unsupervised models can effectively cluster scRNA-seq data, these methods also have challenges. Some suggestions were provided for future research directions.

Simultaneous Improvement of Ionic Conductivity and Mechanical Strength in Block Copolymer Electrolytes with Double Conductive Nanophases.

The most daunting challenge of solid polymer electrolytes (SPEs) is the development of materials with simultaneously high ionic conductivity and mechanical strength. Herein, SPEs of lithium bis-(trifluoromethanesulfonyl)imide (LiTFSI)-doped poly(propylene monothiocarbonate)-b-poly(ethylene oxide) (PPMTC-b-PEO) block copolymers (BCPs) with both blocks associating with Li+ ions are prepared. It is found that the PPMTC-b-PEO/LiTFSI electrolytes with double conductive phases exhibit much higher ionic conductivity (2 × 10-4 S cm-1 at r.t.) than the BCP electrolytes with a single conductive phase. Concurrently, the storage moduli of PPMTCn -b-PEO44 /LiTFSI electrolytes are ≈1-4 orders of magnitude higher than that of the neat PEO/LiTFSI electrolytes. Therefore, simultaneous improvement of ionic conductivity and mechanical properties is achieved by construction of a microphase-separated and disordered structure with double conductive phases.

An Investigation of the Organoborane/Lewis Base Pairs on the Copolymerization of Propylene Oxide with Succinic Anhydride.

The copolymerization of biorenewable succinic anhydride (SA) with propylene oxide (PO) is a promising way to synthesize biodegradable aliphatic polyesters. However, the catalytic systems for this reaction still deserve to be explored because the catalytic activity of the reported catalysts and the molecular weights of produced polyesters are unsatisfied. Herein, we investigate the copolymerization of SA with PO catalyzed by the organoborane/base pairs. The types of Lewis bases, organoboranes, and their loadings all have a large impact on the activity and selectivity of the copolymerization. High ester content of >99% was achieved when performed the PO/SA copolymerization using triethyl borane (TEB)/phosphazene base P1-t-Bu (t-BuP1) pair with a molar ratio of 1/1 at 30-80 °C. Using TEB/t-BuP1 pair with the molar ratio of 4/1 at 80 °C, the turnover of frequency (TOF) was up to 128 h-1 and clearly higher than the known TOF values (0.5-34 h-1) of the PO/SA copolymerization by previously reported catalysts. The number-average molecular weights (Mns) of the resultant polyesters reached up to 20.4 kg/mol when copolymerization was carried out using TEB/t-BuP1 (1/1, in molar ratio) at 30 °C.

Mutant RAMP2 causes primary open-angle glaucoma via the CRLR-cAMP axis.

PURPOSE: Primary open-angle glaucoma (POAG) is the leading cause of irreversible blindness worldwide and mutations in known genes can only explain 5-6% of POAG. This study was conducted to identify novel POAG-causing genes and explore the pathogenesis of this disease. METHODS: Exome sequencing was performed in a Han Chinese cohort comprising 398 sporadic cases with POAG and 2010 controls, followed by replication studies by Sanger sequencing. A heterozygous Ramp2 knockout mouse model was generated for in vivo functional study. RESULTS: Using exome sequencing analysis and replication studies, we identified pathogenic variants in receptor activity-modifying protein 2 (RAMP2) within three genetically diverse populations (Han Chinese, German, and Indian). Six heterozygous RAMP2 pathogenic variants (Glu39Asp, Glu54Lys, Phe103Ser, Asn113Lysfs*10, Glu143Lys, and Ser171Arg) were identified among 16 of 4763 POAG patients, whereas no variants were detected in any exon of RAMP2 in 10,953 control individuals. Mutant RAMP2s aggregated in transfected cells and resulted in damage to the AM-RAMP2/CRLR-cAMP signaling pathway. Ablation of one Ramp2 allele led to cAMP reduction and retinal ganglion cell death in mice. CONCLUSION: This study demonstrated that disruption of RAMP2/CRLR-cAMP axis could cause POAG and identified a potential therapeutic intervention for POAG.

Molecular Network-Based Drug Prediction in Thyroid Cancer.

As a common malignant tumor disease, thyroid cancer lacks effective preventive and therapeutic drugs. Thus, it is crucial to provide an effective drug selection method for thyroid cancer patients. The connectivity map (CMAP) project provides an experimental validated strategy to repurpose and optimize cancer drugs, the rationale behind which is to select drugs to reverse the gene expression variations induced by cancer. However, it has a few limitations. Firstly, CMAP was performed on cell lines, which are usually different from human tissues. Secondly, only gene expression information was considered, while the information about gene regulations and modules/pathways was more or less ignored. In this study, we first measured comprehensively the perturbations of thyroid cancer on a patient including variations at gene expression level, gene co-expression level and gene module level. After that, we provided a drug selection pipeline to reverse the perturbations based on drug signatures derived from tissue studies. We applied the analyses pipeline to the cancer genome atlas (TCGA) thyroid cancer data consisting of 56 normal and 500 cancer samples. As a result, we obtained 812 up-regulated and 213 down-regulated genes, whose functions are significantly enriched in extracellular matrix and receptor localization to synapses. In addition, a total of 33,778 significant differentiated co-expressed gene pairs were found, which form a larger module associated with impaired immune function and low immunity. Finally, we predicted drugs and gene perturbations that could reverse the gene expression and co-expression changes incurred by the development of thyroid cancer through the Fisher's exact test. Top predicted drugs included validated drugs like baclofen, nevirapine, glucocorticoid, formaldehyde and so on. Combining our analyses with literature mining, we inferred that the regulation of thyroid hormone secretion might be closely related to the inhibition of the proliferation of thyroid cancer cells.

Prediction of Protein Subcellular Localization Based on Fusion of Multi-view Features.

The prediction of protein subcellular localization is critical for inferring protein functions, gene regulations and protein-protein interactions. With the advances of high-throughput sequencing technologies and proteomic methods, the protein sequences of numerous yeasts have become publicly available, which enables us to computationally predict yeast protein subcellular localization. However, widely-used protein sequence representation techniques, such as amino acid composition and the Chou's pseudo amino acid composition (PseAAC), are difficult in extracting adequate information about the interactions between residues and position distribution of each residue. Therefore, it is still urgent to develop novel sequence representations. In this study, we have presented two novel protein sequence representation techniques including Generalized Chaos Game Representation (GCGR) based on the frequency and distributions of the residues in the protein primary sequence, and novel statistics and information theory (NSI) reflecting local position information of the sequence. In the GCGR + NSI representation, a protein primary sequence is simply represented by a 5-dimensional feature vector, while other popular methods like PseAAC and dipeptide adopt features of more than hundreds of dimensions. In practice, the feature representation is highly efficient in predicting protein subcellular localization. Even without using machine learning-based classifiers, a simple model based on the feature vector can achieve prediction accuracies of 0.8825 and 0.7736 respectively for the CL317 and ZW225 datasets. To further evaluate the effectiveness of the proposed encoding schemes, we introduce a multi-view features-based method to combine the two above-mentioned features with other well-known features including PseAAC and dipeptide composition, and use support vector machine as the classifier to predict protein subcellular localization. This novel model achieves prediction accuracies of 0.927 and 0.871 respectively for the CL317 and ZW225 datasets, better than other existing methods in the jackknife tests. The results suggest that the GCGR and NSI features are useful complements to popular protein sequence representations in predicting yeast protein subcellular localization. Finally, we validate a few newly predicted protein subcellular localizations by evidences from some published articles in authority journals and books.

Revealing Drug-Target Interactions with Computational Models and Algorithms.

BACKGROUND: Identifying possible drug-target interactions (DTIs) has become an important task in drug research and development. Although high-throughput screening is becoming available, experimental methods narrow down the validation space because of extremely high cost, low success rate, and time consumption. Therefore, various computational models have been exploited to infer DTI candidates. METHODS: We introduced relevant databases and packages, mainly provided a comprehensive review of computational models for DTI identification, including network-based algorithms and machine learning-based methods. Specially, machine learning-based methods mainly include bipartite local model, matrix factorization, regularized least squares, and deep learning. RESULTS: Although computational methods have obtained significant improvement in the process of DTI prediction, these models have their limitations. We discussed potential avenues for boosting DTI prediction accuracy as well as further directions.

Matrix completion with side information and its applications in predicting the antigenicity of influenza viruses.

MOTIVATION: Low-rank matrix completion has been demonstrated to be powerful in predicting antigenic distances among influenza viruses and vaccines from partially revealed hemagglutination inhibition table. Meanwhile, influenza hemagglutinin (HA) protein sequences are also effective in inferring antigenic distances. Thus, it is natural to integrate HA protein sequence information into low-rank matrix completion model to help infer influenza antigenicity, which is critical to influenza vaccine development. RESULTS: We have proposed a novel algorithm called biological matrix completion with side information (BMCSI), which first measures HA protein sequence similarities among influenza viruses (especially on epitopes) and then integrates the similarity information into a low-rank matrix completion model to predict influenza antigenicity. This algorithm exploits both the correlations among viruses and vaccines in serological tests and the power of HA sequence in predicting influenza antigenicity. We applied this model into H3N2 seasonal influenza virus data. Comparing to previous methods, we significantly reduced the prediction root-mean-square error in a 10-fold cross validation analysis. Based on the cartographies constructed from imputed data, we showed that the antigenic evolution of H3N2 seasonal influenza is generally S-shaped while the genetic evolution is half-circle shaped. We also showed that the Spearman correlation between genetic and antigenic distances (among antigenic clusters) is 0.83, demonstrating a globally high correspondence and some local discrepancies between influenza genetic and antigenic evolution. Finally, we showed that 4.4%±1.2% genetic variance (corresponding to 3.11 ± 1.08 antigenic distances) caused an antigenic drift event for H3N2 influenza viruses historically. AVAILABILITY AND IMPLEMENTATION: The software and data for this study are available at http://bi.sky.zstu.edu.cn/BMCSI/. CONTACT: jialiang.yang@mssm.edu or pinganhe@zstu.edu.cn. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.