Exploring cell-specific miRNA regulation with single-cell miRNA-mRNA co-sequencing data.

BACKGROUND: Existing computational methods for studying miRNA regulation are mostly based on bulk miRNA and mRNA expression data. However, bulk data only allows the analysis of miRNA regulation regarding a group of cells, rather than the miRNA regulation unique to individual cells. Recent advance in single-cell miRNA-mRNA co-sequencing technology has opened a way for investigating miRNA regulation at single-cell level. However, as currently single-cell miRNA-mRNA co-sequencing data is just emerging and only available at small-scale, there is a strong need of novel methods to exploit existing single-cell data for the study of cell-specific miRNA regulation. RESULTS: In this work, we propose a new method, CSmiR (Cell-Specific miRNA regulation) to combine single-cell miRNA-mRNA co-sequencing data and putative miRNA-mRNA binding information to identify miRNA regulatory networks at the resolution of individual cells. We apply CSmiR to the miRNA-mRNA co-sequencing data in 19 K562 single-cells to identify cell-specific miRNA-mRNA regulatory networks for understanding miRNA regulation in each K562 single-cell. By analyzing the obtained cell-specific miRNA-mRNA regulatory networks, we observe that the miRNA regulation in each K562 single-cell is unique. Moreover, we conduct detailed analysis on the cell-specific miRNA regulation associated with the miR-17/92 family as a case study. The comparison results indicate that CSmiR is effective in predicting cell-specific miRNA targets. Finally, through exploring cell-cell similarity matrix characterized by cell-specific miRNA regulation, CSmiR provides a novel strategy for clustering single-cells and helps to understand cell-cell crosstalk. CONCLUSIONS: To the best of our knowledge, CSmiR is the first method to explore miRNA regulation at a single-cell resolution level, and we believe that it can be a useful method to enhance the understanding of cell-specific miRNA regulation.

LMSM: A modular approach for identifying lncRNA related miRNA sponge modules in breast cancer.

Until now, existing methods for identifying lncRNA related miRNA sponge modules mainly rely on lncRNA related miRNA sponge interaction networks, which may not provide a full picture of miRNA sponging activities in biological conditions. Hence there is a strong need of new computational methods to identify lncRNA related miRNA sponge modules. In this work, we propose a framework, LMSM, to identify LncRNA related MiRNA Sponge Modules from heterogeneous data. To understand the miRNA sponging activities in biological conditions, LMSM uses gene expression data to evaluate the influence of the shared miRNAs on the clustered sponge lncRNAs and mRNAs. We have applied LMSM to the human breast cancer (BRCA) dataset from The Cancer Genome Atlas (TCGA). As a result, we have found that the majority of LMSM modules are significantly implicated in BRCA and most of them are BRCA subtype-specific. Most of the mediating miRNAs act as crosslinks across different LMSM modules, and all of LMSM modules are statistically significant. Multi-label classification analysis shows that the performance of LMSM modules is significantly higher than baseline's performance, indicating the biological meanings of LMSM modules in classifying BRCA subtypes. The consistent results suggest that LMSM is robust in identifying lncRNA related miRNA sponge modules. Moreover, LMSM can be used to predict miRNA targets. Finally, LMSM outperforms a graph clustering-based strategy in identifying BRCA-related modules. Altogether, our study shows that LMSM is a promising method to investigate modular regulatory mechanism of sponge lncRNAs from heterogeneous data.

miRSM: an R package to infer and analyse miRNA sponge modules in heterogeneous data.

In molecular biology, microRNA (miRNA) sponges are RNA transcripts which compete with other RNA transcripts for binding with miRNAs. Research has shown that miRNA sponges have a fundamental impact on tissue development and disease progression. Generally, to achieve a specific biological function, miRNA sponges tend to form modules or communities in a biological system. Until now, however, there is still a lack of tools to aid researchers to infer and analyse miRNA sponge modules from heterogeneous data. To fill this gap, we develop an R/Bioconductor package, miRSM, for facilitating the procedure of inferring and analysing miRNA sponge modules. miRSM provides a collection of 50 co-expression analysis methods to identify gene co-expression modules (which are candidate miRNA sponge modules), four module discovery methods to infer miRNA sponge modules and seven modular analysis methods for investigating miRNA sponge modules. miRSM will enable researchers to quickly apply new datasets to infer and analyse miRNA sponge modules, and will consequently accelerate the research on miRNA sponges.

Improving the Classification Performance of Esophageal Disease on Small Dataset by Semi-supervised Efficient Contrastive Learning.

The classification of esophageal disease based on gastroscopic images is important in the clinical treatment, and is also helpful in providing patients with follow-up treatment plans and preventing lesion deterioration. In recent years, deep learning has achieved many satisfactory results in gastroscopic image classification tasks. However, most of them need a training set that consists of large numbers of images labeled by experienced experts. To reduce the image annotation burdens and improve the classification ability on small labeled gastroscopic image datasets, this study proposed a novel semi-supervised efficient contrastive learning (SSECL) classification method for esophageal disease. First, an efficient contrastive pair generation (ECPG) module was proposed to generate efficient contrastive pairs (ECPs), which took advantage of the high similarity features of images from the same lesion. Then, an unsupervised visual feature representation containing the general feature of esophageal gastroscopic images is learned by unsupervised efficient contrastive learning (UECL). At last, the feature representation will be transferred to the down-stream esophageal disease classification task. The experimental results have demonstrated that the classification accuracy of SSECL is 92.57%, which is better than that of the other state-of-the-art semi-supervised methods and is also higher than the classification method based on transfer learning (TL) by 2.28%. Thus, SSECL has solved the challenging problem of improving the classification result on small gastroscopic image dataset by fully utilizing the unlabeled gastroscopic images and the high similarity information among images from the same lesion. It also brings new insights into medical image classification tasks.

Correction to: Identifying miRNA synergism using multiple-intervention causal inference.

After publication of this supplement article [1], it was brought to our attention that the Fig. 3 was incorrect. The correct Fig. 3 is as below.

Anti-CRISPRdb: a comprehensive online resource for anti-CRISPR proteins.

CRISPR-Cas is a tool that is widely used for gene editing. However, unexpected off-target effects may occur as a result of long-term nuclease activity. Anti-CRISPR proteins, which are powerful molecules that inhibit the CRISPR-Cas system, may have the potential to promote better utilization of the CRISPR-Cas system in gene editing, especially for gene therapy. Additionally, more in-depth research on these proteins would help researchers to better understand the co-evolution of bacteria and phages. Therefore, it is necessary to collect and integrate data on various types of anti-CRISPRs. Herein, data on these proteins were manually gathered through data screening of the literatures. Then, the first online resource, anti-CRISPRdb, was constructed for effectively organizing these proteins. It contains the available protein sequences, DNA sequences, coding regions, source organisms, taxonomy, virulence, protein interactors and their corresponding three-dimensional structures. Users can access our database at http://cefg.uestc.edu.cn/anti-CRISPRdb/ without registration. We believe that the anti-CRISPRdb can be used as a resource to facilitate research on anti-CRISPR proteins and in related fields.

Identifying miRNA synergism using multiple-intervention causal inference.

BACKGROUND: Studying multiple microRNAs (miRNAs) synergism in gene regulation could help to understand the regulatory mechanisms of complicated human diseases caused by miRNAs. Several existing methods have been presented to infer miRNA synergism. Most of the current methods assume that miRNAs with shared targets at the sequence level are working synergistically. However, it is unclear if miRNAs with shared targets are working in concert to regulate the targets or they individually regulate the targets at different time points or different biological processes. A standard method to test the synergistic activities is to knock-down multiple miRNAs at the same time and measure the changes in the target genes. However, this approach may not be practical as we would have too many sets of miRNAs to test. RESULTS: n this paper, we present a novel framework called miRsyn for inferring miRNA synergism by using a causal inference method that mimics the multiple-intervention experiments, e.g. knocking-down multiple miRNAs, with observational data. Our results show that several miRNA-miRNA pairs that have shared targets at the sequence level are not working synergistically at the expression level. Moreover, the identified miRNA synergistic network is small-world and biologically meaningful, and a number of miRNA synergistic modules are significantly enriched in breast cancer. Our further analyses also reveal that most of synergistic miRNA-miRNA pairs show the same expression patterns. The comparison results indicate that the proposed multiple-intervention causal inference method performs better than the single-intervention causal inference method in identifying miRNA synergistic network. CONCLUSIONS: Taken together, the results imply that miRsyn is a promising framework for identifying miRNA synergism, and it could enhance the understanding of miRNA synergism in breast cancer.

BDB: biopanning data bank.

The BDB database (http://immunet.cn/bdb) is an update of the MimoDB database, which was previously described in the 2012 Nucleic Acids Research Database issue. The rebranded name BDB is short for Biopanning Data Bank, which aims to be a portal for biopanning results of the combinatorial peptide library. Last updated in July 2015, BDB contains 2904 sets of biopanning data collected from 1322 peer-reviewed papers. It contains 25,786 peptide sequences, 1704 targets, 492 known templates, 447 peptide libraries and 310 crystal structures of target-template or target-peptide complexes. All data stored in BDB were revisited, and information on peptide affinity, measurement method and procedures was added for 2298 peptides from 411 sets of biopanning data from 246 published papers. In addition, a more professional and user-friendly web interface was implemented, a more detailed help system was designed, and a new on-the-fly data visualization tool and a series of tools for data analysis were integrated. With these new data and tools made available, we expect that the BDB database would become a major resource for scholars using phage display, with improved utility for biopanning and related scientific communities.

Annotating Early Esophageal Cancers Based on Two Saliency Levels of Gastroscopic Images.

Early diagnoses of esophageal cancer can greatly improve the survival rate of patients. At present, the lesion annotation of early esophageal cancers (EEC) in gastroscopic images is generally performed by medical personnel in a clinic. To reduce the effect of subjectivity and fatigue in manual annotation, computer-aided annotation is required. However, automated annotation of EEC lesions using images is a challenging task owing to the fine-grained variability in the appearance of EEC lesions. This study modifies the traditional EEC annotation framework and utilizes visual salient information to develop a two saliency levels-based lesion annotation (TSL-BLA) for EEC annotations on gastroscopic images. Unlike existing methods, the proposed framework has a strong ability of constraining false positive outputs. What is more, TSL-BLA is also placed an additional emphasis on the annotation of small EEC lesions. A total of 871 gastroscopic images from 231 patients were used to validate TSL-BLA. 365 of those images contain 434 EEC lesions and 506 images do not contain any lesions. 101 small lesion regions are extracted from the 434 lesions to further validate the performance of TSL-BLA. The experimental results show that the mean detection rate and Dice similarity coefficients of TSL-BLA were 97.24 and 75.15%, respectively. Compared with other state-of-the-art methods, TSL-BLA shows better performance. Moreover, it shows strong superiority when annotating small EEC lesions. It also produces fewer false positive outputs and has a fast running speed. Therefore, The proposed method has good application prospects in aiding clinical EEC diagnoses.

A novel method for real-time atrial fibrillation detection in electrocardiograms using multiple parameters.

BACKGROUND: Automatic detection of atrial fibrillation (AF) in electrocardiograms (ECGs) is beneficial for AF diagnosis, therapy, and management. In this article, a novel method of AF detection is introduced. Most current methods only utilize the RR interval as a critical parameter to detect AF; thus, these methods commonly confuse AF with other arrhythmias. METHODS: We used the average number of f waves in a TQ interval as a characteristic parameter in our robust, real-time AF detection method. Three types of clinical ECG data, including ECGs from normal, AF, and non-AF arrhythmia subjects, were downloaded from multiple open access databases to validate the proposed method. RESULTS: The experimental results suggested that the method could distinguish between AF and normal ECGs with accuracy, sensitivity, and positive predictive values (PPVs) of 93.67%, 94.13%, and 98.69%, respectively. These values are comparable to those of related methods. The method was also able to distinguish between AF and non-AF arrhythmias and had performance indexes (accuracy 94.62%, sensitivity 94.13%, and PPVs 97.67%) that were considerably better than those of other methods. CONCLUSIONS: Our proposed method has prospects as a practical tool enabling clinical diagnosis, treatment, and monitoring of AF.

MimoDB 2.0: a mimotope database and beyond.

Mimotopes are peptides with affinities to given targets. They are readily obtained through biopanning against combinatorial peptide libraries constructed by phage display and other display technologies such as mRNA display, ribosome display, bacterial display and yeast display. Mimotopes have been used to infer the protein interaction sites and networks; they are also ideal candidates for developing new diagnostics, therapeutics and vaccines. However, such valuable peptides are not collected in the central data resources such as UniProt and NCBI GenPept due to their 'unnatural' short sequences. The MimoDB database is an information portal to biopanning results of random libraries. In version 2.0, it has 15,633 peptides collected from 849 papers and grouped into 1818 sets. Besides the core data on panning experiments and their results, broad background information on target, template, library and structure is included. An accompanied benchmark has also been compiled for bioinformaticians to develop and evaluate their new models, algorithms and programs. In addition, the MimoDB database provides tools for simple and advanced searches, structure visualization, BLAST and alignment view on the fly. The experimental biologists can easily use the database as a virtual control to exclude possible target-unrelated peptides. The MimoDB database is freely available at http://immunet.cn/mimodb.

Single-Image-Based Deep Learning for Segmentation of Early Esophageal Cancer Lesions.

Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (EEC). However, neither traditional nor deep learning-based methods up to today can meet the clinical requirements, with the mean Dice score - the most important metric in medical image analysis - hardly exceeding 0.75. In this paper, we present a novel deep learning approach for segmenting EEC lesions. Our method stands out for its uniqueness, as it relies solely on a single input image from a patient, forming the so-called "You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data. On the other hand, it avoids nearly all generalization-related problems since each trained network is applied only to the same input image itself. In particular, we can push the training to "over-fitting" as much as possible to increase the segmentation accuracy. Our technical details include an interaction with clinical doctors to utilize their expertise, a geometry-based data augmentation over a single lesion image to generate the training dataset (the biggest novelty), and an edge-enhanced UNet. We have evaluated YOHO over an EEC dataset collected by ourselves and achieved a mean Dice score of 0.888, which is much higher as compared to the existing deep-learning methods, thus representing a significant advance toward clinical applications. The code and dataset are available at: https://github.com/lhaippp/YOHO.

BFE-Net: bilateral fusion enhanced network for gastrointestinal polyp segmentation.

Accurate segmentation of polyp regions in gastrointestinal endoscopic images is pivotal for diagnosis and treatment. Despite advancements, challenges persist, like accurately segmenting small polyps and maintaining accuracy when polyps resemble surrounding tissues. Recent studies show the effectiveness of the pyramid vision transformer (PVT) in capturing global context, yet it may lack detailed information. Conversely, U-Net excels in semantic extraction. Hence, we propose the bilateral fusion enhanced network (BFE-Net) to address these challenges. Our model integrates U-Net and PVT features via a deep feature enhancement fusion module (FEF) and attention decoder module (AD). Experimental results demonstrate significant improvements, validating our model's effectiveness across various datasets and modalities, promising advancements in gastrointestinal polyp diagnosis and treatment.

CEG: a database of essential gene clusters.

BACKGROUND: Essential genes are indispensable for the survival of living entities. They are the cornerstones of synthetic biology, and are potential candidate targets for antimicrobial and vaccine design. DESCRIPTION: Here we describe the Cluster of Essential Genes (CEG) database, which contains clusters of orthologous essential genes. Based on the size of a cluster, users can easily decide whether an essential gene is conserved in multiple bacterial species or is species-specific. It contains the similarity value of every essential gene cluster against human proteins or genes. The CEG_Match tool is based on the CEG database, and was developed for prediction of essential genes according to function. The database is available at http://cefg.uestc.edu.cn/ceg. CONCLUSIONS: Properties contained in the CEG database, such as cluster size, and the similarity of essential gene clusters against human proteins or genes, are very important for evolutionary research and drug design. An advantage of CEG is that it clusters essential genes based on function, and therefore decreases false positive results when predicting essential genes in comparison with using the similarity alignment method.

f-Wave suppression method for improvement of locating T-Wave ends in electrocardiograms during atrial fibrillation.

This article is to propose an algorithm for improving T-wave ends location during atrial fibrillation (AF). The traditional algorithms do not take the irregular baseline fibrillation of AF into consideration, so their location accuracy is relatively low. Based on simple assumptions that AF is a random signal while T waves and QRS complexes are deterministic signals, we suggest a novel method to suppress f wave for improving location of T-wave ends during AF. We firstly define a new cardiac cycle and then match R peaks and T peaks in the three adjacent cardiac cycles. Finally, we suppress the interference of the f wave by averaging. When evaluating with the PhysioNet QT database and simulated AF signals in terms of the mean and the standard deviation of the T-wave ends location errors, the proposed algorithm improves the performance of existing popular methods. Besides, the clinical significance of the proposed method is illustrated.

[Identification of gastric cancer-related differentially expressed genes by combining PCA and ICA].

Screening potential differentially expressed genes can help us to understand the functions of the genes and their roles in disease development. Due to the different emphases of the principal component analysis and independent component analysis, a novel method that combines principal component analysis and independent component analysis is proposed to identify differentially expressed genes associated with gastric cancer for the improvement of accuracy and credibility of results. This method screens out 16 differentially expressed genes which is significantly related to the occurrence and development of gastric cancer from gastric cancer gene expression data with 7129 genes and 29 samples. These genes are worthy to be studied experimentally. The results of this paper are helpful for revealing the occurrence and development mechanism of gastric cancer.

Risk factors and a predictive nomogram for lymph node metastasis in superficial esophageal squamous cell carcinoma.

BACKGROUND: Superficial esophageal squamous cell carcinoma (ESCC) is defined as cancer infiltrating the mucosa and submucosa, regardless of regional lymph node metastasis (LNM). Endoscopic resection of superficial ESCC is suitable for lesions that have no or low risk of LNM. Patients with a high risk of LNM always need further treatment after endoscopic resection. Therefore, accurately assessing the risk of LNM is critical for additional treatment options. AIM: To analyze risk factors for LNM and develop a nomogram to predict LNM risk in superficial ESCC patients. METHODS: Clinical and pathological data of superficial ESCC patients undergoing esophagectomy from January 1, 2009 to January 31, 2016 were collected. Logistic regression analysis was used to predict LNM risk factors, and a nomogram was developed based on risk factors derived from multivariate logistic regression analysis. The receiver operating characteristic (ROC) curve was used to obtain the accuracy of the nomogram model. RESULTS: A total of 4660 patients with esophageal cancer underwent esophagectomy. Of these, 474 superficial ESCC patients were enrolled in the final analysis, with 322 patients in the training set and 142 patients in the validation set. The prevalence of LNM was 3.29% (5/152) for intramucosal cancer and increased to 26.40% (85/322) for submucosal cancer. Multivariate logistic analysis showed that tumor size, invasive depth, tumor differentiation, infiltrative growth pattern, tumor budding, and lymphovascular invasion were significantly correlated with LNM. A nomogram using these six variables showed good discrimination with an area under the ROC curve of 0.789 (95%CI: 0.737-0.841) in the training set and 0.827 (95%CI: 0.755-0.899) in the validation set. CONCLUSION: We developed a useful nomogram model to predict LNM risk for superficial ESCC patients which will facilitate additional decision-making in treating patients who undergo endoscopic resection.

Semi-Supervised Segmentation Framework for Gastrointestinal Lesion Diagnosis in Endoscopic Images.

BACKGROUND: Accurate gastrointestinal (GI) lesion segmentation is crucial in diagnosing digestive tract diseases. An automatic lesion segmentation in endoscopic images is vital to relieving physicians' burden and improving the survival rate of patients. However, pixel-wise annotations are highly intensive, especially in clinical settings, while numerous unlabeled image datasets could be available, although the significant results of deep learning approaches in several tasks heavily depend on large labeled datasets. Limited labeled data also hinder trained models' generalizability under fully supervised learning for computer-aided diagnosis (CAD) systems. METHODS: This work proposes a generative adversarial learning-based semi-supervised segmentation framework for GI lesion diagnosis in endoscopic images to tackle the challenge of limited annotations. The proposed approach leverages limited annotated and large unlabeled datasets in the training networks. We extensively tested the proposed method on 4880 endoscopic images. RESULTS: Compared with current related works, the proposed method validates better results (Dice similarity coefficient = 89.42 ± 3.92, Intersection over union = 80.04 ± 5.75, Precision = 91.72 ± 4.05, Recall = 90.11 ± 5.64, and Hausdorff distance = 23.28 ± 14.36) on the challenging multi-sited datasets, confirming the effectiveness of the proposed framework. CONCLUSION: We explore a semi-supervised lesion segmentation method to employ the full use of multiple unlabeled endoscopic images to improve lesion segmentation accuracy. Experimental results confirmed the potential of our method and outperformed promising results compared with the current related works. The proposed CAD system can minimize diagnostic errors.

Early gastric cancer segmentation in gastroscopic images using a co-spatial attention and channel attention based triple-branch ResUnet.

BACKGROUND AND OBJECTIVE: The artificial segmentation of early gastric cancer (EGC) lesions in gastroscopic images remains a challenging task due to reasons including the diversity of mucosal features, irregular edges of EGC lesions and nuances between EGC lesions and healthy background mucosa. Hence, this study proposed an automatic segmentation framework: co-spatial attention and channel attention based triple-branch ResUnet (CSA-CA-TB-ResUnet) to achieve accurate segmentation of EGC lesions for aiding clinical diagnosis and treatment. METHODS: The input gastroscopic image sequences of the triple-branch segmentation network CSA-CA-TB-ResUnet is firstly generated by the designed multi-branch input preprocessing (MBIP) module in order to fully utilize massive correlation information among multiple gastroscopic images of the same a lesion. Then, the proposed CSA-CA-TB-ResUnet performs the segmentation of EGC lesion, in which the co-spatial attention (CSA) mechanism is designed to activate the spatial location of EGC lesions by leveraging on the correlations among multiple gastroscopic images of the same EGC lesion, and the channel attention (CA) mechanism is introduced to extract subtle discriminative features of EGC lesions by capturing the interdependencies between channel features. Finally, two gastroscopic images datasets from different digestive endoscopic centers in the southwest and northeast regions of China respectively were collected to validate the performances of proposed segmentation method. RESULTS: The correlation information among gastroscopic images was confirmed to be able to improve the accuracy of EGC segmentation. On another unseen dataset, our EGC segmentation method achieves Jaccard similarity index (JSI) of 84.54% (95% confidence interval (CI), 83.49%-85.56%), threshold Jaccard index (TJI) of 81.73% (95% CI, 79.70%-83.61%), Dice similarity coefficient (DSC) of 91.08% (95% CI, 90.40%-91.76%) and pixel-wise accuracy (PA) of 91.18% (95% CI, 90.43%-91.87%), which is superior to other state-of-the-art methods. Even on the challenging small lesions, the segmentation results of our CSA-CA-TB-ResUnet-based method are consistently and significantly better than other state-of-the-art methods. We also compared the segmentation result of our model with the diagnostic accuracy with junior/senior expert. The comparison results indicated that our model performed better than the junior expert. CONCLUSIONS: This study proposed a novel CSA-CA-TB-ResUnet-based EGC segmentation method and it has a potential for real-time application in improving EGC clinical diagnosis and minimally invasive surgery.

Quantitative Analysis on Molecular Characteristics Evolution of Gastric Cancer Progression and Prognosis.

The dynamic changes of key biological characteristics from gastric low-grade intraepithelial neoplasia (LGIN) to high-grade intraepithelial neoplasia (HGIN) to early gastric cancer (EGC) are still unclear, which greatly affect the accurate diagnosis and treatment of EGC and prognosis evaluation of gastric cancer (GC). In this study, bioinformatics methods/tools are applied to quantitatively analyze molecular characteristics evolution of GC progression, and a prognosis model is constructed. This study finds that some dysregulated differentially expressed mRNAs (DEmRNAs) in the LGIN stage may continue to promote the occurrence and development of EGC. Among the LGIN, HGIN, and EGC stages, there are differences and relevance in the transcription expression patterns of DEmRNAs, and the activation related to immune cells is very different. The biological functions continuously changed during the progression from LGIN to HGIN to EGC. The COX model constructed based on the three EGC-related DEmRNAs has GC prognostic risk prediction ability. The evolution of biological characteristics during the development of EGC mined by the authors provides new insight into understanding the molecular mechanism of EGC occurrence and development. The three-gene prognostic risk model provides a new method for assisting GC clinical treatment decisions.