Analysis of expression patterns of the RNAs involved in the triplets and survival rates of cancer patients revealed several interesting findings. First, even for the same cancer type, prognostic lncRNA-miRNA-mRNA triplets can be different depending on whether lncRNA and mRNA show opposite or similar expression patterns. Second, prognostic lncRNA-miRNA-mRNA triplets are often more predictive of survival rates than RNA pairs or individual RNAs. Our approach will be useful for constructing patient-specific lncRNA-miRNA-mRNA networks and for finding prognostic biomarkers from the networks.

Typically patient-specific gene networks are constructed with gene expression data only. Such networks cannot distinguish direct gene interactions from indirect interactions via others such as the effect of epigenetic events to gene activity. There is an increasing evidence of inter-individual variations not only in gene expression but also in epigenetic events such as DNA methylation. In this paper we propose a new method for constructing a cancer patient-specific gene correlation network using both gene expression and DNA methylation data. We derive a patient-specific network from differential second-order partial correlations of gene expression and DNA methylation between normal samples and the patient sample. The network represents direct interactions between genes by controlling the effect of DNA methylation. Using this method, we constructed 4,000 patient-specific networks for 10 types of cancer. The networks are highly effective in classifying different types of cancer and in deriving potential prognostic gene pairs. In particular, potential prognostic gene pairs derived from the networks were powerful in predicting the survival time of cancer patients. This approach will help identify patient-specific gene correlations and predict prognosis of cancer patients.

Analysis of expression patterns of the RNAs involved in the triplets and survival rates of cancer patients revealed several interesting findings. First, even for the same cancer type, prognostic lncRNA-miRNA-mRNA triplets can be different depending on whether lncRNA and mRNA show opposite or similar expression patterns. Second, prognostic lncRNA-miRNA-mRNA triplets are often more predictive of survival rates than RNA pairs or individual RNAs. Our approach will be useful for constructing patient-specific lncRNA-miRNA-mRNA networks and for finding prognostic biomarkers from the networks.

Typically patient-specific gene networks are constructed with gene expression data only. Such networks cannot distinguish direct gene interactions from indirect interactions via others such as the effect of epigenetic events to gene activity. There is an increasing evidence of inter-individual variations not only in gene expression but also in epigenetic events such as DNA methylation. In this paper we propose a new method for constructing a cancer patient-specific gene correlation network using both gene expression and DNA methylation data. We derive a patient-specific network from differential second-order partial correlations of gene expression and DNA methylation between normal samples and the patient sample. The network represents direct interactions between genes by controlling the effect of DNA methylation. Using this method, we constructed 4,000 patient-specific networks for 10 types of cancer. The networks are highly effective in classifying different types of cancer and in deriving potential prognostic gene pairs. In particular, potential prognostic gene pairs derived from the networks were powerful in predicting the survival time of cancer patients. This approach will help identify patient-specific gene correlations and predict prognosis of cancer patients.

Inspired by a newly discovered gene regulation mechanism known as competing endogenous RNA (ceRNA) interactions, several computational methods have been proposed to generate ceRNA networks. However, most of these methods have focused on deriving restricted types of ceRNA interactions such as lncRNA-miRNA-mRNA interactions. Competition for miRNA-binding occurs not only between lncRNAs and mRNAs but also between lncRNAs or between mRNAs. Furthermore, a large number of pseudogenes also act as ceRNAs, thereby regulate other genes. In this study, we developed a general method for constructing integrative networks of all possible interactions of ceRNAs in renal cell carcinoma (RCC). From the ceRNA networks we derived potential prognostic biomarkers, each of which is a triplet of two ceRNAs and miRNA (i.e., ceRNA-miRNA-ceRNA). Interestingly, some prognostic ceRNA triplets do not include mRNA at all, and consist of two non-coding RNAs and miRNA, which have been rarely known so far. Comparison of the prognostic ceRNA triplets to known prognostic genes in RCC showed that the triplets have a better predictive power of survival rates than the known prognostic genes. Our approach will help us construct integrative networks of ceRNAs of all types and find new potential prognostic biomarkers in cancer.

In recent years, research has focused on developing intrusion detection systems (IDS) within vehicle networks to prevent automotive hacking from external cyberattacks. While machine learning (ML) techniques have shown promise in detecting known attacks, their vulnerability to adversarial examples remains a significant challenge. In this study, we introduce a Generative Adversarial Network (GAN)-based method for creating adversarial attacks capable of bypassing ML-based IDS in in-vehicle networks. Our approach involves preprocessing an automotive hacking dataset, training a GAN-based model, and evaluating the generated attacks using accuracy metrics. The results demonstrate that adversarial attacks effectively reduce the detection accuracy of various IDSs to less than 50%, emphasizing the importance of addressing adversarial cases when designing and evaluating ML-based IDSs for in-vehicle networks. Additionally, t-SNE visualization reveals the successful generation of new adversarial attacks, highlighting the need for ongoing research to strengthen the security of in-vehicle systems.

Credit card companies calculate an accurate credit score by utilizing the personal information and credit data of new applicants. To analyze and predict credit ratings, there have been many studies using machine learning. However, previous research had limitations in improving prediction accuracy using single algorithms such as ensembles or deep learning and could not consider the problem of multiple histories of the same customer using different cards. This study proposes a hybrid algorithm that combines heterogeneous ensembles and TabNet, a deep learning algorithm specialized in tabular data, to address these issues. The study conducted comparative experiments with several state-of-the-art machine learning algorithms that have been used for credit card delinquency prediction.

Abstract Background Lymph node metastasis is usually detected based on the images obtained from clinical examinations. Detecting lymph node metastasis from clinical examinations is a direct way of diagnosing metastasis, but the diagnosis is done after lymph node metastasis occurs. Results We developed a new method for predicting lymph node metastasis based on differential correlations of miRNA-mediated RNA interactions in cancer. The types of RNAs considered in this study include mRNAs, lncRNAs, miRNAs, and pseudogenes. We constructed cancer patient-specific networks of miRNA mediated RNA interactions and identified key miRNA–RNA pairs from the network. A prediction model using differential correlations of the miRNA–RNA pairs of a patient as features showed a much higher performance than other methods which use gene expression data. The key miRNA–RNA pairs were also powerful in predicting prognosis of an individual patient in several types of cancer. Conclusions Differential correlations of miRNA–RNA pairs identified from patient-specific networks of miRNA mediated RNA interactions are powerful in predicting lymph node metastasis in cancer patients. The key miRNA–RNA pairs were also powerful in predicting prognosis of an individual patient of solid cancer.

As the most common cause of cancer death, metastasis is a complex process that involves the spread of cancer cells from the original site to other parts of the body. Diagnosis of metastasis is usually confirmed by clinical examinations and imaging, but such diagnosis is made after metastasis occurs. Early detection of metastasis plays an important role in treatment planning, which in turn has an impact on the survival of patients. So far a few methods have been developed to predict lymph node metastasis, but few methods are available for predicting distant metastasis. Motivated by a recently known gene regulation mechanism involving miRNAs, we developed a new method for predicting both lymph node metastasis and distant metastasis. We identified differential correlations of miRNAs and their target RNAs in cancer, and built prediction models using the differential correlations. Testing the method on several types of cancer showed that differential correlations of miRNAs and their target RNAs are much more powerful than expressions of known metastasis predictive genes in predicting distant metastasis as well as lymph node metastasis. Although preliminary, the method developed in this study will be useful in predicting metastasis and thereby in determining treatment options for cancer patients.

Additional file 2. Potential prognostic miRNA–RNA pairs in seven types of cancer.

Additional file 3. TCGA barcodes of all normal samples and tumor samples studied in our work.