A drug repurposing method based on inhibition effect on gene regulatory network.

Numerous computational drug repurposing methods have emerged as efficient alternatives to costly and time-consuming traditional drug discovery approaches. Some of these methods are based on the assumption that the candidate drug should have a reversal effect on disease-associated genes. However, such methods are not applicable in the case that there is limited overlap between disease-related genes and drug-perturbed genes. In this study, we proposed a novel Drug Repurposing method based on the Inhibition Effect on gene regulatory network (DRIE) to identify potential drugs for cancer treatment. DRIE integrated gene expression profile and gene regulatory network to calculate inhibition score by using the shortest path in the disease-specific network. The results on eleven datasets indicated the superior performance of DRIE when compared to other state-of-the-art methods. Case studies showed that our method effectively discovered novel drug-disease associations. Our findings demonstrated that the top-ranked drug candidates had been already validated by CTD database. Additionally, it clearly identified potential agents for three cancers (colorectal, breast, and lung cancer), which was beneficial when annotating drug-disease relationships in the CTD. This study proposed a novel framework for drug repurposing, which would be helpful for drug discovery and development.

Detecting early-warning signals for influenza by dysregulated dynamic network biomarkers.

As a dynamical system, complex disease always has a sudden state transition at the tipping point, which is the result of the long-term accumulation of abnormal regulations. This paper proposes a novel approach to detect the early-warning signals of influenza A (H3N2 and H1N1) outbreaks by dysregulated dynamic network biomarkers (dysregulated DNBs) for individuals. The results of cross-validation show that our approach can detect early-warning signals before the symptom appears successfully. Unlike the traditional DNBs, our dysregulated DNBs are anchored and very few, which is essential for disease early diagnosis in clinical practice. Moreover, the genes of dysregulated DNBs are significantly enriched in the influenza-related pathways. The source code of this paper can be freely downloaded from https://github.com/YanhaoHuo/dysregulated-DNBs.git.

A simple pre-disease state prediction method based on variations of gene vector features.

BACKGROUND: The progression of disease can be divided into three states: normal, pre-disease, and disease. Since a pre-disease state is the tipping point of disease deterioration, accurately predicting pre-disease state may help to prevent the progression of disease and develop feasible treatment in time. METHODS: In the perspective of gene regulatory network, the expression of a gene is regulated by its upstream genes, and then it also regulates that of its downstream genes. In this study, we define the expression value of these genes as a gene vector to depict its state in a specific sample. Then, we propose a novel pre-disease prediction method by such vector features. RESULTS: The results of an influenza virus infection dataset show that our method can successfully predict the pre-disease state. Furthermore, the pre-disease state related genes predicted by our methods are highly associated with each other and enriched in influenza virus infection related pathways. In addition, our method is more time efficient in calculation than previous works. The code of our method is accessed at https://github.com/ZhenshenBao/sPGVF.git.

Analysis of Breast Cancer Based on the Dysregulated Network.

Breast cancer is a heterogeneous disease, and its development is closely associated with the underlying molecular regulatory network. In this paper, we propose a new way to measure the regulation strength between genes based on their expression values, and construct the dysregulated networks (DNs) for the four subtypes of breast cancer. Our results show that the key dysregulated networks (KDNs) are significantly enriched in critical breast cancer-related pathways and driver genes; closely related to drug targets; and have significant differences in survival analysis. Moreover, the key dysregulated genes could serve as potential driver genes, drug targets, and prognostic markers for each breast cancer subtype. Therefore, the KDN is expected to be an effective and novel way to understand the mechanisms of breast cancer.

Detect the early-warning signals of diseases based on signaling pathway perturbations on a single sample.

BACKGROUND: During the pathogenesisof complex diseases, a sudden health deterioration will occur as results of the cumulative effect of various internal or external factors. The prediction of an early warning signal (pre-disease state) before such deterioration is very important in clinical practice, especially for a single sample. The single-sample landscape entropy (SLE) was proposed to tackle this issue. However, the PPI used in SLE was lack of definite biological meanings. Besides, the calculation of multiple correlations based on limited reference samples in SLE is time-consuming and suspect. RESULTS: Abnormal signals generally exert their effect through the static definite biological functions in signaling pathways across the development of diseases. Thus, it is a natural way to study the propagation of the early-warning signals based on the signaling pathways in the KEGG database. In this paper, we propose a signaling perturbation method named SSP, to study the early-warning signal in signaling pathways for single dynamic time-series data. Results in three real datasets including the influenza virus infection, lung adenocarcinoma, and acute lung injury show that the proposed SSP outperformed the SLE. Moreover, the early-warning signal can be detected by one important signaling pathway PI3K-Akt. CONCLUSIONS: These results all indicate that the static model in pathways could simplify the detection of the early-warning signals.

SGL-SVM: A novel method for tumor classification via support vector machine with sparse group Lasso.

At present, with the in-depth study of gene expression data, the significant role of tumor classification in clinical medicine has become more apparent. In particular, the sparse characteristics of gene expression data within and between groups. Therefore, this paper focuses on the study of tumor classification based on the sparsity characteristics of genes. On this basis, we propose a new method of tumor classification-Sparse Group Lasso (least absolute shrinkage and selection operator) and Support Vector Machine (SGL-SVM). Firstly, the primary selection of feature genes is performed on the normalized tumor datasets using the Kruskal-Wallis rank sum test. Secondly, using a sparse group Lasso for further selection, and finally, the support vector machine serves as a classifier for classification. We validate proposed method on microarray and NGS datasets respectively. Formerly, on three two-class and five multi-class microarray datasets it is tested by 10-fold cross-validation and compared with other three classifiers. SGL-SVM is then applied on BRCA and GBM datasets and tested by 5-fold cross-validation. Satisfactory accuracy is obtained by above experiments and compared with other proposed methods. The experimental results show that the proposed method achieves a higher classification accuracy and selects fewer feature genes, which can be widely applied in classification for high-dimensional and small-sample tumor datasets. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/SGL-SVM/.

Feature selection and tumor classification for microarray data using relaxed Lasso and generalized multi-class support vector machine.

At present, the study of gene expression data provides a reference for tumor diagnosis at the molecular level. It is a challenging task to select the feature genes related to the classification from the high-dimensional and small-sample gene expression data and successfully separate the different subtypes of tumor or between the normal and patient. In this paper, we present a new method for tumor classification-relaxed Lasso (least absolute shrinkage and selection operator) and generalized multi-class support vector machine (rL-GenSVM). The tumor datasets are firstly z-score normalized. Secondly, using relaxed Lasso to select feature gene sets on training set, and finally, generalized multi-class support vector machine (GenSVM) serves as a classifier. We select four two-class datasets and four multi-class datasets for experiments. And four classifiers are used to predict and compare the classification accuracy on test set. To compare with other proposed methods, we obtain satisfactory classification accuracy by 10-fold cross-validation on all samples of each dataset. The experimental results show that the method proposed in this paper selects fewer feature genes and achieves higher classification accuracy. rL-GenSVM uses regularization parameters to avoid overfitting and can be widely applied to high-dimensional and small-sample tumor data classification. The source code and all datasets are available at https://github.com/QUST-AIBBDRC/rL-GenSVM/.