Identifying network biomarkers of cancer by sample-specific differential network.

Abundant datasets generated from various big science projects on diseases have presented great challenges and opportunities, which contributed to unfolding the complexity of diseases. The discovery of disease-associated molecular networks for each individual plays an important role in personalized therapy and precision treatment of cancer-based on the reference networks. However, there are no effective ways to distinguish the consistency of different reference networks. In this study, we developed a statistical method, i.e. a sample-specific differential network (SSDN), to construct and analyze such networks based on gene expression of a single sample against a reference dataset. We proved that the SSDN is structurally consistent even with different reference datasets if the reference dataset can follow certain conditions. The SSDN also can be used to identify patient-specific disease modules or network biomarkers as well as predict the potential driver genes of a tumor sample.

Identification of HCC-Related Genes Based on Differential Partial Correlation Network.

Hepatocellular carcinoma (HCC) is one of the most common causes of cancer-related death, but its pathogenesis is still unclear. As the disease is involved in multiple biological processes, systematic identification of disease genes and module biomarkers can provide a better understanding of disease mechanisms. In this study, we provided a network-based approach to integrate multi-omics data and discover disease-related genes. We applied our method to HCC data from The Cancer Genome Atlas (TCGA) database and obtained a functional module with 15 disease-related genes as network biomarkers. The results of classification and hierarchical clustering demonstrate that the identified functional module can effectively distinguish between the disease and the control group in both supervised and unsupervised methods. In brief, this computational method to identify potential functional disease modules could be useful to disease diagnosis and further mechanism study of complex diseases.

Multiple-Tissue and Multilevel Analysis on Differentially Expressed Genes and Differentially Correlated Gene Pairs for HFpEF.

Heart failure with preserved ejection fraction (HFpEF) is a complex disease characterized by dysfunctions in the heart, adipose tissue, and cerebral arteries. The elucidation of the interactions between these three tissues in HFpEF will improve our understanding of the mechanism of HFpEF. In this study, we propose a multilevel comparative framework based on differentially expressed genes (DEGs) and differentially correlated gene pairs (DCGs) to investigate the shared and unique pathological features among the three tissues in HFpEF. At the network level, functional enrichment analysis revealed that the networks of the heart, adipose tissue, and cerebral arteries were enriched in the cell cycle and immune response. The networks of the heart and adipose tissues were enriched in hemostasis, G-protein coupled receptor (GPCR) ligand, and cancer-related pathway. The heart-specific networks were enriched in the inflammatory response and cardiac hypertrophy, while the adipose-tissue-specific networks were enriched in the response to peptides and regulation of cell adhesion. The cerebral-artery-specific networks were enriched in gene expression (transcription). At the module and gene levels, 5 housekeeping DEGs, 2 housekeeping DCGs, 6 modules of merged protein-protein interaction network, 5 tissue-specific hub genes, and 20 shared hub genes were identified through comparative analysis of tissue pairs. Furthermore, the therapeutic drugs for HFpEF-targeting these genes were examined using molecular docking. The combination of multitissue and multilevel comparative frameworks is a potential strategy for the discovery of effective therapy and personalized medicine for HFpEF.

Dynamically characterizing individual clinical change by the steady state of disease-associated pathway.

BACKGROUND: Along with the development of precision medicine, individual heterogeneity is attracting more and more attentions in clinical research and application. Although the biomolecular reaction seems to be some various when different individuals suffer a same disease (e.g. virus infection), the final pathogen outcomes of individuals always can be mainly described by two categories in clinics, i.e. symptomatic and asymptomatic. Thus, it is still a great challenge to characterize the individual specific intrinsic regulatory convergence during dynamic gene regulation and expression. Except for individual heterogeneity, the sampling time also increase the expression diversity, so that, the capture of similar steady biological state is a key to characterize individual dynamic biological processes. RESULTS: Assuming the similar biological functions (e.g. pathways) should be suitable to detect consistent functions rather than chaotic genes, we design and implement a new computational framework (ABP: Attractor analysis of Boolean network of Pathway). ABP aims to identify the dynamic phenotype associated pathways in a state-transition manner, using the network attractor to model and quantify the steady pathway states characterizing the final steady biological sate of individuals (e.g. normal or disease). By analyzing multiple temporal gene expression datasets of virus infections, ABP has shown its effectiveness on identifying key pathways associated with phenotype change; inferring the consensus functional cascade among key pathways; and grouping pathway activity states corresponding to disease states. CONCLUSIONS: Collectively, ABP can detect key pathways and infer their consensus functional cascade during dynamical process (e.g. virus infection), and can also categorize individuals with disease state well, which is helpful for disease classification and prediction.

Multi-Level Comparative Framework Based on Gene Pair-Wise Expression Across Three Insulin Target Tissues for Type 2 Diabetes.

Type 2 diabetes (T2D) is known as a disease caused by gene alterations characterized by insulin resistance, thus the insulin-responsive tissues are of great interest for T2D study. It's of great relevance to systematically investigate commonalities and specificities of T2D among those tissues. Here we establish a multi-level comparative framework across three insulin target tissues (white adipose, skeletal muscle, and liver) to provide a better understanding of T2D. Starting from the ranks of gene expression, we constructed the 'disease network' through detecting diverse interactions to provide a well-characterization for disease affected tissues. Then, we applied random walk with restart algorithm to the disease network to prioritize its nodes and edges according to their association with T2D. Finally, we identified a merged core module by combining the clustering coefficient and Jaccard index, which can provide elaborate and visible illumination of the common and specific features for different tissues at network level. Taken together, our network-, gene-, and module-level characterization across different tissues of T2D hold the promise to provide a broader and deeper understanding for T2D mechanism.

Assessing Image Retrieval Quality at the First Glance.

Image retrieval has achieved remarkable improvements with the rapid progress on visual representation and indexing techniques. Given a query image, search engines are expected to retrieve relevant results in which the top-ranked short list is of most value to users. However, it is challenging to measure the retrieval quality on-the-fly without direct user feedbacks. In this paper, we aim at evaluating the quality of retrieval results at the first glance (i.e., with the top-ranked images). For each retrieval result, we compute a correlation based feature matrix that comprises of contextual information from the retrieval list, and then feed it into a convolutional neural network regression model for retrieval quality evaluation. In this proposed framework, multiple visual features are integrated together for robust representations. We optimize the output of this simpleyet- effective evaluation method to be consistent with Discounted Cumulative Gain (DCG), the intuitive measure for the quality of the top-ranked results. We evaluate our method in terms of prediction accuracy and consistency with the ground truth, and demonstrate its practicability in applications such as rank list selection and database image abundance analyses.

Retrieval Oriented Deep Feature Learning With Complementary Supervision Mining.

Deep convolutional neural networks (CNNs) have been widely and successfully applied in many computer vision tasks, such as classification, detection, semantic segmentation, and so on. As for image retrieval, while off-the-shelf CNN features from models trained for classification task are demonstrated promising, it remains a challenge to learn specific features oriented for instance retrieval. Witnessing the great success of low-level SIFT feature in image retrieval and its complementary nature to the semantic-aware CNN feature, in this paper, we propose to embed the SIFT feature into the CNN feature with a Siamese structure in a learning-based paradigm. The learning objective consists of two kinds of loss, i.e., similarity loss and fidelity loss. The first loss embeds the image-level nearest neighborhood structure with the SIFT feature into CNN feature learning, while the second loss imposes that the CNN feature with the updated CNN model preserves the fidelity of that from the original CNN model solely trained for classification. After the learning, the generated CNN feature inherits the property of the SIFT feature, which is well oriented for image retrieval. We evaluate our approach on the public data sets, and comprehensive experiments demonstrate the effectiveness of the proposed method.

Individual-specific edge-network analysis for disease prediction.

Predicting pre-disease state or tipping point just before irreversible deterioration of health is a difficult task. Edge-network analysis (ENA) with dynamic network biomarker (DNB) theory opens a new way to study this problem by exploring rich dynamical and high-dimensional information of omics data. Although theoretically ENA has the ability to identify the pre-disease state during the disease progression, it requires multiple samples for such prediction on each individual, which are generally not available in clinical practice, thus limiting its applications in personalized medicine. In this work to overcome this problem, we propose the individual-specific ENA (iENA) with DNB to identify the pre-disease state of each individual in a single-sample manner. In particular, iENA can identify individual-specific biomarkers for the disease prediction, in addition to the traditional disease diagnosis. To demonstrate the effectiveness, iENA was applied to the analysis on omics data of H3N2 cohorts and successfully detected early-warning signals of the influenza infection for each individual both on the occurred time and event in an accurate manner, which actually achieves the AUC larger than 0.9. iENA not only found the new individual-specific biomarkers but also recovered the common biomarkers of influenza infection reported from previous works. In addition, iENA also detected the critical stages of multiple cancers with significant edge-biomarkers, which were further validated by survival analysis on both TCGA data and other independent data.

Network biomarkers reveal dysfunctional gene regulations during disease progression.

Extensive studies have been conducted on gene biomarkers by exploring the increasingly accumulated gene expression and sequence data generated from high-throughput technology. Here, we briefly report on the state-of-the-art research and application of biomarkers from single genes (i.e. gene biomarkers) to gene sets (i.e. group or set biomarkers), gene networks (i.e. network biomarkers) and dynamical gene networks (i.e. dynamical network biomarkers). In particular, differential and dynamical network biomarkers are used as representative examples to demonstrate their effectiveness in both detecting early signals for complex diseases and revealing essential mechanisms on disease initiation and progression at a network level.

Spatio-temporal analysis of type 2 diabetes mellitus based on differential expression networks.

T2DM is complex in its dynamical dependence on multiple tissues, disease states, and factors' interactions. However, most existing work devoted to characterizing its pathophysiology from one static tissue, individual factors, or single state. Here we perform a spatio-temporal analysis on T2DM by developing a new form of molecular network, i.e. 'differential expression network' (DEN), which can reflect phenotype differences at network level. Static DENs show that three tissues (white adipose, skeletal muscle, and liver) all suffer from severe inflammation and perturbed metabolism, among which metabolic functions are seriously affected in liver. Dynamical analysis on DENs reveals metabolic function changes in adipose and liver are consistent with insulin resistance (IR) deterioration. Close investigation on IR pathway identifies 'disease interactions', revealing that IR deterioration is earlier than that on SlC2A4 in adipose and muscle. Our analysis also provides evidence that rising of insulin secretion is the root cause of IR in diabetes.