Increasing evidence has indicated that long non-coding RNAs (lncRNAs) are implicated in and associated with many complex human diseases. Despite of the accumulation lncRNA-disease associations, only a few studies had studied roles these associations pathogenesis. In this paper, we investigated from network view to understand contribution lncRNAs Specifically, both properties diseases which were implicated, Regarding fact protein coding genes involved diseases, constructed coding-non-coding...

Phenotypic features associated with genes and diseases play an important role in disease-related studies most of the available methods focus solely on Online Mendelian Inheritance Man (OMIM) database without considering controlled vocabulary. The Human Phenotype Ontology (HPO) provides a standardized vocabulary covering phenotypic abnormalities human diseases, becomes comprehensive resource for computational analysis disease phenotypes. Most existing HPO-based software tools cannot be used...

Abstract Most combination therapies are developed based on targets of existing drugs, which only represent a small portion the human proteome. We introduce network controllability-based method, OptiCon, for de novo identification synergistic regulators as candidates therapy. These jointly exert maximal control over deregulated genes but minimal unperturbed in disease. Using data from three cancer types, we show that 68% predicted either known drug or have critical role development. Predicted...

Computational approaches for predicting drug-disease associations by integrating gene expression and biological network provide great insights to the complex relationships among drugs, targets, disease genes, diseases at a system level. Hepatocellular carcinoma (HCC) is one of most common malignant tumors with high rate morbidity mortality. We an integrative framework predict novel drugs HCC based on multi-source random walk (PD-MRW). Firstly, protein interaction network, we construct...

While a large number of methods for module detection have been developed undirected networks, it is difficult to adapt them handle directed networks due the lack consensus criteria measuring node significance in network. In this paper, we propose novel structural index, control range, motivated by recent studies on controllability large-scale networks. The range quantifies size subnetwork that can effectively control. A related called similarity, also introduced measure similarity between...

The directedness of the links in a network plays critical role determining many dynamical processes among which controllability has received much recent attention. control robustness against malicious attack and random failure also becomes significant issue. In this paper, we propose novel index motivated by studies on global connectivity controllability. its general form, problem optimizing is computationally infeasible for large-scale networks. By analysing influences several directed...

Protein-protein interactions (PPIs) are crucial in cellular processes. Since the current biological experimental techniques time-consuming and expensive, results suffer from problems of incompleteness noise, developing computational methods software tools to predict PPIs is necessary. Although several approaches have been proposed, species supported often limited additional data like homologous other species, protein sequence expression required. And predictive abilities different features...

Topological centrality is a significant measure for characterising the relative importance of node in complex network. For directed networks that model dynamic processes, however, it more practical to quantify vertex's ability dominate (control or observe) state other vertices. In this paper, based on determination controllable and observable subspaces under global minimum-cost condition, we introduce novel direction-specific index, domination centrality, assess intervention capabilities...

An integrated network-based approach is proposed to nominate driver genes. It composed of two steps including a network diffusion step and an aggregated ranking step, which fuses the correlation between gene mutations expression, relationship mutated genes heterogeneous characteristic patient mutation.

Extracting drug-disease correlations is crucial in unveiling disease mechanisms, as well discovering new indications of available drugs, or drug repositioning. Both the interactome and knowledge disease-associated drug-associated genes remain incomplete.We present a method to predict associations between drugs diseases. Our based on module distance, which originally proposed calculate distances modules incomplete human interactome. We first map all combined protein interaction network. Then...

Identifying driver modules or pathways is a key challenge to interpret the molecular mechanisms and pathogenesis underlying cancer. An increasing number of studies suggest that rarely mutated genes are important for development However, consisting with low-frequency mutations not well characterized. To identify genes, we propose functional similarity index quantify relationship between other ones in same module. Then, develop method detect <bold xmlns:mml="http://www.w3.org/1998/Math/MathML"...

Single-cell clustering is an important part of analyzing single-cell RNA-sequencing data. However, the accuracy and robustness existing methods are disturbed by noise. One promising approach for addressing this challenge integrating pathway information, which can alleviate noise improve performance. In work, we studied impact on pathways. We collected 10 state-of-the-art methods, 26 scRNA-seq datasets four databases, combined AUCell method similarity network fusion to integrate data data,...

CTCF-mediated chromatin loops create insulated neighborhoods that constrain promoter-enhancer interactions, serving as a unit of gene regulation. Disruption the CTCF binding sites (CBS) will lead to destruction neighborhoods, which in turn can cause dysregulation contained genes. In recent study, it is found CTCF/cohesin are major mutational hotspot cancer genome. Mutations affect binding, causing disruption neighborhoods. And our analysis reveals significant enrichment well-known...

Network alignment is one of the most common biological network comparison methods. Aligning protein-protein interaction (PPI) networks different species great important to detect evolutionary conserved pathways or protein complexes across through identification interactions, and improve our insight into systems. Global (GNA) problem NP-complete, for which only heuristic methods have been proposed so far. Generally, current GNA fall global seed-and-extend approaches. These can not get best...

Three‐dimensional (3D) facial data offer the potential to overcome difficulties caused by variation of head pose and illumination in 2D face recognition. In 3D recognition, localisation nose tip is essential normalisation, registration correction etc. Most existing methods detection on deal mainly with frontal or near‐frontal poses are rotation sensitive. Many them training‐based model‐based. this study, a novel method proposed. Using pose‐invariant differential surface features – high‐order...

The emergence of cancers involves numerous coding and non-coding genes. Understanding the contribution RNAs (ncRNAs) to cancer neighborhood is crucial for interpreting interaction between molecular markers cancer. However, there a lack systematic studies on involvement ncRNAs in neighborhood. In this paper, we construct an network which encompasses multiple We focus fundamental topological indicator, namely connectivity, evaluate its performance when applied cancer-affected genes using...

Background The complexity of biological systems motivates us to use the underlying networks provide deep understanding disease etiology and human diseases are viewed as perturbations dynamic properties networks. Control theory that deals with has been successfully used capture systems-level knowledge in large amount quantitative interactions. But from perspective system control, ways by which multiple genetic factors jointly perturb a phenotype still remain. Results In this work, we combine...

Precise disease module is conducive to understanding the molecular mechanism of causation and identifying drug targets. However, due fragmentization in incomplete human interactome, how determine connectivity pattern detect a complete neighbourhood based on this still an open question.In paper, we perform exploratory analysis leading important observation that through few intermediate nodes, most separate connected components formed by disease-associated proteins can be effectively...

Background: It is very likely that RNA secondary structures, more so than the sequence itself, are closely related to their functions, especially for mRNAs and even short noncoding RNAs. However, structure of most lncRNAs (long RNAs) remains poorly understood. Method: Here, we perform a large-scale investigation lncRNA structures hairpin structural motif in human mouse based on computational prediction using RNAfold software. Results: The main results show some difference between various...