HiSCF: leveraging higher-order structures for clustering analysis in biological networks

2020 ◽  
Author(s):  
Lun Hu ◽  
Jun Zhang ◽  
Xiangyu Pan ◽  
Hong Yan ◽  
Zhu-Hong You
Keyword(s):  
Biological Networks ◽  
Clustering Analysis ◽  
Biological Network ◽  
Higher Order ◽  
Supplementary Information ◽  
Network Motifs ◽  
Functional Modules ◽  
Connectivity Patterns ◽  
Biological Entities ◽  
Insight Into

Abstract Motivation Clustering analysis in a biological network is to group biological entities into functional modules, thus providing valuable insight into the understanding of complex biological systems. Existing clustering techniques make use of lower-order connectivity patterns at the level of individual biological entities and their connections, but few of them can take into account of higher-order connectivity patterns at the level of small network motifs. Results Here, we present a novel clustering framework, namely HiSCF, to identify functional modules based on the higher-order structure information available in a biological network. Taking advantage of higher-order Markov stochastic process, HiSCF is able to perform the clustering analysis by exploiting a variety of network motifs. When compared with several state-of-the-art clustering models, HiSCF yields the best performance for two practical clustering applications, i.e. protein complex identification and gene co-expression module detection, in terms of accuracy. The promising performance of HiSCF demonstrates that the consideration of higher-order network motifs gains new insight into the analysis of biological networks, such as the identification of overlapping protein complexes and the inference of new signaling pathways, and also reveals the rich higher-order organizational structures presented in biological networks. Availability and implementation HiSCF is available at https://github.com/allenv5/HiSCF. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Quantifying mechanisms in neurodegenerative diseases (NDDs) using candidate mechanism perturbation amplitude (CMPA) algorithm

2019 ◽  
Vol 20 (1) ◽  
Author(s):  
Reagon Karki ◽  
Alpha Tom Kodamullil ◽  
Charles Tapley Hoyt ◽  
Martin Hofmann-Apitius
Keyword(s):  
Biological Networks ◽  
Molecular Mechanisms ◽  
Biological Network ◽  
Expression Patterns ◽  
Cell Types ◽  
Brain Regions ◽  
Heat Diffusion ◽  
Disease Etiology ◽  
The Individual ◽  
Biological Entities

Abstract Background Literature derived knowledge assemblies have been used as an effective way of representing biological phenomenon and understanding disease etiology in systems biology. These include canonical pathway databases such as KEGG, Reactome and WikiPathways and disease specific network inventories such as causal biological networks database, PD map and NeuroMMSig. The represented knowledge in these resources delineates qualitative information focusing mainly on the causal relationships between biological entities. Genes, the major constituents of knowledge representations, tend to express differentially in different conditions such as cell types, brain regions and disease stages. A classical approach of interpreting a knowledge assembly is to explore gene expression patterns of the individual genes. However, an approach that enables quantification of the overall impact of differentially expressed genes in the corresponding network is still lacking. Results Using the concept of heat diffusion, we have devised an algorithm that is able to calculate the magnitude of regulation of a biological network using expression datasets. We have demonstrated that molecular mechanisms specific to Alzheimer (AD) and Parkinson Disease (PD) regulate with different intensities across spatial and temporal resolutions. Our approach depicts that the mitochondrial dysfunction in PD is severe in cortex and advanced stages of PD patients. Similarly, we have shown that the intensity of aggregation of neurofibrillary tangles (NFTs) in AD increases as the disease progresses. This finding is in concordance with previous studies that explain the burden of NFTs in stages of AD. Conclusions This study is one of the first attempts that enable quantification of mechanisms represented as biological networks. We have been able to quantify the magnitude of regulation of a biological network and illustrate that the magnitudes are different across spatial and temporal resolution.

NMDB: NETWORK MOTIF DATABASE ENVISAGED AND EXPLICATED FROM HUMAN DISEASE SPECIFIC PATHWAYS

2014 ◽  
Vol 22 (01) ◽  
pp. 89-100 ◽  
Author(s):  
ABHAY PRATAP ◽  
SETU TALIYAN ◽  
TIRATHA RAJ SINGH
Keyword(s):  
Biological Networks ◽  
Human Disease ◽  
Biological Network ◽  
Metabolic Diseases ◽  
Network Motif ◽  
Network Motifs ◽  
Efficient Detection ◽  
Single Input ◽  
Motif Database ◽  
Disease Specific

The study of network motifs for large number of networks can aid us to resolve the functions of complex biological networks. In biology, network motifs that reappear within a network more often than expected in random networks include negative autoregulation, positive autoregulation, single-input modules, feedforward loops, dense overlapping regulons and feedback loops. These network motifs have their different dynamical functions. In this study, our main objective is to examine the enrichment of network motifs in different biological networks of human disease specific pathways. We characterize biological network motifs as biologically significant sub-graphs. We used computational and statistical criteria for efficient detection of biological network motifs, and introduced several estimation measures. Pathways of cardiovascular, cancer, infectious, repair, endocrine and metabolic diseases, were used for identifying and interlinking the relation between nodes. 3–8 sub-graph size network motifs were generated. Network Motif Database was then developed using PHP and MySQL. Results showed that there is an abundance of autoregulation, feedforward loops, single-input modules, dense overlapping regulons and other putative regulatory motifs in all the diseases included in this study. It is believed that the database will assist molecular and system biologists, biotechnologists, and other scientific community to encounter biologically meaningful information. Network Motif Database is freely available for academic and research purpose at: http://www.bioinfoindia.org/nmdb .

scholarly journals Formal Analysis of Network Motifs

2018 ◽  
Author(s):  
Hillel Kugler ◽  
Sara-Jane Dunn ◽  
Boyan Yordanov
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Formal Analysis ◽  
Building Blocks ◽  
Pulse Generation ◽  
Random Networks ◽  
Network Motifs ◽  
Formal Reasoning ◽  
Dynamical Behaviors ◽  
Stem Cell Pluripotency

AbstractA recurring set of small sub-networks have been identified as the building blocks of biological networks across diverse organisms. These network motifs have been associated with certain dynamical behaviors and define key modules that are important for understanding complex biological programs. Besides studying the properties of motifs in isolation, existing algorithms often evaluate the occurrence frequency of a specific motif in a given biological network compared to that in random networks of similar structure. However, it remains challenging to relate the structure of motifs to the observed and expected behavior of the larger network. Indeed, even the precise structure of these biological networks remains largely unknown. Previously, we developed a formal reasoning approach enabling the synthesis of biological networks capable of reproducing some experimentally observed behavior. Here, we extend this approach to allow reasoning about the requirement for specific network motifs as a way of explaining how these behaviors arise. We illustrate the approach by analyzing the motifs involved in sign-sensitive delay and pulse generation. We demonstrate the scalability and biological relevance of the approach by revealing the requirement for certain motifs in the network governing stem cell pluripotency.

scholarly journals Identifying communities from multiplex biological networks

PeerJ ◽  
2015 ◽  
Vol 3 ◽  
pp. e1525 ◽  
Author(s):  
Gilles Didier ◽  
Christine Brun ◽  
Anaïs Baudot
Keyword(s):  
Protein Interactions ◽  
Biological Networks ◽  
Biological Network ◽  
Random Networks ◽  
Functional Modules ◽  
Source Codes ◽  
Network Layers ◽  
Or Gene ◽  
Multiple Network ◽  
User Friendly

Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression). However, this diversity is classically not considered and the different interaction categories are usually aggregated in a single network. The multiplex framework, where biological relationships are represented by different network layers reflecting the various nature of interactions, is expected to retain more information. Here we assessed aggregation, consensus and multiplex-modularity approaches to detect communities from multiple network sources. By simulating random networks, we demonstrated that the multiplex-modularity method outperforms the aggregation and consensus approaches when network layers are incomplete or heterogeneous in density. Application to a multiplex biological network containing 4 layers of physical or functional interactions allowed recovering communities more accurately annotated than their aggregated counterparts. Overall, taking into account the multiplexity of biological networks leads to better-defined functional modules. A user-friendly graphical software to detect communities from multiplex networks, and corresponding C source codes, are available at GitHub (https://github.com/gilles-didier/MolTi).

scholarly journals PathExt: a general framework for path-based mining of omics-integrated biological networks

2020 ◽  
Author(s):  
Narmada Sambaturu ◽  
Vaidehi Pusadkar ◽  
Sridhar Hannenhalli ◽  
Nagasuma Chandra
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Differentially Expressed ◽  
Single Sample ◽  
Supplementary Information ◽  
Computational Tool ◽  
Practical Applications ◽  
Transcriptomic Data ◽  
General Tool ◽  
Differential Genes

Abstract Motivation Transcriptomes are routinely used to prioritize genes underlying specific phenotypes. Current approaches largely focus on differentially expressed genes (DEGs), despite the recognition that phenotypes emerge via a network of interactions between genes and proteins, many of which may not be differentially expressed. Furthermore, many practical applications lack sufficient samples or an appropriate control to robustly identify statistically significant DEGs. Results We provide a computational tool—PathExt, which, in contrast to differential genes, identifies differentially active paths when a control is available, and most active paths otherwise, in an omics-integrated biological network. The sub-network comprising such paths, referred to as the TopNet, captures the most relevant genes and processes underlying the specific biological context. The TopNet forms a well-connected graph, reflecting the tight orchestration in biological systems. Two key advantages of PathExt are (i) it can extract characteristic genes and pathways even when only a single sample is available, and (ii) it can be used to study a system even in the absence of an appropriate control. We demonstrate the utility of PathExt via two diverse sets of case studies, to characterize (i) Mycobacterium tuberculosis response upon exposure to 18 antibacterial drugs where only one transcriptomic sample is available for each exposure; and (ii) tissue-relevant genes and processes using transcriptomic data for 39 human tissues. Overall, PathExt is a general tool for prioritizing context-relevant genes in any omics-integrated biological network for any condition(s) of interest, even with a single sample or in the absence of appropriate controls. Availabilityand implementation The source code for PathExt is available at https://github.com/NarmadaSambaturu/PathExt. Contact [email protected] or [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Identifying Representative Network Motifs for Inferring Higher-order Structure of Biological Networks

2018 ◽  
Author(s):  
Wang Tao ◽  
Yadong Wang ◽  
Jiajie Peng ◽  
Chen Jin
Keyword(s):  
Biological Networks ◽  
Motif Discovery ◽  
Network Motif ◽  
Low Frequency ◽  
Organizational Structures ◽  
Higher Order ◽  
Order Structure ◽  
Network Motifs ◽  
Scalable Algorithm ◽  
Significant Patterns

AbstractNetwork motifs are recurring significant patterns of inter-connections, which are recognized as fundamental units to study the higher-order organizations of networks. However, the principle of selecting representative network motifs for local motif based clustering remains largely unexplored. We present a scalable algorithm called FSM for network motif discovery. FSM accelerates the motif discovery process by effectively reducing the number of times to do subgraph isomorphism labeling. Multiple heuristic optimizations for subgraph enumeration and subgraph classification are also adopted in FSM to further improve its performance. Experimental results show that FSM is more efficient than the compared models on computational efficiency and memory usage. Furthermore, our experiments indicate that large and frequent network motifs may be more appropriate to be selected as the representative network motifs for discovering higher-order organizational structures in biological networks than small or low-frequency network motifs.

scholarly journals BionetBF: A Novel Bloom Filter for Faster Membership Identification of Paired Biological Network Data

2021 ◽  
Author(s):  
Sabuzima Nayak ◽  
Ripon Patgiri
Keyword(s):  
Biological Networks ◽  
Biological Network ◽  
Biological Process ◽  
Bloom Filter ◽  
Biological Data ◽  
Main Memory ◽  
Tiny Amount ◽  
Cuckoo Filter ◽  
Biological Entities ◽  
False Positive Probability

Biological network represents the interaction or relationship between the biological entities such as proteins and genes of a biological process. A biological network with thousands of millions of vertices makes its processing complex and challenging. In this article, we have proposed a novel Bloom Filter for biological networks, called BionetBF, to provide fast membership identification of the biological network edges or paired biological data. BionetBF is capable of executing millions of operations within a second on datasets having millions of paired biological data while occupying tiny amount of main memory. We have conducted rigorous experiments to prove the performance of BionetBF with large datasets. The experiment is conducted using 12 generated datasets and three biological network datasets. BionetBF demonstrates higher performance while maintaining a 0.001 false positive probability. BionetBF is also compared with other filters: Cuckoo Filter and Libbloom, where BionetBF proves its supremacy by exhibiting higher performance with a smaller sized memory compared with large sized filters of Cuckoo Filter and Libbloom.

scholarly journals Surrogate minimal depth as an importance measure for variables in random forests

2019 ◽  
Vol 35 (19) ◽  
pp. 3663-3671 ◽  
Author(s):  
Stephan Seifert ◽  
Sven Gundlach ◽  
Silke Szymczak
Keyword(s):  
Variable Selection ◽  
Supplementary Information ◽  
Predictor Variables ◽  
Importance Measure ◽  
Surrogate Variables ◽  
Machine Learning Approach ◽  
Complex Relationships ◽  
Minimal Depth ◽  
Insight Into ◽  
Causal Variables

Abstract Motivation It has been shown that the machine learning approach random forest can be successfully applied to omics data, such as gene expression data, for classification or regression and to select variables that are important for prediction. However, the complex relationships between predictor variables, in particular between causal predictor variables, make the interpretation of currently applied variable selection techniques difficult. Results Here we propose a new variable selection approach called surrogate minimal depth (SMD) that incorporates surrogate variables into the concept of minimal depth (MD) variable importance. Applying SMD, we show that simulated correlation patterns can be reconstructed and that the increased consideration of variable relationships improves variable selection. When compared with existing state-of-the-art methods and MD, SMD has higher empirical power to identify causal variables while the resulting variable lists are equally stable. In conclusion, SMD is a promising approach to get more insight into the complex interplay of predictor variables and outcome in a high-dimensional data setting. Availability and implementation https://github.com/StephanSeifert/SurrogateMinimalDepth. Supplementary information Supplementary data are available at Bioinformatics online.

Stacking angle dependent multiple excitonic resonances in bilayer tungsten diselenide

Nanophotonics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 3881-3887
Author(s):  
Ankit Arora ◽  
Pramoda K. Nayak ◽  
Tejendra Dixit ◽  
Kolla Lakshmi Ganapathi ◽  
Ananth Krishnan ◽  
...  
Keyword(s):  
Chemical Vapour ◽  
Optoelectronic Devices ◽  
Interlayer Coupling ◽  
Higher Order ◽  
Doping Effect ◽  
Many Body ◽  
Si Substrate ◽  
Tungsten Diselenide ◽  
Body Dynamics ◽  
Insight Into

AbstractWe report on multiple excitonic resonances in bilayer tungsten diselenide (BL-WSe2) stacked at different angles and demonstrate the use of the stacking angle to control the occurrence of these excitations. BL-WSe2 with different stacking angles were fabricated by stacking chemical vapour deposited monolayers and analysed using photoluminescence measurements in the temperature range 300–100 K. At reduced temperatures, several excitonic features were observed and the occurrences of these exitonic resonances were found to be stacking angle dependent. Our results indicate that by controlling the stacking angle, it is possible to excite or quench higher order excitations to tune the excitonic flux in optoelectronic devices. We attribute the presence/absence of multiple higher order excitons to the strength of interlayer coupling and doping effect from SiO2/Si substrate. Understanding interlayer excitations will help in engineering excitonic devices and give an insight into the physics of many-body dynamics.

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