Novel human microbe-disease associations inference based on network consistency projection.
Shuai Zou1, Jingpu Zhang1, Zuping Zhang2
1School of Information Science and Engineering, Central South University, Changsha, 410083, China.
Scientific Reports
|May 26, 2018
Summary
Scientists developed a new computational model to predict microbe-disease associations, aiding disease prevention and drug discovery. This network consistency projection method integrates microbe and disease similarities for accurate predictions.
Area of Science:
- Microbiology and Computational Biology
- Genomics and Bioinformatics
- Disease Association Studies
Background:
- Growing evidence links microbes to human diseases, highlighting the need for understanding these associations.
- Current understanding of microbe-disease relationships is limited by data scarcity, hindering prediction of novel associations.
- Effective prediction of microbe-disease links is crucial for disease prevention, diagnosis, prognosis, and novel drug discovery.
Purpose of the Study:
- To develop a novel computational model for inferring human microbe-disease associations.
- To integrate diverse data sources, including microbe-disease interaction profiles and symptom-based disease similarity.
- To enhance the prediction accuracy of previously unknown microbe-disease relationships.
Main Methods:
- Developed a computational model named NCPHMDA (Network Consistency Projection for Human Microbe-Disease Associations).
- Employed network consistency projection, integrating Gaussian interaction profile kernel similarity for microbes and diseases.
- Incorporated symptom-based disease similarity to improve prediction accuracy within a non-parametric, global network framework.
Main Results:
- The NCPHMDA model successfully inferred novel human microbe-disease associations.
- Experimental results confirmed the significant contribution of integrated space projection and symptom-based similarity to model performance.
- Cross-validation and case studies demonstrated superior predictive performance compared to existing methods.
Conclusions:
- The developed NCPHMDA model offers a robust computational approach for predicting microbe-disease associations.
- This method advances the understanding of microbe-disease interactions, supporting clinical applications and therapeutic development.
- The findings underscore the potential of network-based computational models in uncovering complex biological relationships.
Related Concept Videos
02:18Fischer Projections
16.6K
Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines. While...
16.6K
02:26Protein Networks
4.6K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.6K
02:06Newman Projections
21.3K
Different notations are used to represent the three-dimensional structure of molecules on two-dimensional surfaces. One of the most commonly used representations is the dash-wedge formula. The dashed wedges, solid wedges, and the plane lines indicate the groups situated behind the plane, coming out of the plane, and in the plane, respectively.
The organic molecules rotate across the single bonds leading to numerous temporary three-dimensional structures of varying energy known as...
The organic molecules rotate across the single bonds leading to numerous temporary three-dimensional structures of varying energy known as...
21.3K
02:18Network Covalent Solids
16.2K
Network covalent solids contain a three-dimensional network of covalently bonded atoms as found in the crystal structures of nonmetals like diamond, graphite, silicon, and some covalent compounds, such as silicon dioxide (sand) and silicon carbide (carborundum, the abrasive on sandpaper). Many minerals have networks of covalent bonds.
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
To break or to melt a covalent network solid, covalent bonds must be broken. Because covalent bonds are relatively strong, covalent network solids are typically...
16.2K
01:15Theory of Attribution I: Correspondent Inference Theory
599
Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
599
01:27Associative Learning
1.4K
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
Classical conditioning, also known...
1.4K