emiley varuni

 Bioinformatics & Systems Biology: Network science applications in genomics and proteomics Bioinformatics and Systems Biology leverage network science to study complex biological systems. Applications include analyzing genomic and proteomic data, mapping gene-protein interactions, and uncovering pathways in disease or cellular functions. These approaches provide insights into biological networks, aiding in drug discovery, personalized medicine, and understanding life processes at a systems level. Applications in Genomics: Gene Regulatory Networks (GRNs) : Identify relationships between genes, understanding how they regulate each other. Uncover mechanisms behind diseases caused by gene mutations or dysregulation. Genome-Wide Association Studies (GWAS) : Analyze connections between genetic variations and diseases using network-based methods. Reveal clusters of genes associated with particular traits or conditions. Pathway Mapping : Discover and analyze biological pathways, unders...

 Social Network Analysis (SNA): Insights into community detection and influence mapping Social Network Analysis (SNA): Overview Social Network Analysis (SNA) is a powerful tool used to study relationships and interactions within a network, such as social media platforms, professional organizations, or communities. It maps and analyzes how individuals (nodes) are connected (via edges) to uncover hidden patterns and relationships. Key Concepts: Community Detection Identifies groups or clusters within the network where nodes are more densely connected to each other than to the rest of the network. Helps understand social groups, shared interests, or collaborative teams. Influence Mapping Highlights key individuals or entities (influencers) that have significant impact on spreading information or resources across the network. These are often central nodes with many connections or strategic positions bridging different clusters. Applications: Social Media : Identifying influencers and a...

 Complex Networks Analysis: Exploring the structure and dynamics of large-scale networks Key Features: Nodes and Edges : Nodes represent entities (e.g., people in social networks, computers in the internet, etc.). Edges represent relationships or connections (e.g., friendships, data links). Clusters of Nodes : Groups of tightly connected nodes indicate communities or sub-networks where interactions are dense. These clusters can reveal patterns like shared interests or mutual dependencies in the network. Color and Size Variations : Color represents different characteristics (e.g., node type, function, or behavior). Size may indicate the importance or influence of a node, like a hub in a transportation network. Flow Patterns : Dynamic flow paths illustrate how information, energy, or goods move across the network. This can show bottlenecks or efficient pathways in the system. Background Gradient : The gradient adds depth, suggesting the vast and intricate nature of large-scale ...

Multilayer Networks: Interconnected Systems Across Domains Multilayer networks are a framework used to model complex systems with multiple types of interactions or relationships between entities. Unlike traditional networks, which typically focus on a single type of connection, multilayer networks capture multiple, interconnected layers of information. Each layer represents a distinct type of relationship, interaction, or network structure, and entities can have connections within the same layer (intra-layer) or across different layers (inter-layer). Key Characteristics of Multilayer Networks: In social networks, one layer might represent friendships, while another represents professional connections. In transportation networks, one layer might represent railways, and another might represent roadways. A city in a transportation network might have connections between its railway and highway systems. A person in a social network could have overlapping roles in personal and professional c...

  Community Detection: Revealing Hidden Structures in Networks Community detection is a key concept in network science and graph theory, focusing on identifying groups or communities within a network where nodes (individual entities) are more densely connected to each other than to nodes outside the group. This technique uncovers hidden structures and patterns within complex networks, providing valuable insights across various domains, such as social networks, biology, economics, and more. 1. What is Community Detection? Community detection refers to the process of identifying clusters or groups of nodes in a network that are more interconnected with each other than with the rest of the network. These communities often correspond to meaningful structures, such as social groups, organizational departments, or biological systems. 2. Types of Communities in Networks Communities can take different forms, depending on the network: Overlapping Communities : Some nodes may belong to multi...

  Robust fiber-optic microphone with modified dual-wavelength demodulation algorithm for low-frequency sound detection Network science provides powerful tools for analyzing and optimizing complex systems, including advanced technologies such as robust fiber-optic microphones. In particular, a modified dual-wavelength demodulation algorithm for low-frequency sound detection can greatly benefit from network science principles, enabling precise, high-quality signal capture in challenging environments. This innovative microphone uses fiber optics to achieve sensitivity and durability, making it well-suited for applications requiring accurate low-frequency sound detection, such as industrial monitoring, environmental acoustics, and even biomedical sensing. By incorporating network science into its design, the fiber-optic system can be optimized for enhanced signal reliability and reduced noise through strategic data and signal flow pathways. The dual-wavelength demodulation algorithm, a...