Extraction of diverse gene groups with individual relationship from gene co-expression networks
Motivation: Modules in gene coexpression networks (GCN) can be regarded as gene groups with individual relationships. No studies have optimized module detection methods to extract diverse gene groups from GCN, especially for data from clinical specimens. Results: Here, we optimized the flow from transcriptome data to gene modules, aiming to cover diverse gene relationships. We found the prediction accuracy of relationships in benchmark networks of non-mammalian was not always suitable for evaluating gene relationships of human and employed network based metrics. We also proposed a module detection method involving a combination of graphical embedding and recursive partitioning, and confirmed its stable and high performance in biological plausibility of gene groupings. Analysis of differentially ex-pressed genes of several reported cancers using the extracted modules successfully added relational information consistent with previous reports, confirming the usefulness of our framework.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Causal Learning for Heterogeneous Subgroups Based on Nonlinear Causal Kernel Clustering
Due to the challenge posed by multi-source and heterogeneous data collected from diverse environments, causal relationships among features can exhibit variations influenced by different time spans, regions, or strategies…
ClusteringDiversityAtomicRAG: Atom-Entity Graphs for Retrieval-Augmented Generation
Recent GraphRAG methods integrate graph structures into text indexing and retrieval, using knowledge graph triples to connect text chunks, thereby improving retrieval coverage and precision. However, we observe that trea…
Entity LinkingSAGE: A Unified Algebra and Self-Adaptive Execution for AI Functions in SQL
SQL systems increasingly expose AI functions for tasks such as classification, extraction, filtering, ranking, retrieval, joining, and summarization. Despite their diverse APIs, these functions play only three relational…
Modelling Reciprocating Relationships with Hawkes Processes
We present a Bayesian nonparametric model that discovers implicit social structure from interaction time-series data. Social groups are often formed implicitly, through actions among members of groups. Yet many models of…
Time SeriesTime Series AnalysisGreater than the sum of its parts: The role of minority and majority status in collaborative problem-solving communication
Collaborative problem-solving (CPS) is a vital skill used both in the workplace and in educational environments. CPS is useful in tackling increasingly complex global, economic, and political issues and is considered a c…
Diversity