AngLPCM: An Enhanced Similarity Measure

Gene Co-Expression Network (GCN) Analysis is fundamental for understanding gene-gene interactions and cellular processes. A co-expressed gene pair may exhibit patterns such as absolute, alternate, shifting, scaling, and shifting-scaling. To identify co-expressed genes among several genes, patterns must be identified first. Among the existing similarity measures, LPCM is one which is robust to noise while analyzing gene expression data. However, challenges remain in capturing subtle co-expression patterns in highly noisy datasets. In this paper, we propose an enhancement to LPCM by introducing angular deviation-based transformations. This modified measure further reduces noise sensitivity and improves the detection of co-expression patterns. Experiments demonstrate that the proposed measure consistently outperforms traditional approaches under varying noise conditions.