NoiBic: a noise-tolerant biclustering algorithm for high-throughput gene expression data analysis.

NoiBic accurately recovers gene expression biclusters with over 90% precision in noisy, high-dimensional transcriptomic datasets, enhancing module detection.

• Why it matters: Effective identification of gene modules is hindered by technical and biological noise in large-scale transcriptomic data, limiting insights into cellular functions and disease mechanisms.
• What they did: We developed NoiBic, a biclustering algorithm combining seed identification via longest approximate common subsequences with noise-aware column expansion, tested on simulated and real RNA-seq data.
• The result: NoiBic reliably detects biologically meaningful gene modules and cell-type biclusters even under high noise and overlap, facilitating more accurate functional analysis of transcriptomic data.