Written by: Summary: MEALR – Smarter Motif Discovery, Faster Insights MEALR (Motif Enrichment Analysis by Logistic Regression) [1] is a next-generation tool that finds which transcription factor motifs really matter in your data — without arbitrary cutoffs or guesswork. Instead of looking at each motif in isolation, MEALR uses sparse…
Cancer remains one of the greatest medical challenges of our time, and the development of new treatment strategies is crucial. At geneXplain, we have pioneered a unique computational methodology – upstream analysis – which has enabled us to identify and develop a new family of anticancer compounds, now patented under…
Written by: When you think of cell signaling, it’s tempting to imagine a single, straight road—but NF-κB runs two. The canonical pathway fires fast to tackle stress and infection, while the non-canonical route, driven by NIK, works more slowly and selectively, shaping immune and developmental responses. TNF superfamily members are…
Written by: Discovering Overlapping Mechanisms with Disease Similarity Maps Understanding the molecular relationships between diseases has never been more critical. With the explosion of omics data, we are now able to uncover not only what makes a disease unique — but also what it might share with others. One powerful…
Written by: We would be happy to share our in-depth white paper detailing the construction of the RT-PCR gene expression panel and its role in identifying patient-specific disease signatures during COPD acute exacerbations. We participated in the FEBS Congress 2025 in Milan. At this event, we presented our latest study on personalized treatment selection for…
Written by: Quick Summary Upstream analysis is a bioinformatics method developed by geneXplain to identify master regulators—key transcription factors, kinases, or signaling proteins that drive observed gene expression changes in diseases like cancer. Unlike traditional enrichment analyses that show downstream effects, upstream analysis works backwards from differentially expressed genes (DEGs)…
End of content
End of content