AI & Bioinformatics Data Analysis
Converting High-Dimensional Mass Spec Datasets into Insights
Yatiri Bio's B2B AI & Bioinformatics Service processes high-dimensional mass-spectrometry datasets, incorporating machine-learning classification and pathway network enrichment to isolate biomarkers. We deliver analysis results through proprietary visualization systems, including the ProteoBrowser™ portal and ProteoPathway™ network map.
Taming High-Dimensional Proteomic Complexity
Unbiased mass spectrometry generates massive datasets, typically mapping thousands of peptides and proteins across dozens of sample cohorts. Standard statistical packages fail to account for the deep covariate structures, spatial-temporal signaling, and cascade interactions that define cell systems.
Our computational biology team utilizes customized machine learning algorithms—specifically optimized for sparse, high-dimensional biological inputs. We screen raw intensity and spectral counts through feature-selection filters, isolating the core protein sets that explain the variance between drug-sensitive and drug-resistant models.
Proprietary Portals: ProteoBrowser™ & ProteoPathway™
Rather than delivering simple, static Excel tables, Yatiri Bio provides access to our interactive computational data engines:
- ProteoBrowser™: An interactive cloud database portal that hosts your study's proteomic data. Partners can search specific gene/protein symbols, monitor abundance across treatment arms, run differential expression analysis (Volcano plots), and download publication-ready figures.
- ProteoPathway™: An algorithmic mapping system that overlays proteomic changes onto validated intracellular signaling networks. It visually isolates which pathway cascades (such as PI3K/Akt/mTOR, MAPK, or apoptosis cascades) are hyper-activated or down-regulated by compound treatment.
Computational Capability Stack
We configure bioinformatics analyses for various objectives, tailored to our partners' needs:
- Differential Expression Profiling (LFQ intensity, TMT multiplex normalization).
- Kinase-Substrate Relation Mapping (deducing upstream active kinases using phosphoproteomic data).
- Predictive Biomarker Classifier Design (building Random Forest/SVM classifiers to categorize treatment responder subsets).
- Cross-platform integration (matching proteomics to public RNA-seq or genomics datasets).
AI Bioinformatics Buyer Questions
When should a team add AI analysis?
Add AI analysis when protein tables need feature selection, pathway interpretation, classifier design, or a browsable portal for cross-functional teams.
What makes the outputs useful for trials?
Outputs connect high-dimensional protein measurements to interpretable pathways, candidate biomarkers, and patient-stratification hypotheses.
Request Project Quote
Bioinformatics and AI campaigns are structured as add-ons to mass-spectrometry studies or as standalone processing contracts for third-party datasets.