Enabling more sophisticated proteomic profile analysis

Quantitative comparison of samples is central to proteomics. However, biomarkers identified in one batch are quite often not consistent and not reproducible in another batch of samples. We developed techniques based on biological networks to more reproducibly and consistently identify biomarkers and achieve more reliable proteomic-based diagnosis.

• Computational Biology

Dealing with confounders in omics analysis

Universality and reproducibility problems are commonly encountered in analyzing omics data due to etiology and human variability, but also batch effects, poor experiment design, inappropriate sample size, and misapplied statistics. Here, we explore a deeper rethink on the mechanics of applying statistical tests, and design analysis techniques that are robust on omics data.

• Computational Biology

Transcription factor interaction prediction and classification

Regulatory mechanisms often involve several transcription factors (TF), binding together and attaching to the DNA as a single complex. But only a fraction of the regulation partners of each TF is currently known. We developed techniques for predicting the physical interaction between TFs, as well as for predicting the nature of their interactions (i.e. co-operative, competitive, or others).

• Computational Biology

From iteration on multiple collections in synchrony to fast general interval joins

Synchrony iterator captures a programming pattern for synchronized iterations. It is a conservative extension that enhances the repertoire of algorithms expressible in comprehension syntax. In particular, efficient general synchronized iterations, e.g. linear-time algorithms for low-selectivity database non-equijoins, become expressible naturally in comprehensinon syntax.

• Computational Biology, Security

TRL 4

Recovering missing proteins based on biological complexes

We propose a novel ranking strategy for missing-protein recovery based on protein complexes. We postulate that protein complexes provide a good context for making inference of a protein's presence and its abundance. Notably, it is applicable for predicting whether a protein is present even when there is only one sample.

• Computational Biology

TRL 4