Kluger Lab

Role and Vision

In recent years it became evident that integration of mathematical, statistical and computer science concepts is essential for accelerating discoveries in biology and medicine. There are two levels needed for understanding living systems. The first is based on expansion and implementation of data mining approaches developed in applied math, computer science, statistics and engineering disciplines for the identification of molecular patterns associated with various biological states and cell types. This quest is directly relevant for biomarker discovery, diagnosis, prognosis and studying of responses to drugs and treatments.

The second level, which is more complex, is that of inferring biological pathways or mechanisms from perturbation studies. Towards the goals of increasing our knowledge and understanding at both levels, our laboratory foster close collaborations between scientists employing genome-wide technologies (biologists and translational researchers who generate massive amounts of genomics and proteomics data in diverse range of problems), other computational biology faculty, COB program students and colleagues in quantitative disciplines at NYU and other institutions.

These collaborations provide opportunities to find novel mathematical and visual data representations of biological systems and suggest rational manipulations of these systems. The main functions of our integrative quantitative bioinformatics and computational biology laboratory are identification and adaptation of sophisticated quantitative approaches to explore complex systems and heterogonous data to facilitate deeper insights into the underlying biological processes. These approaches are typically not implemented in widely used bioinformatics toolboxes. 

There are two rules that guide our lab in choosing which problems to study:

a) the problem is associated with a practical biological or medical question; and

b) it involves applications or development of mathematical, statistical or modeling approaches. Our goal is to continue in this research venue by collaborating with basic and clinical biomedical researchers, mathematicians, statisticians, and computer scientists.

Research Interests

Our research lab has four key research interests involving computational genomics/proteomics:

• Integrative Bioinformatics
• Biomarker Identification
• Signal Processing of new high throughput technologies
• Discovering complex genomics patterns in GWAS studies and cancer evolution 

Our current activities include the following areas:

a) uncovering direct and collective regulatory relationships between (TFs, epigenomic marks and miRNAs) regulators and their target genes by integration of heterogeneous Omics datasets and DNA sequences,

b) developing approaches to analyze high dimensional data from genomics and proteomics platforms for biomarker discovery and personal medicine,

c) data preprocessing tasks for identification of relevant biological signals in high throughput experiments (e.g. identification of copy number events in multi-clonal cancer samples, signal denoising in protein arrays and next generation platforms,  and dimixing of cell types in heterogeneous samples)

d) combining biological knowledge with advanced applied math methods for searching  complex local and non-local genomic patterns that may discriminate affected vs. healthy individuals in large scale GWAS studies or cancer patients with good vs. poor outcomes in SNP/CNV studies.