- About Us
- Collaboration & Service
Yuval Kluger, Ph.D.
- Associate Professor of Bioinformatics,Yale University School of Medicine
with CHIBI and Department of Cell Biology (joint appointment with Yale
Dr. Kluger has a total of 21 years of formal graduate and post-graduate training in physics and bioinformatics. He has held staff membership position in Physics at Los Alamos National Laboratory between 1997 and 1999. In September 1999 Dr. Kluger switched career paths from physics to computational biology. He was a recipient of the Sloan Foundation/ DOE computational biology fellowship between 1999 and 2001. He then received the Anna Fuller fellowship in cancer bioinformatics to work with Dr. Sherman Weissman at the Department of Genetics and Dr. Mark Gerstein at the Department of Molecular Biophysics and Biochemistry, Yale University.
In November of 2003 Dr Kluger was hired by NYU. He holds appointments as Assistant Professor of Cell Biology (with Courtesy appointments as an Assistant Professor in Courant Institute of Mathematical Science at NYU and as a visiting scientist at the Departments of Genetics and Dermatology at Yale University). Dr Kluger is also a member of the Biostatistics and Bioinformatics Core, Yale SPORE in Skin Cancer.
Dr. Kluger has authored and co-authored a number of papers in leading journals such as Physical Review Letters, Science, Nature Biotechnology and PNAS in his fields of expertise (i.e., Physics and Bioinformatics and Medical Informatics). Dr Kluger has mentored 5 doctoral students and postdoctoral fellows. His alumni postdoctoral fellows continued their research in prominent research institutes.
Dr. Kluger research involves analysis of genomics and proteomics experiments. These include computational analyses of output from high-throughput datasets generated from experiments involving breast cancer, hematopoeisis, cell cycle genomics, and protein-protein interactions. The central focus of his earlier studies was to reveal functional and regulatory gene modules using genome-wide data generated in various Omics experiments and auxiliary information from genomics databases. His first goal in analyzing cDNA microarray data was to address issues of normalization and artifacts in microarrays. Subsequently, he developed a novel spectral method for bi-directional clustering of cancer microarray data to reveal regulatory gene modules. Consequently, his work has shifted focus to extracting meaningful biological information from experimental systems by assessing the co-expression of genes regulated by various transcription factors, evaluating pathway expression and building genetic networks based on functionality rather than pure expression. This approach is a step forward for identifying genes in regulatory networks that are disrupted by mutations of tumor suppressors and oncogenes and could shed light on the process of malignant transformation. His bioinformatic research also involves the integration of sequence information with genome-wide transcriptome and epigenome profiles. This analysis allowed his group and collaborators to reveal non-unique sequence recognition motifs of transcription factors in an in vivo context and to predict combinatorial regulation partners of few transcription factors. Moreover, this approach allowed them to find spatial organization of transcription factor binding events as well as their relationships with other epigenomics marks.
The Kluger lab current activities include the following areas: a) uncovering direct and collective regulatory relationships between regulators (TFs, epigenomic marks and miRNAs), and their target genes by integration of heterogeneous Omics datasets and DNA sequences, b) developing approaches to the analysis of 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-subclonal cancer samples, signal denoising in protein arrays and next generation platforms, and de-mixing of cell types in heterogeneous samples) d) combining biological knowledge with advanced applied math methods to search 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. In summary, there are two rules that guide the Kluger 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.
540 First Avenue
Skirball Institute of Biomolecular Medicine, 3rd Fl., Lab. 7
New York University Langone Medical Center
New York, NY 10016, USA