Bioinformatics Projects
Analysis of Genome-wide Association Studies for Common Diseases
In these studies sets of cases (individuals carrying a disease) and controls (background population) are collected, and genotyped for genetic variants, normally single nucleotide polymorphisms (SNPs). Our group is collaborating closely with groups of geneticists and epidimiologists who have collected such samples. We take part in the analysis of these studies, and in some cases also in the design of the studies. Our current collaborations include studies of cardiovascular diseases, ovarian cancer, breast cancer, and non-Hodgkin's lymphoma; for the latter, see our recent publication in Nature Genetics.
Computational Methods for the Identification of Disease-Genotype Associations
We develop copmutational methods that aid in the analysis of genome-wide association studies, or other studies that involve the inference of a relation between a genetic variant such as a SNP or a copy-number variant (CNV), with a given phenotype that was measured for the studied population. These methods include haplotype inference methods, ancestry inference methods, and the incorporation of these in a statistical or machine-learning framework that is used to test for an association of a genetic marker with a phenotype.
Statistical Genetics and Populaton Genetics
We develop computational methods for the inference of evolutionary and genetic characteristics, such as the inference of recombination events, estimation of mutation rates, human history, etc., from genetic data.
Methods for the Analysis of High-throughput Sequencing Data
We are currently developing methods for the design and analysis of studies that involve high-throghput sequencing technologies, such as the Solexa, 454, or Solid platrforms.
Genome Rearrangements
Researchers in the Algorithms group have developed
a 1.5-approximation algorithm for the problem of sorting genome
rearrangement by transpositions and transreversals, improving on the
1.75 known ratio for this problem.
SNP Genotyping
SNP genotyping avoids disruptive
cross-hybridization between universal components of a system to genotype
single nucleotide polymorphisms (SNPs) using a universal DNA tag
array.
Transcriptional Regulation
Dissection of regulatory networks that conrol gene
transcription is one of the greatest challenges of functional genomics.
The Algorithms group addressed the problem of modeling generic features
of structurally but not textually related DNA motifs.
The work divides into several parts: (1) A new approach to the
recognition of transcription-factor binding sites, based on the
principle that transcription factors divide naturally into families, and
that the binding site motifs for transcription factors within the same
family have common features. (2) An algorithm and an associated
web-based tool for finding recurrent cis-regulatory modules in the
promoter regions of human genes. (3) An algorithm for minimizing the
number of gene perturbation pathways whose regulatory structures can be
described within the mathematical framework of chain functions. (4)
Algorithms for discovering protein complexes and regulatory pathways
that are conserved in evolotion, using protein sequence data and
protein-protien interaction data for two or more organisms.
Direct Reinforcement Learning
Stochastic Direct Reinforcment Algorithms
Researchers at ICSI are developing Stochastic Direct
Reinforcement algorithms, which show promise of being a superior
alternative to traditional reinforcement learning methods for solving
real world applications.
Risk, Reward and Reinforcement
Professor John Moody leads this NSF sponsored
project on computational finance and risk. Researchers are developing
applications of reinforcement learning to computational finance.
More about the Algorithms Research Group
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