Bioinformatics Projects
HAP: Haplotype Resolution Using Imperfect Phylogeny
HAP is a software haplotyping program created by
researchers at ICSI and UCSD. It is used to separate sequences of
genetic data into haplotypes, which are used in research on the genetic
components of complex diseases such as Alzheimer's disease, cancer of
Multiple Sclerosis. HAP has been used by hundreds of scientists to aid
in finding correlation between complex disease and specific genes. HAP
improves on other available haplotyping programs in terms of speed,
while maintaining accuracy. The software can be used online via the HAP
webserver.
Genome Rearrangements
Researchers in the Algorithmss 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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