The statistical analysis of compositional data
The statistical analysis of compositional data
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
A simple construction of d-disjunct matrices with certain constant weights
Discrete Mathematics
A revolution: belief propagation in graphs with cycles
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Probe Design for Compressive Sensing DNA Microarrays
BIBM '08 Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine
Message-passing and local heuristics as decimation strategies for satisfiability
Proceedings of the 2009 ACM symposium on Applied Computing
Information, Physics, and Computation
Information, Physics, and Computation
Recursive constructions of detecting matrices for multiuser coding: a unifying approach
IEEE Transactions on Information Theory
Compressed sensing approach for high throughput carrier screen
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
Code construction for the T-user noiseless adder channel
IEEE Transactions on Information Theory
New constructions of superimposed codes
IEEE Transactions on Information Theory
Factor graphs and the sum-product algorithm
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
IEEE Transactions on Information Theory
Bacterial community reconstruction using compressed sensing
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Superimposed codes and threshold group testing
Information Theory, Combinatorics, and Search Theory
Search for sparse active inputs: a review
Information Theory, Combinatorics, and Search Theory
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 0.00 |
Over the past three decades we have steadily increased our knowledge on the genetic basis of many severe disorders. Nevertheless, there are still great challenges in applying this knowledge routinely in the clinic, mainly due to the relatively tedious and expensive process of genotyping. Since the genetic variations that underlie the disorders are relatively rare in the population, they can be thought of as a sparse signal. Using methods and ideas from compressed sensing and group testing, we have developed a cost-effective genotyping protocol to detect carriers for severe genetic disorders. In particular, we have adapted our scheme to a recently developed class of high throughput DNA sequencing technologies. The mathematical framework presented here has some important distinctions from the "traditional" compressed sensing and group testing frameworks in order to address biological and technical constraints of our setting.