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Li-Juan Zhao

Publications and source records attributed to Li-Juan Zhao.

5 recordsLinked to original sources
PubMed2005

[Cytogenetic maps and their applications in plants].

Integrated cytogenetic maps encompass the information from both genetic maps and cytological maps. It is possible for cytogenetic maps to simultaneously report the cytological and genetic position of a maker. To constructure cytogenetic maps it is necessary to relate the markers mapped across linkage groups to cytological position on chromosomes. Cytogenetic maps have been constructured primarily in two ways. The first general strategy is to utilize the chromosome breakpoints to determine the location of genetically mapped markers on the chromosomes. A second way is by the direct hybridization of genetically mapped sequences onto chromosomes by FISH. In addition, a novel approach is to use RN-cM maps to predict the physical position of genetic markers on the chromosomes. Cytogenetic maps suggest that both the density of genes and the frequency of recombination increase towards the distal regions of chromosome arms, and they play significant roles in revealing gene colinearity between two species, exploring the evolution relationship between both of them and in map-based gene isolation.

Chromosome Mapping↗
PubMed2004

Mathematical algorithm for discovering states of expression from direct genetic comparison by microarrays.

Highly specific direct genome-scale expression discovery from two biological samples facilitates functional discovery of molecular systems. Here, expression data from cDNA arrays are ranked and curve-fitted. The algorithm uses filters based on the derivatives (slopes) of the curve fits. The rules are set to (i) filter the largest number of artifactual ratios from same-to-same datasets and (ii) maximize discovery from direct comparisons of different samples. The unsupervised discovery is optimized without lowering specificity. The false discovery rates are significantly lower than other methods. The discovered states of genetic expression facilitate functional discovery and are validated by real-time RT-PCR. Better quality improves sensitivity.

Algorithms↗
PubMed2003

Genomic expression discovery predicts pathways and opposing functions behind phenotypes.

Discovering states of genetic expression that are true to a high degree of certainty is likely to predict gene function behind biological phenotypes. The states of expression (up- or down-regulated) of 19200 cDNAs in 10 meningiomas are compared with normal brain by an algorithm that detects only 1 false measurement per 192000; 364 genes are discovered. The expression data accurately predict activation of signaling pathways and link gene function to specific phenotypes. Meningiomas appear to acquire aberrant phenotypes by disturbing the balanced expression of molecules that promote opposing functions. The findings expose interconnected genes and propose a role of genomic expression discovery in functional genomics of living systems.

Algorithms↗
PubMed2002

Mathematical modeling of noise and discovery of genetic expression classes in gliomas.

The microarray array experimental system generates noisy data that require validation by other experimental methods for measuring gene expression. Here we present an algebraic modeling of noise that extracts expression measurements true to a high degree of confidence. This work profiles the expression of 19 200 cDNAs in 35 human gliomas; the experiments are designed to generate four replicate spots/gene with switching of probes. The validity of the extracted measurements is confirmed by: (1) cluster analysis that generates a molecular classification differentiating glioblastoma from lower-grade tumors and radiation necrosis; (2) By what other investigators have reported in gliomas using paradigms for assaying molecular expression other than gene profiling; and (3) Real-time RT-PCR. The results yield a genetic analysis of gliomas and identify classes of genetic expression that link novel genes to the biology of gliomas.

Brain Neoplasms↗