Application of inter simple sequence repeat (ISSR) markers to plant genetics

Microsatellites or simple sequence repeats (SSRs) are ubiquitous in eukaryotic genomes. Single-locus SSR markers have been developed for a number of species, although there is a major bottleneck in developing SSR markers whereby flanking sequences must be known to design 5'-anchors for polymerase chain reaction (PCR) primers. Inter SSR (ISSR) fingerprinting was developed such that no sequence knowledge was required. Primers based on a repeat sequence, such as (CA)(n), can be made with a degenerate 3'-anchor, such as (CA)(8)RG or (AGC)(6)TY. The resultant PCR reaction amplifies the sequence between two SSRs, yielding a multilocus marker system useful for fingerprinting, diversity analysis and genome mapping. PCR products are radiolabelled with P-32 or P-33 via end-labelling or PCR incorporation, and separated on a polyacrylamide sequencing gel prior to autoradiographic visualisation. A typical reaction yields 20-100 bands per lane depending on the species and primer. We have used ISSR fingerprinting in a number of plant species, and report here some results on two important tropical species, sorghum and banana. Previous investigators have demonstrated that ISSR analysis usually detects a higher level of polymorphism than that detected with restriction fragment length polymorphism (RFLP) or random amplified polymorphic DNA (RAPD) analyses. Our data indicate that this is not a result of greater polymorphism genetically, but rather technical reasons related to the detection methodology used for ISSR analysis.

The application of adaptive partitioned random search in feature selection problem

Feature selection is one of important and frequently used techniques in data preprocessing. It can improve the efficiency and the effectiveness of data mining by reducing the dimensions of feature space and removing the irrelevant and redundant information. Feature selection can be viewed as a global optimization problem of finding a minimum set of M relevant features that describes the dataset as well as the original N attributes. In this paper, we apply the adaptive partitioned random search strategy into our feature selection algorithm. Under this search strategy, the partition structure and evaluation function is proposed for feature selection problem. This algorithm ensures the global optimal solution in theory and avoids complete randomness in search direction. The good property of our algorithm is shown through the theoretical analysis.