Sentence examples for microarray studies published from inspiring English sources

However, the data produced by the thousands of microarray studies published annually are confounded by "batch effects," the systematic error introduced when samples are processed in multiple batches.

To identify genes modulated in PCa that could explain hormone-therapy refractory growth through bypass of the AR pathway, we linked the 276-gene signature from PC346DCC with data from seven PCa microarray studies published previously (Table 5) [29], [30], [31], [32], [33], [34], [34].

Two of the largest childhood ALL microarray studies published so far demonstrated the presence of distinct gene expression patterns in six known prognostic subgroups [ 13, 14].

To our knowledge, in all microarray studies published before 2010, the number of follicular tumours ranged from 7 to 28 [ 20, 22].

A major difference between this study and the microarray studies published recently [ 12] is that we determined the actual sequences of the meiotic products, rather than inferring about the SNP genotypes on the basis of differential hybridization signals.

In a microarray study published previously [ 13, 14], we detected multiple silkworm ABC genes that are highly expressed during molting and pupation, exhibiting possible regulation by 20E.

In a microarray study published previously [ 13, 14], we detected multiple silkworm ABC genes exhibiting possible regulation by the molting hormone, 20-hydroxyecdysone (20E).

In lieu of this, we utilized a microarray study published by Lory and colleagues (∆ retS vs wild-type) in order to generate a list of proteins under Gac/Rsm control.

Gene expression databases have been expanding rapidly since the first high-throughput microarray studies were published in the 1990s [ 5, 6].

In this context it can be noted that in the current study the number of differentially expressed genes is lower than some of the other microarray studies previously published in the field of plant-microbe interactions.

Moreover, a recently published review of published microarray studies that focus on cancer related outcomes showed that the most common flaw in classification studies is a biased estimation of the accuracy (present in 12 of 28 studies published in 2004 [ 23]).