Transcriptional Characterization of Glioma Neural Stem Cells Diva ...

9.3. Concluding Remarks Discussion
in context with other genomic information in order to elucidate regulatory
function. In an attempt to elucidate the answer to the research question of
interest, the hypothesis put forward by Alexiou et al [16] that combinations of
algorithms predict more accurately than the single algorithm alone was chal-
lenged. Through the analysis described in this thesis it is clear that the target
prediction algorithm ElMMo [155]is at the head of the list sorted by accuracy
score out of the 258 combinations tested for. Although this analysis shows
that ElMMo in particular has the most accurate target prediction algorithm
within the pool of eight tested prediction algorithms, it must be noted that
there is a substantial discrepancy between the score achieved by ElMMo and
the next best score for a single target prediction algorithm. Before Diana-
microT [237,316], the second highest solo score, many combinations of two to
three target prediction algorithms appear earlier in the list, demonstrating the
potential for better accuracy than another solo prediction algorithm. Although
it is refreshing to see that one target prediction algorithm can fare better than
the rest, the question remains as to whether the variables and factors that are
taken into consideration, and the ones that are not, are fairly judged in the
algorithm so as to simulate the intricate regulations happening within the cell.
In fact, during this analysis the accuracy score of each prediction algorithm
decreased greatly when a background gene list was not used to filter out all
the genes that were predicted but were not tissue specific.
While the results of this analysis indicate that some algorithms alone and some
group of algorithms are better than others, it highlights at the same time the
importance of re-assessing the factors that are taken into consideration to
make the microRNA target predictions. If we have reached the limit of what
sequence-based algorithms can achieve, we ought to start thinking of adding
another dimension to the field of microRNA target prediction algorithms that
takes factors in components of the tissue-specific regulatory actions of microR-
NAs.
9.3 Concluding Remarks
In this thesis I aimed to characterise the transcriptional landscape of Glioma
Neural Stem Cells (GNS) in the most comprehensive way possible. Several
approaches were taken to examine the expression profiling data that gave in-
sights into the stem cell component of the biology of these cells, which had
232