  We are in the era where the Big Data analytics has changed the way of
interpreting the various biomedical phenomena, and as the generated data
increase, the need for new machine learning methods to handle this evolution
grows. An indicative example is the single-cell RNA-seq (scRNA-seq), an
emerging DNA sequencing technology with promising capabilities but significant
computational challenges due to the large-scaled generated data. Regarding the
classification process for scRNA-seq data, an appropriate method is the k
Nearest Neighbor (kNN) classifier since it is usually utilized for large-scale
prediction tasks due to its simplicity, minimal parameterization, and
model-free nature. However, the ultra-high dimensionality that characterizes
scRNA-seq impose a computational bottleneck, while prediction power can be
affected by the "Curse of Dimensionality". In this work, we proposed the
utilization of approximate nearest neighbor search algorithms for the task of
kNN classification in scRNA-seq data focusing on a particular methodology
tailored for high dimensional data. We argue that even relaxed approximate
solutions will not affect the prediction performance significantly. The
experimental results confirm the original assumption by offering the potential
for broader applicability.
