  Transcriptomics response of SK-N-AS cells to methamidophos (an acetylcholine
esterase inhibitor) exposure was measured at 10 time points between 0.5 and 48
h. The data was analyzed using a combination of traditional statistical methods
and novel machine learning algorithms for detecting anomalous behavior and
infer causal relations between time profiles. We identified several processes
that appeared to be upregulated in cells treated with methamidophos including:
unfolded protein response, response to cAMP, calcium ion response, and
cell-cell signaling. The data confirmed the expected consequence of
acetylcholine buildup. In addition, transcripts with potentially key roles were
identified and causal networks relating these transcripts were inferred using
two different computational methods: Siamese convolutional networks and time
warp causal inference. Two types of anomaly detection algorithms, one based on
Autoencoders and the other one based on Generative Adversarial Networks (GANs),
were applied to narrow down the set of relevant transcripts.
