Price,Quentin
7/17/23
Sudha Sharma Lab
Manuscript Summary and Critique
Different forms of cancer and the cancer cells that lead to carcinogenesis are already
hard to fight against due to their ability to multiply; However, an even more advanced survival
strategy for these cells is their unique ability to go undetected in many forms of cancer
treatment. In this study, scientists developed a deep learning algorithm called “RINN”
(redundant input neural network) as an attempt to detect and make connections between latent
somatic genomic alterations (SGA), and differentially expressed genes (DEG). RINN is similar
to any other deep learning algorithm, but with slight modifications in its architecture. The
important difference between RINN and other neural networks is that RINN is able to establish
links between not only the first hidden layer, but every subsequent layer as well. This aids in the
detection of latent SGA’s and the connection to DEG’s. In order to do this, different RINN
models are used to predict expression data from genomic alteration data, and then recover
causal relationships in the weights of a deep neural network. The RINN model is programmed
with different sequences for tumors, and used to help determine cancer signaling systems.
The scientists predict that RINN models with the most consistent weight structure that
can accurately depict the relationship between somatic genomic alterations and differentially
expressed genes will most likely have learned the “optimal representation”, of SGA’s in cancer
cells. It is also hypothesized that if genomic alterations from a pathway are linked to a similar set
of shared hidden nodes in different RINN models, then that shows RINN models can somewhat
consistently detect the common impact of the SGA’s, and detect their impact on DEG’s. RINN’s
success was determined by comparing it with three of the highest performing deep neural
network models, after performing a cosine similarity experiment for each data set.
After analysis, RINN showed more complex and less defined results than typical graphs
from similar analysis, indicating that RINN’s current algorithm may be less efficient than other
DNN processes; However the scientists report that the hypothesis about the RINN model
proved to be correct. It was proven that RINN and DNN’s can detect and capture genomic
alterations at a relatively high success rate, and that RINN can capture within its hidden variable
and assigned weights. The main difference in the success of RINN versus a typical DNN is
shown in the DNN’s inability to clearly show data for the latent hidden variables within a SGA;
Whereas, RINN models are able to interpret such data in a simpler, less complex way.
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What is different in the RINN algorithm that isn’t present in typical deep neural networks?
How was it developed and is the development as efficient as other DNN’s?
The RINN model was outperformed by the DNN on a pathway that was observed in this
experiment. If the DNN is still able to perform better in certain pathways, what makes
RINN a better overall model? Why would RINN be used instead of DNN’s when they
might not perform as well in a certain situation?
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It is also reported that RINN and DNN performed similarly across all metrics. Why opt for
RINN when you will get similar results from DNN?
Strengths and weaknesses are presented for both models, but no there is no definitive
answer to which model is better overall.