  Machine learning can precisely identify different cancer tumors at any stage
by classifying cancerous and healthy samples based on their genomic profile. We
have developed novel methods of MLAC (Machine Learning Against Cancer)
achieving perfect results with perfect precision, sensitivity, and specificity.
We have used the whole genome sequencing data acquired by next-generation RNA
sequencing techniques in The Cancer Genome Atlas and Genotype-Tissue Expression
projects for cancerous and healthy tissues respectively. Moreover, we have
shown that unsupervised machine learning clustering has great potential to be
used for cancer diagnosis. Indeed, a creative way to work with data and general
algorithms has resulted in perfect classification i.e. all precision,
sensitivity, and specificity are equal to 1 for most of the different tumor
types even with a modest amount of data, and the same method works well on a
series of cancers and results in great clustering of cancerous and healthy
samples too. Our system can be used in practice because once the classifier is
trained, it can be used to classify any new sample of new potential patients.
One advantage of our work is that the aforementioned perfect precision and
recall are obtained on samples of all stages including very early stages of
cancer; therefore, it is a promising tool for diagnosis of cancers in early
stages. Another advantage of our novel model is that it works with normalized
values of RNA sequencing data, hence people's private sensitive medical data
will remain hidden, protected, and safe. This type of analysis will be
widespread and economical in the future and people can even learn to receive
their RNA sequencing data and do their own preliminary cancer studies
themselves which have the potential to help the healthcare systems. It is a
great step forward toward good health that is the main base of sustainable
societies.

Genomics;Machine Learning;Machine Learning