Tumor Hypoxia Diagnosis’ using Deep CNN Learning strategy- A theranostic pharmacogenomic approach

Vaisali B, Parvathy C R, Hima Vyshnavi A M, and Krishnan Namboori P K
Publication Target: 
Publication Issue: 
Special Issue PHM for Human Health and Performance
Submission Type: 
Technical Brief
ijphm_19_007.pdf639.55 KBFebruary 23, 2019 - 8:51pm

Tumor hypoxia results in most of the anticancer drugs
becoming ineffective. However, due to lack of proper signaling
in the hypoxic microenvironment, the condition cannot be
detected in advance, leading into an unnecessary delay in the
diagnosis and treatment. The main objective of the work is to
identify the 'hypoxia prone SNPs to help the patients to predict
their possibility of hypoxia formation and to Design and
develop a machine helping in diagnosing the hypoxia from
pathological images using deep learning with 'convolution
neural network'. The genetic signatures corresponding to
'tumor hypoxia development' have been identified by the pharmacogenomic method, comprising of genomics,
epigenomics, metagenomics and environmental genomics. All
the common hypoxia-related mutations have been included in
the study. The formation of the hypoxia condition has to be
carefully identified and monitored during the process of
treatment to ensure that the right drug is being administered. In
the present manuscript, a novel method of elucidating the
condition using 'deep convolution network' from the simple
pathological image has been suggested. The efficiency of the
suggested machine is found to be 92.8% making it as a potential
device for prediction of hypoxia mutation and thereby helping
us to monitor the hypoxic conditions effectively. Thus, the
hypoxia prone SNPs corresponding to common mutations have
been identified. The patients having the hypoxia prone SNPs
are advised to guard against hypoxia formation with the help of
diagnostic tests using the machine. The machine helps to warn
the patients against the respective mutations from the simple
pathological image of the tumor cells.

Publication Year: 
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Submission Keywords: 
Tumor hypoxia
Deep CNN
Submission Topic Areas: 
Data-driven methods for fault detection, diagnosis, and prognosis

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