Affiliation:
1. Department of Pharmacology, Institute of Pharmaceutical Research, GLA University, Mathura, Uttar Pradesh, India
Abstract
Abstract:
In recent years, the use of natural compounds derived from plants for the treatment of skin
cancer has gained significant attention due to their potential therapeutic effects and minimal side effects.
This review focuses on the innovative approach of utilizing biocomponents sourced from plants
in combination with backpropagation neural networks (BPNN) for the screening and analysis of skin
cancer treatments. The integration of plant-derived compounds and AI-driven algorithms holds promise
for enhancing the precision and effectiveness of skin cancer therapies. The review begins by highlighting
the escalating global burden of skin cancer and the limitations of conventional treatment approaches.
With the rise in concerns about the adverse effects of synthetic drugs, researchers have
turned their attention towards exploring the therapeutic potential of plant-derived biocomponents.
These natural compounds are known for their rich bioactive constituents that exhibit anti-cancer
properties, making them suitable candidates for skin cancer treatment. One of the key challenges in
harnessing the potential of plant-derived compounds is the need for accurate screening and analysis
of their effects. This is where backpropagation neural networks, a type of artificial neural network,
comes into play. These networks can process complex data and recognize intricate patterns, enabling
them to predict the efficacy of various biocomponents in combating skin cancer. The review delves
into the functioning of BPNN and its applications in drug discovery and treatment evaluation. Furthermore,
the review explores several case studies that demonstrate the successful integration of
plant-derived compounds with BPNN in the context of skin cancer treatment. These studies provide
evidence of how this synergistic approach can lead to improved treatment outcomes by minimizing
adverse effects and maximizing therapeutic benefits. The methodology section discusses the steps
involved in training the neural network using relevant datasets and optimizing its performance for
accurate predictions. While the integration of plant-derived compounds and BPNN shows great
promise, the review also addresses the existing challenges and limitations. These include the need for
comprehensive and standardized datasets, potential biases in training data, and the complexity of neural
network architectures. The regulatory considerations surrounding plant-based therapies are also
discussed, highlighting the importance of rigorous testing and validation.
Publisher
Bentham Science Publishers Ltd.
Subject
Cancer Research,Oncology,Molecular Medicine