Gray code representation of the universal genetic code: Generation of never born protein sequences using Toeplitz matrix approach

Muthugobal, Bagayalakshmi Karuna Nidhi; Ramesh, G.; Parthasarathy, S.; Suvaithenamudhan, Suvaiyarasan; Prasath, Karuppasamy Muthuvel

doi:10.1016/j.biosystems.2020.104280

Cited by 4 publications

(1 citation statement)

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“…Then VNN will generate a placeholder node to store and read this input tensor. The Gray code conversion is a reliable encoding method that minimizes errors [ 29 , 30 ]. It effectively prevents the phenomenon of using binary encoding that may deviate from the optimal solution and fail to achieve stability.…”

Section: Methodsmentioning

confidence: 99%

Prediction of anticancer drug sensitivity using an interpretable model guided by deep learning

Pang,

Chen,

Qin

2024

BMC Bioinformatics

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Background The prediction of drug sensitivity plays a crucial role in improving the therapeutic effect of drugs. However, testing the effectiveness of drugs is challenging due to the complex mechanism of drug reactions and the lack of interpretability in most machine learning and deep learning methods. Therefore, it is imperative to establish an interpretable model that receives various cell line and drug feature data to learn drug response mechanisms and achieve stable predictions between available datasets. Results This study proposes a new and interpretable deep learning model, DrugGene, which integrates gene expression, gene mutation, gene copy number variation of cancer cells, and chemical characteristics of anticancer drugs to predict their sensitivity. This model comprises two different branches of neural networks, where the first involves a hierarchical structure of biological subsystems that uses the biological processes of human cells to form a visual neural network (VNN) and an interpretable deep neural network for human cancer cells. DrugGene receives genotype input from the cell line and detects changes in the subsystem states. We also employ a traditional artificial neural network (ANN) to capture the chemical structural features of drugs. DrugGene generates final drug response predictions by combining VNN and ANN and integrating their outputs into a fully connected layer. The experimental results using drug sensitivity data extracted from the Cancer Drug Sensitivity Genome Database and the Cancer Treatment Response Portal v2 reveal that the proposed model is better than existing prediction methods. Therefore, our model achieves higher accuracy, learns the reaction mechanisms between anticancer drugs and cell lines from various features, and interprets the model’s predicted results. Conclusions Our method utilizes biological pathways to construct neural networks, which can use genotypes to monitor changes in the state of network subsystems, thereby interpreting the prediction results in the model and achieving satisfactory prediction accuracy. This will help explore new directions in cancer treatment. More available code resources can be downloaded for free from GitHub (https://github.com/pangweixiong/DrugGene).

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Section: Methodsmentioning

confidence: 99%