Authors: Sanad Aburass, Osama Dorgham, Jamil Al Shaqsi

Published on: July 24, 2023

Impact Score: 7.6

Arxiv code: Arxiv:2307.14361

Summary

• What is new: Introduction of a novel hybrid ensemble model combining LSTM, BiLSTM, CNN, GRU, and GloVe embeddings which surpasses the performance of advanced transformer models in gene mutation classification.
• Why this is important: Need for accurate and efficient classification of gene mutations in cancer to enable precise personalized treatment plans.
• What the research proposes: A hybrid ensemble model that integrates multiple deep learning architectures to enhance classification accuracy.
• Results: Achieved a training accuracy of 80.6%, precision of 81.6%, recall of 80.6%, F1 score of 83.1%, and significantly reduced MSE of 2.596.

Technical Details

Technological frameworks used: Keras, TensorFlow

Models used: LSTM, BiLSTM, CNN, GRU, GloVe

Data used: Kaggle’s Personalized Medicine: Redefining Cancer Treatment dataset

Potential Impact

Precision medicine, healthcare providers, medical diagnostics industry, genomic analysis firms

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