An autoencoder with Gaussian noise injection in its latent space generates synthetic wafer maps to balance classes, and a CNN trained on the augmented data reports 98.56% accuracy on WM-811K with AUC and AP of 1.0000.
An Evaluation of Large Language Models in Bioinformatics Research
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abstract
Large language models (LLMs) such as ChatGPT have gained considerable interest across diverse research communities. Their notable ability for text completion and generation has inaugurated a novel paradigm for language-interfaced problem solving. However, the potential and efficacy of these models in bioinformatics remain incompletely explored. In this work, we study the performance LLMs on a wide spectrum of crucial bioinformatics tasks. These tasks include the identification of potential coding regions, extraction of named entities for genes and proteins, detection of antimicrobial and anti-cancer peptides, molecular optimization, and resolution of educational bioinformatics problems. Our findings indicate that, given appropriate prompts, LLMs like GPT variants can successfully handle most of these tasks. In addition, we provide a thorough analysis of their limitations in the context of complicated bioinformatics tasks. In conclusion, we believe that this work can provide new perspectives and motivate future research in the field of LLMs applications, AI for Science and bioinformatics.
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Wafer Map Defect Classification Using Autoencoder-Based Data Augmentation and Convolutional Neural Network
An autoencoder with Gaussian noise injection in its latent space generates synthetic wafer maps to balance classes, and a CNN trained on the augmented data reports 98.56% accuracy on WM-811K with AUC and AP of 1.0000.