AI Models in Bioinformatics: Some General Tips
The use of deep neural network (DNN) models, also known as artificial intelligence (AI) models, has increased significantly in the biological and biomedical fields in recent years. Unlike data for image classification tasks like recognizing dogs and cats, biological and biomedical data can be costly to acquire. As a result, the latest and most data-intensive AI techniques are not often used in these fields. Even more established DNN techniques like adversarial networks and auto-encoders are not commonly used in biological and biomedical applications. In fact, around 90% of AI applications in bioinformatics and biomedical informatics involve supervised learning for classification tasks, and about 90% of these use convolutional neural networks (CNNs) for image, signal trace, or nucleic acid and protein sequence data.
Therefore, if you are skilled at building CNN-based classification models for image and sequence data, you will likely be well-equipped to tackle a large portion of AI problems in the biological and biomedical domains.
In this series of blogs, I will provide general tips for building these useful AI models in bioinformatics and biomedical informatics.
Tip #1: Augmentation Is King
Tip #2: Do Transfer Learning When You Can
Tip #3: Watch Your Own Data
Tip #4: Follow Tested Practices
Tip #5: My Starting-Point CNN Model
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Read Tip #1: Augmentation Is King
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