ML/AI
"Power biology by Machine and deep learning models "
"Power biology by Machine and deep learning models "
Uncover patterns in vast biological datasets with ease, using sophisticated machine and deep learning models. For instance, Random Forest models are invaluable in comprehending which features contribute significantly to determining the dependent feature. These models significantly reduce the manual labour required for feature generation.
ML models can be trained to classify biological data into different categories. For example, they can classify genes based on their function or predict protein-protein interactions. Models like these allow researchers to identify potential drug targets, diagnose diseases, and understand biological processes.
Unsupervised learning algorithms can uncover hidden patterns in unlabeled data, revealing previously unknown relationships or outliers within the biological dataset.
This exploration could lead to the discovery of novel genes, mutations, and other significant insights.
Deep learning excels at processing complex, high-dimensional data such as images and protein sequences.
Convolutional Neural Networks (CNNs) are adept at analysing a) protein structures to predict their functions, b) classifying objects of interest from imaging data , etc.
Conversely, Recurrent Neural Networks (RNNs) effectively analyse DNA sequences to identify regulatory elements and other patterns.