Several tools and techniques are commonly employed in ML for bioanalytical research:
Supervised Learning: Algorithms like support vector machines (SVM) and random forests that learn from labeled training data. Unsupervised Learning: Techniques like clustering and principal component analysis (PCA) that analyze data without predefined labels. Deep Learning: Advanced ML techniques, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs), that can model complex relationships in data. Bioinformatics Software: Tools such as Bioconductor, TensorFlow, and Scikit-learn are frequently used for implementing ML algorithms.
