GR-504 Synthetic DNA Sequence Generation and Classification for Species Discrimination

The two main goals of this research are to apply machine learning models in computational biology to classify DNA sequences from different species and to create synthetic DNA sequences using GANs. Generative Adversarial Networks (GANs) synthesize DNA sequences while preserving key characteristics like sequence length and GC content. The dataset is enhanced by these artificial sequences, which makes classification jobs better. The classification accuracy of black rat and human genome sequences is evaluated using machine learning models, including Random Forest, SVM, and Logistic Regression. Notably, when trained with synthetic data, all models perform better

Fast differentiable DNA and protein sequence optimization for molecular design

Designing DNA and protein sequences with improved function has the potential to greatly accelerate synthetic biology. Machine learning models that accurately predict biological fitness from sequence are becoming a powerful tool for molecular design. Activation maximization offers a simple design strategy for differentiable models: one-hot coded sequences are first approximated by a continuous representation which is then iteratively optimized with respect to the predictor oracle by gradient ascent. While elegant, this method suffers from vanishing gradients and may cause predictor pathologies leading to poor convergence. Here, we build on a previously proposed straight-through approximation method to optimize through discrete sequence samples. By normalizing nucleotide logits across positions and introducing an adaptive entropy variable, we remove bottlenecks arising from overly large or skewed sampling parameters. The resulting algorithm, which we call Fast SeqProp, achieves up to 100-fold faster convergence compared to previous versions of activation maximization and finds improved fitness optima for many applications. We demonstrate Fast SeqProp by designing DNA and protein sequences for six deep learning predictors, including a protein structure predictor.Comment: All code available at http://www.github.com/johli/seqprop; Moved example sequences from Suppl to new Figure 2, Added new benchmark comparison to Section 4.3, Moved some technical comparisons to Suppl, Added new Methods sectio