Abstract: An apparatus for training a tunable data structure to predict internal ribosome entry site (IRES) activity includes at least a processor and a memory containing instructions configuring the at least a processor to assemble a training set including a plurality of nucleotide sequence data examples IRES sequences and a plurality of correlated observed IRES activity, partition the training set into at least a first section and a second section, train, using the first section at least an activity data structure to generate probable IRES activity using nucleotide sequence data, and iteratively retrain the at least an activity data structure using the second section, wherein each iteration of the iterative retraining includes generating a predicted IRES activity value using the at least an activity neural network and a nucleotide sequence data example, evaluating an error function, and tuning the activity data structure.

Abstract: The computer system applies machine learning techniques to train a computational model using data representing researched items and their known properties. The computer system applies the trained computational model to data representing the potential candidate items to predict whether such items have such properties. The trained computational model outputs one or more predictions about whether the potential candidate items are likely to have a property from among the plurality of types of properties that the computational model is trained to predict. The computer system allows multiple machine learning experiments to be defined, and then allows predictions from those multiple machine learning experiments to be queried, including accessing aggregate statistics for those predictions. In some implementations, a machine learning experiment can specify a computational model that is an ensemble of multiple models.