Apparatuses, systems, and methods are provided that may analyze deoxyribonucleic add (DNA) sequence data using a natural language processing (NLP) model to, for example, identify genetic elements such as known and/or novel cis-regulatory elements {e.g., known and/or putative novel drought-responsive cis-regulatory elements (DREs)). Apparatuses, systems, and methods are also provided that may identify transcriptional regulators {e.g., upstream transcriptional regulators of a novel putative DRE) based on natural language processing (NLP) model data and expression genome-wide association study (eGWAS) data. Apparatuses, systems, and methods are also provided that may verify putative novel cis-regulatory elements based on a comparison of natural language processing (NLP) model output data and other model output data.

An instance weighted learning (IWL) machine learning model. In one example embodiment, a method of employing an IWL machine learning model to train a classifier may include determining a quality value that should be associated with each machine learning training instance in a temporal sequence of reinforcement learning machine learning training instances, associating the corresponding determined quality value with each of the machine learning training instances, and training a classifier using each of the machine learning training instances. Each of the machine learning training instances includes a state-action pair and is weighted during the training based on its associated quality value using a weighting factor that weights different quality values differently such that the classifier learns more from a machine learning training instance with a higher quality value than from a machine learning training instance with a lower quality value.

Systems, methods and apparatus of machine-learning-predictive-analytics, the method performed by a predictive analytic control computer and including receiving from a second computer a training-profile data that describes one or more contributions of resources that are associated and identified with particular entities, receiving from a third computer a training-profile data that are associated and identified with the particular entities, that does not describe one or more contributions of resources the training-profile data, and that includes data that is that is received from additional computers that host websites and applications that focus on communication, community-based input, interaction, content-sharing and collaboration that describe a first set of features and representations of issues of interest of the particular entities, generating a machine-learning-predictive-analytic model by a machine learning-predictive-analytic trainer in reference to the training-profile data, generating predictions from the machine-learning-predictive-analytic model and from a second set of features and representations of issues of interest.