Abstract: Embodiments are directed to a platform for zebrafish phenotyping that includes a user, images and videos containing zebrafish and elements pertaining to the zebrafish (such as organs, organ systems, and tissues), a first set of machine learning models used to predict zebrafish phenotypes based on the image and video data, and a second set of machine learning models used to automatically detect elements in one of the images and videos containing zebrafish. This platform may be used to detect changes in phenotypes and may be used to detect alteration is specific organs in zebrafish.

Abstract: Some embodiments relate to methods, systems, and frameworks for data analytics using machine learning, such as methods and systems for preprocessing of biomedical data on a parallel cloud computing network while ensuring bi-directional data security. The system may include a processor that is configured to store and run a biomedical predictive model that processes proprietary data. The system may also include an administrative account that is configured to control the parallel cloud computing network and proprietary data, plurality of other accounts that are configured to access to the parallel computing network and a network that facilitates the transportation of the biomedical predictive model as well as proprietary data while ensuring bi-directional data security.

Abstract: Some embodiments relate to methods, systems, and frameworks for data analytics using machine learning, such as methods and systems for preprocessing of biomedical data, using machine learning, for input to a predictive model. The method may include receiving data from a data source, using at least one machine learning (ML) algorithm from a plurality of ML algorithms to obtain at least one combination of preprocessing steps, and computing an accuracy score for each of the at least one combination based on accuracy of prediction of the predictive model. The method may further include using at least one ML algorithm to optimize the feature selection of the predictive model, combining a plurality of datasets into a single dataset, and using a parallel computing network to provide a framework for executing such predictive model.

Abstract: Some embodiments relate to methods, systems, and frameworks for data analytics using machine learning, such as methods and systems for preprocessing of biomedical data, using machine learning, for input to a predictive model. The method may include receiving data from a data source, using at least one machine learning (ML) algorithm from a plurality of ML algorithms to obtain at least one combination of preprocessing steps, and computing an accuracy score for each of the at least one combination based on accuracy of prediction of the predictive model. The method may further include using at least one ML algorithm to optimize the feature selection of the predictive model, combining a plurality of datasets into a single dataset, and using a parallel computing network to provide a framework for executing such predictive model.

Abstract: Some embodiments relate to methods, systems, and frameworks for data analytics using machine learning, such as methods and systems for preprocessing of biomedical data, using machine learning, for input to a predictive model. The method may include receiving data from a data source, using at least one machine learning (ML) algorithm from a plurality of ML algorithms to obtain at least one combination of preprocessing steps, and computing an accuracy score for each of the at least one combination based on accuracy of prediction of the predictive model. The method may further include using at least one ML algorithm to optimize the feature selection of the predictive model, combining a plurality of datasets into a single dataset, and using a parallel computing network to provide a framework for executing such predictive model.

Abstract: Some embodiments relate to methods, systems, and frameworks for data analytics using machine learning, such as methods and systems for preprocessing of biomedical data, using machine learning, for input to a predictive model. The method may include receiving data from a data source, using at least one machine learning (ML) algorithm from a plurality of ML algorithms to obtain at least one combination of preprocessing steps, and computing an accuracy score for each of the at least one combination based on accuracy of prediction of the predictive model. The method may further include using at least one ML algorithm to optimize the feature selection of the predictive model, combining a plurality of datasets into a single dataset, and using a parallel computing network to provide a framework for executing such predictive model.