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: Embodiments are directed to a method for determining relationships between genes, phenotypes, and diseases that includes extracting a first set of biological data containing gene data, phenotype data, and disease data from a database, extracting a second set of biological data from a database, creating nodes on a network pertaining to each individual gene, phenotype, and disease data extracted from one of a database and documents, training multiple machine learning (ML) algorithms on a set of extracted data with matching empirical results, using at least one of the ML algorithms and the second set of biological data for determining relationships between the nodes, creating at least one of a gene-disease association score, gene-phenotype association score, and disease-phenotype association score for the relationships based on the relative association of the nodes, and displaying at least one of the association scores to a user.

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: A method for linking a natural product and gene cluster is disclosed. In some embodiments, monomers of natural products are predicted from a gene sequence. In other embodiments, monomers of natural products are predicted from a chemical structure of a natural product. In another embodiment, monomers predicted from gene sequences are aligned with monomers predicted from chemical structures.

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: A method for linking a natural product and gene cluster is disclosed. In some embodiments, monomers of natural products are predicted from a gene sequence. In other emboidments, monomers of natural products are predicted from a chemical structure of a natural product. In another embodiment, monomers predicted from gene sequences are aligned with monomers predicted from chemical structures.