Abstract: An interactive learning cycle includes an operator, a computer and a pool of images. The operator produces a sparsely-labeled data set. A back-end system produces live feedback: a densely-labeled training set which is displayed on the computer. Immediate feedback is displayed in color on the operator computer in less than about five seconds. A labeling tool displays a user interface and for every labeling project a region is defined that is downloaded as an image data batch. The operator annotates on a per-image basis in the region and uses several UI tools to mark features in the image and group them to a predefined label class. The back-end system includes processes that run in parallel and feed back into each other, each executing a model. A local model is used independently of the global model. The global model accepts sparsely-labeled images from numerous operator computers.

Abstract: System and methods are disclosed to perform peptide-MHC interaction prediction by applying a high-order kernel function to determine a similarity between peptide sequences; applying one or more supervised strategies to the kernel to encode relevant physicochemical and interaction information about peptide sequence and MHC molecule; and applying a classifier to the kernel to identify the peptide-MHC interaction of interest in response to a query.

Abstract: A method for peptide binding prediction includes receiving a peptide sequence descriptor and descriptors of contacting amino acids on major histocompatibility complex (MHC) protein-peptide interaction structure; generating a model with an ensemble of high order neural network; pre-training the model by high order semi-restricted Boltzmann machine (RBM) or high-order denoising autoencoder; and generating a prediction as a binary output or continuous output with initial model parameters pre-trained using binary output data if available. A systematic learning method for leveraging high-order interactions/associations among items for better collaborative filtering and item recommendation.

Abstract: Systems and methods are disclosed to perform relation extraction in text by applying a convolution strategy to determine a kernel between sentences; applying one or more semi-supervised strategies to the kernel to encode syntactic and semantic information to recover a relational pattern of interest; and applying a classifier to the kernel to identify the relational pattern of interest in the text in response to a query.

Abstract: Systems and methods are disclosed to perform relation extraction in text by applying a convolution strategy to determine a kernel between sentences; applying one or more semi-supervised strategies to the kernel to encode syntactic and semantic information to recover a relational pattern of interest; and applying a classifier to the kernel to identify the relational pattern of interest in the text in response to a query.