Abstract: A Convolutional Neural network (CNNs) based system and method based on the same for image analysis including for computer aided detection/diagnosis is provided, enabling an efficient and accurate delineation of diseased volumes in images/medical images. More particularly, the system of the present invention is adapted to incorporate deformable convolution (DC) module based processor means, along the down- and up-sampling pathways of the underlying U-Net architectural framework of said system for effective understanding and attending to deformable geometry of unknown transformations, considering deformable convolution (DC), unlike the basic convolution is advantageously not constrained by predefined geometrical structures of the kernels.

Abstract: Disclosed are methods and custom computing apparatuses for identifying gene-gene interactions from gene expression data, based on which a gene regulatory sub-network can be built. In particular, relationships in which multiple genes co-regulate one target gene can also be identified.

Abstract: Disclosed are methods and custom computing apparatuses for identifying gene-gene interactions from gene expression data, based on which a gene regulatory sub-network can be built. In particular, relationships in which multiple genes co-regulate one target gene can also be identified.

Abstract: Provided in part herein are processes and machines that can be used to reduce a large amount of information into meaningful data and reduce the dimensionality of a data set. Such processes and machines can, for example, reduce dimensionality by eliminating redundant data, irrelevant data or noisy data. Processes and machines described herein are applicable to data in biotechnology and other fields.