Incremental dimension reduction of tensors with random index

We present an incremental, scalable and efficient dimension reduction technique for tensors that is based on sparse random linear coding. Data is stored in a compactified representation with fixed size, which makes memory requirements low and predictable. Component encoding and decoding are performed on-line without computationally expensive re-analysis of the data set. The range of tensor indices can be extended dynamically without modifying the component representation. This idea originates from a mathematical model of semantic memory and a method known as random indexing in natural language processing. We generalize the random-indexing algorithm to tensors and present signal-to-noise-ratio simulations for representations of vectors and matrices. We present also a mathematical analysis of the approximate orthogonality of high-dimensional ternary vectors, which is a property that underpins this and other similar random-coding approaches to dimension reduction. To further demonstrate the properties of random indexing we present results of a synonym identification task. The method presented here has some similarities with random projection and Tucker decomposition, but it performs well at high dimensionality only (n>10^3). Random indexing is useful for a range of complex practical problems, e.g., in natural language processing, data mining, pattern recognition, event detection, graph searching and search engines. Prototype software is provided. It supports encoding and decoding of tensors of order >= 1 in a unified framework, i.e., vectors, matrices and higher order tensors.Comment: 36 pages, 9 figure

A platform for phenotypic discovery of therapeutic antibodies and targets applied on Chronic Lymphocytic Leukemia

Development of antibody drugs against novel targets and pathways offers great opportunities to improve current cancer treatment. We here describe a phenotypic discovery platform enabling efficient identification of therapeutic antibody-target combinations. The platform utilizes primary patient cells throughout the discovery process and includes methods for differential phage display cell panning, high-throughput cell-based specificity screening, phenotypic in vitro screening, target deconvolution, and confirmatory in vivo screening. In this study the platform was applied on cancer cells from patients with Chronic Lymphocytic Leukemia resulting in discovery of antibodies with improved cytotoxicity in vitro compared to the standard of care, the CD20-specific monoclonal antibody rituximab. Isolated antibodies were found to target six different receptors on Chronic Lymphocytic Leukemia cells; CD21, CD23, CD32, CD72, CD200, and HLA-DR of which CD32, CD200, and HLA-DR appeared as the most potent targets for antibody-based cytotoxicity treatment. Enhanced antibody efficacy was confirmed in vivo using a patient-derived xenograft model