Abstract: A method, system, and computer product is presented for mapping a set of patterns into an m-dimensional space so as to preserve relationships that may exist between these patterns. A subset of the input patterns is chosen and mapped into the m-dimensional space using an iterative nonlinear mapping process based on subset refinements. A set of n attributes are determined for each pattern, and one or more neural networks or other supervised machine learning techniques are then trained in accordance with the mapping produced by the iterative process. Additional input patterns not in the subset are mapped into the m-dimensional space by determining their n input attributes and using the neural networks in a feed-forward (prediction) mode.

Abstract: A method and computer product is presented for mapping n-dimensional input patterns into an m-dimensional space so as to preserve relationships that may exist in the n-dimensional space. A subset of the input patterns is chosen and mapped into the m-dimensional space using an iterative nonlinear mapping process. A set of locally defined neural networks is created, then trained in accordance with the mapping produced by the iterative process. Additional input patterns not in the subset are mapped into the m-dimensional space by using one of the local neural networks. In an alternative embodiment, the local neural networks are only used after training and use of a global neural network. The global neural network is trained in accordance with the mapping produced by the iterative process. Input patterns are initially projected into the m-dimensional space using the global neural network. Local neural networks are then used to refine the results of the global network.

Abstract: A system, method, and computer program product for visualizing and interactively analyzing data relating to chemical compounds. A user selects a plurality of compounds to map, and also selects a method for evaluating similarity/dissimilarity between the selected compounds. A non-linear map is generated in accordance with the selected compounds and the selected method. The non-linear map has a point for each of the selected compounds, wherein a distance between any two points is representative of similarity/dissimilarity between the corresponding compounds. A portion of the non-linear map is then displayed. Users are enabled to interactively analyze compounds represented in the non-linear map.

Abstract: A screening method for identifying a therapeutic candidate for a coronary heart disease or an inflammatory condition is disclosed. The screening method tests for the presence or absence of an effect by a putative therapeutic agent on a component of a sphingosine kinase signaling pathway.

Abstract: The present invention determines properties of combinatorial library products from features of library building blocks. At least one feature is determined for each building block of a combinatorial library having a plurality of products. A training subset of products is selected from the products, and at least one property is determined for each product of the training subset. A building block set is identified for each product of the training subset, and an input features vector is formed from the features of the identified building blocks for each product of the training subset. A supervised machine learning approach is used to infer a mapping function that transforms the input features vector for each product of the training subset to the corresponding at least one property for each product of the training subset. After the mapping function is inferred, it is used for determining properties of other products of the library from their corresponding input features vectors.

Abstract: Enzymes are the metabolic catalysts that affect a multitude of physiological processes and responses. Tight control of enzyme activity is therefore essential in maintaining the steady state of all organisms. Keywords: feedback inhibition; phosphorylation; allosteric regulation; covalent modification; proenzymes