TL;DR: In this article, a method, system and computer program product for scaling, or dimensionally reducing, multi-dimensional data sets is presented, where a sample of points from an n-dimensional dataset is selected and non-linearly mapped to obtain the corresponding set of m-dimensional points.
Abstract: A method, system and computer program product for scaling, or dimensionally reducing, multi-dimensional data sets. The invention selects a sample of points from an n-dimensional data set and non-linearly maps the sample of points to obtain the corresponding set of m-dimensional points. Any suitable non-linear mapping or multi-dimensional scaling technique can be employed. The process then trains a system (e.g., a neural network), using the corresponding sets of points. During, or at the conclusion of the training process, the system develops or determines a relationship between the two sets of points. In an embodiment, the relationship is in the form of one or more non-linear functions. The one or more non-linear functions are then implemented in a system. Thereafter, additional n-dimensional points are provided to the system, which maps the additional points using the one or more non-linear functions.
TL;DR: In this article, the authors present a set of protein molecules and derivatives, analogues, chemical equivalents and mimetics thereof capable of modulating cellular activity and, in particular, modulating the cellular activity via the modulation of signal transduction, which are useful in a range of therapeutic, prophylactic and diagnostic applications.
Abstract: The present invention relates generally to novel protein molecules and to derivatives, analogues, chemical equivalents and mimetics thereof capable of modulating cellular activity and, in particular, modulating cellular activity via the modulation of signal transduction. More particularly, the present invention relates to human sphingosine kinase and to derivatives, analogues, chemical equivalents and mimetics thereof. The present invention also contemplates genetic sequences encoding said protein molecules and derivatives, analogues, chemical equivalents and mimetics thereof. The molecules of the present invention are useful in a range of therapeutic, prophylactic and diagnostic applications.
TL;DR: In this paper, a method for ranking the affinity of each of a multiplicity of different molecules for a target molecule which is capable of denaturing due to a thermal change is presented.
Abstract: The present invention is a method for ranking the affinity of each of a multiplicity of different molecules for a target molecule which is capable of denaturing due to a thermal change. The method comprises contacting the target molecule with one molecule of the multiplicity of different molecules in each of a multiplicity of containers, simultaneously heating the multiplicity of containers, measuring in each of the containers a physical change associated with the thermal denaturation of the target molecule resulting from the heating in each of the containers, generating a thermal denaturation curve for the target molecule as a function of temperature for each of the containers and determining a midpoint temperature (Tm) therefrom, comparing the Tm of each of the thermal denaturation curves with the Tm of a thermal denaturation curve obtained for the target molecule in the absence of any of the molecules in the multiplicity of different molecules, and ranking the affinities of the multiplicity of different molecules according the change in Tm of each of the thermal denaturation curves.
TL;DR: In this article, the authors present a system, method, and computer program product for fast and efficient searching of large virtual combinatorial libraries based on a fitness function, where a first set of N reagent combinations are selected, for example, at random, from a VCL, and each reagent combination in the first set is then enumerated to produce a set of enumerated compounds.
Abstract: A system, method, and computer program product for fast and efficient searching of large virtual combinatorial libraries based on a fitness function. According to the method of the present invention, a first set of N reagent combinations are selected, for example, at random, from a virtual combinatorial library. Each reagent combination in the first set is then enumerated to produce a first set of enumerated compounds. M number of compounds of the first set of enumerated compounds are selected based on the fitness function. The M compounds are then deconvoluted into reagents to generate a focused library. Substantially every reagent combination associated with the focused library is enumerated to produce a second set of enumerated compounds. K number of compounds of the second set of enumerated compounds are then selected based on the fitness function. These K compounds represent a near optimal selection of compounds based on the fitness function.