Abstract: Embodiments of this invention include application of new inferential methods to analysis of complex biological information, including gene networks. In some embodiments, disruptant data and/or drug induction/inhibition data are obtained simultaneously for a number of genes in an organism. New methods include modifications of Boolean inferential methods and application of those methods to determining relationships between expressed genes in organisms. Additional new methods include modifications of Bayesian inferential methods and application of those methods to determining cause and effect relationships between expressed genes, and in some embodiments, for determining upstream effectors of regulated genes. Additional modifications of Bayesian methods include use of heterogeneous variance and different curve fitting methods, including spline functions, to improve estimation of graphs of networks of expressed genes.

Abstract: Embodiments of this invention include application of new inferential methods to analysis of complex biological information, including gene networks. New methods include modifications of Bayesian inferential methods and application of those methods to determining cause and effect relationships between expressed genes, and in some embodiments, for determining upstream effectors of regulated genes. Additional modifications of Bayesian methods include use of time course data and use of gene disruption data to infer causal relationships between expressed genes. Other embodiments include the use of bootstrapping methods and determination of edge effects to more accurately provide network information between expressed genes. Information about gene networks can be stored in a memory device and can be transmitted to an output device, or can be transmitted to remote location.

Abstract: The present invention provides a method for constructing cell lineage that is less labor intensive and requires less time as a result of using a computer. The method comprises the step for obtaining a plurality of 2D images different in focal plane and a time by taking a plurality of the 2D images by changing the focal plane and taking a plurality of the 2D images in a time series for a cell constituting the subject of observation. The method further comprises the steps of extracting a nucleus area by carrying out image processing for individual 2D images; unifying the nucleus area, which is derived from the identical nucleus, from the nucleus area extracted from the individual 2D images; and constructing the cell lineage from a time and a position, where the nucleus area appears and disappears in the image, in the unified nucleus area.

Abstract: Embodiments of this invention include application of new inferential methods to analysis of complex biological information, including gene networks. In some embodiments, time course data obtained simultaneously for a number of genes in an organism. New methods include modifications of Bayesian inferential methods and application of those methods to determining cause and effect relationships between expressed genes, and in some embodiments, for determining upstream effectors of regulated genes. Additional modifications of Bayesian methods include use of time course data to infer causal relationships between expressed genes. Other embodiments include the use of bootstrapping methods and determination of edge effects to more accurately provide network information between expressed genes. Information about gene networks can be stored in a memory device and can be transmitted to an output device, or can be transmitted to remote location.

Abstract: Embodiments of methods are provided that can be used to estimate network relationships between genes of an organism using time course expression data and a set of linear differential equations. Aikaike's Information Criterion and mask tools can be used to reduce the number of elements in a matrix by determining which elements are zero or not significantly changed under the conditions of the study. Maximum likelihood estimation and new statistical methods are used to evaluate the significance of a proposed network relationship.

Abstract: Embodiments of this invention include application of new inferential methods to analysis of complex biological information, including gene networks. In some embodiments, disruptant data and/or drug induction/inhibition data are obtained simultaneously for a number of genes in an organism. New methods include modifications of Boolean inferential methods and application of those methods to determining relationships between expressed genes in organisms. Additional new methods include modifications of Bayesian inferential methods and application of those methods to determining cause and effect relationships between expressed genes, and in some embodiments, for determining upstream effectors of regulated genes. Additional modifications of Bayesian methods include use of heterogeneous variance and different curve fitting methods, including spline functions, to improve estimation of graphs of networks of expressed genes.

Abstract: The present invention provides a method for constructing cell lineage that is less labor intensive and requires less time as a result of using a computer. The method comprises the step for obtaining a plurality of 2D images different in focal plane and a time by taking a plurality of the 2D images by changing the focal plane and taking a plurality of the 2D images in a time series for a cell constituting the subject of observation. The method further comprises the steps of extracting a nucleus area by carrying out image processing for individual 2D images; unifying the nucleus area, which is derived from the identical nucleus, from the nucleus area extracted from the individual 2D images; and constructing the cell lineage from a time and a position, where the nucleus area appears and disappears in the image, in the unified nucleus area.