Abstract: A method for optimizing protein expression comprises obtaining a plurality of amino acid sequences and corresponding known efficiency values, each known efficiency value indicating efficiency of expressing a protein having a corresponding amino acid sequence; for the plurality of prediction algorithms, obtaining a prediction function, wherein the prediction function outputs a predicted efficiency value for expressing a protein having an amino acid sequence corresponding to an input numerical vector; evaluating the prediction function by comparing outputted predicted efficiency values with the known efficiency values; selecting a prediction algorithm based on said evaluating; predicting, using the prediction algorithm and the prediction function, efficiency values for expressing proteins respectively having specified amino acid sequences; and outputting the specified amino acid sequences and the efficiency values predicted for the specified amino acid sequences.

Abstract: An example method comprises receiving a new observation characterizing at least one parameter of an entity; inputting the new observation to a deep neural network having hidden layers; obtaining a second set of intermediate output values that are output from at least one of the hidden layers by inputting the received new observation to the deep neural network; mapping the second set of intermediate output values to a second set of projected values; determining whether or not the received new observation is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values, calculating a prediction for the new observation; and determining a result indicative of the occurrence of at least one anomaly in the entity based on the prediction and the determination whether or not the new observation is an outlier.

Abstract: A computer-implemented method for analysis of cell images comprises obtaining a deep neural network and a training dataset, the deep neural network comprising a plurality of hidden layers; obtaining first sets of intermediate output values that are output from at least one of the plurality of hidden layers; constructing a latent variable model using the first sets of intermediate output values, the latent variable model mapping the first sets of intermediate output values to first sets of projected values in a sub-space that has a dimension lower than the sets of the intermediate outputs; obtaining a second set of intermediate output values by inputting a received new cell image to the deep neural network; mapping, using the latent variable model, the second set of intermediate output values to a second set of projected values; and determining whether the received new cell image is an outlier.

Abstract: A computer-implemented method for data analysis is provided.

Abstract: A computer implemented and a system for adapting control of a cell culture in a production-scale vessel with regard to a starting medium are provided. The method comprises providing multiple production-scale process trajectories, receiving a media lot for the cell culture, and sampling first media from the media lot for possible use in the production-scale vessel. The method also comprises starting a seed train using the first media to achieve inoculation of the production-scale vessel, providing a plurality of micro-scale vessels in a process control device, and sampling second media from the media lot for the micro-scale vessels. Cells from the seed train can be introduced into the micro-scale vessels to start cell cultures in each of the micro-scale vessels.

Abstract: A method is described for training a machine learning model to predict virus titer from an image or a sequence of images of a cell culture containing a virus population. The trained machine learning model allows a prediction of virus titer to be made much earlier than in the standard virus plaque assay, for example in 6 or 8 hours after initial inoculation of the cell culture with the virus sample. The method includes the steps of: (1) obtaining a training set in the form of a plurality of sets of images of virus-treated cell cultures from a plurality of experiments at one or more time points from a start time t0 to a final time tfinal, (2) for each experiment, recording at least one numeric virus titer readout of the virus-treated cell culture at the final time tfinal, (3) processing all the images in the training set to acquire a numeric representation of each image, and (4) training one or more machine learning models to make a prediction of a final virus titer on the training set numeric representations.

Abstract: A computer-implemented method for analysis of cell images comprises obtaining a deep neural network and a training dataset, the deep neural network comprising a plurality of hidden layers; obtaining first sets of intermediate output values that are output from at least one of the plurality of hidden layers; constructing a latent variable model using the first sets of intermediate output values, the latent variable model mapping the first sets of intermediate output values to first sets of projected values in a sub-space that has a dimension lower than the sets of the intermediate outputs; obtaining a second set of intermediate output values by inputting a received new cell image to the deep neural network; mapping, using the latent variable model, the second set of intermediate output values to a second set of projected values; and determining whether the received new cell image is an outlier.

Abstract: An example method comprises receiving a new observation characterizing at least one parameter of an entity; inputting the new observation to a deep neural network having hidden layers; obtaining a second set of intermediate output values that are output from at least one of the hidden layers by inputting the received new observation to the deep neural network; mapping the second set of intermediate output values to a second set of projected values; determining whether or not the received new observation is an outlier with respect to the training dataset based on the latent variable model and the second set of projected values, calculating a prediction for the new observation; and determining a result indicative of the occurrence of at least one anomaly in the entity based on the prediction and the determination whether or not the new observation is an outlier.

Abstract: A computer-implemented method for data analysis is provided.

Abstract: The present invention is related to a set of genes, which when modified in plants gives altered lignin properties. The invention provides DNA construct such as a vector useful in the method of the invention. Further, the invention relates to a plant cell or plant progeny of the plants and wood produced by the plants according to the invention Lower lignin levels will result in improved saccharification for bio-refining and ethanol production and improved pulp and paper. Increased lignin levels will utilise lignin properties for energy production. The genes and DNA constructs may be used for the identification of plants having altered lignin characteristics as compared to the wild-type. According to the invention genes and DNA constructs may also be used as candidate genes in marker assisted breeding.

Abstract: A method and system for partitioning (clustering) large amounts of data in a relatively short processing time. The method involves providing a first data matrix and a second data matrix where each of the first and second data matrices includes one or more variables, and a plurality of data points. The method also involves determining a first score from the first data matrix using a partial least squares (PLS) analysis or orthogonal PLS (OPLS) analysis and partitioning the first and second data matrices (e.g., row-wise) into a first group and a second group based on the sorted first score, the variance of the first data matrix, and a variance of the first and second groups relative to the variances of the first and second data matrices.

Abstract: The present invention is related to a set of genes, which when modified in plants gives altered lignin properties. The invention provides DNA construct such as a vector useful in the method of the invention. Further, the invention relates to a plant cell or plant progeny of the plants and wood produced by the plants according to the invention Lower lignin levels will result in improved saccharification for bio-refining and ethanol production and improved pulp and paper. Increased lignin levels will utilise lignin properties for energy production. The genes and DNA constructs may be used for the identification of plants having altered lignin characteristics as compared to the wild-type. According to the invention genes and DNA constructs may also be used as candidate genes in marker assisted breeding.

Abstract: A method and system for partitioning (clustering) large amounts of data in a relatively short processing time. The method involves providing a first data matrix and a second data matrix where each of the first and second data matrices includes one or more variables, and a plurality of data points. The method also involves determining a first score from the first data matrix using a partial least squares (PLS) analysis or orthogonal PLS (OPLS) analysis and partitioning the first and second data matrices (e.g., row-wise) into a first group and a second group based on the sorted first score, the variance of the first data matrix, and a variance of the first and second groups relative to the variances of the first and second data matrices.

Abstract: The invention provides a method and an arrangement for filtering or pre-processing most any type of multivariate data exemplified by NIR or NMR spectra measured on samples in order to remove systematic noise such as base-line variation and multiplicative scatter effects. This is accomplished by differentiating the spectra to first or second derivatives, by Multiplicative Signal Correction (MSC), or by similar filtering methods. The pre-processing may, however, also remove information from the spectra, as well as other multiple measurement arrays, regarding (Y) (the response variables). Provided is a variant of PLS that can be used to achieve a signal correction that is as close to orthogonal as possible to a given (y) vector or (Y) matrix. Hence, ensuring that the signal correction removes as little information as possible regarding (Y). A filter according to the present invention is named Orthogonal Partial Least Squares (OPLS).

Abstract: The invention regards a method and an arrangement for filtering or pre-processing most any type of multivariate data exemplified by NIR or NMR spectra measured on samples in order to remove systematic noise such as base line variation and multiplicative scatter effects. This is accomplished by differentiating the spectra to first or second derivatives, by Multiplicative Signal Correction (MSC), or by similar filtering methods. The pre-processing may, however, also remove information from the spectra, as well as other multiple measurement arrays, regarding (Y) (the response variables). Provided is a variant of PLS that can be used to achieve a signal correction that is as close to orthogonal as possible to a given (y) vector or (Y) matrix. Hence, ensuring that the signal correction removes as little information as possible regarding (Y). A filter according to the present invention is named Orthogonal Partial Least Squares (OPLS).