Abstract: A computer-implemented method for analyzing data obtained for a chemical and/or biological process comprises: obtaining a result of statistical data analysis on the data obtained with respect to the chemical and/or biological process; calculating, for values of process parameters obtained at groups of time points during batch processes of the chemical and/or biological process, a ratio of a correlation value to a confidence value of the correlation value, the correlation value indicating a correlation between the values of the process parameter and at a process output value; calculating, for process parameters, an average of absolute values of the ratios calculated for the values of the process parameter obtained at different groups of time points during the batch processes; excluding the values of one of the process parameters having a smallest average; and iterating, until at least one specified condition is met.

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 method of predicting a parameter of a medium to be observed in a bioprocess based on Raman spectroscopy including the steps of acquiring a first series of preparatory Raman spectra of an aqueous medium using a first measuring assembly; normalizing the first series of preparatory Raman spectra based on a characteristic band of water from at least one Raman spectrum acquired with the first measuring assembly; building a multivariate model for the parameter based on the normalized preparatory Raman spectra; acquiring predictive Raman spectra of the medium to be observed during the bioprocess with another measuring assembly; normalizing the predictive Raman spectra based on a characteristic band of water from at least one Raman spectrum acquired with the other measuring assembly; and applying the built model to the predictive Raman spectra for predicting the parameter.

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

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: Computer-implemented monitoring of monoclonal quality of cell growth is specifically applicable to development of cell lines for the manufacturing of biopharmaceuticals. In one aspect, a computer-implemented method comprises: acquiring a sequence of images of a cell culture taken at different times during cell growth; processing each image in the sequence of images to identify cell locations of cells in the cell culture; determining for at least some of the images in the sequence of images the number of cells from the identified cell locations; determining for at least one image in the sequence of images a spatial distribution of cells from the identified cell locations; evaluating compliance of the determined numbers of cells and the determined spatial distribution of cells with predetermined evaluation conditions being characteristic of monoclonal growth; and assessing and outputting a monoclonal quality indicator based on the evaluated compliance with the predetermined evaluation conditions.

Abstract: A computer-implemented method for data analysis comprises obtaining a plurality of first observations, each one of the plurality of first observations including one or more values of one or more first parameters, the plurality of first observations grouped into a plurality of groups; constructing a first histogram using the values of at least one of the one or more first parameters, included in the plurality of first observations; constructing, for each one of the plurality of groups, a second histogram having bins corresponding to bins of the first histogram, wherein each one of the bins of the second histogram includes a count of the first observations, among the first observations that belong to the one of the plurality of groups, having one or more values corresponding to the one of the bins for the at least one of the one or more first parameters; and outputting the second histograms.

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.