Abstract: The present invention relates to a computer-implemented method for modelling genomic data represented in an unsupervised neural network, trVAE, comprising a conditional variational autoencoder, CVAE, with an encoder (f) and a decoder (g).

Abstract: The present invention relates to a method of determining whether a subject is at risk of developing type 1 diabetes by determining the genetic risk score (GRS) of a subject. The present invention also comprises a pharmaceutical composition comprising insulin and a pharmaceutical acceptable carrier for use in a method for preventing type 1 diabetes in a subject having a genetic risk score as determined by the method mentioned above. Further, it encompasses a kit for use in a method of determining whether a subject is at risk of developing type 1 diabetes by determining the genetic risk score of a subject and a type 1 diabetes antigen for use in a method of immunizing a subject against type 1 diabetes having a genetic risk score as determined by the method mentioned above.

Abstract: Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. Transcriptome-wide identification of regulatory heterogeneities can be robustly achieved by randomly collecting small numbers of cells followed by statistical analysis. However, this stochastic-profiling approach blurs out the expression states of the individual cells in each pooled sample. Various aspects of the disclosure show that the underlying distribution of single-cell regulatory states can be deconvolved from stochastic-profiling data through maximum-likelihood inference. Guided by the mechanisms of transcriptional regulation, the disclosure provides mixture models for cell-to-cell regulatory heterogeneity which result in likelihood functions to infer model parameters.

Abstract: Regulated changes in gene expression underlie many biological processes, but globally profiling cell-to-cell variations in transcriptional regulation is problematic when measuring single cells. Transcriptome-wide identification of regulatory heterogeneities can be robustly achieved by randomly collecting small numbers of cells followed by statistical analysis. However, this stochastic-profiling approach blurs out the expression states of the individual cells in each pooled sample. Various aspects of the disclosure show that the underlying distribution of single-cell regulatory states can be deconvolved from stochastic-profiling data through maximum-likelihood inference. Guided by the mechanisms of transcriptional regulation, the disclosure provides mixture models for cell-to-cell regulatory heterogeneity which result in likelihood functions to infer model parameters.

Abstract: A method for humanizing a music sequence (S), the music sequence (S) comprising a multitude of sounds (s1, . . . , sn) occurring on times (t1, . . . , tn) comprises the steps generating, for each time (ti) a random offset (oi), adding the random offset (oi) to the time (ti) in order to obtain a modified time (ti+oi); and outputting a humanized music sequence (S?) wherein each sound (si) occurs on the modified time (ti+oi). According to the invention, the power spectral density of the random offsets has the form 1 f ? . wherein 0<?<2.

Abstract: A method for humanizing a music sequence (S), the music sequence (S) comprising a multitude of sounds (s1, . . . , sn) occurring on times (t1, . . . , tn) comprises the steps generating, for each time (ti) a random offset (oi), adding the random offset (oi) to the time (ti) in order to obtain a modified time (ti+oi); and outputting a humanized music sequence (S?) wherein each sound (si) occurs on the modified time (ti+oi). According to the invention, the power spectral density of the random offsets has the form 1 f ? . wherein 0<?<2.