The present invention relates to a method for determining one or more intrinsic properties of a DNA component from a plurality of measurements obtained over a time period from a cell culture, with each cell comprising the DNA component, wherein the DNA component is involved in transcription of one or more target signals, wherein the plurality of measurements comprises measurements relating to the density of the cell culture over the time period and measurements relating to the amount of the one or more target signals in the cell culture over the time period.

The present invention is related to the field of bio/chemical sampling, sensing, assays and applications. Particularly, the present invention is related to how to make the sampling/sensing/assay become simple to use, fast to results, highly sensitive, easy to use, using tiny sample volume (e.g. 0.5 uL or less), operated by a person without any professionals, reading by mobile-phone, or low cost, or a combination of them.

The present invention is related to the field of bio/chemical sampling, sensing, assays and applications. Particularly, the present invention is related to how to make the sampling/sensing/assay become simple to use, fast to results, highly sensitive, easy to use, using tiny sample volume (e.g. 0.5 uL or less), operated by a person without any professionals, reading by mobile-phone, or low cost, or a combination of them.

Membrane protein expression can be predicted using statistical frameworks to provide an enhanced subset of sequences, out of an initial larger set of potentially expressing sequences, by using features derived from sequences and a model parameterized through a dataset of known expression levels. Also, membrane protein experimentation protocols can be designed using the statistical frameworks in concert known outcomes to identify which laboratory conditions are most likely to produce successful results.

Membrane protein expression can be predicted using statistical frameworks to provide an enhanced subset of sequences, out of an initial larger set of potentially expressing sequences, by using features derived from sequences and a model parameterized through a dataset of known expression levels. Also, membrane protein experimentation protocols can be designed using the statistical frameworks in concert known outcomes to identify which laboratory conditions are most likely to produce successful results.

A computer-implemented method and system uses a map which maps from gene expression data for a plurality of training tumors in a tumor atlas to gene expression data representing single cells derived from mammal samples in developmental stages in a single-cell atlas. The method and system: (A) use the map to extract, from the plurality of training tumors, a plurality of biological components, thereby generating, for each training tumor-biological component pair, a corresponding biological component score; and (B) construct, based on the two atlases and the map, a machine learning perceptron classifier that outputs a tumor type of an input tumor based on its gene expression data. The method and system may generate the map before using it. The method and system may apply the machine learning perceptron classifier to the input tumor's gene expression data to generate the tumor type of the input tumor.

A computer-implemented method and system uses a map which maps from gene expression data for a plurality of training tumors in a tumor atlas to gene expression data representing single cells derived from mammal samples in developmental stages in a single-cell atlas. The method and system: (A) use the map to extract, from the plurality of training tumors, a plurality of biological components, thereby generating, for each training tumor-biological component pair, a corresponding biological component score; and (B) construct, based on the two atlases and the map, a machine learning perceptron classifier that outputs a tumor type of an input tumor based on its gene expression data. The method and system may generate the map before using it. The method and system may apply the machine learning perceptron classifier to the input tumor's gene expression data to generate the tumor type of the input tumor.

The invention is notably directed to a method for encoding information. This method first comprises generating an encryption key according to polymorphic features of nucleic acids from one or more entities. Next, information is encrypted based on the generated key. Finally, the encrypted information is encoded into synthetic DNA. Another aspect concerns a method for retrieving information. Consistently with the above encoding scheme, synthetic DNA in provided, which encodes encrypted information. Such information is read by sequencing the synthetic DNA and by decrypting the information read using a decryption key. The latter is generated according to polymorphic features of nucleic acids from one or more entities (e.g., from the legitimate individual(s) requesting access to information). Thus, the encoded information cannot be interpreted unless a suitable decryption key is available. The invention is further directed to related DNA samples and systems, including DNA vaults.

A method is presented for analyzing interactions in a human genome. The method includes: receiving a biological sample of a cell from a subject; extracting read data from the biological sample, where the read data includes a set of reads; and constructing, by a computer processor, a hypergraph from the read data, where each node in the hypergraph represents a locus and hyperedges in the hypergraph represent interactions between two or more loci. The hypergraphs may be used for different applications including determining entropy, comparing different biological samples and reporting multi-way contacts in a set of transcription clusters.

A method is presented for analyzing interactions in a human genome. The method includes: receiving a biological sample of a cell from a subject; extracting read data from the biological sample, where the read data includes a set of reads; and constructing, by a computer processor, a hypergraph from the read data, where each node in the hypergraph represents a locus and hyperedges in the hypergraph represent interactions between two or more loci. The hypergraphs may be used for different applications including determining entropy, comparing different biological samples and reporting multi-way contacts in a set of transcription clusters.