A method for application of a deep learning neural network (NN) for predicting the probability distribution of a biological phenotype does not require any assumption or prior knowledge of the probability distributions. The NN may be a recurrent neural network (RNN) or a long short-term memory (LSTM) network. The NN includes a loss function, which is trained on limited observations, as low as one observation, which is obtained from a large data set related to a biological system. The NN with the trained loss function is capable of calculating if readings that are outside of the mean for the data set are inherent to the biological system or are outlier readings. The output of the method is a continuous probability distribution of the biological phenotypes for each input parameter or set of parameters from the biological data set.

High-throughput production of modified microbes is achieved through optimization of directed build graph data structures representing biological workflows. Portions of otherwise unrelated workflows may be combined where they share common biological reaction steps, and processed by a genetic manufacturing facility to take advantage of operational efficiencies. Workflows may be mapped to physical laboratory equipment in a manner that optimizes material transfers. Different automated platforms running different machines in different languages are coordinated in a device-agnostic and language-agnostic manner.

A system for generating an alimentary plan is disclosed. The system comprises a computing device which is configured to receive an input that includes physiological data related to a skin sample. Computing device is configured to extract a plurality of biological indicators related to disease state from the physiological data. Computing device is configured to determine a biological indicator score for each biological score for each biological indicator of the plurality of biological indicators. Computing device is configured to generate a skin disorder classifier by receiving skin disorder training data. The computing device is configured to classify, using the skin disorder classifier, the at least one biological indicator and the biological indicator score to a positive result for a skin disorder. Computing device is configured to generate an alimentary plan as a function of the positive result. A method for generating an alimentary plan is also disclosed.

A system for generating an alimentary plan is disclosed. The system comprises a computing device which is configured to receive an input that includes physiological data related to a skin sample. Computing device is configured to extract a plurality of biological indicators related to disease state from the physiological data. Computing device is configured to determine a biological indicator score for each biological score for each biological indicator of the plurality of biological indicators. Computing device is configured to generate a skin disorder classifier by receiving skin disorder training data. The computing device is configured to classify, using the skin disorder classifier, the at least one biological indicator and the biological indicator score to a positive result for a skin disorder. Computing device is configured to generate an alimentary plan as a function of the positive result. A method for generating an alimentary plan is also disclosed.

Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.

Technical solutions for classifying patients with respect to multiple cancer classes are provided. The classification can be done using cell-free whole genome sequencing information from subjects. A reference set of subjects is used to train classifiers to recognize genomic markers that distinguish such cancer classes. The classifier training includes dividing the reference genome into a set of non-overlapping bins, applying a dimensionality reduction method to obtain a feature set, and using the feature set to train classifiers. For subjects with unknown cancer class, the trained classifiers provide probabilities or likelihoods that the subject has a respective cancer class for each cancer in a set of cancer classes. The present disclosure thus describes methods to improve the screening and detection of cancer class from among several cancer classes. This serves to facilitate early and appropriate treatment for subjects afflicted with cancer.

The present disclosure is directed to a computer-implemented method and system for anatomical tree structure analysis. The method includes receiving model inputs for a set of positions in an anatomical tree structure. The method further includes applying, by a processor, a learning network to the model inputs. The learning network comprises a set of encoders and a neural network modeling the anatomical tree structure, wherein each encoder provides features extracted from the model input at a corresponding position. The neural network has a plurality of nodes constructed according to the anatomical tree structure and each node is configured to process the extracted features from one or more of the encoders. The method additionally includes providing an output of the learning network as an analysis result of the anatomical tree structure analysis.

The present disclosure is directed to a computer-implemented method and system for anatomical tree structure analysis. The method includes receiving model inputs for a set of positions in an anatomical tree structure. The method further includes applying, by a processor, a learning network to the model inputs. The learning network comprises a set of encoders and a neural network modeling the anatomical tree structure, wherein each encoder provides features extracted from the model input at a corresponding position. The neural network has a plurality of nodes constructed according to the anatomical tree structure and each node is configured to process the extracted features from one or more of the encoders. The method additionally includes providing an output of the learning network as an analysis result of the anatomical tree structure analysis.

Disclosed is a computer-implemented method for determining a measure correlated to the probability that two mutated sequence reads derive from the same sequence comprising mutations. The method comprises receiving mutated sequence reads each corresponding to a subsequence of a sequence comprising mutations compared to a sequence not comprising mutations, applying a common minimizer function to each mutated sequence read, to determining minimizers for each mutated sequence read, determining positions of the one or more minimizers in each mutated sequence read, determining positions of mutations in each mutated sequence read, and for at least two mutated sequence reads with a common minimizer, counting the number of mutations with matching position and/or mismatching position when the respective minimizers are aligned. Also disclosed is a corresponding method for determining at least a portion of a sequence of at least one target template nucleic acid molecule.

Disclosed is a computer-implemented method for determining a measure correlated to the probability that two mutated sequence reads derive from the same sequence comprising mutations. The method comprises receiving mutated sequence reads each corresponding to a subsequence of a sequence comprising mutations compared to a sequence not comprising mutations, applying a common minimizer function to each mutated sequence read, to determining minimizers for each mutated sequence read, determining positions of the one or more minimizers in each mutated sequence read, determining positions of mutations in each mutated sequence read, and for at least two mutated sequence reads with a common minimizer, counting the number of mutations with matching position and/or mismatching position when the respective minimizers are aligned. Also disclosed is a corresponding method for determining at least a portion of a sequence of at least one target template nucleic acid molecule.