Abstract: A method for identifying a key module and a key node of a biomolecular complex network by fusing multiple methods on the basis of a topological structure of the biomolecular complex network which may be such as a protein-protein interaction network, a gene expression regulation network, a biological metabolism network, an epigenetic network, a phenotypic network or a signaling network, comprising the following main steps: according to the biomolecular network and a module division for the network, comprehensively and quantitatively identifying the key module and key node on the basis of the topological structure by various measuring methods from multi-angle.

Abstract: The present invention relates to an artificial-intelligence-based cancer diagnosis and cancer type prediction method, and, more particularly, to an artificial-intelligence-based cancer diagnosis and cancer type prediction method, which extracts nucleic acids from a biological sample to acquire sequence information, and thus generate vectorized data on the basis of aligned nucleic acid fragments, and then inputs same into a trained artificial intelligence model to analyze a calculated value. Compared with a conventional method, which uses a step of determining the number of chromosomes on the basis of a read count and utilizes each related value as a normalized value, the artificial-intelligence-based cancer diagnosis and cancer type prediction method according to the present invention generates vectorized data to perform an analysis using an AI algorithm, and thus is useful in that similar effects can be exhibited even when read coverage is low.

Abstract: An analysis method of analyzing a nucleic acid sequence of a patient sample with a computer, may include: obtaining first nucleic acid sequence data derived from a tumor cell collected from a patient, and second nucleic acid sequence data derived from a non-tumor cell collected from the patient; detecting a somatic mutation based on the first nucleic acid sequence data; detecting a germline mutation based on the second nucleic acid sequence data; selecting a presentation form for information on the germline mutation among candidate forms; and creating an analysis report in the selected form.

Abstract: Disclosed herein is a method for predicting a patient response to a sodium ion channel blocker such as mexiletine when the patient has LQT syndrome or an arrhythmia. The method generally comprises determining a plurality of parameters associated with sodium ion channels; generating a model for patient response by using a partial least squared (PLS) regression analysis on said plurality of parameters; and using the model to predict the patient response if the patient is administered a sodium ion channel blocker such as mexiletine.

Abstract: An approach is disclosed for activating cell-mediated herd immunity (CMHI) in a group of people. A scalable infrastructure is provided with one or more compliance protocol targets to facilitate activation of at least one of the four types of individual cell-mediated immunity (CMI): absolute humidity CMI, vitamin D CMI, gut microbiome CMI, and antiviral priming CMI, in the group of people. The activation is for at least one of: absolute humidity CMHI, vitamin D CMHI, gut microbiome CMHI, and antiviral priming CMHI in the group of people. The CMHI is achieved in the group of people when the compliance protocol targets are achieved.

Abstract: Provided is a deep learning algorithm that analyzes fragments of biological sequences. The input for the deep learning algorithm is a biological sequence fragment of unknown origin and the output is the closest known biological genome that could share phenotypic properties with the biological species of unknown origin. The workflow thus has application for genomic classification, identification of mutations within known genomes, and the identification of the closest class for unknown species.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining a predicted structure of a protein that is specified by an amino acid sequence. In one aspect, a method comprises: obtaining a multiple sequence alignment for the protein; determining, from the multiple sequence alignment and for each pair of amino acids in the amino acid sequence of the protein, a respective initial embedding of the pair of amino acids; processing the initial embeddings of the pairs of amino acids using a pair embedding neural network comprising a plurality of self-attention neural network layers to generate a final embedding of each pair of amino acids; and determining the predicted structure of the protein based on the final embedding of each pair of amino acids.

Abstract: Processes to reveal biological attributes from nucleic acids are provided. In some instances, nucleic acids are used to develop frequency sequence signal maps, construct V-plots, and/or to train computational models. In some instances, trained computational models are used to predict features that reveal biological attributes.

Abstract: Methods and systems are provided herein to track an original biological deposit that is withdrawn from a public repository. In some aspects, the public repository may include the ATCC or any other public repository in which biological materials are deposited for access by the general public. These techniques may be used to identify a product or biological deposit from a third party that is derived from the original biological deposit.

Abstract: Embodiments of the present invention may provide automated techniques for signal analysis that may continuously provide up-to-date results that link EEG and behaviors that are important for daily activities. Such techniques may provide automation, objectivity, real-time monitoring and portability. In an embodiment of the present invention, a computer-implemented method for monitoring neural activity may comprise receiving data representing at least one signal representing neural activity of a test subject, pre-processing the received data by performing at least one of band-pass filtering, artifact removal, identifying common spatial patterns, and temporally segmentation, processing the pre-processed data by performing at least one of time domain processing, frequency domain processing, and time-frequency domain processing, generating a machine learning model using the processed data as a training dataset, and outputting a characterization of the neural activity based on the machine learning model.

Abstract: Methods of accurately determining DNA barcodes using a cylindrical nanopore system. The methods include steps of leveraging the average velocity of a double-stranded DNA segment passing through a single cylindrical nanopore that is measured through repeated scanning to accurately determine protein tag locations on the double-stranded DNA segment. As such, the methods provide for the accurate calculation of a barcode for the double-stranded DNA segment based on protein tag locations without underestimation or overestimate issues. The underlying concept and the methods are equally applicable to other multi-nanopore systems which use the dwell time and time of flight velocities to measure the barcodes.

Abstract: Provided herein are systems, methods, and computer program products using tumor phylogeny, mutation rates, and machine learning to produce a clinical projection, such as patient survival, risk of malignancy, and therapeutic options. The method includes generating sequence variation data that identifies, characterizes, or quantifies at least one mutation in tumor sequence data of a tumor of a patient. The method also includes generating a phylogenic tree depicting clonal evolution of cells in the tumor of the patient. The method further includes determining at least one feature of the phylogenic tree including at least one value quantifying rates of mutation and/or at least one value representing at least one aspect of a structure of the phylogenic tree. The method further includes training a machine learning model to be configured to generate a projection for the patient comprising a clinical outcome or disease progression.

Abstract: A system, method, and article for diagnosing a test set of biological cells. For example, in one embodiment a normal set of cells is characterized using flow cytometry. A centroid and radius are defined for a set of clusters in an n-dimensional space corresponding to a normal maturation for a cell lineage in the normal set of cells. A test set of cells is characterized using flow cytometry and the characterization is compared to the set of clusters.

Abstract: A device for determining a heart rate of a user has a PPG sensor and an accelerometer to compensate for acceleration artifacts within the PPG signal. The device transforms time domain PPG and accelerometer signals into the frequency domain using a Fourier transformation and utilizes the Fourier coefficient magnitudes as indicative of the probability of candidate heart rate values. Candidate heart rate values are determined at sampling times over a time interval and a most probable heart rate path during the time interval is determined using a reward/penalty algorithm.

Abstract: The disclosure notably relates to a computer-implemented method for simulating evolution of a tumor associated to an oncogene. The method includes providing a plurality of pieces of data, each corresponding to a given cell of the tumor, and includes a degree of activation of the oncogene in the given cell. The method further includes providing a model configured to take an input piece of data and to output information on proliferation of the respective given cell corresponding to the input piece of data. The information on proliferation depends on the degree of activation of the oncogene. The method further includes running the model on one or more pieces of data of the plurality of pieces of data and updating the plurality of pieces of data based on the result of the running. Such a method improves the simulation of the evolution of a tumor.

Abstract: The disclosure provides systems and methods for data analysis of experimental data. The analysis can include reference data that are not directly generated from the present experiment, which reference data may be values of the experimental parameters that were either provided by a user, computed by the system with input from a user, or computed by the system without using any input from a user. Another example of such reference data may be information about the instrument, such as the calibration method of the instrument.

Abstract: The present disclosure provides systems and methods for controllable protein generation. According to some embodiments, the systems and methods leverage neural network models and techniques that have been developed for other fields, in particular, natural language processing (NLP). In some embodiments, the systems and methods use or employ models implemented with transformer architectures developed for language modeling and apply the same to generative modeling for protein engineering.

Abstract: Aspects of the present disclosure include methods for identifying a set of fluorophore-biomolecule reagent pairs for characterizing a sample by flow cytometry. Methods according to certain embodiments include calculating a spectral spillover spreading parameter for a plurality of fluorophores, pairing each fluorophore with a biomolecule that is specific for a biomarker of a cell in the sample to generate a plurality of fluorophore-biomolecule reagent pairs, generating an adjusted spillover spreading matrix for the fluorophore-biomolecule reagent pairs based on the spectral spillover spreading parameter of each fluorophore and a biomarker classification parameter and identifying an optimal set of fluorophore-biomolecule reagent pairs based on the calculated spillover spreading values from the adjusted spillover spreading matrix. Systems and non-transitory computer readable storage medium for practicing the subject methods are also provided.

Abstract: A data storage medium is disclosed comprising a solid support matrix including an optional stabilising reagent or reagents in a dry form, for use as a support for artificially synthesised oligonucleotide sequences encoded with data. Preferably the matrix is fibrous (for example cellulose, or glass, fibres) formed into a support of sufficient strength to hold the oligonucleotide sequences. The stabilising reagents are preferably a combination of a weak base, and a chelating agent, optionally, uric acid or a urate salt, and optionally an anionic surfactant.

Abstract: Provided herein are methods, processes, systems, machines and apparatuses for non-invasive assessment of chromosome alterations.