An artificial intelligence-based system comprises an input preparation module that accesses a sequence database and generates an input base sequence. The input base sequence comprises a target base sequence with target bases, wherein the target base sequence is flanked by a right base sequence with downstream context bases, and a left base sequence with upstream context bases. A sequence-to-sequence model processes the input base sequence and generates an alternative representation of the input base sequence. An output module processes the alternative representation of the input base sequence and produces at least one per-base output for each of the target bases in the target base sequence. The per-base output specifies, for a corresponding target base, signal levels of a plurality of epigenetic tracks.

Exemplary embodiments described herein provide improved techniques for identifying and accounting for molecule variants when modeling a fragmentation of the molecule. The variants may be identified by comparing possible modifications of molecule fragments against experimental data to rank or score the possible modifications. Possible modifications may be shown in a variant interface where a modification may be selected as a candidate for comparison to experimental data.

Exemplary embodiments described herein provide improved techniques for matching an experimental mass spectrometry fragmentation against a known or predicted fragmentation from a library. Among other improvements, exemplary embodiments provide more accessible interfaces that are easier to interpret, thus allowing for more accurate and faster matches. They also may automatically accumulate multiple experimental results to determine whether several runs of a given sample cumulatively represent a library fragmentation pattern. Furthermore, exemplary embodiments provide simplified techniques for identifying and accounting for molecule variants.

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 of assessing the analytical performance of a biochemical measured using a multi-analyte assay is described. The method includes analytically validating a measurement of the level of a first biochemical in a sample, wherein the first biochemical has been previously analytically validated for three or more analytical validation conditions; measuring the level of a second biochemical in a sample, wherein the second biochemical is structurally or biochemically related to the first biochemical; and comparing validation parameters of the first biochemical with validation parameters of the second biochemical to determine whether the performance of the second biochemical is acceptable based on the comparison results.

A method of assessing the analytical performance of a biochemical measured using a multi-analyte assay is described. The method includes analytically validating a measurement of the level of a first biochemical in a sample, wherein the first biochemical has been previously analytically validated for three or more analytical validation conditions; measuring the level of a second biochemical in a sample, wherein the second biochemical is structurally or biochemically related to the first biochemical; and comparing validation parameters of the first biochemical with validation parameters of the second biochemical to determine whether the performance of the second biochemical is acceptable based on the comparison results.

Disclosed herein are methods and systems for storing data and/or information on nucleic acid molecules, storing the nucleic acid molecules, and retrieving the data and/or information. These methods and systems have broad applications for data storage, including in improving the efficiency and accuracy of retrieving data.

A method of diagnosing central nervous system injuries such as acquired brain injury (ABI) and/or acquired spinal cord injury (ASI), including mild TBI (concussion or blast wave), mild ASI (contusion, stretch or partial cord transection), non-TBI brain injury and/or non-TSI spinal cord injury in a subject (animal or human). The method includes (a) obtaining a biological test sample from the subject, identifying metabolites in the subject's sample using metabolomics thereby obtaining a subject's metabolite matrix and generating a subject's profile using the patient's metabolite matrix; and (b) using multivariate statistical analysis and machine learning to compare the subject's profile with predetermined set of profiles of CNS injuries and a predetermined set of profiles of controls to determine if the subject has a CNS injury.

To improve the reliability of mutual diagnosis in a cancer determination by machine learning, m/z values of ions originating from tumor markers or similar substances used in other related tests are stored in a particular m/z-value database. A spectrum information filtering section deletes signal intensities at the m/z values stored in the particular m/z-value database from a large number of mass spectra classified by the presence or absence of cancer. Using the data which remain after the deletion as training data, a training processor obtains training-result information and stores it in a training result database. A judgment processor similarly deletes signal intensities at the predetermined m/z values from mass spectrum data obtained for a target sample to be judged. Then, based on the training-result information stored in the training-result database, the judgment processor determines whether the target sample should be classified into a cancerous group or non-cancerous group.

Methods and processes to identify neoplastic tissue antigens derived from alternative splicing (AS) are described, in accordance with various embodiments of the invention. Also described are novel tumor antigens that are useful as targets in various immunotherapeutic approaches to treating brain cancer as well as novel engineered T cell Receptors (TCRs) and chimeric antigen receptors (CARs) that target these antigenic peptides.