Abstract: In some aspects, the present disclosure provides a computer-implemented method for quantifying a molecule using a machine learning algorithm. The computer-implemented method can comprise providing an input dataset comprising one or more features representing a quantity of the molecule measured using at least a first condition. The computer-implemented method can comprise processing the input dataset, using a machine learning algorithm, to generate an adjusted quantity of the molecule at a second condition.

Abstract: In some aspects, the present disclosure provides a method for determining a risk or state of a neurodegenerative disease of a subject. In some embodiments, the method compriseses detecting a presence of a biomarker in a biological sample from the subject, wherein the biomarker comprises at least one of: E7EUF1, O94812, P02549, P02730, P05019, P05154, P05546, P13497, P16157, P16452, P17936, P24593, P27918, P35858, P41218, Q12797, Q13214, Q13822, Q8NI99, Q96IY4, Q99715, Q9BXN1, Q9H0B8, or a proteoform thereof. In some embodiments, the method comprises detecting a presence of a biomarker in a biological sample from the subject, wherein the biomarker comprises at least one of: P54803, P14625, P30043, P00742, A0A0D9SG88, Q5TFM2, P54803, P54803-3, P54803-4, P04196, or a proteoform thereof. In some embodiments, the method comprises determining the risk or state of the neurodegenerative disease of the subject based on the presence of the biomarker in the biological sample.

Abstract: The present disclosure describes a method for assaying a biological sample. In some cases, the method comprises assaying a set of nucleic acids from the biological sample to obtain genotypic information of the biological sample. In some cases, the method comprises generating, based at least in part on the genotypic information, a set of expressible proteoforms that can be expressed from the set of nucleic acids. In some cases, the method comprises assaying a set of polyamino acids from the biological sample to generate proteomic information of the biological sample, wherein the proteomic information comprises a set of identifications for the set of polyamino acids. In some cases, the method comprises mapping the set of identifications to the set of expressible proteoforms, thereby determining a set of expressed proteoforms in the biological sample.

Abstract: In some aspects, the present disclosure provides a method for determining a polyamino acid descriptor associated with a biological state. The method can comprise removing technical variation from a proteomic dataset to generate a refined proteomic dataset, the technical variation arising from a predetermined non-biological factor, by training a neural network using a loss function. The loss function can be configured to increase a similarity between a first set of latent embeddings that are based on a first subset of polyamino acid descriptors in the proteomic dataset, wherein the first subset of polyamino acid descriptors are obtained from the same sample. The loss function can be configured to decrease the similarity between a second set of latent embeddings that are based on a second subset of polyamino acid descriptors in the proteomic dataset, wherein the second subset of polyamino acid descriptors are obtained from different samples.

Abstract: The present disclosure describes methods and systems for analyzing mass spectrometry data. The methods and systems can comprise an operation of contacting a plurality of biomolecules with a plurality of surfaces. The methods and systems can further comprise performing mass spectrometry on the plurality of biomolecules, or a portion or derivative thereof. The methods and systems can further comprise classifying a sample based on the mass spectra. The methods and systems of the disclosure may be used for identifying evidence of operational errors in mass spectrometry datasets.

Abstract: In some aspects, the present disclosure describes a method for determining a biological state associated with a polyamino acid descriptor. In some cases, the method comprises receiving the polyamino acid descriptor comprising at least one dimension representing a polyamino acid association with a given assay method. In some cases, the method comprises generating, in a latent space, a latent descriptor based at least in part on the polyamino acid descriptor, and wherein the latent descriptor comprises sufficiently fewer dimensions than the polyamino acid descriptor such that at least a portion of information in the polyamino acid descriptor is lost in the latent descriptor. In some cases, the method comprises determining, based at least in part on the latent descriptor, the biological state associated with the polyamino acid descriptor.

Abstract: A computer-implemented method for linking individuals' datasets in a database may include receiving a target individual dataset of a target individual and a plurality of additional individual datasets. A computing server may generate a plurality of sub-cluster pairs of first parental groups and second parental groups. At least one of sub-cluster pairs includes a first parental group of matched segments and a second parental group of matched segments. A computing server may link the first parental groups and the second parental groups across the plurality of sub-cluster pairs to generate at least one super-cluster of a parental side. A computing server may assign metadata to one or more additional individual datasets of the plurality of additional individual datasets. The metadata may specify that the one or more additional individual datasets are connected to the target individual dataset by the parental side of the super-cluster.

Abstract: Embodiments of the present invention include methods for discovering deleterious human variants for a given human whole genome sequence or genotype and predicting the functional consequence of the variants.