A method for generating binding peptides presented by any given Major Histocompatibility Complex (MHC) protein is presented. The method includes, given a peptide and an MHC protein pair, enabling a Reinforcement Learning (RL) agent to interact with and exploit a peptide mutation environment by repeatedly mutating the peptide and observing an observation score of the peptide, learning to form a mutation policy, via a mutation policy network, to iteratively mutate amino acids of the peptide to obtain desired presentation scores, and generating, based on the desired presentation scores, qualified peptides and binding motifs of MHC Class I proteins.

Disclosed herein are systems and methods for direct classification of biological datasets. The datasets may include raw mass spectrometry data. Some aspects include training a classifier for direct classification of raw data, and some aspects include applying the classifier.

Disclosed herein are systems and methods for direct classification of biological datasets. The datasets may include raw mass spectrometry data. Some aspects include training a classifier for direct classification of raw data, and some aspects include applying the classifier.

Disclosed is a method of qualifying a subgroup of target binding biomolecules from a larger group of target binding biomolecules for analysis. A competitive immunoassay including a target protein is used to identify 100 interactions between different pairs of the target binding biomolecules and interaction profiles are generated 200. Each target binding biomolecule is allocated 300 to a bin representing an epitope family and identified bins are associated in a circular or semi-circular bin chart on a display with identified respective target binding biomolecule(s). Based on the association 400 between identified bins and identified respective target binding molecule(s) in the bin chart, a subgroup of target binding biomolecules is selected 500 for further analysis by selecting one or more of the target binding biomolecule(s) of one or more of the bins.

Disclosed is a method of qualifying a subgroup of target binding biomolecules from a larger group of target binding biomolecules for analysis. A competitive immunoassay including a target protein is used to identify 100 interactions between different pairs of the target binding biomolecules and interaction profiles are generated 200. Each target binding biomolecule is allocated 300 to a bin representing an epitope family and identified bins are associated in a circular or semi-circular bin chart on a display with identified respective target binding biomolecule(s). Based on the association 400 between identified bins and identified respective target binding molecule(s) in the bin chart, a subgroup of target binding biomolecules is selected 500 for further analysis by selecting one or more of the target binding biomolecule(s) of one or more of the bins.

Disclosed is a method for classifying monitoring results from an analytical sensor system (20) arranged to monitor molecular interactions at a sensing surface, wherein detection curves representing progress of the molecular interactions with time are produced. The method comprises steps of: acquiring (100) a set of detection curves, fitting (101) a first mathemati- cal model to the set of detection curves; calculating (102) a set of features from the set of detection curves and fitted mathematical model; based on the calculated set of features, classifying (103) each detection curve into qual- ity classification group; and based on the classification determining which detection curves to use in kinetic analysis of the monitored molecular inter- actions.

Disclosed is a method for classifying monitoring results from an analytical sensor system (20) arranged to monitor molecular interactions at a sensing surface, wherein detection curves representing progress of the molecular interactions with time are produced. The method comprises steps of: acquiring (100) a set of detection curves, fitting (101) a first mathemati- cal model to the set of detection curves; calculating (102) a set of features from the set of detection curves and fitted mathematical model; based on the calculated set of features, classifying (103) each detection curve into qual- ity classification group; and based on the classification determining which detection curves to use in kinetic analysis of the monitored molecular inter- actions.

Cell free nucleic acids from a test sample obtained from an individual are analyzed to identify possible fusion events. Cell free nucleic acids are sequenced and processed to generate fragments. Fragments are decomposed into kmers and the kmers are either analyzed de novo or compared to targeted nucleic acid sequences that are known to be associated with fusion gene pairs of interest. Thus, kmers that may have originated from a fusion event can be identified. These kmers are consolidated to generate gene ranges from various genes that match sequences in the fragment. A candidate fusion event can be called given the spanning of one or more gene ranges across the fragment.

Cell free nucleic acids from a test sample obtained from an individual are analyzed to identify possible fusion events. Cell free nucleic acids are sequenced and processed to generate fragments. Fragments are decomposed into kmers and the kmers are either analyzed de novo or compared to targeted nucleic acid sequences that are known to be associated with fusion gene pairs of interest. Thus, kmers that may have originated from a fusion event can be identified. These kmers are consolidated to generate gene ranges from various genes that match sequences in the fragment. A candidate fusion event can be called given the spanning of one or more gene ranges across the fragment.

Various embodiments disclosed relate to method for identification of preferred binding sites on intrinsically disorganized proteins (IDPs). The present disclosure includes methods including generating an IDP ensemble comprising one or more of the IDPs, sampling ligand interactions with the IDP ensemble to produce sampled ligand interactions, subjecting each of the sampled ligand interactions to an IDP ensemble docking, producing a differential binding score (DIBS) based on the sampled ligand interactions with the IDP ensemble docking, and modeling the DIBS to identify binding sites on the IDP ensemble.