The present invention relates to the discovery of a method for identifying a treatment regimen for a patient diagnosed with cancer, predicting patient resistance to therapeutic agents and identifying new therapeutic agents. Specifically, the present invention relates to the use of an algorithm to identify a mutation in a kinase, determine if the mutation is an activation or resistance mutation and then to suggest an appropriate therapeutic regimen. The invention also relates to the use of a pattern matching algorithm and a crystal structure library to predict the functionality of a gene mutation, predict the specificity of small molecule kinase inhibitors and for the identification of new therapeutic agents.

The disclosure provides methods to assemble genomes of eukaryotic or prokaryotic organisms. The disclosure further provides methods for haplotype phasing and meta-genomics assemblies.

Embodiments herein concern systems, compositions, methods and uses for in vivo selection of optimum target proteins of use in designing genomically-engineered cells or organisms. Some embodiments relate to compositions and methods for generating barcoded constructs of use in systems and methods described.

A method for at least one of characterizing, diagnosing, and treating an autoimmune disorder in at least a subject, the method comprising: receiving an aggregate set of biological samples from a population of subjects; generating at least one of a microbiome composition dataset and a microbiome functional diversity dataset for the population of subjects; generating a characterization of the autoimmune condition based upon features extracted from at least one of the microbiome composition dataset and the microbiome functional diversity dataset; based upon the characterization, generating a therapy model configured to correct the autoimmune condition; and at an output device associated with the subject, promoting a therapy to the subject based upon the characterization and the therapy model.

A method and system for in-silico testing of actives on human skin is described. The present invention discloses a micro and macroscopic level model of the skins upper protective layer Stratum-Corneum. The invention presents a multi-scale modeling framework for the calculation of diffusion and release profile of different actives like drugs, particles and cosmetics through developed skin model using molecular dynamics simulations and computational fluid dynamics approach. The systems consist of a molecular model of the skin's upper layer stratum corneum and permeate molecules. The system also consists of a macroscopic transport model of stratum corneum. The transport model is used to generate the release profile of the active molecule.

A method for designing a protein capable of binding in an RNA base selective manner or RNA base sequence specific manner is provided. The protein of the present invention is a protein containing one or more of PPR motifs (preferably 2 to 14 PPR motifs) each consisting of a polypeptide of 30- to 38-amino acid length represented by the formula 1 (wherein Helix A is a moiety of 12-amino acid length capable of forming an -helix structure, and is represented by the formula 2, wherein, in the formula 2, A.sub.1 to A.sub.12 independently represent an amino acid; X does not exist, or is a moiety of 1- to 9-amino acid length; Helix B is a moiety of 11- to 13-amino acid length capable of forming an -helix structure; and L is a moiety of 2- to 7-amino acid length represented by the formula 3, wherein, in the formula 3, the amino acids are numbered i (1), ii (2), and so on from the C-terminus side, provided that L.sub.iii to L.sub.vii may not exist), and combination of three amino acids A.sub.1, A.sub.4 and L.sub.ii, or combination of two amino acids A.sub.4, and L.sub.ii is a combination corresponding to a target RNA base or base sequence.

The present invention relates to peptides, nucleic acids and cells for use in immunotherapeutic methods. In particular, the present invention relates to the immunotherapy of cancer. The present invention furthermore relates to tumor-associated cytotoxic T cell (CTL) peptide epitopes, alone or in combination with other tumor-associated peptides that serve as active pharmaceutical ingredients of vaccine compositions that stimulate anti-tumor immune responses. The present invention relates to several novel peptide sequences and their variants derived from HLA class I and HLA class II molecules of human tumor cells that can be used in vaccine compositions for eliciting anti-tumor immune responses.

Disclosed herein are systems and uses of systems operating between fully quantum coherent and fully classical states. Such systems operate in what is termed the Poised realm and exhibit unique behaviors that can be applied to a number of useful applications. Non-limiting examples include drug discovery, computers, and artificial intelligence

Systems and methods for in silico prediction of HLA type of a patient are presented in which patient sequence reads and a reference sequence with known and distinct HLA alleles are used in a de Bruijn graph. A composite match score is then used to rank HLA alleles, thus providing a first HLA type. A second HLA type is identified by re-ranking using an adjusted composite match score.

The present invention provides novel methods and assays for high-throughput screening of combinatorial libraries to identify FPR1 and/or FPR2 ligands (e.g., agonists and/or antagonists), preferably FPR1 agonists and/or FPR2 antagonists, by positional scanning deconvolution. The invention also provides novel FPR1 and FPR2 ligands (e.g, agonists and antagonists), related pharmaceutical compositions and methods of treating FPR1 and FPR2-related disorders.