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1.20210319847PEPTIDE-BASED VACCINE GENERATION SYSTEM
US 14.10.2021
Int.Class G16B 15/30
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
15ICT specially adapted for analysing two-dimensional or three-dimensional molecular structures, e.g. structural or functional relations or structure alignment
30Drug targeting using structural data; Docking or binding prediction
Appl.No 17197166 Applicant NEC Laboratories America, Inc. Inventor Renqiang Min

A method is provided for peptide-based vaccine generation. The method receives a dataset of positive and negative binding peptide sequences. The method pre-trains a set of peptide binding property predictors on the dataset to generate training data. The method trains a Wasserstein Generative Adversarial Network (WGAN) only on the positive binding peptide sequences, in which a discriminator of the WGAN is updated to distinguish generated peptide sequences from sampled positive peptide sequences from the training data, and a generator of the WGAN is updated to fool the discriminator. The method trains the WGAN only on the positive binding peptide sequences while simultaneously updating the generator to minimize a kernel Maximum Mean Discrepancy (MMD) loss between the generated peptide sequences and the sampled peptide sequences and maximize prediction accuracies of a set of pre-trained peptide binding property predictors with parameters of the set of pre-trained peptide binding property predictors being fixed.

2.20210317533UNBIASED IDENTIFICATION OF TUMOR REJECTION MEDIATING NEOEPITOPES
US 14.10.2021
Int.Class C12Q 1/6886
CCHEMISTRY; METALLURGY
12BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
QMEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
1Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
68involving nucleic acids
6876Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
6883for diseases caused by alterations of genetic material
6886for cancer
Appl.No 17225374 Applicant University of Connecticut Inventor Pramod K. Srivastava

Described herein is an unbiased method of identifying tumor rejection mediating neoepitopes (TRMNs). Putative neoepitopes from a cancer cell exome sequence from a cancer patient are putative neoepitopes are unbiased by MHC binding and/or CD8T* reactivity. By plotting the putative neoepitope IC50s on one axis, and the non-mutated amino acid sequence IC50s on a perpendicular axis to provide a bivariate scatter plot, novel TRMNs are identified TRMNs the neoepitopes in the bivariate scatter plot which are in the space greater than 501 nM on the x-axis and greater than 501 nM on the y-axis. Peptides and nucleic acids for expressing peptides including the TRMNs are also described.

3.20210319851OLIGONUCLEOTIDE-BASED MACHINE LEARNING
US 14.10.2021
Int.Class G16B 35/20
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
35ICT specially adapted for in silico combinatorial libraries of nucleic acids, proteins or peptides
20Screening of libraries
Appl.No 17220725 Applicant Creyon Bio, Inc. Inventor Swagatam MUKHOPADHYAY

A machine-learned model can be trained on and applied to oligonucleotide data. The machine-learned model can be, for example, a neural network, a random forest classifier, or a regression model, and can be trained in one or more stages. The machine-learned model can be applied in design settings, for instance by being configured to predict biophysical effects corresponding to oligonucleotides, by processing real-world experimental or laboratory data, and by retraining the machine-learned model in response to the processed data.

4.WO/2021/206544METHOD FOR IDENTIFYING SIGNATURES FOR PREDICTING TREATMENT RESPONSE
WO 14.10.2021
Int.Class G16B 20/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
20ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
Appl.No PCT/NL2021/050220 Applicant SKYLINEDX B.V. Inventor DE RIDDER, Jeroen
The disclosure relates to methods of signatures which can be used in order to classify patients and predict responsiveness to therapy. In particular, the disclosure relates to RAINFOREST (tReAtment benefIt prediction using raNdom FOREST), a new method to discover signatures capable of identifying a subgroup of patients more likely to benefit from a specific treatment as compared to another treatment.
5.WO/2021/207684PREDICTING LIKELIHOOD AND SITE OF METASTASIS FROM PATIENT RECORDS
WO 14.10.2021
Int.Class G16B 25/10
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
25ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
10Gene or protein expression profiling; Expression-ratio estimation or normalisation
Appl.No PCT/US2021/026696 Applicant TEMPUS LABS, INC. Inventor HAFEZ, Ashraf
Systems and methods are provided for predicting metastasis of a cancer in a subject. A plurality of data elements for the subject's cancer is obtained, including sequence features comprising relative abundance values for gene expression in a cancer biopsy of the subject, optional personal characteristics about the subject, and optional clinical features related to the stage, histopathological grade, diagnosis, symptom, comorbidity, and/or treatment of the cancer in the subject, and/or a temporal element associated therewith. One or more models are applied to the plurality of data elements, determining one or more indications of whether the cancer will metastasize. A clinical report comprising the one or more indications is generated.
6.20210319848SYSTEMS AND METHODS FOR IDENTIFYING ASSOCIATIONS BETWEEN MICROBIAL STRAINS AND PHENOTYPIC FEATURES
US 14.10.2021
Int.Class G16B 20/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
20ICT specially adapted for functional genomics or proteomics, e.g. genotype-phenotype associations
Appl.No 17225991 Applicant FRED HUTCHINSON CANCER RESEARCH CENTER Inventor Samuel S. MINOT

Provided herein are systems and methods for identifying associations, or lack thereof, between microbial strains (e.g., bacterial strains) and phenotypic features (e.g., demographic characteristics, physical statistics, and/or medical history) of a subject.

7.20210318331COMBINATORIAL ANTIBODY DIAGNOSTIC
US 14.10.2021
Int.Class G01N 33/68
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
33Investigating or analysing materials by specific methods not covered by groups G01N1/-G01N31/131
48Biological material, e.g. blood, urine; Haemocytometers
50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
68involving proteins, peptides or amino acids
Appl.No 17355707 Applicant PharmaSeq, Inc. Inventor Wlodek Mandecki

Provided among other things is an indexed library on one or more solid phase supports of a substantial representation of all theoretical peptide combinations having a certain length of 3 to 5 amino acids, or a combination thereof, and being formed with a certain collection of amino acids that numbers as follows:

# of amino acids in Length collection 3 6 to 18 4 4 to 18 5 4 to 18

the peptides spaced apart from the supports sufficiently such that one or more of the peptides binds an antibody composition substantially more strongly than others.

8.20210320905CHROMOSOMAL IDENTIFICATION
US 14.10.2021
Int.Class H04L 29/06
HELECTRICITY
04ELECTRIC COMMUNICATION TECHNIQUE
LTRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION
29Arrangements, apparatus, circuits or systems, not covered by a single one of groups H04L1/-H04L27/136
02Communication control; Communication processing
06characterised by a protocol
Appl.No 17250612 Applicant Paul Andrew Croall Inventor Paul Andrew Croall

The present invention relates to a method, apparatus, and system for communication with a user's family members using the DNA of the user without making the DNA profile public. According to a first aspect, there is provided a computer implemented method of locating one or more members of a familial network, comprising the steps of: generating one or more encryption keys derived from a first genomic sequence; encrypting a message using the or each encryption key to form an encrypted message; sending the encrypted message to one or more remote devices wherein decrypting the encrypted message at the one or more remote devices uses one or more encryption keys derived from a second genomic sequence; and receiving a confirmation regarding whether the decryption of the encrypted message was successful by any of the one or more remote devices.

9.WO/2021/206570PREDICTIVE CHROMATOGRAPHY OF ORGANIC PLANT EXTRACTS
WO 14.10.2021
Int.Class G16B 40/00
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
BBIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
Appl.No PCT/PH2020/050004 Applicant JUANICO, Drandreb Earl Inventor JUANICO, Drandreb Earl
The present disclosure draws attention to a method and system for predicting the phytochemical composition of a plant extract, as would have been determined from tedious laboratory procedures, from the time-series sensor data of the environment conditions in which the plant grew and the laboratory conditions in which the extraction would take place. The efficient encoding of the relational patterns between the laboratory-determined chromatographic profile of an extract (the output) and the time-series sensor data of environmental conditions and laboratory specifications (the input) is necessary for the standardization of herbal formulations.
10.20210318317METHOD FOR ACQUIRING AUXILIARY INFORMATION
US 14.10.2021
Int.Class G01N 33/574
GPHYSICS
01MEASURING; TESTING
NINVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
33Investigating or analysing materials by specific methods not covered by groups G01N1/-G01N31/131
48Biological material, e.g. blood, urine; Haemocytometers
50Chemical analysis of biological material, e.g. blood, urine; Testing involving biospecific ligand binding methods; Immunological testing
53Immunoassay; Biospecific binding assay; Materials therefor
574for cancer
Appl.No 17272848 Applicant Konica Minolta, Inc. Inventor Tomonori KANEKO

The present invention provides a method for acquiring auxiliary information useful to assist a diagnosis or treatment of prostate cancer. The method for acquiring auxiliary information of the present invention is a method for acquiring auxiliary information to assist a diagnosis or treatment of prostate cancer, and includes a step (C) of dividing a concentration value of a prostate specific antigen having a β-N-acetylgalactosamine residue at a non-reducing terminal of a sugar chain, contained in a sample derived from a living body, by a volume value of prostate of the living body to calculate the concentration value of the GalNAc-PSA per prostate volume.