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Analysis

1.20230034559AUTOMATED PREDICTION OF CLINICAL TRIAL OUTCOME
US 02.02.2023
Int.Class G16H 50/50
GPHYSICS
16INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
HHEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA
50ICT specially adapted for medical diagnosis, medical simulation or medical data mining; ICT specially adapted for detecting, monitoring or modelling epidemics or pandemics
50for simulation or modelling of medical disorders
Appl.No 17749065 Applicant Sunstella Technology Corporation Inventor Tianfan Fu

A system for prediction of clinical trial outcome. The system includes: a processor of a trial prediction (TP) node connected to at least one cloud server node over a network configured to host a machine learning (ML) module; a memory on which are stored machine-readable instructions that when executed by the processor, cause the processor to: receive a clinical trial (CT) data, parse the CT data to derive drug molecules data, disease information data, and trial protocols data, encode the drug molecules data, the disease information data, and the trial protocols data into corresponding embeddings, generate knowledge pre-trained embeddings using external knowledge data, and provide the knowledge pre-trained embeddings to the ML module for prediction of the CT outcome.

2.WO/2023/010069ADAPTIVE BASE CALLING SYSTEMS AND METHODS
WO 02.02.2023
Int.Class G16B 30/20
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
30ICT specially adapted for sequence analysis involving nucleotides or amino acids
20Sequence assembly
Appl.No PCT/US2022/074246 Applicant ULTIMA GENOMICS, INC. Inventor ETZIONI, Yoav
Methods for updating a system comprising a sequencer are described herein. In some exemplary methods, the system is updated through generating sequencing data for a plurality of nucleic acid molecule colonies, selecting sequencing data for a subset of the nucleic acid molecule colonies, calling preliminary sequences for the subset of the nucleic acid colonies, mapping the called preliminary sequences to a known reference sequence, and updating the pre-trained sequencer- specific machine-learning model. Also described herein are systems for carrying out such methods and computer readable memory for storing such methods.
3.20230030539METHOD FOR ANALYZING THE METABOLIC CONTENT OF A BIOLOGICAL SAMPLE
US 02.02.2023
Int.Class G16B 40/10
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
40ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
10Signal processing, e.g. from mass spectrometry or from PCR
Appl.No 17782290 Applicant BASF PLANT SCIENCE COMPANY GMBH Inventor Elie Fux

The invention relates to a method of analyzing the metabolic content of a biological sample comprising: i) providing one or more samples of extracted metabolites from the biological sample; ii) performing a chromatography coupled mass spectrometry analysis of the extracted metabolites to generate a full raw data set for full scan ions; iii) generating a full data cluster set from the full raw data set obtained in step ii) by grouping full scan ions according to isotope and adduct values; iv) performing a tandem mass spectrometry analysis of the extracted metabolites with a plurality of mass selection windows to generate a raw SWATH® data set for fragment ions; v) generating a SWATH® data cluster set from the raw SWATH® data set obtained in step iv) by grouping fragment ions according to retention time and mass values; vi) aligning the SWATH® data cluster set with the full data cluster set to generate characteristic profile for each extracted metabolite; vii) comparing the data using R characteristic profile of each extracted metabolite obtained in step vi) with a reference library of characteristic profiles of metabolites to provide the metabolic content of the biological sample.

4.20230029970QUALITY SCORE CALIBRATION OF BASECALLING SYSTEMS
US 02.02.2023
Int.Class G16B 30/00
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
30ICT specially adapted for sequence analysis involving nucleotides or amino acids
Appl.No 17839387 Applicant ILLUMINA, INC. Inventor Rohan PAUL

A method of generating base calls by a base caller is disclosed. The method includes receiving a plurality of sensor data from a flow cell, wherein the plurality of sensor data is within a first range and identifying a second range, such that at least a threshold percentage of the plurality of sensor data are within the second range. At least a subset of the plurality of sensor data, that are within the second range, are mapped to a third range, thereby generating a plurality of normalized sensor data. The plurality of normalized sensor data is processed in a base caller, to call, for the plurality of normalized sensor data, one or more corresponding bases.

5.20230036568PROGRAMS AND FUNCTIONS IN DNA-BASED DATA STORAGE
US 02.02.2023
Int.Class C12Q 1/686
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
6844Nucleic acid amplification reactions
686Polymerase chain reaction
Appl.No 17693705 Applicant CATALOG TECHNOLOGIES, INC. Inventor Nathaniel Roquet

Systems and methods are provided herein for encoding and storing information in nucleic acids. Encoded information is partitioned and stored in nucleic acids having native key-value pairs that allow for storage of metadata or other data objects. Computation on the encoded information is performed by chemical implementation of if-then-else operations. Numerical data is stored in nucleic acids by producing samples having nucleic acid sequences copy counts corresponding to the numerical data. Data objects of a dataset are encoded by partitioning of bytes into parts and encoding of parts along distinct libraries of nucleic acids. These libraries can be used as inputs for computation on the dataset.

6.WO/2023/008673MACHINE LEARNING-BASED COMPLEX MARKER FOR DETERMINING NONALCOHOLIC STEATOHEPATITIS, AND USE THEREOF
WO 02.02.2023
Int.Class C12Q 1/6883
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
Appl.No PCT/KR2022/002773 Applicant SOOKMYUNG WOMEN'S UNIVERSITY INDUSTRY-ACADEMIC COOPERATION FOUNDATION Inventor RYU, Kyung Hyun
The present invention relates to a biomarker composition for diagnosing chronic liver diseases. In order to select, as biomarkers, genes exhibiting a difference in expression between patients with fatty liver and patients with nonalcoholic steatohepatitis, genes selected through a differentially expressed gene (DEG) method and genes selected using a feature set are collected so that a set of genes, with high accuracy, of CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6 is selected, and it has been identified that fatty liver and nonalcoholic steatohepatitis can be distinguished with high accuracy by using the selected gene set in a patient group-based data clinical model, and thus the set of genes composed of CAPG, HYAL3, WIPI1, TREM2, SPP1, and RNASE6 are provided as biomarkers for the diagnosis of chronic liver diseases and nonalcoholic steatohepatitis.
7.WO/2023/006348SYMMETRIC PROTEINS
WO 02.02.2023
Int.Class C07K 14/00
CCHEMISTRY; METALLURGY
07ORGANIC CHEMISTRY
KPEPTIDES
14Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof
Appl.No PCT/EP2022/068475 Applicant KATHOLIEKE UNIVERSITEIT LEUVEN Inventor CLARKE, David
The invention relates to protein building block named the Self-Assembling Kelch (SAKe) protein. The protein has a stable, symmetric design with readily accessible loops that can be varied in both sequence and length to later bind larger molecules or scaffold a catalytic site.
8.20230033547SYSTEMS AND METHODS FOR PREDICTING THE TASTE OF A USER
US 02.02.2023
Int.Class G06F 16/9035
GPHYSICS
06COMPUTING; CALCULATING OR COUNTING
FELECTRIC DIGITAL DATA PROCESSING
16Information retrieval; Database structures therefor; File system structures therefor
90Details of database functions independent of the retrieved data types
903Querying
9035Filtering based on additional data, e.g. user or group profiles
Appl.No 17963532 Applicant KPN INNOVATIONS, LLC. Inventor Kenneth Neumann

A system for determining user taste changes using a plurality of biological extraction data and artificial intelligence includes at least a computing device, wherein the computing device is designed and configured to receive, from a user, at least a first element of biological extraction data, calculate at least a first taste index of the user, wherein calculating further comprises training a first machine learning process as a function of training data correlating biological extraction data with taste indices, calculating the at least a first taste index as a function of the first machine learning process and the at least a first element of biological extraction data, generate a taste profile using the first taste index, and determine, using at least a second element of biological extraction data and a second machine learning process, at least a change in user taste profile.

9.20230031082METHOD FOR WHOLE GENOME SEQUENCING OF PICOGRAM QUANTITIES OF DNA
US 02.02.2023
Int.Class C12Q 1/6874
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
6869Methods for sequencing
6874involving nucleic acid arrays, e.g. sequencing by hybridisation
Appl.No 17783300 Applicant Oxford University Innovation Limited Inventor Ahmed Ashour AHMED

The present invention relates to a method of whole genome sequencing of a single cell or cell-group for identification of single nucleotide variants, determining chromosome structural variations, or determining phasing information in the genome of the single cell or cell-group. Methods of preparing an indexed DNA library for sequencing of nucleic acid molecules; preparing an indexed DNA library for whole genome sequencing of single cells or cell-groups for the identification of single nucleotide variants, determining chromosome structural variations, or determining phasing information in the genome of the single cells or cell-groups; and whole genome sequencing of a single cell or cell-group to provide data for the identification of single nucleotide variants (SNVs), determining chromosome structural variations, or determining phasing information in the genome of the single cell or cell-group are also described.

10.20230035954METHOD FOR ESTABLISHING MEDICINE SYNERGISM PREDICTION MODEL, PREDICTION METHOD AND CORRESPONDING APPARATUS
US 02.02.2023
Int.Class G16B 45/00
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
45ICT specially adapted for bioinformatics-related data visualisation, e.g. displaying of maps or networks
Appl.No 17844094 Applicant BEIJING BAIDU NETCOM SCIENCE TECHNOLOGY CO., LTD. Inventor Jing HU

The present disclosure discloses a method for establishing a medicine synergism prediction model, a prediction method and corresponding apparatus, and relates to deep learning and artificial intelligence (AI) medical technologies in the field of AI technologies. A specific implementation solution includes: acquiring a relation graph, nodes in the relation graph including medicine nodes and protein nodes, and edges indicating that interaction exists between the nodes; collecting, from the relation graph, a medicine node pair with definite synergism and a label of whether the medicine node pair has synergism as training samples; and training the medicine synergism prediction model by taking the medicine node pair in the training samples as input to the medicine synergism prediction model and taking the label of whether the medicine node pair has synergism as target output; wherein the medicine synergism prediction model is obtained by learning the relation graph based on a graph convolutional network.