公开(公告)号：US11482307B2

公开(公告)日：2022-10-25

申请号：US15910845

申请日：2018-03-02

申请人： Drexel University

发明人： Zhengqiao Zhao ,  Gail Rosen

IPC分类号： G16B50/10 ,  G06K9/62 ,  G16B50/00 ,  G16B40/00 ,  G16B40/30 ,  G06F16/28 ,  G16B40/20 ,  G16B50/30

摘要： An incremental author disambiguation framework may create new clusters to accommodate new data based on the existing cluster results and newly added data. The proposed system may provide frequent update of taxonomic classification, name disambiguation and many other applications because it takes less time to generate new results. In addition, the proposed methods may reduce the time needed for updating the model and help improve the performance with the limited computational resource.

公开(公告)号：US20220284316A1

公开(公告)日：2022-09-08

申请号：US17828533

申请日：2022-05-31

申请人： Ro5 Inc.

发明人： Alwin Bucher ,  Alvaro Prat ,  Orestis Bastas ,  Gintautas Kamuntavicius ,  Zeyu Yang ,  Charles Dazler Knuff ,  Zygimantas Jocys ,  Roy Tal ,  Hisham Abdel Aty

IPC分类号： G06N5/02 ,  G06F16/951 ,  G06K9/62 ,  G06N3/08 ,  G16B15/00 ,  G16B40/00 ,  G16B45/00 ,  G16B50/10

摘要： A system and method for accelerating the calculations of free energy differences by automating FEP-path-decision-making and replacing the standard series of alchemical interpolations typically created by molecular dynamic (MD) simulations with voxelated interpolated states. A novel machine learning approach comprising a restricted variational autoencoder (ResVAE) is used which can reduce the computational-cost associated with interpolations by restricting the dimensions of a molecular latent space. The ResVAE generates a model based on flow-based transformations of a 3D-VAE latent point that is trained to maximize the log-likelihood of MD samples which enables the model to compute transformations more efficiently between molecules and also handle deletions of atoms more efficiently during iterative FEP calculation steps.

公开(公告)号：US20220251537A1

公开(公告)日：2022-08-11

申请号：US17516596

申请日：2021-11-01

申请人： WISCONSIN ALUMNI RESEARCH FOUNDATION

发明人： Tony Goldberg ,  Samuel D. Sibley

IPC分类号： C12N15/10 ,  G16B30/00 ,  G16B50/00 ,  G16B50/30 ,  G16B50/10 ,  C12Q1/6806 ,  C12Q1/6853 ,  C12Q1/686 ,  C12Q1/6869

摘要： The disclosure relates to laboratory and bioinformatics methods for isolating, detecting, and characterizing microbes in biological samples using metagenomic approaches.

公开(公告)号：US20220198286A1

公开(公告)日：2022-06-23

申请号：US17540153

申请日：2021-12-01

申请人： Ro5 Inc.

发明人： Alvaro Prat ,  Alwin Bucher ,  Zygimantas Jocys ,  Roy Tal

IPC分类号： G06N5/02 ,  G06F16/951 ,  G06K9/62 ,  G06N3/08 ,  G16B15/00 ,  G16B40/00 ,  G16B45/00 ,  G16B50/10

摘要： A system and method comprising a transmoler that identifies common substructures of a given 3D conformer and predicts its structural information. First, based on contrastive learning, substructure embeddings are learned in an unsupervised manner. Secondly, a novel oriented 3D object regressor predicts the dimensions and directions of each substructure in a conformer as well as its fingerprint embedding which are used to create differentiable junction tree molecular graphs. Lastly, using the junction tree graphs, molecular representations such as DeepSMILES are generated which represent new and novel molecules. The system may also generate conformers directly from a pocket. A pocket may be input to the model and the model learns to generate structures which can fit that pocket by conditioning the generative system. Furthermore, structure-based contrastive embeddings generated for transmoler can be recycled in structure-based generative modelling.

公开(公告)号：US11347704B2

公开(公告)日：2022-05-31

申请号：US14885192

申请日：2015-10-16

申请人： Seven Bridges Genomics Inc.

发明人： Vladimir Semenyuk

IPC分类号： G06F16/22 ,  G06F16/901 ,  G06F16/2455 ,  G16B50/00 ,  G16B50/20 ,  G16B30/10 ,  G16B50/30 ,  G16B50/10 ,  G16B50/50 ,  G16B30/00

摘要： Methods of the invention include representing biological data in a memory subsystem within a computer system with a data structure that is particular to a location in the memory subsystem and serializing the data structure into a stream of bytes that can be deserialized into a clone of the data structure. In a preferred genomic embodiment, the biological data comprises genomic sequences and the data structure comprises a genomic directed acyclic graph (DAG) in which objects have adjacency lists of pointers that indicate the location of any object adjacent to that object. After serialization and deserialization, the clone genomic DAG has the same structure as the original to represent the same sequences and relationships among them as the original.

公开(公告)号：US11342048B2

公开(公告)日：2022-05-24

申请号：US16351394

申请日：2019-03-12

申请人： THE SCRIPPS RESEARCH INSTITUTE

发明人： Ali Torkamani ,  Nicholas Schork

IPC分类号： G06F16/16 ,  G16B50/10 ,  G16B50/00 ,  G16B20/00 ,  G16B20/20 ,  G16B20/40 ,  G16B20/10 ,  G16B20/30

摘要： A computer-based genomic annotation system, including a database configured to store genomic data, non-transitory memory configured to store instructions, and at least one processor coupled with the memory, the processor configured to implement the instructions in order to implement an annotation pipeline and at least one module filtering or analysis of the genomic data.

公开(公告)号：US20220108768A1

公开(公告)日：2022-04-07

申请号：US17429477

申请日：2020-02-27

申请人： NANTOMICS, LLC

发明人： John Zachary SANBORN

IPC分类号： G16B20/20 ,  G16B30/10 ,  G16B50/10 ,  G06F16/901 ,  G06F16/25

摘要： A locus tester or locust database has stored therein DNA or RNA sequence information for one or more loci of interest. The sequence information may include a list of k-mers in a given DNA or RNA sequence, an identification of whether each k-mer in the list of k-mers appears in a reference sequence or in a variation of the reference sequence, and a count of how many times each k-mer in the list of k-mers has been identified in sequence information for the locus of interest in question. Sequence data for the locus in question received from a data source may be broken into fragments, with each fragment containing one or more k-mers. These k-mers may be quickly compared to the list of k-mers in the locust database to determine whether the sequence data corresponds to the reference sequence or to a variation of the reference sequence.

公开(公告)号：US20210398612A1

公开(公告)日：2021-12-23

申请号：US17284302

申请日：2019-10-09

申请人： GeneCentric Therapeutics, Inc. ,  The University of North Carolina at Chapel Hill

发明人： Greg Mayhew ,  Yoichiro Shibata ,  Myla Lai-Goldman ,  Charles Perou ,  Joel Parker

IPC分类号： G16B20/20 ,  G16B30/10 ,  G16B50/10

摘要： Methods and compositions are provided for determining TMB in a tumor sample using transcriptome profiling data. Also provided herein are methods and compositions for determining the response of an individual with a specific TMB to a therapy such as immunotherapy.

公开(公告)号：US11176462B1

公开(公告)日：2021-11-16

申请号：US17171494

申请日：2021-02-09

申请人： Ro5 Inc.

发明人： Orestis Bastas ,  Alwin Bucher ,  Aurimas Pabrinkis ,  Mikhail Demtchenko ,  Zeyu Yang ,  Cooper Stergis Jamieson ,  {circumflex over (Z)}ygimantas Joĉys ,  Roy Tal ,  Charles Dazler Knuff

IPC分类号： G01N31/00 ,  G06N5/02 ,  G06K9/62 ,  G06F16/951 ,  G06N3/08 ,  G16B45/00 ,  G16B15/00 ,  G16B40/00 ,  G16B50/10

摘要： A system and method for computationally tractable prediction of protein-ligand interactions and their bioactivity. According to an embodiment, the system and method comprise two machine learning processing streams and concatenating their outputs. One of the machine learning streams is trained using information about ligands and their bioactivity interactions with proteins. The other machine learning stream is trained using information about proteins and their bioactivity interactions with ligands. After the machine learning algorithms for each stream have been trained, they can be used to predict the bioactivity of a given protein-ligand pair by inputting a specified ligand into the ligand processing stream and a specified protein into the protein processing stream. The machine learning algorithms of each stream predict possible protein-ligand bioactivity interactions based on the training data.

公开(公告)号：US11162090B2

公开(公告)日：2021-11-02

申请号：US15694249

申请日：2017-09-01

申请人： Wisconsin Alumni Research Foundation

发明人： Tony L. Goldberg ,  Samuel D. Sibley

IPC分类号： C12N15/10 ,  C12Q1/6806 ,  C12Q1/6853 ,  C12Q1/686 ,  C12Q1/6869 ,  C12Q1/6888 ,  G16B30/00 ,  G16B50/00 ,  G16B50/30 ,  G16B50/10

摘要： Metagenomic data are increasingly useful for detecting microbes, such as viruses and other microorganisms in a wide range of sample types. Methods and systems for isolating, detecting, and characterizing microbes in biological samples using metagenomic data are provided. Also disclosed are methods for identifying microbes in nucleic acid libraries.