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公开(公告)号：US20220083864A1
公开(公告)日：2022-03-17
申请号：US17538946
申请日：2021-11-30
申请人： InstaDeep Ltd
发明人： Alexandre LATERRE
IPC分类号： G06N3/08 , G06N3/04 , G06F30/394
摘要： A computer-implemented method, a machine learning system, and non-transitory computer-readable storage medium for training a neural network are provided. The neural network is used to instruct an agent to select actions for interacting with an environment to determine a solution to a specified problem. In the computer-implemented method a state signal representing a current state of the environment is received. A Sequential Monte Carlo process is then used to perform a search to determine target action selection data associated with the current state of the environment. This target action selection data is stored in association with the state signal and the current state of the environment is updated by providing an action selection signal based on the target action selection data. The Sequential Monte Carlo process involves generating a plurality of simulations using the neural network to determine the target action selection data.
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公开(公告)号：US20240355417A1
公开(公告)日：2024-10-24
申请号：US18618412
申请日：2024-03-27
申请人： BioNTech SE , InstaDeep Ltd
发明人： Alexander Muik , Ugur Sahin , Asaf Poran , Alexandra Walls , Yunguan Fu , Rafal Okuniewski , Nicolas Winfried Pfeuffer
摘要： The present disclosure, among other things, provides technologies for identifying, characterizing, and/or monitoring variant sequences of a particular reference infections agent. Among other things, systems, methods, and architectures described herein provide visualization and decision support tools that can, e.g., facilitate decision making processes by local authorities and improve pandemic response in terms of, e.g., resource allocation, policy making, and speed tailored vaccine development. The present disclosure also provides tools for analyzing circulating variants to predict mutations likely to increase immune evasion of infectious agents.
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公开(公告)号：US20240321387A1
公开(公告)日：2024-09-26
申请号：US18289424
申请日：2022-05-04
申请人： BioNTech SE , InstaDeep Ltd
发明人： Alexander Muik , Asaf Poran , Yunpeng Liu , Ugur Sahin , Karim Beguir , Marcin Skwark , Thomas Pierrot , Yunguan Fu
摘要： A method is provided for identifying a number of variants of concern of a reference disease associated immunogen. The method uses a language model to perform inference on data representing each of a plurality of variants and data representing the reference immunogen. For each of the plurality of variants and the reference immunogen, a characteristic vector is derived from an output feature map of a hidden layer of the language model. For each of the plurality of variants, a measure of distance is generated for the variant that includes calculating a measure of distance between the characteristic vector of the variant and the characteristic vector of the reference immunogen. A semantic change score is calculated for each variant based on the generated measure of distance for that variant. A variant of the reference immunogen is selected based, at least in part, on the generated the semantic change scores.
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公开(公告)号：US20240339174A1
公开(公告)日：2024-10-10
申请号：US18289425
申请日：2022-05-04
申请人： BioNTech SE , InstaDeep Ltd.
发明人： Alexander Muik , Asaf Poran , Yunpeng Liu , Ugur Sahin , Karim Beguir , Marcin Skwark , Thomas Pierrot , Yunguan Fu
IPC分类号： G16B20/20 , C07K14/005 , G16B15/30 , G16B40/20
CPC分类号： G16B20/20 , C07K14/005 , G16B15/30 , G16B40/20 , C12N2770/20022
摘要： The present disclosure provides technologies for identifying, characterizing, and/or monitoring sequences of a variant of a reference infectious agent (e.g., but not limited to viral variants, for example in some embodiments SARS-CoV-2 variants) for transmissibility factors and/or immune escape potential, and/or for detecting and/or monitoring variants in environmental or biological samples, and/or for designing, preparing, and/or administering vaccines for such variants.
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公开(公告)号：US20220083723A1
公开(公告)日：2022-03-17
申请号：US17538987
申请日：2021-11-30
申请人： InstaDeep Ltd
发明人： Yunguan FU , Nabil CHOUBA , Alexandre LATERRE
IPC分类号： G06F30/3953 , G06N3/04
摘要： A computer-implemented method, a system, and non-transitory computer-readable storage medium for designing electrical circuits are provided. In the method input data is received and processed to generate a representation of the electrical circuit. A first process is repeatedly performed to identify a plurality of candidate routes for connecting a first and second circuit element based on the representation. A candidate route is selected from the plurality of candidate routes based on a look ahead search. The first process includes selecting a first point in the representation, executing a second process to identify a set of candidate points, and selecting a second point from the set of candidate points. The second process comprises evaluating at least one candidate path extending in a linear direction from the first point to identify the set of candidate points based on at least a constraint and a topology of the electrical circuit.
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公开(公告)号：US20220083720A1
公开(公告)日：2022-03-17
申请号：US17538961
申请日：2021-11-30
申请人： InstaDeep Ltd
发明人： Nabil CHOUBA , Alexandre LATERRE
IPC分类号： G06F30/394 , G06N20/00
摘要： A computer-implemented method, a machine learning system, and non-transitory computer-readable storage medium for designing electrical circuits are provided. In the computer-implemented method input data is received and processed to generate a representation of the electrical circuit. A plurality of candidate routes for connecting a first and second circuit element of the electrical circuit are identified. A candidate route is then selected by iteratively selecting candidate sub-routes. Selecting candidate sub-routes is performed by using a Sequential Monte Carlo process to perform a look ahead search of a subset of the plurality of candidate routes, the Sequential Monte Carlo process being guided by a neural network. The representation of the electrical circuit is then updated with an action selection signal representing a selection of a candidate sub-route.
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公开(公告)号：US12019968B2
公开(公告)日：2024-06-25
申请号：US17538987
申请日：2021-11-30
申请人： InstaDeep Ltd
发明人： Yunguan Fu , Nabil Chouba , Alexandre Laterre
IPC分类号： G06F30/3953 , G06F30/3308 , G06F30/394 , G06F30/398 , G06N3/04 , G06N3/047 , G06N3/08 , G06N20/00 , G06F111/08
CPC分类号： G06F30/3953 , G06F30/3308 , G06F30/394 , G06F30/398 , G06N3/04 , G06N3/047 , G06N3/08 , G06N20/00 , G06F2111/08
摘要： A computer-implemented method, a system, and non-transitory computer-readable storage medium for designing electrical circuits are provided. In the method input data is received and processed to generate a representation of the electrical circuit. A first process is repeatedly performed to identify a plurality of candidate routes for connecting a first and second circuit element based on the representation. A candidate route is selected from the plurality of candidate routes based on a look ahead search. The first process includes selecting a first point in the representation, executing a second process to identify a set of candidate points, and selecting a second point from the set of candidate points. The second process comprises evaluating at least one candidate path extending in a linear direction from the first point to identify the set of candidate points based on at least a constraint and a topology of the electrical circuit.
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公开(公告)号：US11842136B2
公开(公告)日：2023-12-12
申请号：US17538977
申请日：2021-11-30
申请人： InstaDeep Ltd
发明人： Nabil Chouba , Alexandre Laterre
IPC分类号： G06F30/3953 , G06F30/398 , G06F30/3308 , G06N3/04 , G06F30/394 , G06N3/08 , G06N20/00 , G06N3/047 , G06F111/08
CPC分类号： G06F30/3953 , G06F30/3308 , G06F30/394 , G06F30/398 , G06N3/04 , G06N3/047 , G06N3/08 , G06N20/00 , G06F2111/08
摘要： A computer-implemented method, a machine learning system, and non-transitory computer-readable storage medium for designing electrical circuits are provided. In the method input data, comprising an indication of a plurality of connections including a first and second connection is processed to generate a representation of the electrical circuit. Routes for the first and second connections are determined using an iterative process that includes defining one or more orders in which to determine routes for the first and second connections. A Sequential Monte Carlo process is used to perform a look ahead search of each defined order by generating simulations in respect of routes to be determined for the connections in the orders, the Sequential Monte Carlo process being guided by a neural network. A connection is selected and a route for the selected connection is determined. The representation is updated by providing an action selection signal representing the determined route.
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公开(公告)号：US20220392573A1
公开(公告)日：2022-12-08
申请号：US17827309
申请日：2022-05-27
申请人： InstaDeep Ltd
摘要： A computer-implemented method for evaluating an amino acid chain is provided. The method includes obtaining first data including a representation of an amino acid chain and performing a process to generate second data comprising a set of one or more probability values. The representation comprises a sequence of two or more letters, each letter representing a respective amino acid. The second process comprises, for a said position in the sequence of letters, applying a language models to the sequence of letters to determine at least one probability value associated with the said position, wherein the language model is trained using one or more datasets representing amino acid chains. A computer system configured to implement the method, and a non-transitory computer-readable storage medium, storing instructions for implementing the method, is also provided.
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公开(公告)号：US20220083722A1
公开(公告)日：2022-03-17
申请号：US17538977
申请日：2021-11-30
申请人： InstaDeep Ltd
发明人： Nabil CHOUBA , Alexandre LATERRE
IPC分类号： G06F30/3953 , G06F30/398 , G06F30/3308 , G06N3/04
摘要： A computer-implemented method, a machine learning system, and non-transitory computer-readable storage medium for designing electrical circuits are provided. In the method input data, comprising an indication of a plurality of connections including a first and second connection is processed to generate a representation of the electrical circuit. Routes for the first and second connections are determined using an iterative process that includes defining one or more orders in which to determine routes for the first and second connections. A Sequential Monte Carlo process is used to perform a look ahead search of each defined order by generating simulations in respect of routes to be determined for the connections in the orders, the Sequential Monte Carlo process being guided by a neural network. A connection is selected and a route for the selected connection is determined. The representation is updated by providing an action selection signal representing the determined route.
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