Quantum-si Incorporated

Umut Eser

Michael Meyer

Jonathan Marc Rothberg

Henri Lichenstein

Jonathan M Rothberg

Marylens Hernandez

Tian Xu

A method includes obtaining, from one or more sequencing devices, raw data detected from luminescent labels associated with nucleotides during nucleotide incorporation events; and processing the raw data to perform a comparison of base calls produced by a learning enabled, automatic base calling module of the one or more sequencing devices with actual values associated with the raw data, wherein the base calls identify one or more individual nucleotides from the raw data. Based on the comparison, an update to the learning enabled, automatic base calling module is created using at least some of the obtained raw data, and the update is made available to the one or more sequencing devices.

Machine learning enabled pulse and base calling for sequencing devices

Methods and apparatus for predicting an association between input data in a first modality and data in a second modality using a statistical model trained to represent interactions between data having a plurality of modalities including the first modality and the second modality, the statistical model comprising a plurality of encoders and decoders, each of which is trained to process data for one of the plurality of modalities, and a joint-modality representation coupling the plurality of encoders and decoders. The method comprises selecting, based on the first modality and the second modality, an encoder/decoder pair or a pair of encoders, from among the plurality of encoders and decoders, and processing the input data with the joint-modality representation and the selected encoder/decoder pair or pair of encoders to predict the association between the input data and the data in the second modality.

Methods and apparatus for multi-modal prediction using a trained statistical model

Methods and apparatus for making biological predictions using a trained multi-modal statistical model

Techniques for performing a prediction task using a multi-modal statistical model configured to receive input data from multiple modalities including input data from a first modality and input data from a second modality different from the first modality. The techniques include:

Systems and methods for unifying statistical models for different data modalities

Techniques for performing a prediction task using a multi-modal statistical model configured to receive input data from multiple modalities including input data from a first modality and input data from a second modality different from the first modality. The techniques include: obtaining information specifying the multi-modal statistical model including values of parameters of each of multiple components of the multi-modal statistical model, the multiple components including first and second encoders for processing input data for the first and second modalities, respectively, first and second modality embeddings, a joint-modality representation, and a predictor; obtaining first input data for the first data modality; providing the first input data to the first encoder to generate a first feature vector; identifying a second feature vector using the joint-modality representation, the first modality embedding and the first feature vector; and generating a prediction for the prediction task using the predictor and the second feature vector.

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Lexington, MA, United States of America

Umut Eser