title: Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain.

authors: Taft, Joseph M.

subjects: artificial intelligence

abstract: The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine-learning-guided protein engineering technology, which is used to investigate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as Omicron, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19.

text: Deep mutational learning predicts ACE2 binding and antibody escape to combinatorial mutations in the SARS-CoV-2 receptor-binding domain. The continual evolution of SARS-CoV-2 and the emergence of variants that show resistance to vaccines and neutralizing antibodies threaten to prolong the COVID-19 pandemic. Selection and emergence of SARS-CoV-2 variants are driven in part by mutations within the viral spike protein and in particular the ACE2 receptor-binding domain (RBD), a primary target site for neutralizing antibodies. Here, we develop deep mutational learning (DML), a machine-learning-guided protein engineering technology, which is used to investigate a massive sequence space of combinatorial mutations, representing billions of RBD variants, by accurately predicting their impact on ACE2 binding and antibody escape. A highly diverse landscape of possible SARS-CoV-2 variants is identified that could emerge from a multitude of evolutionary trajectories. DML may be used for predictive profiling on current and prospective variants, including highly mutated variants such as Omicron, thus guiding the development of therapeutic antibody treatments and vaccines for COVID-19.

year: 2022

title: Mining data streams using option trees (revised edition, 2004)

authors: Holmes, Geoffrey

subjects: Machine learning

abstract: The data stream model for data mining places harsh restrictions on a learning algorithm. A model must be induced following the briefest interrogation of the data, must use only available memory and must update itself over time within these constraints. Additionally, the model must be able to be used for data mining at any point in time.
This paper describes a data stream classi_cation algorithm using an ensemble of option trees. The ensemble of trees is induced by boosting and iteratively combined into a single interpretable model. The algorithm is evaluated using benchmark datasets for accuracy against state-of-the-art algorithms that make use of the entire dataset.

text: Mining data streams using option trees (revised edition, 2004) The data stream model for data mining places harsh restrictions on a learning algorithm. A model must be induced following the briefest interrogation of the data, must use only available memory and must update itself over time within these constraints. Additionally, the model must be able to be used for data mining at any point in time.
This paper describes a data stream classi_cation algorithm using an ensemble of option trees. The ensemble of trees is induced by boosting and iteratively combined into a single interpretable model. The algorithm is evaluated using benchmark datasets for accuracy against state-of-the-art algorithms that make use of the entire dataset.

year: 2004-01-01

title: Pōwhiri process in mental health research

authors: McClintock, Kahu

subjects: indigenous mental health research

abstract: Within the health research context, indigenous people globally have a commitment to provide their own solutions. Māori, the indigenous people of Aotearoa (New Zealand) value the traditional Pōwhiri process of engagement and participation in mental health research. The practices and protocols within the Pōwhiri process (use in the Doctorate of Philosophy (2010) and Auckland University) are premised on the notion of respect and positive relationships between the tangata whenua (hosts or research participants) and manuwhiri (guests or researchers). This paper briefly describes the Pōwhiri process, which may be a model applicable to research with other indigenous cultures.

text: Pōwhiri process in mental health research Within the health research context, indigenous people globally have a commitment to provide their own solutions. Māori, the indigenous people of Aotearoa (New Zealand) value the traditional Pōwhiri process of engagement and participation in mental health research. The practices and protocols within the Pōwhiri process (use in the Doctorate of Philosophy (2010) and Auckland University) are premised on the notion of respect and positive relationships between the tangata whenua (hosts or research participants) and manuwhiri (guests or researchers). This paper briefly describes the Pōwhiri process, which may be a model applicable to research with other indigenous cultures.

year: 2010