Abstract: A machine learning model is generated for drug efficacy prediction in treatment of genetic disease from a dataset correlating gene expression data for disease-cell samples with drug efficacy values for the samples. Bias weights are stored that correspond to respective genes in the samples. Each bias weight is dependent on predetermined relevance of the respective gene to drug efficacy. The model is generated by processing the dataset via a tree ensemble method wherein decision trees are grown with splits corresponding to respective genes in the samples. The gene for each split is chosen from a respective subset of the genes, and genes are selected for inclusion in this subset with respective probabilities dependent on the corresponding bias weights. The model is stored, and can be applied to gene expression data measured for a patient to obtain a personalized drug efficacy prediction for devising a personalized course of treatment.

Abstract: A neural network model is generated for drug efficacy prediction in treatment of genetic disease from a dataset correlating biomolecular data for disease-cell samples with drug efficacy values for a plurality of drugs, including defining training data pairs each including biomolecular data for a sample represented in the dataset and a string representation of a drug represented in the dataset; and using the training data pairs and the drug efficacy values corresponding to respective pairs to train weights of the model including a first attention-based encoder which encodes the biomolecular data of each pair, to produce encoded biomolecular data, a second attention-based encoder which encodes the string representation of each pair, to produce encoded string data, and a set of neural network layers which process the encoded biomolecular and string data for each pair, to produce a model output corresponding to drug efficacy.

Abstract: Provide a reinforcement learning model including an agent and a critic; the critic includes a neural network pre-trained to generate, from input biomolecular data characterizing tissue cells and input compound data defining a compound molecule, a property value for said biomolecular action of that molecule on those tissue cells. The agent includes a neural network adapted to generate the compound data in dependence on input biomolecular data. Supply biomolecular data characterizing patient tissue cells to the agent and supply that data, and the compound data generated therefrom, to the critic to obtain a property value in an iterative training process in which reward values, dependent on the property values, are used to progressively train the agent to optimize the reward value. After training the agent, supply target biomolecular data, characterizing the target tissue cells, to the agent to generate compound data corresponding to a set of drug compounds.

Abstract: A neural network model is generated for drug efficacy prediction in treatment of genetic disease from a dataset correlating biomolecular data for disease-cell samples with drug efficacy values for a plurality of drugs, including defining training data pairs each including biomolecular data for a sample represented in the dataset and a string representation of a drug represented in the dataset; and using the training data pairs and the drug efficacy values corresponding to respective pairs to train weights of the model including a first attention-based encoder which encodes the biomolecular data of each pair, to produce encoded biomolecular data, a second attention-based encoder which encodes the string representation of each pair, to produce encoded string data, and a set of neural network layers which process the encoded biomolecular and string data for each pair, to produce a model output corresponding to drug efficacy.

Abstract: Provide a reinforcement learning model including an agent and a critic; the critic includes a neural network pre-trained to generate, from input biomolecular data characterizing tissue cells and input compound data defining a compound molecule, a property value for said biomolecular action of that molecule on those tissue cells. The agent includes a neural network adapted to generate the compound data in dependence on input biomolecular data. Supply biomolecular data characterizing patient tissue cells to the agent and supply that data, and the compound data generated therefrom, to the critic to obtain a property value in an iterative training process in which reward values, dependent on the property values, are used to progressively train the agent to optimize the reward value. After training the agent, supply target biomolecular data, characterizing the target tissue cells, to the agent to generate compound data corresponding to a set of drug compounds.

Abstract: An apparatus for a vacuum-driven microfluidic probe includes a body with an apex and a processing surface, at an end of the body. The apparatus also includes a partially open cavity formed as a recess on the processing surface and a set of apertures in the cavity, where the set of apertures include a sample outlet aperture intersected by a vertical axis of the cavity. The apparatus also includes aspiration apertures radially distributed around said vertical axis, wherein the apex is further configured to generate a pressure in the cavity upon aspirating an external liquid through the aspiration apertures that causes to aspirate a liquid sample from the sample outlet aperture, so as to eject the aspirated liquid sample from the probe.

Abstract: A machine learning model is generated for drug efficacy prediction in treatment of genetic disease from a dataset correlating gene expression data for disease-cell samples with drug efficacy values for the samples. Bias weights are stored that correspond to respective genes in the samples. Each bias weight is dependent on predetermined relevance of the respective gene to drug efficacy. The model is generated by processing the dataset via a tree ensemble method wherein decision trees are grown with splits corresponding to respective genes in the samples. The gene for each split is chosen from a respective subset of the genes, and genes are selected for inclusion in this subset with respective probabilities dependent on the corresponding bias weights. The model is stored, and can be applied to gene expression data measured for a patient to obtain a personalized drug efficacy prediction for devising a personalized course of treatment.

Abstract: An apparatus for a vacuum-driven microfluidic probe includes a body with an apex and a processing surface, at an end of the body. The apparatus also includes a partially open cavity formed as a recess on the processing surface and a set of apertures in the cavity, where the set of apertures include a sample outlet aperture intersected by a vertical axis of the cavity. The apparatus also includes aspiration apertures radially distributed around said vertical axis, wherein the apex is further configured to generate a pressure in the cavity upon aspirating an external liquid through the aspiration apertures that causes to aspirate a liquid sample from the sample outlet aperture, so as to eject the aspirated liquid sample from the probe.