Philip Morris Products S.a.

Other

Selventa, Inc.

Yang Xiang

Julia Hoeng

Florian Martin

Hoeng, Julia

Martin, Florian

Peitsch, Manuel Claude

Sewer, Alain

Talikka, Marja

Manuel Claude Peitsch

Alain Sewer

Dmitry Vasilyev

Ty Matthew Thomson

Simon Goepfert

Gregor Nicholas Bindler

Nicholas Bakaher

Michel Philippe Blanc

Marja Talikka

Gregor Nicolas Bindler

Vincenzo Belcastro

Carine Poussin

Stephanie Boue

Nicolas Bakaher

Systems and methods are provided for assessing a sample obtained from a subject. The computerized method includes receiving, by receiving circuitry, a dataset associated with the sample, the dataset comprising quantitative expression data for LRRN3, CDKN1C, PALLD, SASH1, RGL1, and TNFRSF17. A processor generates, based on the received dataset, a score that is indicative of a predicted smoking status of the subject. The predicted smoking status may classify the subject as a current smoker or as a non-current smoker.

Systems and methods for predicting a smoking status of an individual

Systems and methods are described for quantifying the response of a biological system to one or more perturbations. First and second datasets corresponding to a response of a biological system to first and second treatments are received. A plurality of computational network models that represent the biological system are provided, each model including nodes representing a plurality of biological entities and edges representing relationships between the nodes in the model. A first set of scores is generated, representing the perturbation of the biological system based on the first dataset and the plurality of models, and a second set of scores representing the perturbation of the biological system based on the second dataset and the plurality of computational models. One or more biological impact factors are generated based on each of the first set and second set of scores that represent the biological impact of the perturbation on the biological system.

Systems and methods for quantifying the impact of biological perturbations

Disclosed herein are system, method, and computer program product embodiments for determining a score for a degree of activation of a biological network. An embodiment constructs a tree graph comprising a root node and a set of child nodes. The root node represents a biological network having a reference node and causal connections among a set of nodes that represent biological entities, biological processes, or other biological networks. Each child node represents a particular node in the biological network and is connected to the root node by a signed, directed edge pointing from the root node to that child node. Each child node also has an associated weight based on one or more paths connecting the child node to the reference node in the biological network. The embodiment further scores the tree graph based on scores assigned to the child nodes and the signs of the singed, directed edges.

Quantitative assessment of biological impact by scoring directed tree graphs of causally inconsistent biological networks

Described herein are systems and methods for classifying a data set using an ensemble classification technique. Classifiers are iteratively generated by applying machine learning techniques to a training data set, and training class sets are generated by classifying the elements in the training data set according to the classifiers. Objective values are computed based on the training class sets, and objective values associated with different classifiers are compared until a desired number of iterations is reached, and a final training class set is output.

Systems and methods for generating biomarker signatures with integrated dual ensemble and generalized simulated annealing techniques

Described herein are systems and methods for correcting a data set and classifying the data set in an integrated manner. A training data set, a training class set, and a test data set are received. A first classifier is generated for the training data set by applying a machine learning technique to the training data set and the training class set, and a first test class set is generated by classifying the elements in the test data set according to the first classifier. For each of multiple iterations, the training data set is transformed, the test data set is transformed, and a second classifier is generated by applying a machine learning technique to the transformed training data set. A second test class set is generated according to the second classifier, and the first test class set is compared to the second test class set.

Systems and methods for generating biomarker signatures with integrated bias correction and class prediction

The present invention relates to a mutant, non-naturally occurring or transgenic plant cell comprising: (i) at least one polynucleotide comprising, consisting or consisting essentially of a sequence encoding an isopropylmalate synthase and having at least 60% sequence identity to SEQ ID NO:1 or SEQ ID NO:10 or SEQ ID NO: 12 or SEQ ID NO:14; or (ii) a polypeptide encoded by said polynucleotide(s); or (iii) a polypeptide having at least 60% sequence identity to SEQ ID NO:2 or SEQ ID NO:11 or SEQ ID NO:13 or SEQ ID NO:15; or (iv) a construct, vector or expression vector comprising said polynucleotide sequence(s), optionally wherein said construct, vector or expression vector additionally comprises a promoter comprising, consisting or consisting essentially of the sequence set forth in SEQ ID NO:8 or a variant thereof with at least about 60% identity thereto or a trichome promoter.

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Peseux, Switzerland

Florian Martin