Artificial Intelligence approaches for personalized medicine – WiDS Zürich 2019: Lightning Talk Maria Rodriguez Martinez

Artificial Intelligence approaches for personalized medicine – WiDS Zürich 2019: Lightning Talk Maria Rodriguez Martinez

Publication date: Apr 11, 2019

Maria Rodriguez Martinez, Technical Lead of Systems Biology at IBM Research, Zurich, Switzerland

Artificial Intelligence approaches for personalized medicine

In recent years, deep learning has become one of the most active fields in machine learning with astounding performances in a broad area of applications such as computer vision, speech recognition, and natural language processing. In computational biology, the recent availability of large amounts of data generated by word-wide consortia together with technical developments facilitating the implementation and training of more performant models have made possible the broad application of deep learning to a vast set of problems. In this talk, I will present current activities at the Computational Systems Biology group in IBM Research, Zurich, that illustrate the application of AI approaches to integrate disparate data types. Specifically, I will explain how a multi-modal neural network can be trained to ingest disparate data types, such as compound molecular structure, transcriptomic data, and prior molecular knowledge, and predict drug sensitivity in cancer cell lines.

Concepts Keywords
Active Learning Deep learning
Artificial Intelligence Computational neuroscience
Astounding Bioinformatics
Computational Biology Artificial intelligence
Computer Vision Formal sciences
Deep Learning Academic disciplines
IBM Research Emerging technologies
Lightning Talk Technology
Modal Articles
Neural Network Modal neural network
Speech Recognition Disparate types
Transcriptomic Computational biology
Zurich IBM
Neural network
Artificial intelligence
Speech recognition

Semantics

Type Source Name
disease DOID cancer
disease MESH cancer
drug DRUGBANK Sulpiride
disease MESH multi
gene UNIPROT SET
gene UNIPROT LARGE1

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