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Items: 4

1.

Explainable machine-learning predictions for the prevention of hypoxaemia during surgery.

Lundberg SM, Nair B, Vavilala MS, Horibe M, Eisses MJ, Adams T, Liston DE, Low DK, Newman SF, Kim J, Lee SI.

Nat Biomed Eng. 2018 Oct;2(10):749-760. doi: 10.1038/s41551-018-0304-0. Epub 2018 Oct 10.

2.

AIControl: replacing matched control experiments with machine learning improves ChIP-seq peak identification.

Hiranuma N, Lundberg SM, Lee SI.

Nucleic Acids Res. 2019 Jun 4;47(10):e58. doi: 10.1093/nar/gkz156.

3.

A machine learning approach to integrate big data for precision medicine in acute myeloid leukemia.

Lee SI, Celik S, Logsdon BA, Lundberg SM, Martins TJ, Oehler VG, Estey EH, Miller CP, Chien S, Dai J, Saxena A, Blau CA, Becker PS.

Nat Commun. 2018 Jan 3;9(1):42. doi: 10.1038/s41467-017-02465-5.

4.

ChromNet: Learning the human chromatin network from all ENCODE ChIP-seq data.

Lundberg SM, Tu WB, Raught B, Penn LZ, Hoffman MM, Lee SI.

Genome Biol. 2016 Apr 30;17:82. doi: 10.1186/s13059-016-0925-0.

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