M. D. Anderson Breaks With IBM Watson, Raising Questions About Artificial Intelligence in Oncology

Schmidt, Charlie

doi:10.1093/jnci/djx113

Cited by 58 publications

(32 citation statements)

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“…DeepMind Health recently partnered with Moorfields Eye Hospital to develop models for diagnosing common retinal pathologies based on optical coherence tomography scans 9. IBM’s Watson for Oncology seeks to provide personalised cancer care based on health records,10 although the project has run into numerous procurement problems, cost over-runs, and delays 11…”

Section: Predictions Versus Explanationsmentioning

confidence: 99%

Clinical applications of machine learning algorithms: beyond the black box

Watson

Krutzinna

Bruce

et al. 2019

BMJ

256

156

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Section: Predictions Versus Explanationsmentioning

confidence: 99%

Clinical applications of machine learning algorithms: beyond the black box

Watson

Krutzinna

Bruce

et al. 2019

BMJ

256

156

View full text Add to dashboard Cite

show abstract

“…Oncology seeks to provide personalised cancer care based on health records, 10 although the project has run into numerous procurement problems, cost overruns, and delays. 11 One frequently cited obstacle to machine learning's wider clinical adoption is a lack of understanding among patients and doctors about how predictions are made. 12 This is especially true of some top performing algorithms, like the deep neural networks used in image recognition software.…”

Section: Ibm's Watson Formentioning

confidence: 99%

Clinical Applications of Machine Learning Algorithms: Beyond the Black Box

et al. 2019

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“…Successful applications of deep learning methods to multi-omics data have been recently reported, such as in Reference [12]. One should also notice that there exists a certain level of controversy in assessing the actual success of this rapidly growing area [13] and an important methodological discussion on the “deep” versus “shallow” methods in real applications [14]. Reviewing any statistical method today should necessarily take into account the existing intrinsic competition between this relatively recent trend and more “classical” areas of machine learning, even though many of them, including ICA, are rooted in the artificial neural network theory [15].…”

Section: Introductionmentioning

confidence: 99%

Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets

Sompairac

Nazarov

Czerwińska

et al. 2019

IJMS

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Independent component analysis (ICA) is a matrix factorization approach where the signals captured by each individual matrix factors are optimized to become as mutually independent as possible. Initially suggested for solving source blind separation problems in various fields, ICA was shown to be successful in analyzing functional magnetic resonance imaging (fMRI) and other types of biomedical data. In the last twenty years, ICA became a part of the standard machine learning toolbox, together with other matrix factorization methods such as principal component analysis (PCA) and non-negative matrix factorization (NMF). Here, we review a number of recent works where ICA was shown to be a useful tool for unraveling the complexity of cancer biology from the analysis of different types of omics data, mainly collected for tumoral samples. Such works highlight the use of ICA in dimensionality reduction, deconvolution, data pre-processing, meta-analysis, and others applied to different data types (transcriptome, methylome, proteome, single-cell data). We particularly focus on the technical aspects of ICA application in omics studies such as using different protocols, determining the optimal number of components, assessing and improving reproducibility of the ICA results, and comparison with other popular matrix factorization techniques. We discuss the emerging ICA applications to the integrative analysis of multi-level omics datasets and introduce a conceptual view on ICA as a tool for defining functional subsystems of a complex biological system and their interactions under various conditions. Our review is accompanied by a Jupyter notebook which illustrates the discussed concepts and provides a practical tool for applying ICA to the analysis of cancer omics datasets.

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M. D. Anderson Breaks With IBM Watson, Raising Questions About Artificial Intelligence in Oncology

Cited by 58 publications

References 0 publications

Clinical applications of machine learning algorithms: beyond the black box

Clinical applications of machine learning algorithms: beyond the black box

Clinical Applications of Machine Learning Algorithms: Beyond the Black Box

Independent Component Analysis for Unraveling the Complexity of Cancer Omics Datasets

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