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Erschienen in:
Chemical Neural Networks and Synthetic Cell Biotechnology: Preludes to Chemical AI Kapitel lesen Erstes Kapitel lesen

2022 | OriginalPaper | Buchkapitel

The Need of Standardised Metadata to Encode Causal Relationships: Towards Safer Data-Driven Machine Learning Biological Solutions

verfasst von : Beatriz Garcia Santa Cruz, Carlos Vega, Frank Hertel

Erschienen in: Computational Intelligence Methods for Bioinformatics and Biostatistics

Verlag: Springer International Publishing

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Abstract

In this paper, we discuss the importance of considering causal relations in the development of machine learning solutions to prevent factors hampering the robustness and generalisation capacity of the models, such as induced biases. This issue often arises when the algorithm decision is affected by confounding factors. In this work, we argue that the integration of research assumptions as causal relationships can help identify potential confounders. Together with metadata information, it can enable meta-comparison of data acquisition pipelines. We call for standardised meta-information practices as a crucial step for proper machine learning solutions development, validation, and data sharing. Such practices include detailing the data acquisition process, aiming for automatic integration of causal relationships and actionable metadata.

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Vorheriges Kapitel Explainable AI Models for COVID-19 Diagnosis Using CT-Scan Images and Clinical Data
Nächstes Kapitel Deep Recurrent Neural Networks for the Generation of Synthetic Coronavirus Spike Protein Sequences
Literatur
1.
A survey on deep learning in medical image analysis. Med. Image Anal. 42, 60–88 (2017)
2.
3.
Repecka, D., et al.: Expanding functional protein sequence spaces using generative adversarial networks. Nat. Mach. Intell. 3, 324–333 (2021)CrossRef
4.
Xu, C., Jackson, S.: Machine learning and complex biological data. Genome Biol. 20, 1–4 (2019)CrossRef
5.
Wilkinson, M., et al.: The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 1–9 (2016)CrossRef
6.
Walsh, I., et al.: DOME: recommendations for supervised machine learning validation in biology. Nat. Methods 18, 1122–1127 (2021)CrossRefPubMed
7.
Bzdok, D., Altman, N., Krzywinski, M.: Statistics versus machine learning. Natu. Methods 15, 233 (2018)CrossRef
8.
Smuha, N.: The EU approach to ethics guidelines for trustworthy artificial intelligence. Comput. Law Rev. Int. 20, 97–106 (2019)CrossRef
9.
Hutchinson, B., et al.: Towards accountability for machine learning datasets: Practices from software engineering and infrastructure. In: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency, pp. 560–575 (2021)
10.
Mora-Cantallops, M., Sanchez-Alonso, S., Garcia-Barriocanal, E., Sicilia, M.: Traceability for trustworthy AI: a review of models and tools. Big Data Cogn. Comput. 5, 20 (2021)CrossRef
11.
Paschali, M., Conjeti, S., Navarro, F., Navab, N.: Generalizability vs. robustness: adversarial examples for medical imaging. ArXiv Preprint ArXiv:​1804.​00504 (2018)
12.
Sambasivan, N., Kapania, S., Highfill, H., Akrong, D., Paritosh, P., Aroyo, L.: Everyone wants to do the model work, not the data work: data cascades in high-stakes AI. In: Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems, pp. 1–15 (2021)
13.
Mitchell, M., et al.: Model cards for model reporting. In: Proceedings Of The Conference On Fairness, Accountability, And Transparency, pp. 220–229 (2019)
14.
Santa Cruz, B., Bossa, M., Sölter, J., Husch, A.: Public Covid-19 X-ray datasets and their impact on model bias-a systematic review of a significant problem. Med. Image Anal. 74, 102225 (2021)CrossRef
15.
Castro, D., Walker, I., Glocker, B.: Causality matters in medical imaging. Nat. Commun. 11, 1–10 (2020)CrossRef
16.
Zhu, Y., et al.: Converting tabular data into images for deep learning with convolutional neural networks. Sci. Rep. 11, 1–11 (2021)
17.
Bazgir, O., Zhang, R., Dhruba, S., Rahman, R., Ghosh, S., Pal, R.: Representation of features as images with neighborhood dependencies for compatibility with convolutional neural networks. Nat. Commun. 11, 1–13 (2020)CrossRef
18.
Mäkinen, S., Skogström, H., Laaksonen, E., Mikkonen, T.: Who needs MLOps: what data scientists seek to accomplish and how can MLOps Help?. In: 2021 IEEE/ACM 1st Workshop on AI Engineering-Software Engineering For AI (WAIN), pp. 109–112 (2021)
19.
Sweenor, D., Hillion, S., Rope, D., Kannabiran, D., Hill, T., O’Connell, M.: ML Ops: Operationalizing Data Science. O’Reilly Media, Incorporated (2020)
20.
Vega, C.: From Hume to Wuhan: an epistemological journey on the problem of induction in COVID-19 machine learning models and its impact upon medical research. IEEE Access. 9, 97243–97250 (2021)CrossRefPubMed
21.
22.
23.
24.
Altevogt, B., Davis, M., Pankevich, D., Norris, S.: Improving and Accelerating Therapeutic Development for Nervous System Disorders: Workshop Summary. National Academies Press, Washington (2014)
25.
Ching, T., et al.: Opportunities and obstacles for deep learning in biology and medicine. J. Roy. Soc. Interface. 15, 20170387 (2018)CrossRef
26.
Leek, J., et al.: Tackling the widespread and critical impact of batch effects in high-throughput data. Nat. Rev. Genet. 11, 733–739 (2010)CrossRefPubMed
27.
28.
Griffith, G., et al.: Others collider bias undermines our understanding of COVID-19 disease risk and severity. Nat. Commun. 11, 1–12 (2020)CrossRef
29.
Leipzig, J., Nüst, D., Hoyt, C., Ram, K., Greenberg, J.: The role of metadata in reproducible computational research. Patterns. 2, 100322 (2021)CrossRefPubMedPubMedCentral
30.
31.
32.
Shimoni, Y., et al.: An evaluation toolkit to guide model selection and cohort definition in causal inference. ArXiv Preprint ArXiv:​1906.​00442 (2019)
33.
34.
Touré, V., Flobak, A., Niarakis, A., Vercruysse, S., Kuiper, M.: The status of causality in biological databases: data resources and data retrieval possibilities to support logical modeling. Briefings Bioinform. 22, bbaa390 (2021)
35.
Juty, N., Le Novere, N., Laibe, C.: Identifiers. org and MIRIAM Registry: community resources to provide persistent identification. Nucleic Acids Res. 40, D580–D586 (2012)
Metadaten
Titel
The Need of Standardised Metadata to Encode Causal Relationships: Towards Safer Data-Driven Machine Learning Biological Solutions
verfasst von
Beatriz Garcia Santa Cruz
Carlos Vega
Frank Hertel
Copyright-Jahr
2022
Buch
Computational Intelligence Methods for Bioinformatics and Biostatistics

Print ISBN: 978-3-031-20836-2

Electronic ISBN: 978-3-031-20837-9

Copyright-Jahr: 2022

DOI
https://doi.org/10.1007/978-3-031-20837-9_16

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