Interpretable machine learning methods for predictions in systems biology from omics data

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU146244" target="_blank" >RIV/00216305:26220/22:PU146244 - isvavai.cz</a>

Result on the web

<a href="https://www.frontiersin.org/articles/10.3389/fmolb.2022.926623/full" target="_blank" >https://www.frontiersin.org/articles/10.3389/fmolb.2022.926623/full</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.3389/fmolb.2022.926623" target="_blank" >10.3389/fmolb.2022.926623</a>

Result language

angličtina

Original language name

Interpretable machine learning methods for predictions in systems biology from omics data

Original language description

Machine learning has become a powerful tool for systems biologists, from diagnosing cancer to optimizing kinetic models and predicting the state, growth dynamics, or type of a cell. Potential predictions from complex biological data sets obtained by “omics” experiments seem endless, but are often not the main objective of biological research. Often we want to understand the molecular mechanisms of a disease to develop new therapies, or we need to justify a crucial decision that is derived from a prediction. In order to gain such knowledge from data, machine learning models need to be extended. A recent trend to achieve this is to design “interpretable” models. However, the notions around interpretability are sometimes ambiguous, and a universal recipe for building well-interpretable models is missing. With this work, we want to familiarize systems biologists with the concept of model interpretability in machine learning. We consider data sets, data preparation, machine learning methods, and software tools relevant to omics research in systems biology. Finally, we try to answer the question: “What is interpretability?” We introduce views from the interpretable machine learning community and propose a scheme for categorizing studies on omics data. We then apply these tools to review and categorize recent studies where predictive machine learning models have been constructed from non-sequential omics data.

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF19_073%2F0016948" target="_blank" >EF19_073/0016948: Quality internal grants at BUT</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2022

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Frontiers in Molecular Biosciences

ISSN

2296-889X

e-ISSN

—

Volume of the periodical

9

Issue of the periodical within the volume

October 2022

Country of publishing house

CH - SWITZERLAND

Number of pages

28

Pages from-to

1-28

UT code for WoS article

000884481200001

EID of the result in the Scopus database

2-s2.0-85141880659