Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening

Omta, Wienand; Heesbeen, Roy G van; Shen, I.; Nobel, Jacob de; Robers, Desmond; Velden, Lieke M. van der; Medema, René H.; Siebes, Arno; Feelders, Ad; Brinkkemper, Sjaak; Klumperman, Judith; Spruit, Marco; Brinkhuis, Matthieu; Egan, David A.

doi:10.1177/2472555220919345

Cited by 15 publications

(7 citation statements)

References 51 publications

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“…FA or principal component analysis are more often used to find out the major conditioning factors in LSM [49,50]. "Dimension Trouble" can be alleviated to some extent through omitting the factors with limited weight.…”

Section: Comparison Of Unsupervised and Supervised Learning For Lsmmentioning

confidence: 99%

A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

et al. 2021

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Landslides cause huge damage to social economy and human beings every year. Landslide susceptibility mapping (LSM) occupies an important position in land use and risk management. This study is to investigate a hybrid model which makes full use of the advantage of supervised learning model (SLM) and unsupervised learning model (ULM). Firstly, ten continuous variables were used to develop a ULM which consisted of factor analysis (FA) and k-means cluster for a preliminary landslide susceptibility map. Secondly, 351 landslides with “1” label were collected and the same number of non-landslide samples with “0” label were selected from the very low susceptibility area in the preliminary map, constituting a new priori condition for a SLM, and thirteen factors were used for the modeling of gradient boosting decision tree (GBDT) which represented for SLM. Finally, the performance of different models was verified using related indexes. The results showed that the performance of the pretreated GBDT model was improved with sensitivity, specificity, accuracy and the area under the curve (AUC) values of 88.60%, 92.59%, 90.60% and 0.976, respectively. It can be concluded that a pretreated model with strong robustness can be constructed by increasing the purity of samples.

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Section: Comparison Of Unsupervised and Supervised Learning For Lsmmentioning

confidence: 99%

A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

et al. 2021

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“…Omta et al 15 describe an approach utilizing unsupervised analysis followed by a supervised analysis carried out on a previously analyzed data set from an image-based genetic screen using siRNA libraries. The authors show that the combination of unsupervised and supervised data analytics methods has the potential to enhance the ability to identify new knowledge in functional genomics screens when compared with the use of unsupervised methods alone.…”

Section: Computational Pipeline and Data Analyticsmentioning

confidence: 99%

Functional Genomics for Target Identification

Gianni

Farrow

2020

SLAS Discovery

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“…This cycle touches on other related ways people have used unsupervised ML as a precursor to or in a cycle with supervised ML. These approaches have included semisupervised machine learning, 13 pretraining a neural network, 14 T h i s c o n t e n t i s feature selection or generation, 15 and human-in-the-loop learning, 16 which have been used in a multitude of fields, such as gene expression 17 and marketing. 18 Given the ubiquity of concerns about the scope and bias when constructing training datasets in supervised ML, we propose that our approach, which we term Unsupervised Validation of Classes (UVC), has relevance beyond the present case of X-ray spectroscopies as well as contributes to efforts to close the loop between artificial intelligence and scientific understanding.…”

Section: Introductionmentioning

confidence: 99%

Informed Chemical Classification of Organophosphorus Compounds via Unsupervised Machine Learning of X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy

et al. 2022

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We analyze an ensemble of organophosphorus compounds to form an unbiased characterization of the information encoded in their X-ray absorption near-edge structure (XANES) and valence-to-core X-ray emission spectra (VtC-XES). Data-driven emergence of chemical classes via unsupervised machine learning, specifically cluster analysis in the Uniform Manifold Approximation and Projection (UMAP) embedding, finds spectral sensitivity to coordination, oxidation, aromaticity, intramolecular hydrogen bonding, and ligand identity. Subsequently, we implement supervised machine learning via Gaussian process classifiers to identify confidence in predictions that match our initial qualitative assessments of clustering. The results further support the benefit of utilizing unsupervised machine learning as a precursor to supervised machine learning, which we term Unsupervised Validation of Classes (UVC), a result that goes beyond the present case of X-ray spectroscopies.

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Combining Supervised and Unsupervised Machine Learning Methods for Phenotypic Functional Genomics Screening

Cited by 15 publications

References 51 publications

A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

A Hybrid Model Consisting of Supervised and Unsupervised Learning for Landslide Susceptibility Mapping

Functional Genomics for Target Identification

Informed Chemical Classification of Organophosphorus Compounds via Unsupervised Machine Learning of X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy

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