Benchmarking feature selection methods for compressing image information in high-content screening

Siegismund, Daniel; Fassler, Matthias; Heyse, Stephan; Steigele, Stephan

doi:10.1016/j.slast.2021.10.015

Cited by 8 publications

(8 citation statements)

References 39 publications

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“…First, we selected features of interest for our phenotypic end point associated with DILI categories. It has been shown that Cell Painting can benefit from supervised feature selection and that a smaller size of features is more informative . To do the feature selection, we used a Mann–Whitney test, which allowed us to propose sufficient morphology features that allow discrimination between DILI versus non-DILI chemicals.…”

Section: Discussionmentioning

confidence: 99%

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Lejal,

Cerisier,

Rouquié

et al. 2023

Chem. Res. Toxicol.

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Drug-induced liver injury (DILI) is a significant concern in drug development, often leading to drug withdrawal. Although many studies aim to identify biomarkers and gene/pathway signatures related to liver toxicity and aim to predict DILI compounds, this remains a challenge in drug discovery. With a strong development of high-content screening/imaging (HCS/HCI) for phenotypic screening, we explored the morphological cell perturbations induced by DILI compounds. In the first step, cell morphological signatures were associated with two datasets of DILI chemicals (DILIRank and eTox). The mechanisms of action were then analyzed for chemicals having transcriptomics data and sharing similar morphological perturbations. Signaling pathways associated with liver toxicity (cell cycle, cell growth, apoptosis, ...) were then captured, and a hypothetical relation between cell morphological perturbations and gene deregulation was illustrated within our analysis. Finally, using the cell morphological signatures, machine learning approaches were developed to predict chemicals with a potential risk of DILI. Some models showed relevant performance with validation set balanced accuracies between 0.645 and 0.739. Overall, our findings demonstrate the utility of combining HCI with transcriptomics data to identify the morphological and gene expression signatures related to DILI chemicals. Moreover, our protocol could be extended to other toxicity end points, offering a promising avenue for comprehensive toxicity assessment in drug discovery.

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Section: Discussionmentioning

confidence: 99%

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Lejal,

Cerisier,

Rouquié

et al. 2023

Chem. Res. Toxicol.

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“…There are many approaches where more features lead to better ML models. This can be seen in the work of Siegismund et al [16] Nevertheless, this does not always have to be the case, as some features, unless excluded, could potentially lead to noisy data.…”

Section: Resultsmentioning

confidence: 99%

“…However, it was only slightly better than the best model that used all features ( Supplementary Material 1 ). As a rule, it is assumed that more features are more useful [16] , but more features are only useful if they really provide information and not noise. Since feature selection has helped somewhat, many features are probably orthogonal to each other or cause unnecessary noise.…”

Section: Discussionmentioning

confidence: 99%

“…However, the HT and high number of features per sample results in large and multidimensional data sets resulting complex data for subsequent data analysis. These data sets are often analysed with classical statistical methods [3] , [9] , [10] , [11] , [12] , or more recently also with machine learning (ML) approaches [13] , [14] , [15] , [16] , [17] , a subfield of AI. An example for an application using ML is the classification of morphological differences.…”

Section: Introductionmentioning

confidence: 99%

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Unleashing high content screening in hit detection – Benchmarking AI workflows including novelty detection

Kupczyk

Schorpp

Hadian

et al. 2022

Computational and Structural Biotechnology Journal

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“…Another study proposed a U-Net architecture to synthesize AT8-pTau image given two DAPI and YFP-tau image channels (15). With the potential of DL architectures in extracting meaningful features directly from microscopic images, recent studies proposed self-supervised learning frameworks, including a framework for studying the temporal drug effect on cancer cell images, or a framework to learn phenotypic embeddings of HCS images using self-supervised triplet network (16,17). While these advancements in DL application to HCS images offer the potential to accelerate drug discovery, so far there is only very little work about the analysis and prediction of regulated cell death.…”

Section: Introductionmentioning

confidence: 99%

CellDeathPred: A Deep Learning framework for Ferroptosis and Apoptosis prediction based on cell painting

Schorpp

Bessadok

Biibosunov

et al. 2023

Preprint

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Cell death, such as apoptosis and ferroptosis, play essential roles in the process of development, homeostasis, and pathogenesis of acute and chronic diseases. The increasing number of studies investigating cell death types in various diseases, particularly cancer and degenerative diseases, has raised hopes for their modulation in disease therapies. However, identifying the presence of a particular cell death type is not an obvious task, as it requires computationally intensive work and costly experimental assays. To address this challenge, we present CellDeathPred, a novel deep learning framework that uses high-content-imaging based on cell painting to distinguish cells undergoing ferroptosis or apoptosis from healthy cells. In particular, we incorporate a deep neural network that effectively embeds microscopic images into a representative and discriminative latent space, classifies the learned embedding into cell death modalities and optimizes the whole learning using the supervised contrastive loss function. We assessed the efficacy of the proposed framework using cell painting microscopy datasets from human HT-1080 cells, where multiple inducers of ferroptosis and apoptosis were used to trigger cell death. Our model confidently separates ferroptotic and apoptotic cells from healthy controls, with an averaged accuracy of 95% on non-confocal datasets, supporting the capacity of the CellDeathPred framework for cell death discovery.

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Benchmarking feature selection methods for compressing image information in high-content screening

Cited by 8 publications

References 39 publications

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Assessment of Drug-Induced Liver Injury through Cell Morphology and Gene Expression Analysis

Unleashing high content screening in hit detection – Benchmarking AI workflows including novelty detection

CellDeathPred: A Deep Learning framework for Ferroptosis and Apoptosis prediction based on cell painting

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