Machine learning-based approaches for identifying human blood cells harboring CRISPR-mediated fetal chromatin domain ablations

Li, Yi; Zaheri, Shadi; Nguyen, Khai T.; Liu, Li; Hassanipour, Fatemeh; Bleris, Leonidas

doi:10.1038/s41598-022-05575-3

“…For the current study, four supervised learning algorithms were investigated [45,46]: logistical regression, k-nearest neighbor, random forest, and multilayer perceptron (MLP). Logistical regression is a linear model that predicts the probability of a binary outcome based on input features.…”

Section: Machine Learning Model Training and Testingmentioning

confidence: 99%

NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage

Li,

Frederick,

George

et al. 2024

J. Neural Eng.

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Objective. The safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters. Approach. A literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation. Main Results. We compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using a k value of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%. Significance. This novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models.

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“…Machine learning approaches have also been utilized to identify specific biological changes in cells. For instance, Li Y. et al (2022) proposed two unique models utilizing multilayer perceptron algorithms and deep learning to detect human blood cells with CRISPR-mediated fetal chromatin domain (FCD) ablations. The models displayed promising predictive abilities for genetically edited cells.…”

Section: Deep Learning For Prediction Of Crispr-cas Editing Outcomesmentioning

confidence: 99%

Deep learning in CRISPR-Cas systems: a review of recent studies

Lee

¹

2023

Front. Bioeng. Biotechnol.

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In genetic engineering, the revolutionary CRISPR-Cas system has proven to be a vital tool for precise genome editing. Simultaneously, the emergence and rapid evolution of deep learning methodologies has provided an impetus to the scientific exploration of genomic data. These concurrent advancements mandate regular investigation of the state-of-the-art, particularly given the pace of recent developments. This review focuses on the significant progress achieved during 2019–2023 in the utilization of deep learning for predicting guide RNA (gRNA) activity in the CRISPR-Cas system, a key element determining the effectiveness and specificity of genome editing procedures. In this paper, an analytical overview of contemporary research is provided, with emphasis placed on the amalgamation of artificial intelligence and genetic engineering. The importance of our review is underscored by the necessity to comprehend the rapidly evolving deep learning methodologies and their potential impact on the effectiveness of the CRISPR-Cas system. By analyzing recent literature, this review highlights the achievements and emerging trends in the integration of deep learning with the CRISPR-Cas systems, thus contributing to the future direction of this essential interdisciplinary research area.

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“…A Dell desktop computer (Intel Core i7-10700 CPU @ 2.90 GHz, Windows 10 enterprise 64-bit OS and For the current study, four supervised learning algorithms were investigated 47,48 : logistical regression, k-nearest neighbor, random forest, and multilayer perceptron (MLP). Logistical regression is a linear model that predicts the probability of a binary outcome based on input features.…”

Section: Machine Learning Model Training and Testingmentioning

confidence: 99%

“…A modeling pipeline inspired from our previous study was adopted 47 . First, we screened all models with the One-Hot encoded/standardized training dataset using tenfold cross-validation and adopted two filtering conditions: (1) mean accuracy > 0.75, and (2) standard deviation of accuracy < 0.10.…”

Section: Machine Learning Model Training and Testingmentioning

confidence: 99%

NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage

Li,

Frederick,

George

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Objective. The safe delivery of electrical current to neural tissue depends on many factors, yet previous methods for predicting tissue damage rely on only a few stimulation parameters. Here, we report the development of a machine learning approach that could lead to a more reliable method for predicting electrical stimulation-induced tissue damage by incorporating additional stimulation parameters. Approach. A literature search was conducted to build an initial database of tissue response information after electrical stimulation, categorized as either damaging or non-damaging. Subsequently, we used ordinal encoding and random forest for feature selection, and investigated four machine learning models for classification: Logistic Regression, K-nearest Neighbor, Random Forest, and Multilayer Perceptron. Finally, we compared the results of these models against the accuracy of the Shannon equation. Main Results. We compiled a database with 387 unique stimulation parameter combinations collected from 58 independent studies conducted over a period of 47 years, with 195 (51%) categorized as non-damaging and 190 (49%) categorized as damaging. The features selected for building our model with a Random Forest algorithm were: waveform shape, geometric surface area, pulse width, frequency, pulse amplitude, charge per phase, charge density, current density, duty cycle, daily stimulation duration, daily number of pulses delivered, and daily accumulated charge. The Shannon equation yielded an accuracy of 63.9% using a k value of 1.79. In contrast, the Random Forest algorithm was able to robustly predict whether a set of stimulation parameters was classified as damaging or non-damaging with an accuracy of 88.3%. Significance. This novel Random Forest model can facilitate more informed decision making in the selection of neuromodulation parameters for both research studies and clinical practice. This study represents the first approach to use machine learning in the prediction of stimulation-induced neural tissue damage, and lays the groundwork for neurostimulation driven by machine learning models.

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Machine learning-based approaches for identifying human blood cells harboring CRISPR-mediated fetal chromatin domain ablations

Cited by 6 publications

References 38 publications

NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage

NeurostimML: a machine learning model for predicting neurostimulation-induced tissue damage

Deep learning in CRISPR-Cas systems: a review of recent studies

NeurostimML: A machine learning model for predicting neurostimulation-induced tissue damage

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