scholarly journals Machine Learning Identifies Cellular and Exosomal MicroRNA Signatures of Lyssavirus Infection in Human Stem Cell-Derived Neurons

2021 ◽  
Vol 11 ◽  
Author(s):  
Ryan J. Farr ◽  
Nathan Godde ◽  
Christopher Cowled ◽  
Vinod Sundaramoorthy ◽  
Diane Green ◽  
...  
Keyword(s):  
Machine Learning ◽  
Stem Cell ◽  
Differential Expression Analysis ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
P Value ◽  
Human Stem Cell ◽  
Cellular Mirnas ◽  
Neural Lineage

Despite being vaccine preventable, rabies (lyssavirus) still has a significant impact on global mortality, disproportionally affecting children under 15 years of age. This neurotropic virus is deft at avoiding the immune system while travelling through neurons to the brain. Until recently, research efforts into the role of non-coding RNAs in rabies pathogenicity and detection have been hampered by a lack of human in vitro neuronal models. Here, we utilized our previously described human stem cell-derived neural model to investigate the effect of lyssavirus infection on microRNA (miRNA) expression in human neural cells and their secreted exosomes. Conventional differential expression analysis identified 25 cellular and 16 exosomal miRNAs that were significantly altered (FDR adjusted P-value <0.05) in response to different lyssavirus strains. Supervised machine learning algorithms determined 6 cellular miRNAs (miR-99b-5p, miR-346, miR-5701, miR-138-2-3p, miR-651-5p, and miR-7977) were indicative of lyssavirus infection (100% accuracy), with the first four miRNAs having previously established roles in neuronal function, or panic and impulsivity-related behaviors. Another 4-miRNA signatures in exosomes (miR-25-3p, miR-26b-5p, miR-218-5p, miR-598-3p) can independently predict lyssavirus infected cells with >99% accuracy. Identification of these robust lyssavirus miRNA signatures offers further insight into neural lineage responses to infection and provides a foundation for utilizing exosome miRNAs in the development of next-generation molecular diagnostics for rabies.

scholarly journals Correlating in vitro performance with physico-chemical characteristics of nanofibrous scaffolds for skin tissue engineering using supervised machine learning algorithms

2020 ◽  
Vol 7 (12) ◽  
pp. 201293
Author(s):  
Lakshmi Y. Sujeeun ◽  
Nowsheen Goonoo ◽  
Honita Ramphul ◽  
Itisha Chummun ◽  
Fanny Gimié ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tissue Engineering ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Cell Material ◽  
Nanofibrous Scaffolds ◽  
Polymeric Scaffolds ◽  
Physico Chemical

The engineering of polymeric scaffolds for tissue regeneration has known a phenomenal growth during the past decades as materials scientists seek to understand cell biology and cell–material behaviour. Statistical methods are being applied to physico-chemical properties of polymeric scaffolds for tissue engineering (TE) to guide through the complexity of experimental conditions. We have attempted using experimental in vitro data and physico-chemical data of electrospun polymeric scaffolds, tested for skin TE, to model scaffold performance using machine learning (ML) approach. Fibre diameter, pore diameter, water contact angle and Young's modulus were used to find a correlation with 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay of L929 fibroblasts cells on the scaffolds after 7 days. Six supervised learning algorithms were trained on the data using Seaborn/Scikit-learn Python libraries. After hyperparameter tuning, random forest regression yielded the highest accuracy of 62.74%. The predictive model was also correlated with in vivo data. This is a first preliminary study on ML methods for the prediction of cell–material interactions on nanofibrous scaffolds.

Exploring the Use of Machine Learning to Automate the Qualitative Coding of Church-related Tweets

2020 ◽  
Vol 14 (2) ◽  
pp. 140-159
Author(s):  
Anthony-Paul Cooper ◽  
Emmanuel Awuni Kolog ◽  
Erkki Sutinen
Keyword(s):  
Machine Learning ◽  
Online Community ◽  
High Volume ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Social Media Data ◽  
Twitter Data ◽  
Resource Intensity ◽  
Media Data ◽  
Better Than

This article builds on previous research around the exploration of the content of church-related tweets. It does so by exploring whether the qualitative thematic coding of such tweets can, in part, be automated by the use of machine learning. It compares three supervised machine learning algorithms to understand how useful each algorithm is at a classification task, based on a dataset of human-coded church-related tweets. The study finds that one such algorithm, Naïve-Bayes, performs better than the other algorithms considered, returning Precision, Recall and F-measure values which each exceed an acceptable threshold of 70%. This has far-reaching consequences at a time where the high volume of social media data, in this case, Twitter data, means that the resource-intensity of manual coding approaches can act as a barrier to understanding how the online community interacts with, and talks about, church. The findings presented in this article offer a way forward for scholars of digital theology to better understand the content of online church discourse.

scholarly journals A hydrogel platform for in vitro three dimensional assembly of human stem cell-derived islet cells and endothelial cells

2019 ◽  
Vol 97 ◽  
pp. 272-280 ◽  
Author(s):  
Punn Augsornworawat ◽  
Leonardo Velazco-Cruz ◽  
Jiwon Song ◽  
Jeffrey R. Millman
Keyword(s):  
Endothelial Cells ◽  
Stem Cell ◽  
Islet Cells ◽  
Three Dimensional ◽  
Human Stem Cell

scholarly journals Crop price prediction using supervised machine learning algorithms

2021 ◽  
Vol 1916 (1) ◽  
pp. 012042
Author(s):  
Ranjani Dhanapal ◽  
A AjanRaj ◽  
S Balavinayagapragathish ◽  
J Balaji
Keyword(s):  
Machine Learning ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Supervised Machine Learning ◽  
Price Prediction

scholarly journals Computational Methods for Structure-to-Function Analysis of Diet-Derived Catechins-Mediated Targeting of In Vitro Vasculogenic Mimicry

2021 ◽  
Vol 20 ◽  
pp. 117693512110092
Author(s):  
Abicumaran Uthamacumaran ◽  
Narjara Gonzalez Suarez ◽  
Abdoulaye Baniré Diallo ◽  
Borhane Annabi
Keyword(s):  
Machine Learning ◽  
Cancer Cells ◽  
Structural Changes ◽  
Function Analysis ◽  
Vasculogenic Mimicry ◽  
Machine Learning Algorithms ◽  
Emergent Behavior ◽  
Molecular Signature ◽  
Ovarian Cancer Cells

Background: Vasculogenic mimicry (VM) is an adaptive biological phenomenon wherein cancer cells spontaneously self-organize into 3-dimensional (3D) branching network structures. This emergent behavior is considered central in promoting an invasive, metastatic, and therapy resistance molecular signature to cancer cells. The quantitative analysis of such complex phenotypic systems could require the use of computational approaches including machine learning algorithms originating from complexity science. Procedures: In vitro 3D VM was performed with SKOV3 and ES2 ovarian cancer cells cultured on Matrigel. Diet-derived catechins disruption of VM was monitored at 24 hours with pictures taken with an inverted microscope. Three computational algorithms for complex feature extraction relevant for 3D VM, including 2D wavelet analysis, fractal dimension, and percolation clustering scores were assessed coupled with machine learning classifiers. Results: These algorithms demonstrated the structure-to-function galloyl moiety impact on VM for each of the gallated catechin tested, and shown applicable in quantifying the drug-mediated structural changes in VM processes. Conclusions: Our study provides evidence of how appropriate 3D VM compression and feature extractors coupled with classification/regression methods could be efficient to study in vitro drug-induced perturbation of complex processes. Such approaches could be exploited in the development and characterization of drugs targeting VM.

scholarly journals Application of Supervised Machine Learning to Recognize Competent Level and Mixed Antinuclear Antibody Patterns Based on ICAP International Consensus

Diagnostics ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 642
Author(s):  
Yi-Da Wu ◽  
Ruey-Kai Sheu ◽  
Chih-Wei Chung ◽  
Yen-Ching Wu ◽  
Chiao-Chi Ou ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Excellent Agreement ◽  
Antinuclear Antibody ◽  
Disease Diagnosis ◽  
Machine Learning Algorithms ◽  
Observer Agreement ◽  
Supervised Machine Learning ◽  
International Consensus ◽  
Testing Data

Background: Antinuclear antibody pattern recognition is vital for autoimmune disease diagnosis but labor-intensive for manual interpretation. To develop an automated pattern recognition system, we established machine learning models based on the International Consensus on Antinuclear Antibody Patterns (ICAP) at a competent level, mixed patterns recognition, and evaluated their consistency with human reading. Methods: 51,694 human epithelial cells (HEp-2) cell images with patterns assigned by experienced medical technologists collected in a medical center were used to train six machine learning algorithms and were compared by their performance. Next, we choose the best performing model to test the consistency with five experienced readers and two beginners. Results: The mean F1 score in each classification of the best performing model was 0.86 evaluated by Testing Data 1. For the inter-observer agreement test on Testing Data 2, the average agreement was 0.849 (?) among five experienced readers, 0.844 between the best performing model and experienced readers, 0.528 between experienced readers and beginners. The results indicate that the proposed model outperformed beginners and achieved an excellent agreement with experienced readers. Conclusions: This study demonstrated that the developed model could reach an excellent agreement with experienced human readers using machine learning methods.

scholarly journals Improving Manufacturing Applications of Machine Learning by Understanding Defect Classification and the Critical Error Threshold

2021 ◽  
Author(s):  
David Blondheim
Keyword(s):  
Machine Learning ◽  
Machine Learning Algorithms ◽  
Error Threshold ◽  
Supervised Machine Learning ◽  
Secondary Process ◽  
Defect Classification ◽  
Critical Error ◽  
Manufacturing Applications ◽  
Applications Of Machine Learning ◽  
Manufacturing Environments

AbstractMachine learning (ML) is unlocking patterns and insight into data to provide financial value and knowledge for organizations. Use of machine learning in manufacturing environments is increasing, yet sometimes these applications fail to produce meaningful results. A critical review of how defects are classified is needed to appropriately apply machine learning in a production foundry and other manufacturing processes. Four elements associated with defect classification are proposed: Binary Acceptance Specifications, Stochastic Formation of Defects, Secondary Process Variation, and Visual Defect Inspection. These four elements create data space overlap, which influences the bias associated with training supervised machine learning algorithms. If this influence is significant enough, the predicted error of the model exceeds a critical error threshold (CET). There is no financial motivation to implement the ML model in the manufacturing environment if its error is greater than the CET. The goal is to bring awareness to these four elements, define the critical error threshold, and offer guidance and future study recommendations on data collection and machine learning that will increase the success of ML within manufacturing.

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