A DNA tetrahedron-based molecular computation device for the logic sensing of dual microRNAs in living cells

2020 ◽  
Vol 56 (39) ◽  
pp. 5303-5306
Author(s):  
Qing Lin ◽  
Anmin Wang ◽  
Shiyuan Liu ◽  
Jing Li ◽  
Jiaoli Wang ◽  
...  
Keyword(s):  
Mirna Expression ◽  
Prediction Accuracy ◽  
Expression Patterns ◽  
Living Cells ◽  
Cell Type ◽  
Molecular Computation ◽  
High Prediction Accuracy ◽  
Dna Tetrahedron ◽  
High Prediction

Endogenous miRNA expression patterns are specific to cell type and thus offer high prediction accuracy with regard to different cell identities compared to single miRNA analysis.

Dual-MicroRNA-Regulation of Singlet Oxygen Generation by DNA-Tetrahedron-Based Molecular Logic Device

2021 ◽  
Author(s):  
Qing Lin ◽  
Shijun Cai ◽  
Bing Zhou ◽  
Kemin Wang ◽  
Lixin Jian ◽  
...  
Keyword(s):  
Logic Gate ◽  
Expression Patterns ◽  
Cell Type ◽  
Logic Device ◽  
Microrna Regulation ◽  
Oxygen Generation ◽  
Molecular Logic ◽  
Dna Tetrahedron ◽  
Cell Type Specific ◽  
High Prediction

Endogenous miRNA expression patterns are well cell type-specific, thereby offering high prediction accuracy regard to different cell identities. DNA-tetrahedron-based “AND” logic gate is utilized as a molecular device that recognize...

scholarly journals xMD-miRNA-seq to generate near in vivo miRNA expression estimates in colon epithelial cells

2018 ◽  
Author(s):  
Avi Z. Rosenberg ◽  
Carrie Wright ◽  
Karen Fox-Talbot ◽  
Anandita Rajpurohit ◽  
Courtney Williams ◽  
...  
Keyword(s):  
Epithelial Cells ◽  
Small Rna ◽  
Mirna Expression ◽  
Low Cost ◽  
Expression Patterns ◽  
Small Rna Sequencing ◽  
Rna Seq ◽  
Cell Type

AbstractAccurate, RNA-seq based, microRNA (miRNA) expression estimates from primary cells have recently been described. However, this in vitro data is mainly obtained from cell culture, which is known to alter cell maturity/differentiation status, significantly changing miRNA levels. What is needed is a robust method to obtain in vivo miRNA expression values directly from cells. We introduce expression microdissection miRNA small RNA sequencing (xMD-miRNA-seq), a method to isolate cells directly from formalin fixed paraffin-embedded (FFPE) tissues. xMD-miRNA-seq is a low-cost, high-throughput, immunohistochemistry-based method to capture any cell type of interest. As a proof-of-concept, we isolated colon epithelial cells from two specimens and performed low-input small RNA-seq. We generated up to 600,000 miRNA reads from the samples. Isolated epithelial cells, had abundant epithelial-enriched miRNA expression (miR-192; miR-194; miR-200b; miR-200c; miR-215; miR-375) and overall similar miRNA expression patterns to other epithelial cell populations (colonic enteroids and flow-isolated colon epithelium). xMD-derived epithelial cells were generally not contaminated by other adjacent cells of the colon as noted by t-SNE analysis. xMD-miRNA-seq allows for simple, economical, and efficient identification of cell-specific miRNA expression estimates. Further development will enhance rapid identification of cell-specific miRNA expression estimates in health and disease for nearly any cell type using archival FFPE material.

scholarly journals Characteristics of microRNAs enriched in specific cell types and primary tissue types in solid organs

2013 ◽  
Vol 45 (23) ◽  
pp. 1144-1156 ◽  
Author(s):  
Alison J. Kriegel ◽  
Yong Liu ◽  
Pengyuan Liu ◽  
Maria Angeles Baker ◽  
Matthew R. Hodges ◽  
...  
Keyword(s):  
Mirna Expression ◽  
Expression Patterns ◽  
Cell Types ◽  
Differentially Expressed ◽  
Tissue Type ◽  
Motor Nucleus ◽  
Specific Cell ◽  
Thick Ascending Limb ◽  
Cell Type ◽  
Primary Tissue

Knowledge of miRNA expression and function in specific cell types in solid organs is limited because of difficulty in obtaining appropriate specimens. We used laser capture microdissection to obtain nine tissue regions from rats, including the nucleus of the solitary tract, hypoglossal motor nucleus, ventral respiratory column/pre-Bötzinger complex, and midline raphe nucleus from the brain stem, myocardium and coronary artery from the heart, and glomerulus, proximal convoluted tubule, and medullary thick ascending limb from the kidney. Each tissue region consists of or is enriched for a specific cell type. Differential patterns of miRNA expression obtained by deep sequencing of minute amounts of laser-captured cells were highly consistent with data obtained from real-time PCR analysis. miRNA expression patterns correctly clustered the specimens by tissue regions and then by primary tissue types (neural, muscular, or epithelial). The aggregate difference in miRNA profiles between tissue regions that contained the same primary tissue type was as large as one-half of the aggregate difference between primary tissue types. miRNAs differentially expressed between primary tissue types are more likely to be abundant miRNAs, while miRNAs differentially expressed between tissue regions containing the same primary tissue type were distributed evenly across the abundance spectrum. The tissue type-enriched miRNAs were more likely to target genes enriched for specific functional categories compared with either cell type-enriched miRNAs or randomly selected miRNAs. These data indicate that the role of miRNAs in determining characteristics of primary tissue types may be different than their role in regulating cell type-specific functions in solid organs.

scholarly journals Modelling postural discomfort perception using CHAID decision tree algorithm

2019 ◽  
Vol 63 (1) ◽  
pp. 1749-1750
Author(s):  
Soomin Hyun ◽  
Woojin Park
Keyword(s):  
Decision Tree ◽  
Prediction Accuracy ◽  
Prediction Models ◽  
Body Part ◽  
Upper Body ◽  
Good Prediction ◽  
Practical Applications ◽  
High Prediction Accuracy ◽  
Good Prediction Accuracy ◽  
High Prediction

Developing quantitative models that predict discomfort levels of working postures has been an important ergonomics research topic. Such modeling not only has practical applications, but also may serve as a useful research method to improve our understanding of the human postural discomfort perception process. While the existing models have focused on achieving high prediction accuracy, less attention has been given to model interpretability, which is vital for understanding a process through modeling. Research is needed to identify the model types or modeling methods that offer high interpretability as well as good prediction accuracy. The goal of this study was to evaluate the utility of the Chi-square Automatic Interaction Detector (CHAID) decision tree modeling method in developing postural discomfort prediction models. Ten individual-specific decision tree models were developed, which predicted overall upper-body discomfort from local body part discomfort ratings. The prediction models were found to achieve high prediction accuracy and interpretability. (150 words)

Profiling the baseline performance and limits of machine learning models for adaptive immune receptor repertoire classification

2021 ◽  
Author(s):  
Chakravarthi Kanduri ◽  
Milena Pavlović ◽  
Lonneke Scheffer ◽  
Keshav Motwani ◽  
Maria Chernigovskaya ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Regression ◽  
Prediction Accuracy ◽  
Immune Receptor ◽  
Adaptive Immune ◽  
High Prediction Accuracy ◽  
Wide Range ◽  
Benchmark Datasets ◽  
High Prediction ◽  
Penalized Logistic Regression

Background: Machine learning (ML) methodology development for classification of immune states in adaptive immune receptor repertoires (AIRR) has seen a recent surge of interest. However, so far, there does not exist a systematic evaluation of scenarios where classical ML methods (such as penalized logistic regression) already perform adequately for AIRR classification. This hinders investigative reorientation to those scenarios where further method development of more sophisticated ML approaches may be required. Results: To identify those scenarios where a baseline method is able to perform well for AIRR classification, we generated a collection of synthetic benchmark datasets encompassing a wide range of dataset architecture-associated and immune state-associated sequence pattern (signal) complexity. We trained ≈1300 ML models with varying assumptions regarding immune signal on ≈850 datasets with a total of ≈210000 repertoires containing ≈42 billion TCRβ CDR3 amino acid sequences, thereby surpassing the sample sizes of current state-of-the-art AIRR ML setups by two orders of magnitude. We found that L1-penalized logistic regression achieved high prediction accuracy even when the immune signal occurs only in 1 out of 50000 AIR sequences. Conclusions: We provide a reference benchmark to guide new AIRR ML classification methodology by: (i) identifying those scenarios characterised by immune signal and dataset complexity, where baseline methods already achieve high prediction accuracy and (ii) facilitating realistic expectations of the performance of AIRR ML models given training dataset properties and assumptions. Our study serves as a template for defining specialized AIRR benchmark datasets for comprehensive benchmarking of AIRR ML methods.

Stock Price’s Prediction with Decision Tree

2011 ◽  
Vol 48-49 ◽  
pp. 1116-1121
Author(s):  
Tun Li ◽  
Gong Shen Liu
Keyword(s):  
Decision Tree ◽  
Stock Prices ◽  
Prediction Accuracy ◽  
Price Change ◽  
Financial Data ◽  
Average Error ◽  
Careful Selection ◽  
High Prediction Accuracy ◽  
High Prediction ◽  
Error Distance

Establishment of one process and some ameliorations of decision tree’s algorithm in order to predict the second day’s price change. The experiment builds a J48 tree, which is comfortable with continuous attributes, based on 10 years historical stock prices. After careful selection and preprocessing of financial data, high prediction accuracy is obtained. An introduction of dynamic-constructed tree reduces tree’s cost, and increases prediction’s quality on accuracy as well as average error distance.

The DGPS Maximum Speed Prediction Based on AR Model in Full-Scale Speed and Power Trial

2012 ◽  
Vol 424-425 ◽  
pp. 265-269
Author(s):  
Ming Yu Gu ◽  
Yan Ying Wang
Keyword(s):  
Detailed Analysis ◽  
Process Parameters ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Full Scale ◽  
Ar Model ◽  
Maximum Speed ◽  
High Prediction Accuracy ◽  
Speed Prediction ◽  
High Prediction

It is difficult to decide when the speed of a ship reaches to stability only by the experience of a test personnel in a speed and power trial. In this paper, a speed prediction method based on AR model is proposed to obtain the maximum speed automatically during the speed and power trial. In order to get high prediction accuracy, a detailed analysis is made on prediction process parameters. The results show that the related error between the prediction maximum speed and the actual value is low enough to meet the engineering requirements

Using machine learning to discover shape descriptors for predicting emulsion stability in a microfluidic channel

Soft Matter ◽  
2019 ◽  
Vol 15 (6) ◽  
pp. 1361-1372 ◽  
Author(s):  
Jian Wei Khor ◽  
Neal Jean ◽  
Eric S. Luxenberg ◽  
Stefano Ermon ◽  
Sindy K. Y. Tang
Keyword(s):  
Machine Learning ◽  
Prediction Accuracy ◽  
Emulsion Stability ◽  
Microfluidic Channel ◽  
Shape Descriptor ◽  
Shape Descriptors ◽  
High Prediction Accuracy ◽  
High Prediction

A novel shape descriptor identified by machine learning captures diverse droplet shapes and achieves high prediction accuracy of droplet instability.

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