scholarly journals Quantitative Models Identify Histone Signatures of Poised Genes Prior to Cellular Differentiation

2017 ◽  
Author(s):  
Rui Tian
Keyword(s):  
Bayesian Model ◽  
Cellular Differentiation ◽  
Predictive Accuracy ◽  
Es Cells ◽  
Support Vector ◽  
Full Model ◽  
Histone Marks ◽  
Mouse Es Cells ◽  
Multipotent Cells ◽  
Histone Modification Data

AbstractBackgroundRecent studies have shown that histone marks are involved in pre-programming gene fates during cellular differentiation. Bivalent domains (marked by both H3K4me3 and H3K27me3) have been proposed to act in the histone pre-patterning of poised genes; however, bivalent genes could also resolve into monovalent silenced states during differentiation. Thus, the histone signatures of poised genes need to be more precisely characterized.ResultsUsing a support vector machine (SVM), we show that the collective histone modification data from human blood hematopoietic cells (HSCs) could predict poised genes with fairly high predictive accuracy within the model of directed erythrocyte differentiation from HSCs. Surprisingly, models with single histone marks (e.g., H3K4me3 or H2A.Z) could reach comparable predictive powers to the full model built with all of the nine histone marks. We also derived an H2A.Z and H3K9me3-based Naive Bayesian model for inferring poised genes, and the validity of this model was supported by data from several other pluripotent/multipotent cells (including mouse ES cells).ConclusionOur work represents a systematic quantitative study that verified that histone marks play a role in pre-programming the activation or repression of specific genes during cellular differentiation. Our results suggest that the relative quantities of H2A.Z modification and H3K9me3 modification are important in determining a corresponding gene’s fate during cellular differentiation.

Gene Targeting

1999 ◽  
Keyword(s):  
Homologous Recombination ◽  
Gene Targeting ◽  
Mammalian Cells ◽  
Es Cells ◽  
Practical Approach ◽  
Simple Method ◽  
Mouse Es Cells ◽  
Cell Clones ◽  
Previous Edition ◽  
Pros And Cons

Since the publication of the first edition of Gene Targeting: A Practical Approach in 1993 there have been many advances in gene targeting and this new edition has been thoroughly updated and rewritten to include all the major new techniques. It provides not only tried-and-tested practical protocols but detailed guidance on their use and applications. As with the previous edition Gene Targeting: A Practical Approach 2e concentrates on gene targeting in mouse ES cells, but the techniques described can be easily adapted to applications in tissue culture including those for human cells. The first chapter covers the design of gene targeting vectors for mammalian cells and describes how to distinguish random integrations from homologous recombination. It is followed by a chapter on extending conventional gene targeting manipulations by using site-specific recombination using the Cre-loxP and Flp-FRT systems to produce 'clean' germline mutations and conditionally (in)activating genes. Chapter 3 describes methods for introducing DNA into ES cells for homologous recombination, selection and screening procedures for identifying and recovering targeted cell clones, and a simple method for establishing new ES cell lines. Chapter 4 discusses the pros and cons or aggregation versus blastocyst injection to create chimeras, focusing on the technical aspects of generating aggregation chimeras and then describes some of the uses of chimeras. The next topic covered is gene trap strategies; the structure, components, design, and modification of GT vectors, the various types of GT screens, and the molecular analysis of GT integrations. The final chapter explains the use of classical genetics in gene targeting and phenotype interpretation to create mutations and elucidate gene functions. Gene Targeting: A Practical Approach 2e will therefore be of great value to all researchers studying gene function.

scholarly journals Modeling of Cutting Force in the Turning of AISI 4340 Using Gaussian Process Regression Algorithm

2021 ◽  
Vol 11 (9) ◽  
pp. 4055
Author(s):  
Mahdi S. Alajmi ◽  
Abdullah M. Almeshal
Keyword(s):  
Gaussian Process ◽  
Cutting Force ◽  
Predictive Accuracy ◽  
Gaussian Process Regression ◽  
Machining Process ◽  
Support Vector ◽  
Process Data ◽  
Cutting Force Prediction ◽  
Artificial Neural Network Ann ◽  
Aisi 4340

Machining process data can be utilized to predict cutting force and optimize process parameters. Cutting force is an essential parameter that has a significant impact on the metal turning process. In this study, a cutting force prediction model for turning AISI 4340 alloy steel was developed using Gaussian process regression (GPR), support vector machines (SVM), and artificial neural network (ANN) methods. The GPR simulations demonstrated a reliable prediction of surface roughness for the dry turning method with R2 = 0.9843, MAPE = 5.12%, and RMSE = 1.86%. Performance comparisons between GPR, SVM, and ANN show that GPR is an effective method that can ensure high predictive accuracy of the cutting force in the turning of AISI 4340.

Remote sensing inversion of water quality in coastal sea area based on machine learning: a case study of Shenzhen bay, China

2021 ◽  
Author(s):  
Xiaotong Zhu ◽  
Jinhui Jeanne Huang
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Water Quality ◽  
Predictive Accuracy ◽  
Water Environment ◽  
Quality Parameters ◽  
Machine Learning Algorithms ◽  
Dynamic Monitoring ◽  
Support Vector ◽  
Seawater Quality

<p>Remote sensing monitoring has the characteristics of wide monitoring range, celerity, low cost for long-term dynamic monitoring of water environment. With the flourish of artificial intelligence, machine learning has enabled remote sensing inversion of seawater quality to achieve higher prediction accuracy. However, due to the physicochemical property of the water quality parameters, the performance of algorithms differs a lot. In order to improve the predictive accuracy of seawater quality parameters, we proposed a technical framework to identify the optimal machine learning algorithms using Sentinel-2 satellite and in-situ seawater sample data. In the study, we select three algorithms, i.e. support vector regression (SVR), XGBoost and deep learning (DL), and four seawater quality parameters, i.e. dissolved oxygen (DO), total dissolved solids (TDS), turbidity(TUR) and chlorophyll-a (Chla). The results show that SVR is a more precise algorithm to inverse DO (R<sup>2</sup> = 0.81). XGBoost has the best accuracy for Chla and Tur inversion (R<sup>2</sup> = 0.75 and 0.78 respectively) while DL performs better in TDS (R<sup>2</sup> =0.789). Overall, this research provides a theoretical support for high precision remote sensing inversion of offshore seawater quality parameters based on machine learning.</p>

Application of machine learning in predicting construction project profit in Ghana using Support Vector Regression Algorithm (SVRA)

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emmanuel Adinyira ◽  
Emmanuel Akoi-Gyebi Adjei ◽  
Kofi Agyekum ◽  
Frank Desmond Kofi Fugar
Keyword(s):  
Machine Learning ◽  
Support Vector Regression ◽  
Cash Flow ◽  
Predictive Accuracy ◽  
Model Development ◽  
Construction Project ◽  
Support Vector ◽  
Sensitivity Index ◽  
Content Type ◽  
Hyperparameter Selection

PurposeKnowledge of the effect of various cash-flow factors on expected project profit is important to effectively manage productivity on construction projects. This study was conducted to develop and test the sensitivity of a Machine Learning Support Vector Regression Algorithm (SVRA) to predict construction project profit in Ghana.Design/methodology/approachThe study relied on data from 150 institutional projects executed within the past five years (2014–2018) in developing the model. Eighty percent (80%) of the data from the 150 projects was used at hyperparameter selection and final training phases of the model development and the remaining 20% for model testing. Using MATLAB for Support Vector Regression, the parameters available for tuning were the epsilon values, the kernel scale, the box constraint and standardisations. The sensitivity index was computed to determine the degree to which the independent variables impact the dependent variable.FindingsThe developed model's predictions perfectly fitted the data and explained all the variability of the response data around its mean. Average predictive accuracy of 73.66% was achieved with all the variables on the different projects in validation. The developed SVR model was sensitive to labour and loan.Originality/valueThe developed SVRA combines variation, defective works and labour with other financial constraints, which have been the variables used in previous studies. It will aid contractors in predicting profit on completion at commencement and also provide information on the effect of changes to cash-flow factors on profit.

Analysis of GATA and its related factors using in vitro differentiation of mouse ES cells

2007 ◽  
Vol 38 (2) ◽  
pp. 161
Author(s):  
Toru Nakano ◽  
Jie Zheng ◽  
Daijiro Sugiyama ◽  
Hilo Yen ◽  
Kenji Kitajima
Keyword(s):  
Es Cells ◽  
In Vitro Differentiation ◽  
Related Factors ◽  
Mouse Es Cells

scholarly journals Novel Genetic Variants of Hepatitis B Virus in Fulminant Hepatitis

2017 ◽  
Vol 2017 ◽  
pp. 1-6 ◽  
Author(s):  
Jack Bee Chook ◽  
Yun Fong Ngeow ◽  
Kok Keng Tee ◽  
Suat Cheng Peh ◽  
Rosmawati Mohamed
Keyword(s):  
Hepatitis B Virus ◽  
Hepatitis B ◽  
Predictive Value ◽  
Fulminant Hepatitis ◽  
Predictive Accuracy ◽  
Stop Codon ◽  
Acute Infection ◽  
Support Vector ◽  
Svm Model ◽  
B Virus

Fulminant hepatitis (FH) is a life-threatening liver disease characterised by intense immune attack and massive liver cell death. The common precore stop codon mutation of hepatitis B virus (HBV), A1896, is frequently associated with FH, but lacks specificity. This study attempts to uncover all possible viral nucleotides that are specifically associated with FH through a compiled sequence analysis of FH and non-FH cases from acute infection. We retrieved 67 FH and 280 acute non-FH cases of hepatitis B from GenBank and applied support vector machine (SVM) model to seek candidate nucleotides highly predictive of FH. Six best candidates with top predictive accuracy, 92.5%, were used to build a SVM model; they are C2129 (85.3%), T720 (83.0%), Y2131 (82.4%), T2013 (82.1%), K2048 (82.1%), and A2512 (82.1%). This model gave a high specificity (99.3%), positive predictive value (95.6%), and negative predictive value (92.1%), but only moderate sensitivity (64.2%). We successfully built a SVM model comprising six variants that are highly predictive and specific for FH: four in the core region and one each in the polymerase and the surface regions. These variants indicate that intracellular virion/core retention could play an important role in the progression to FH.

scholarly journals Hoxa1 and TALE proteins display cross-regulatory interactions and form a combinatorial binding code on Hoxa1 targets

2016 ◽  
Author(s):  
Bony De Kumar ◽  
Hugo J. Parker ◽  
Ariel Paulson ◽  
Mark E. Parrish ◽  
Irina Pushel ◽  
...  
Keyword(s):  
Es Cells ◽  
Hox Proteins ◽  
Binding Motifs ◽  
Functional Roles ◽  
Regulatory Interactions ◽  
Mouse Es Cells ◽  
Genome Wide ◽  
A Genome ◽  
Combinatorial Interactions ◽  
Insight Into

AbstractHoxa1 has diverse functional roles in differentiation and development. We have identified and characterized properties of regions bound by Hoxa1 on a genome-wide basis in differentiating mouse ES cells. Hoxa1 bound regions are enriched for clusters of consensus binding motifs for Hox, Pbx and Meis and many display co-occupancy of Pbx and Meis. Pbx and Meis are members of the TALE family and genome-wide analysis of multiple TALE members (Pbx, Meis, TGIF, Prep1 and Prep2) show that nearly all Hoxa1 targets display occupancy of one or more TALE members. The combinatorial binding patterns of TALE proteins defines distinct classes of Hoxa1 targets and indicates a role as cofactors in modulating the specificity of Hox proteins. We also discovered extensive auto- and cross-regulatory interactions among the Hoxa1 and TALE genes. This study provides new insight into a regulatory network involving combinatorial interactions between Hoxa1 and TALE proteins.

scholarly journals Establishing a Credit Risk Evaluation System for SMEs Using the Soft Voting Fusion Model

Risks ◽  
2021 ◽  
Vol 9 (11) ◽  
pp. 202
Author(s):  
Ge Gao ◽  
Hongxin Wang ◽  
Pengbin Gao
Keyword(s):  
Credit Risk ◽  
Evaluation System ◽  
Predictive Accuracy ◽  
Assessment System ◽  
Gradient Boosting ◽  
Support Vector ◽  
Fusion Model ◽  
Light Gradient ◽  
Extreme Gradient Boosting ◽  
The Government

In China, SMEs are facing financing difficulties, and commercial banks and financial institutions are the main financing channels for SMEs. Thus, a reasonable and efficient credit risk assessment system is important for credit markets. Based on traditional statistical methods and AI technology, a soft voting fusion model, which incorporates logistic regression, support vector machine (SVM), random forest (RF), eXtreme Gradient Boosting (XGBoost), and Light Gradient Boosting Machine (LightGBM), is constructed to improve the predictive accuracy of SMEs’ credit risk. To verify the feasibility and effectiveness of the proposed model, we use data from 123 SMEs nationwide that worked with a Chinese bank from 2016 to 2020, including financial information and default records. The results show that the accuracy of the soft voting fusion model is higher than that of a single machine learning (ML) algorithm, which provides a theoretical basis for the government to control credit risk in the future and offers important references for banks to make credit decisions.

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