Insight to Gene Expression From Promoter Libraries With the Machine Learning Workflow Exp2Ipynb

Metabolic engineering relies on modifying gene expression to regulate protein concentrations and reaction activities. The gene expression is controlled by the promoter sequence, and sequence libraries are used to scan expression activities and to identify correlations between sequence and activity. We introduce a computational workflow called Exp2Ipynb to analyze promoter libraries maximizing information retrieval and promoter design with desired activity. We applied Exp2Ipynb to seven prokaryotic expression libraries to identify optimal experimental design principles. The workflow is open source, available as Jupyter Notebooks and covers the steps to 1) generate a statistical overview to sequence and activity, 2) train machine-learning algorithms, such as random forest, gradient boosting trees and support vector machines, for prediction and extraction of feature importance, 3) evaluate the performance of the estimator, and 4) to design new sequences with a desired activity using numerical optimization. The workflow can perform regression or classification on multiple promoter libraries, across species or reporter proteins. The most accurate predictions in the sample libraries were achieved when the promoters in the library were recognized by a single sigma factor and a unique reporter system. The prediction confidence mostly depends on sample size and sequence diversity, and we present a relationship to estimate their respective effects. The workflow can be adapted to process sequence libraries from other expression-related problems and increase insight to the growing application of high-throughput experiments, providing support for efficient strain engineering.

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Exp2Ipynb: A general machine-learning workflow for the analysis of promoter libraries

10.1101/2020.12.14.422740 ◽

2020 ◽

Author(s):

Ulf W. Liebal ◽

Sebastian Koebbing ◽

Lars M. Blank

Keyword(s):

Machine Learning ◽

Sample Size ◽

Learning Strategies ◽

Promoter Sequence ◽

Strain Engineering ◽

Reporter System ◽

Promoter Sequences ◽

Expression Activity ◽

Multiple Promoter ◽

High Throughput Experiments

Strain engineering in biotechnology modifies metabolic pathways in microorganisms to overproduce target metabolites. To modify metabolic pathway activity in bacteria, gene expression is an effective and easy manipulated process, specifically the promoter sequence recognized by sigma factors. Promoter libraries are generated to scan the expression activity of different promoter sequences and to identify sequence positions that predict activity. To maximize information retrieval, a well-designed experimental setup is required. We present a computational workflow to analyse promoter libraries; by applying this workflow to seven libraries, we aim to identify critical design principles. The workflow is based on a Python Jupyter Notebook and covers the following steps: (i) statistical sequence analysis, (ii) sequence-input to expression-output predictions, (iii) estimator performance evaluation, and (iv) new sequence prediction with defined activity. The workflow can process multiple promoter libraries, across species or reporter proteins, and classify or regress expression activity. The strongest predictions in the sample libraries were achieved when the promoters in the library were recognized by a single sigma factor and a unique reporter system. A trade-off between sample size and sequence diversity reduces prediction quality, and we present a relationship to estimate the minimum sample size. The workflow guides the user through analysis and machine-learning training, is open source and easily adaptable to include alternative machine-learning strategies and to process sequence libraries from other expression-related problems. The workflow is a contribution to increase insight to the growing application of high-throughput experiments and provides support for efficient strain engineering.

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Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽

10.3390/ma14154068 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4068

Author(s):

Xu Huang ◽

Mirna Wasouf ◽

Jessada Sresakoolchai ◽

Sakdirat Kaewunruen

Keyword(s):

Machine Learning ◽

Search Algorithm ◽

Weather Conditions ◽

Prediction Performance ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Gradient Boosting ◽

Support Vector ◽

Self Healing ◽

Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

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Comparison of Ensemble Machine Learning Methods for Soil Erosion Pin Measurements

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10010042 ◽

2021 ◽

Vol 10 (1) ◽

pp. 42

Author(s):

Kieu Anh Nguyen ◽

Walter Chen ◽

Bor-Shiun Lin ◽

Uma Seeboonruang

Keyword(s):

Machine Learning ◽

Soil Erosion ◽

Ensemble Methods ◽

Machine Learning Algorithms ◽

Multivariate Adaptive Regression Splines ◽

Gradient Boosting ◽

Support Vector ◽

Ensemble Machine Learning ◽

Boosting Method ◽

Bagging Method

Although machine learning has been extensively used in various fields, it has only recently been applied to soil erosion pin modeling. To improve upon previous methods of quantifying soil erosion based on erosion pin measurements, this study explored the possible application of ensemble machine learning algorithms to the Shihmen Reservoir watershed in northern Taiwan. Three categories of ensemble methods were considered in this study: (a) Bagging, (b) boosting, and (c) stacking. The bagging method in this study refers to bagged multivariate adaptive regression splines (bagged MARS) and random forest (RF), and the boosting method includes Cubist and gradient boosting machine (GBM). Finally, the stacking method is an ensemble method that uses a meta-model to combine the predictions of base models. This study used RF and GBM as the meta-models, decision tree, linear regression, artificial neural network, and support vector machine as the base models. The dataset used in this study was sampled using stratified random sampling to achieve a 70/30 split for the training and test data, and the process was repeated three times. The performance of six ensemble methods in three categories was analyzed based on the average of three attempts. It was found that GBM performed the best among the ensemble models with the lowest root-mean-square error (RMSE = 1.72 mm/year), the highest Nash-Sutcliffe efficiency (NSE = 0.54), and the highest index of agreement (d = 0.81). This result was confirmed by the spatial comparison of the absolute differences (errors) between model predictions and observations using GBM and RF in the study area. In summary, the results show that as a group, the bagging method and the boosting method performed equally well, and the stacking method was third for the erosion pin dataset considered in this study.

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Prediction and Analysis of Gold Prices using Ensemble Machine Learning Algorithms

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.36028 ◽

2021 ◽

Vol 9 (VI) ◽

pp. 4367-4374

Author(s):

Gudipally Chandrashakar

Keyword(s):

Machine Learning ◽

Time Series ◽

Time Series Data ◽

Gold Price ◽

Machine Learning Algorithms ◽

Series Data ◽

Gradient Boosting ◽

Support Vector ◽

Average Value ◽

Ensemble Machine Learning

In this article, we used historical time series data up to the current day gold price. In this study of predicting gold price, we consider few correlating factors like silver price, copper price, standard, and poor’s 500 value, dollar-rupee exchange rate, Dow Jones Industrial Average Value. Considering the prices of every correlating factor and gold price data where dates ranging from 2008 January to 2021 February. Few algorithms of machine learning are used to analyze the time-series data are Random Forest Regression, Support Vector Regressor, Linear Regressor, ExtraTrees Regressor and Gradient boosting Regression. While seeing the results the Extra Tree Regressor algorithm gives the predicted value of gold prices more accurately.

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MODIS-FIRMS and ground-truthing based wildfire likelihood mapping of Sikkim Himalaya using machine learning algorithms.

10.21203/rs.3.rs-750123/v1 ◽

2021 ◽

Author(s):

Polash Banerjee

Keyword(s):

Machine Learning ◽

Machine Learning Algorithms ◽

Tree Cover ◽

Anthropogenic Factors ◽

Gradient Boosting ◽

Support Vector ◽

Learning Methods ◽

Sikkim Himalaya ◽

Environmental Features ◽

Machine Learning Methods

Abstract Wildfires in limited extent and intensity can be a boon for the forest ecosystem. However, recent episodes of wildfires of 2019 in Australia and Brazil are sad reminders of their heavy ecological and economical costs. Understanding the role of environmental factors in the likelihood of wildfires in a spatial context would be instrumental in mitigating it. In this study, 14 environmental features encompassing meteorological, topographical, ecological, in situ and anthropogenic factors have been considered for preparing the wildfire likelihood map of Sikkim Himalaya. A comparative study on the efficiency of machine learning methods like Generalized Linear Model (GLM), Support Vector Machine (SVM), Random Forest (RF) and Gradient Boosting Model (GBM) has been performed to identify the best performing algorithm in wildfire prediction. The study indicates that all the machine learning methods are good at predicting wildfires. However, RF has outperformed, followed by GBM in the prediction. Also, environmental features like average temperature, average wind speed, proximity to roadways and tree cover percentage are the most important determinants of wildfires in Sikkim Himalaya. This study can be considered as a decision support tool for preparedness, efficient resource allocation and sensitization of people towards mitigation of wildfires in Sikkim.

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Techniques for Detecting Malware Traffic: A Comprehensive Approach to Feature Selection and Classification

International Journal for Research in Applied Science and Engineering Technology ◽

10.22214/ijraset.2021.39088 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1-10

Author(s):

Harsha A K

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Random Forest ◽

Learning Algorithms ◽

Malware Detection ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Steady Increase ◽

Extreme Gradient Boosting

Abstract: Since the advent of encryption, there has been a steady increase in malware being transmitted over encrypted networks. Traditional approaches to detect malware like packet content analysis are inefficient in dealing with encrypted data. In the absence of actual packet contents, we can make use of other features like packet size, arrival time, source and destination addresses and other such metadata to detect malware. Such information can be used to train machine learning classifiers in order to classify malicious and benign packets. In this paper, we offer an efficient malware detection approach using classification algorithms in machine learning such as support vector machine, random forest and extreme gradient boosting. We employ an extensive feature selection process to reduce the dimensionality of the chosen dataset. The dataset is then split into training and testing sets. Machine learning algorithms are trained using the training set. These models are then evaluated against the testing set in order to assess their respective performances. We further attempt to tune the hyper parameters of the algorithms, in order to achieve better results. Random forest and extreme gradient boosting algorithms performed exceptionally well in our experiments, resulting in area under the curve values of 0.9928 and 0.9998 respectively. Our work demonstrates that malware traffic can be effectively classified using conventional machine learning algorithms and also shows the importance of dimensionality reduction in such classification problems. Keywords: Malware Detection, Extreme Gradient Boosting, Random Forest, Feature Selection.

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Gene Expression Assay: A New Panel for Early Metastatic Risk Estimation for Breast Cancer

10.21203/rs.3.rs-279461/v1 ◽

2021 ◽

Author(s):

Melih Agraz ◽

Umut Agyuz ◽

E. Celeste Welch ◽

Kaymaz Yasin ◽

Kuyumcu Birol

Keyword(s):

Breast Cancer ◽

Gene Expression ◽

Machine Learning ◽

T Cell ◽

Zinc Finger ◽

Learning Algorithms ◽

Machine Learning Algorithms ◽

Cytotoxic T Cell ◽

Gradient Boosting ◽

Shh Pathway

Abstract Background Metastasis is one of the most challenging problems in cancer diagnosis and treatment, as its causes have not been yet well characterized. Prediction of the metastatic status of breast cancer is important in cancer research because it has the potential to save lives. However, the systems biology behind metastasis is complex and driven by a variety of factors beyond those that have already been characterized for various cancer types. Furthermore, prediction of cancer metastasis is a challenging task due to the variation in parameters and conditions specific to individual patients and mutation of the sub-types. Results In this paper, we apply tree-based machine learning algorithms for gene expression data analysis in the estimation of metastatic potentials within a group of 490 breast cancer patients. Hence, we utilize tree-based machine learning algorithms, decision trees, gradient boosting, and extremely randomized trees to assess the variable importance.Conclusions We obtained highly accurate values from all three algorithms, we observed the highest accuracy from the Gradient Boost method which is 0.8901. Finally, we were able to determine the 10 most important genetic variables used in the boosted algorithms, as well as their respective importance scores and biological importance. Common important genes for our algorithms are found as CD8, PB1, THP-1. CD8, also known as CD8A is a receptor for the TCR, or T-cell receptor, which facilitates cytotoxic T-cell activity and its association with cancer is defined in the paper. PB1, PBRM1 or polybromo 1 is a tumor suppressor gene. THP-1 or GLI2 is a zinc finger protein referred to as ”Glioma-Associated Oncogene Family Zinc Finger 2”. This gene encodes a protein for the zinc finger, which binds DNA and mediate Sonic hedgehog signaling (SHH). Disruption in the SHH pathway have long been associated with cancer and cellular proliferation.

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Evaluating Variable Selection and Machine Learning Algorithms for Estimating Forest Heights by Combining Lidar and Hyperspectral Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9090507 ◽

2020 ◽

Vol 9 (9) ◽

pp. 507

Author(s):

Sanjiwana Arjasakusuma ◽

Sandiaga Swahyu Kusuma ◽

Stuart Phinn

Keyword(s):

Machine Learning ◽

Feature Selection ◽

Learning Algorithms ◽

Principal Component ◽

Hyperspectral Data ◽

Machine Learning Algorithms ◽

Gradient Boosting ◽

Support Vector ◽

Forest Height ◽

Extreme Gradient Boosting

Machine learning has been employed for various mapping and modeling tasks using input variables from different sources of remote sensing data. For feature selection involving high- spatial and spectral dimensionality data, various methods have been developed and incorporated into the machine learning framework to ensure an efficient and optimal computational process. This research aims to assess the accuracy of various feature selection and machine learning methods for estimating forest height using AISA (airborne imaging spectrometer for applications) hyperspectral bands (479 bands) and airborne light detection and ranging (lidar) height metrics (36 metrics), alone and combined. Feature selection and dimensionality reduction using Boruta (BO), principal component analysis (PCA), simulated annealing (SA), and genetic algorithm (GA) in combination with machine learning algorithms such as multivariate adaptive regression spline (MARS), extra trees (ET), support vector regression (SVR) with radial basis function, and extreme gradient boosting (XGB) with trees (XGbtree and XGBdart) and linear (XGBlin) classifiers were evaluated. The results demonstrated that the combinations of BO-XGBdart and BO-SVR delivered the best model performance for estimating tropical forest height by combining lidar and hyperspectral data, with R2 = 0.53 and RMSE = 1.7 m (18.4% of nRMSE and 0.046 m of bias) for BO-XGBdart and R2 = 0.51 and RMSE = 1.8 m (15.8% of nRMSE and −0.244 m of bias) for BO-SVR. Our study also demonstrated the effectiveness of BO for variables selection; it could reduce 95% of the data to select the 29 most important variables from the initial 516 variables from lidar metrics and hyperspectral data.

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A Comparative Analysis of Enhanced Machine Learning Algorithms for Smart Grid Stability Prediction

10.36227/techrxiv.16863145.v1 ◽

2021 ◽

Author(s):

ANKIT GHOSH ◽

ALOK KOLE

Keyword(s):

Machine Learning ◽

Comparative Analysis ◽

Smart Grid ◽

Smart Grids ◽

Machine Learning Algorithms ◽

Electricity Sector ◽

Stochastic Gradient Descent ◽

Energy Output ◽

Gradient Boosting ◽

Support Vector

<p>Smart grid is an essential concept in the transformation of the electricity sector into an intelligent digitalized energy network that can deliver optimal energy from the source to the consumers. Smart grids being self-sufficient systems are constructed through the integration of information, telecommunication, and advanced power technologies with the existing electricity systems. Artificial Intelligence (AI) is an important technology driver in smart grids. The application of AI techniques in smart grid is becoming more apparent because the traditional modelling optimization and control techniques have their own limitations. Machine Learning (ML) being a sub-set of AI enables intelligent decision-making and response to sudden changes in the customer energy demands, unexpected disruption of power supply, sudden variations in renewable energy output or any other catastrophic events in a smart grid. This paper presents the comparison among some of the state-of-the-art ML algorithms for predicting smart grid stability. The dataset that has been selected contains results from simulations of smart grid stability. Enhanced ML algorithms such as Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbour (KNN), Naïve Bayes (NB), Decision Tree (DT), Random Forest (RF), Stochastic Gradient Descent (SGD) classifier, XGBoost and Gradient Boosting classifiers have been implemented to forecast smart grid stability. A comparative analysis among the different ML models has been performed based on the following evaluation metrics such as accuracy, precision, recall, F1-score, AUC-ROC, and AUC-PR curves. The test results that have been obtained have been quite promising with the XGBoost classifier outperforming all the other models with an accuracy of 97.5%, recall of 98.4%, precision of 97.6%, F1-score of 97.9%, AUC-ROC of 99.8% and AUC-PR of 99.9%. </p>

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Predicting Forest Fires using Supervised and Ensemble Machine Learning Algorithms

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b2878.078219 ◽

2019 ◽

Vol 8 (2) ◽

pp. 3697-3705 ◽

Cited By ~ 1

Keyword(s):

Machine Learning ◽

Logistic Regression ◽

Forest Fires ◽

Principal Component ◽

Climatic Conditions ◽

Machine Learning Algorithms ◽

Machine Learning Techniques ◽

Gradient Boosting ◽

Support Vector ◽

Physical Factors

Forest fires have become one of the most frequently occurring disasters in recent years. The effects of forest fires have a lasting impact on the environment as it lead to deforestation and global warming, which is also one of its major cause of occurrence. Forest fires are dealt by collecting the satellite images of forest and if there is any emergency caused by the fires then the authorities are notified to mitigate its effects. By the time the authorities get to know about it, the fires would have already caused a lot of damage. Data mining and machine learning techniques can provide an efficient prevention approach where data associated with forests can be used for predicting the eventuality of forest fires. This paper uses the dataset present in the UCI machine learning repository which consists of physical factors and climatic conditions of the Montesinho park situated in Portugal. Various algorithms like Logistic regression, Support Vector Machine, Random forest, K-Nearest neighbors in addition to Bagging and Boosting predictors are used, both with and without Principal Component Analysis (PCA). Among the models in which PCA was applied, Logistic Regression gave the highest F-1 score of 68.26 and among the models where PCA was absent, Gradient boosting gave the highest score of 68.36.

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