scholarly journals Multi-label pathway prediction based on active dataset subsampling

2020 ◽  
Author(s):  
Abdur Rahman M. A. Basher ◽  
Steven J. Hallam
Keyword(s):  
Machine Learning ◽  
Microbial Communities ◽  
Performance Metrics ◽  
Class Imbalance ◽  
Training Data ◽  
Great Promise ◽  
Biological Organization ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Pathway Prediction

AbstractMachine learning methods show great promise in predicting metabolic pathways at different levels of biological organization. However, several complications remain that can degrade prediction performance including inadequately labeled training data, missing feature information, and inherent imbalances in the distribution of enzymes and pathways within a dataset. This class imbalance problem is commonly encountered by the machine learning community when the proportion of instances over class labels within a dataset are uneven, resulting in poor predictive performance for underrepresented classes. Here, we present leADS, multi-label learning based on active dataset subsampling, that leverages the idea of subsampling points from a pool of data to reduce the negative impact of training loss due to class imbalance. Specifically, leADS performs an iterative process to: (i)-construct an acquisition model in an ensemble framework; (ii) select informative points using an appropriate acquisition function; and (iii) train on selected samples. Multiple base learners are implemented in parallel where each is assigned a portion of labeled training data to learn pathways. We benchmark leADS using a corpora of 10 experimental datasets manifesting diverse multi-label properties used in previous pathway prediction studies, including manually curated organismal genomes, synthetic microbial communities and low complexity microbial communities. Resulting performance metrics equaled or exceeded previously reported machine learning methods for both organismal and multi-organismal genomes while establishing an extensible framework for navigating class imbalances across diverse real world datasets.Availability and implementationThe software package, and installation instructions are published on github.com/[email protected]

scholarly journals Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

2021 ◽  
Vol 10 (4) ◽  
pp. 199
Author(s):  
Francisco M. Bellas Aláez ◽  
Jesus M. Torres Palenzuela ◽  
Evangelos Spyrakos ◽  
Luis González Vilas
Keyword(s):  
Machine Learning ◽  
Performance Metrics ◽  
Prediction Models ◽  
Support Vector ◽  
False Alarms ◽  
Learning Approaches ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Rías Baixas ◽  
New Algorithms

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.

scholarly journals Possibility of Autonomous Estimation of Shiba Goat’s Estrus and Non-Estrus Behavior by Machine Learning Methods

Animals ◽  
2020 ◽  
Vol 10 (5) ◽  
pp. 771
Author(s):  
Toshiya Arakawa
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Random Forest ◽  
Markov Models ◽  
Tracking System ◽  
Video Tracking ◽  
Training Data ◽  
Support Vector ◽  
Learning Methods ◽  
Machine Learning Methods

Mammalian behavior is typically monitored by observation. However, direct observation requires a substantial amount of effort and time, if the number of mammals to be observed is sufficiently large or if the observation is conducted for a prolonged period. In this study, machine learning methods as hidden Markov models (HMMs), random forests, support vector machines (SVMs), and neural networks, were applied to detect and estimate whether a goat is in estrus based on the goat’s behavior; thus, the adequacy of the method was verified. Goat’s tracking data was obtained using a video tracking system and used to estimate whether they, which are in “estrus” or “non-estrus”, were in either states: “approaching the male”, or “standing near the male”. Totally, the PC of random forest seems to be the highest. However, The percentage concordance (PC) value besides the goats whose data were used for training data sets is relatively low. It is suggested that random forest tend to over-fit to training data. Besides random forest, the PC of HMMs and SVMs is high. However, considering the calculation time and HMM’s advantage in that it is a time series model, HMM is better method. The PC of neural network is totally low, however, if the more goat’s data were acquired, neural network would be an adequate method for estimation.

Machine Learning and Value-Based Software Engineering

2012 ◽  
pp. 287-301
Author(s):  
Du Zhang
Keyword(s):  
Machine Learning ◽  
Software Engineering ◽  
Full Range ◽  
Training Data ◽  
Learning Methods ◽  
Engineering Research ◽  
Machine Learning Methods ◽  
Machine Learning Applications ◽  
Value Propositions ◽  
Software Engineering Research

Software engineering research and practice thus far are primarily conducted in a value-neutral setting where each artifact in software development such as requirement, use case, test case, and defect, is treated as equally important during a software system development process. There are a number of shortcomings of such value-neutral software engineering. Value-based software engineering is to integrate value considerations into the full range of existing and emerging software engineering principles and practices. Machine learning has been playing an increasingly important role in helping develop and maintain large and complex software systems. However, machine learning applications to software engineering have been largely confined to the value-neutral software engineering setting. In this paper, the general message to be conveyed is to apply machine learning methods and algorithms to value-based software engineering. The training data or the background knowledge or domain theory or heuristics or bias used by machine learning methods in generating target models or functions should be aligned with stakeholders’ value propositions. An initial research agenda is proposed for machine learning in value-based software engineering.

scholarly journals Machine learning methods for metabolic pathway prediction

2010 ◽  
Vol 11 (1) ◽  
pp. 15 ◽  
Author(s):  
Joseph M Dale ◽  
Liviu Popescu ◽  
Peter D Karp
Keyword(s):  
Machine Learning ◽  
Metabolic Pathway ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Pathway Prediction

scholarly journals TADA: phylogenetic augmentation of microbiome samples enhances phenotype classification

2019 ◽  
Vol 35 (14) ◽  
pp. i31-i40 ◽  
Author(s):  
Erfan Sayyari ◽  
Ban Kawas ◽  
Siavash Mirarab
Keyword(s):  
Machine Learning ◽  
Sample Size ◽  
Data Augmentation ◽  
Training Data ◽  
Supplementary Information ◽  
High Dimensional ◽  
Learning Methods ◽  
Machine Learning Methods ◽  
Phenotype Classification ◽  
Microbiome Data

Abstract Motivation Learning associations of traits with the microbial composition of a set of samples is a fundamental goal in microbiome studies. Recently, machine learning methods have been explored for this goal, with some promise. However, in comparison to other fields, microbiome data are high-dimensional and not abundant; leading to a high-dimensional low-sample-size under-determined system. Moreover, microbiome data are often unbalanced and biased. Given such training data, machine learning methods often fail to perform a classification task with sufficient accuracy. Lack of signal is especially problematic when classes are represented in an unbalanced way in the training data; with some classes under-represented. The presence of inter-correlations among subsets of observations further compounds these issues. As a result, machine learning methods have had only limited success in predicting many traits from microbiome. Data augmentation consists of building synthetic samples and adding them to the training data and is a technique that has proved helpful for many machine learning tasks. Results In this paper, we propose a new data augmentation technique for classifying phenotypes based on the microbiome. Our algorithm, called TADA, uses available data and a statistical generative model to create new samples augmenting existing ones, addressing issues of low-sample-size. In generating new samples, TADA takes into account phylogenetic relationships between microbial species. On two real datasets, we show that adding these synthetic samples to the training set improves the accuracy of downstream classification, especially when the training data have an unbalanced representation of classes. Availability and implementation TADA is available at https://github.com/tada-alg/TADA. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Prediction of Collapsibility of Loess of Construction Sites in Xining Based on Machine Learning Methods

2021 ◽  
Author(s):  
Qifei Zhao ◽  
Xiaojun Li ◽  
Yunning Cao ◽  
Zhikun Li ◽  
Jixin Fan
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Training Data ◽  
Support Vector ◽  
Engineering Practice ◽  
Burial Depth ◽  
Learning Methods ◽  
Data Set ◽  
Machine Learning Methods ◽  
North East

Abstract Collapsibility of loess is a significant factor affecting engineering construction in loess area, and testing the collapsibility of loess is costly. In this study, A total of 4,256 loess samples are collected from the north, east, west and middle regions of Xining. 70% of the samples are used to generate training data set, and the rest are used to generate verification data set, so as to construct and validate the machine learning models. The most important six factors are selected from thirteen factors by using Grey Relational analysis and multicollinearity analysis: burial depth、water content、specific gravity of soil particles、void rate、geostatic stress and plasticity limit. In order to predict the collapsibility of loess, four machine learning methods: Support Vector Machine (SVM), Random Subspace Based Support Vector Machine (RSSVM), Random Forest (RF) and Naïve Bayes Tree (NBTree), are studied and compared. The receiver operating characteristic (ROC) curve indicators, standard error (SD) and 95% confidence interval (CI) are used to verify and compare the models in different research areas. The results show that: RF model is the most efficient in predicting the collapsibility of loess in Xining, and its AUC average is above 80%, which can be used in engineering practice.

scholarly journals Quantified uncertainties in fission yields from machine learning

2020 ◽  
Vol 242 ◽  
pp. 05003
Author(s):  
A.E. Lovell ◽  
A.T. Mohan ◽  
P. Talou ◽  
M. Chertkov
Keyword(s):  
Machine Learning ◽  
Nuclear Physics ◽  
Experimental Error ◽  
Training Data ◽  
Learning Methods ◽  
Mixture Density ◽  
Machine Learning Methods ◽  
Predicted Values ◽  
The Impact ◽  
Fission Yields

As machine learning methods gain traction in the nuclear physics community, especially those methods that aim to propagate uncertainties to unmeasured quantities, it is important to understand how the uncertainty in the training data coming either from theory or experiment propagates to the uncertainty in the predicted values. Gaussian Processes and Bayesian Neural Networks are being more and more widely used, in particular to extrapolate beyond measured data. However, studies are typically not performed on the impact of the experimental errors on these extrapolated values. In this work, we focus on understanding how uncertainties propagate from input to prediction when using machine learning methods. We use a Mixture Density Network (MDN) to incorporate experimental error into the training of the network and construct uncertainties for the associated predicted quantities. Systematically, we study the effect of the size of the experimental error, both on the reproduced training data and extrapolated predictions for fission yields of actinides.

scholarly journals An evaluation of machine-learning for predicting phenotype: studies in yeast, rice, and wheat

2019 ◽  
Vol 109 (2) ◽  
pp. 251-277 ◽  
Author(s):  
Nastasiya F. Grinberg ◽  
Oghenejokpeme I. Orhobor ◽  
Ross D. King
Keyword(s):  
Machine Learning ◽  
Population Structure ◽  
Missing Data ◽  
Statistical Genetics ◽  
Great Promise ◽  
Lasso Regression ◽  
Learning Methods ◽  
Phenotype Prediction ◽  
Machine Learning Methods ◽  
Prediction Problems

Abstract In phenotype prediction the physical characteristics of an organism are predicted from knowledge of its genotype and environment. Such studies, often called genome-wide association studies, are of the highest societal importance, as they are of central importance to medicine, crop-breeding, etc. We investigated three phenotype prediction problems: one simple and clean (yeast), and the other two complex and real-world (rice and wheat). We compared standard machine learning methods; elastic net, ridge regression, lasso regression, random forest, gradient boosting machines (GBM), and support vector machines (SVM), with two state-of-the-art classical statistical genetics methods; genomic BLUP and a two-step sequential method based on linear regression. Additionally, using the clean yeast data, we investigated how performance varied with the complexity of the biological mechanism, the amount of observational noise, the number of examples, the amount of missing data, and the use of different data representations. We found that for almost all the phenotypes considered, standard machine learning methods outperformed the methods from classical statistical genetics. On the yeast problem, the most successful method was GBM, followed by lasso regression, and the two statistical genetics methods; with greater mechanistic complexity GBM was best, while in simpler cases lasso was superior. In the wheat and rice studies the best two methods were SVM and BLUP. The most robust method in the presence of noise, missing data, etc. was random forests. The classical statistical genetics method of genomic BLUP was found to perform well on problems where there was population structure. This suggests that standard machine learning methods need to be refined to include population structure information when this is present. We conclude that the application of machine learning methods to phenotype prediction problems holds great promise, but that determining which methods is likely to perform well on any given problem is elusive and non-trivial.

scholarly journals Performance Evaluation of Machine Learning Methods for Forest Fire Modeling and Prediction

Symmetry ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1022 ◽  
Author(s):  
Binh Thai Pham ◽  
Abolfazl Jaafari ◽  
Mohammadtaghi Avand ◽  
Nadhir Al-Ansari ◽  
Tran Dinh Du ◽  
...  
Keyword(s):  
Machine Learning ◽  
National Park ◽  
Performance Metrics ◽  
Characteristic Curve ◽  
Residential Areas ◽  
Learning Methods ◽  
Explanatory Variables ◽  
Machine Learning Methods ◽  
Modeling Methodology ◽  
In Fire

Predicting and mapping fire susceptibility is a top research priority in fire-prone forests worldwide. This study evaluates the abilities of the Bayes Network (BN), Naïve Bayes (NB), Decision Tree (DT), and Multivariate Logistic Regression (MLP) machine learning methods for the prediction and mapping fire susceptibility across the Pu Mat National Park, Nghe An Province, Vietnam. The modeling methodology was formulated based on processing the information from the 57 historical fires and a set of nine spatially explicit explanatory variables, namely elevation, slope degree, aspect, average annual temperate, drought index, river density, land cover, and distance from roads and residential areas. Using the area under the receiver operating characteristic curve (AUC) and seven other performance metrics, the models were validated in terms of their abilities to elucidate the general fire behaviors in the Pu Mat National Park and to predict future fires. Despite a few differences between the AUC values, the BN model with an AUC value of 0.96 was dominant over the other models in predicting future fires. The second best was the DT model (AUC = 0.94), followed by the NB (AUC = 0.939), and MLR (AUC = 0.937) models. Our robust analysis demonstrated that these models are sufficiently robust in response to the training and validation datasets change. Further, the results revealed that moderate to high levels of fire susceptibilities are associated with ~19% of the Pu Mat National Park where human activities are numerous. This study and the resultant susceptibility maps provide a basis for developing more efficient fire-fighting strategies and reorganizing policies in favor of sustainable management of forest resources.

scholarly journals An Overview of Opportunities for Machine Learning Methods in Underground Rock Engineering Design

Geosciences ◽  
2019 ◽  
Vol 9 (12) ◽  
pp. 504
Author(s):  
Josephine Morgenroth ◽  
Usman T. Khan ◽  
Matthew A. Perras
Keyword(s):  
Machine Learning ◽  
Engineering Design ◽  
Performance Metrics ◽  
Mining Industry ◽  
Learning Methods ◽  
Rock Engineering ◽  
Input Selection ◽  
Machine Learning Methods ◽  
And Performance ◽  
Rock Engineering Design

Machine learning methods for data processing are gaining momentum in many geoscience industries. This includes the mining industry, where machine learning is primarily being applied to autonomously driven vehicles such as haul trucks, and ore body and resource delineation. However, the development of machine learning applications in rock engineering literature is relatively recent, despite being widely used and generally accepted for decades in other risk assessment-type design areas, such as flood forecasting. Operating mines and underground infrastructure projects collect more instrumentation data than ever before, however, only a small fraction of the useful information is typically extracted for rock engineering design, and there is often insufficient time to investigate complex rock mass phenomena in detail. This paper presents a summary of current practice in rock engineering design, as well as a review of literature and methods at the intersection of machine learning and rock engineering. It identifies gaps, such as standards for architecture, input selection and performance metrics, and areas for future work. These gaps present an opportunity to define a framework for integrating machine learning into conventional rock engineering design methodologies to make them more rigorous and reliable in predicting probable underlying physical mechanics and phenomenon.

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