Hybrid model for efficient prediction of poly(A) signals in human genomic DNA

Albalawi, Fahad; Chahid, Abderrazak; Guo, Xingang; Albaradei, Somayah; Magana-Mora, Arturo; Janković, Boris R.; Uludağ, Mahmut; Neste, Christophe Van; Essack, Magbubah; Laleg‐Kirati, Taous‐Meriem; Bajic, Vladimir B.

doi:10.1016/j.ymeth.2019.04.001

Cited by 22 publications

(7 citation statements)

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“…Based on the results, the proposed hybrid method has a higher performance and accuracy compared with the necessary methods. Albalawi et al [75] developed a hybrid HybPAS including the integration of linear regression-deep neural network models for the estimation of ply (a) signals in DNA in the presence of sequence-based features and signal processing-based statistical as input values. Based on the results, the hybrid method could successfully increase the accuracy and performance by 30.29 %.…”

Section: Hybrid Methodsmentioning

confidence: 99%

Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods<strong> </strong>

Ardabili¹,

Mosavi²,

Várkonyi-Kóczy³

2019

Preprint

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The conventional machine learning (ML) algorithms are continuously advancing and evolving at a fast-paced by introducing the novel learning algorithms. ML models are continually improving using hybridization and ensemble techniques to empower computation, functionality, robustness, and accuracy aspects of modeling. Currently, numerous hybrid and ensemble ML models have been introduced. However, they have not been surveyed in a comprehensive manner. This paper presents the state of the art of novel ML models and their performance and application domains through a novel taxonomy.

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Section: Hybrid Methodsmentioning

confidence: 99%

Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods<strong> </strong>

Ardabili¹,

Mosavi²,

Várkonyi-Kóczy³

2019

Preprint

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“…For example, Wang et al (2004) presented a hybrid Flexible NBTree model: a decision tree consisting of leaf nodes that contain General Naive Bayes algorithm, a variant of the standard Naive Bayesian classifier. More recently, including the integration of linear regression-deep neural network models, a hybrid HybPAS model was proposed by Albalawi et al (2019). Hence, it is important to note that this review focuses mostly on large-scale trends of individual ML categories and algorithms, while details of the specific F I G U R E 1 Main goals of machine learning applications hybrid models merit another review.…”

Section: Data Collectionmentioning

confidence: 99%

Machine learning applications in river research: Trends, opportunities and challenges

Goethals

2022

Methods Ecol Evol

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1. As one of the earth's key ecosystems, rivers have been intensively studied and modelled through the application of machine learning (ML). With the amount of large data available, these computer algorithms are ever increasing in numerous fields, although there is ongoing scepticism and scholars still question the actual impact and deliverables of algorithms.2. This study aims to provide a systematic review of the state-of-the-art ML-based techniques, trends, opportunities and challenges in river research by applying text mining and automated content analysis.3. Unsupervised and supervised learning have dominated river research while neural networks and deep learning have also gradually gained popularity. Matrix factorisation and linear models have been the most popular ML algorithms, with around 1300 and 800 publications on these topics in 2020 respectively. In contrast, river researchers have had few applications in multiclass and multilabel algorithm, associate rule and Naïve Bayes. 4. The current article proposes an end-to-end workflow of ML applications in river research in order to tackle major ML challenges, including four steps: (1) data collection and preparation; (2) model evaluation and selection; (3) model application; and (4) feedback loops. Within this workflow, river modellers have to balance numerous trade-offs related to model traits, such as complexity, accuracy, interpretability, bias, data privacy and accessibility and spatial and temporal scales. Any choices made when balancing the trade-offs can lead to different model outcomes affecting the final applications. Hence, it is necessary to carefully consider and specify modelling goals, understand the data collected and maintain feedback loops in order to continuously improve model performance and eventually reach the research objectives. Moreover, it remains crucial to address the users' needs and demands that often entail additional elements, such as computational cost, development time and the quantity, quality and compatibility of data. Furthermore, river researchers should account for new technologies and regulations in data collection and protection that are transforming the development and applications of ML, most notably data warehouse and

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“…The application of artificial intelligence algorithms has achieved significant milestones over the past few years. Particularly, with the increase of computational resources and amount of available data, ML algorithms have developed rapidly and been adopted widely across various domains and industries [15][16][17][18]. Mitchell [19] defines an ML algorithm as a process where "a computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E".…”

Section: Machine Learning Modelmentioning

confidence: 99%

Drilling Fluid Properties Prediction: A Machine Learning Approach to Automate Laboratory Experiments

Aljubran

AlBahrani

Ramasamy

et al. 2022

Preprint

Self Cite

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Providing access to underground energy resources such as oil, gas, and geothermal energy requires drilling through subterranean rock formations. Drilling fluids are commonly used to enable this drilling process by serving a variety of functions. Chief among these is circulating into the drilled wellbore to remove the produced rock cuttings. Other functions include exerting the hydrostatic pressure necessary to prevent the flow of underground fluids into the wellbore while drilling, minimizing the invasion of solids and undesired fluids into the wellbore rock, and ensuring the fluid remains flowable within the means of the pumps available on site. To assess the ability of a drilling fluid to serve these functions, it has to have certain rheological properties, which are conventionally measured using specialized equipment. Considering that the formulation and components of these drilling fluids can vary greatly for different scenarios, the process of preparing samples and measuring their rheological properties, which is usually performed on a daily basis on a drilling site, is unavoidably time-consuming, repetitive, and error-prone. Based on this, it is apparent that there is a need for a computational model that can accurately predict drilling fluids properties based on the proposed concentration of their components without the need for further laboratory testing. This study describes a novel methodology to train a machine learning model derived from over 6,878 drilling fluid formulations to successfully predict water-based drilling fluids properties with a resulting R2 of 91.07 ± 6.35%.

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Hybrid model for efficient prediction of poly(A) signals in human genomic DNA

Cited by 22 publications

References 50 publications

Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods<strong> </strong>

Advances in Machine Learning Modeling Reviewing Hybrid and Ensemble Methods<strong> </strong>

Machine learning applications in river research: Trends, opportunities and challenges

Drilling Fluid Properties Prediction: A Machine Learning Approach to Automate Laboratory Experiments

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