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Prediction of human functional genetic networks from heterogeneous data using RVM-based ensemble learning

Chia-Chin Wu, Shahab Asgharzadeh, Timothy J. Triche, David Z. D'Argenio
2010 Computer applications in the biosciences : CABIOS  
Motivation: Three major problems confront the construction of a human genetic network from heterogeneous genomics data using kernel-based approaches: definition of a robust gold-standard negative set,  ...  Results: The proposed graph-based approach generates a robust GSN for the training process of genetic network construction.  ...  We now focus on how the ensemble framework can address the two remaining problems for prediction of human genetic networks.  ... 
doi:10.1093/bioinformatics/btq044 pmid:20134029 pmcid:PMC2832827 fatcat:vjfl4tmfsbhczhuqv2wa5oqfum

A SURVEY ON VARIOUS MACHINE LEARNING APPROACHES USED FOR BREAST CANCER DETECTION

Neha Kumari, Bansal Institute of Science and Technology, Bhopal, India
2019 International Journal of Advanced Research in Computer Science  
In the past few decades, several machine learning approach has been used by various researchers.  ...  There are several classification approaches that can be used in cancer detection. This paper discusses the comparative analysis of some of the existing cancer detection approaches.  ...  In 2012, Muhammad Rafi et al. used SVM and RVM techniques for documentclassification without using minimum accuracy limit and find that predicting accuracy of RVM is much higher than SVM [16] .  ... 
doi:10.26483/ijarcs.v10i3.6438 fatcat:v33tojozhvhl7koylrrqni6zq4

Machine Learning and Integrative Analysis of Biomedical Big Data

Bilal Mirza, Wei Wang, Jie Wang, Howard Choi, Neo Christopher Chung, Peipei Ping
2019 Genes  
Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods.  ...  In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing  ...  Network-based Integration of Multi-omics Data (NetICS) integrates multi-omics data on a directed functional interaction network.  ... 
doi:10.3390/genes10020087 pmid:30696086 pmcid:PMC6410075 fatcat:vopnjgke4fculmr7t3n43ewfiy

A Review of Ensemble Methods in Bioinformatics

Pengyi Yang, Yee Hwa Yang, Bing B. Zhou, Albert Y. Zomaya
2010 Current Bioinformatics  
Recent work in computational biology has seen an increasing use of ensemble learning methods due to their unique advantages in dealing with small sample size, high-dimensionality, and complexity data structures  ...  First, it is to provide a review of the most widely used ensemble learning methods and their application in various bioinformatics problems, including the main topics of gene expression, mass spectrometry-based  ...  Acknowledgement We thank Professor Joachim Gudmundsson for critical comments and constructive suggestions which have greatly improve the early version of this article.  ... 
doi:10.2174/157489310794072508 fatcat:muzcldjxifc23kl4tynz4lwjlu

TARGETgene: A Tool for Identification of Potential Therapeutic Targets in Cancer

Chia-Chin Wu, David D'Argenio, Shahab Asgharzadeh, Timothy Triche, Ying Xu
2012 PLoS ONE  
The predictions in these two applications were then satisfactorily validated by several ways, including known cancer genes, results of RNAi screens, gene function annotations, and target genes of drugs  ...  Users can find, select, and explore identified target genes and compounds of interest (e.g., novel cancer genes and their enriched biological processes), and validate predictions using user-defined benchmark  ...  Methods Construction of Gene-Gene Functional Relationship Network Seventeen heterogeneous genomic and proteomic data were integrated using the RVM-based ensemble model reported in [11] in order to  ... 
doi:10.1371/journal.pone.0043305 pmid:22952662 pmcid:PMC3432038 fatcat:5ibnwngmmven5hti6kzcddpcwe

A Novel Hybrid Machine Learning Algorithm for Limited and Big Data Modelling with Application in Industry 4.0

Hamid Khayyam, Ali Jamali, Alireza Bab-Hadiashar, Thomas Esch, Seeram Ramakrishna, Mahdi Jalili, Minoo Naebe
2020 IEEE Access  
In particular, an intelligent algorithm is proposed for robust data modeling of nonlinear systems based on input-output data.  ...  The transformation is assisted by employment of machine learning techniques that can deal with modeling both big or limited data.  ...  Using Nondominated Sorting Genetic Algorithm (NSGA)-II [15] , Pareto optimum nondominated models are obtained from the point of view of these two objective functions.  ... 
doi:10.1109/access.2020.2999898 fatcat:pw4nc3y5dfaszmys5n6p3hdb3a

Tackling Faults in the Industry 4.0 Era—A Survey of Machine-Learning Solutions and Key Aspects

Angelos Angelopoulos, Emmanouel T. Michailidis, Nikolaos Nomikos, Panagiotis Trakadas, Antonis Hatziefremidis, Stamatis Voliotis, Theodore Zahariadis
2019 Sensors  
In this survey, we focus on the vital processes of fault detection, prediction and prevention in Industry 4.0 and present recent developments in ML-based solutions.  ...  Towards this end, a detailed overview of ML-based human–machine interaction techniques is provided, allowing humans to be in-the-loop of the manufacturing processes in a symbiotic manner with minimal errors  ...  be used in various kernelised learning algorithms and in function approximation, and the deep belief learning-based DL (DBL-DL) that includes four layers, that is, one visible layer handling inputs from  ... 
doi:10.3390/s20010109 pmid:31878065 pmcid:PMC6983262 fatcat:n4muoguq5jalrfwqkq4264vswe

Machine Learning—A Review of Applications in Mineral Resource Estimation

Nelson K. Dumakor-Dupey, Sampurna Arya
2021 Energies  
Mineral resource estimation involves the determination of the grade and tonnage of a mineral deposit based on its geological characteristics using various estimation methods.  ...  This study presents a comprehensive review of papers that have employed machine learning to estimate mineral resources.  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/en14144079 fatcat:cuey4wzsurawndkoqz6h6d4qry

Machine Learning Techniques for Anxiety Disorder

Elif ALTINTAŞ, Zeyneb UYLAŞ AKSU, Zeynep GÜMÜŞ DEMİR
2021 European Journal of Science and Technology  
Considering the clinical heterogeneity of the data obtained from anxiety patients, we conclude that artificial intelligence techniques can provide important information to clinicians and researchers in  ...  In recent years, artificial intelligence based applications have been improved and used to improve the timing, sensitivity and quality of diagnosis of psychiatric diseases.  ...  Kurban determined the closeness of text-based fields with each other using machine learning methods [47] .  ... 
doi:10.31590/ejosat.999914 fatcat:apaj6rt4y5f3pm5u3nn2epkbi4

A survey of multiple classifier systems as hybrid systems

Michał Woźniak, Manuel Graña, Emilio Corchado
2014 Information Fusion  
This paper presents an up-todate survey on multiple classifier system (MCS) from the point of view of Hybrid Intelligent Systems.  ...  These systems perform information fusion of classification decisions at different levels overcoming limitations of traditional approaches based on single classifiers.  ...  Dasarathy, who encouraged us to write this survey for this prestigious journal. MichałWoź niak was supported by The Polish National Science Centre under the Grant No.  ... 
doi:10.1016/j.inffus.2013.04.006 fatcat:4pps7i2g2rbxpci7wjkqy2pska

Grant Report on PREDICT-ADFTD: Multimodal Imaging Prediction of AD/FTD and Differential Diagnosis

2019 Journal of Psychiatry and Brain Science  
Several machine learning experiments have shown high classification accuracy in the prediction of disease based on Convolutional Neural Networks utilizing MRI images as input.  ...  The overall goal of the project is to study neurodegeneration within Alzheimer's Disease, Frontotemporal Dementia, and related neurodegenerative disorders, using a variety of brain imaging and computational  ...  ACKNOWLEDGMENTS ADNI data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department  ... 
doi:10.20900/jpbs.20190017 pmid:31754634 pmcid:PMC6868780 fatcat:f65tcg424rcv7cbymqwa7pvkdi

A Modified Functional Link Neural Network for Data Classification [chapter]

Toktam Babaei, Chee Peng Lim, Hamid Abdi, Saeid Nahavandi
2017 Series in BioEngineering  
effectiveness of the proposed rFLNN-based individual and ensemble models for data classification.  ...  Extensive experiments covering benchmark classification problems from the machine learning repository of the University of California, Irvine and KEEL-data set repository are performed to evaluate the  ...  The artificial neural network (ANN) is one of key data-based learning AI methodologies.  ... 
doi:10.1007/978-981-10-3957-7_13 fatcat:lfjfchicyfeqvlprvrqpp4op5e

Machine Learning in Compiler Optimisation [article]

Zheng Wang, Michael O'Boyle
2018 arXiv   pre-print
In the last decade, machine learning based compilation has moved from an an obscure research niche to a mainstream activity.  ...  This paper provides both an accessible introduction to the fast moving area of machine learning based compilation and a detailed bibliography of its main achievements.  ...  Machine learning predicts an outcome for a new data point based on prior data. In its simplest guise it can be considered a from of interpolation.  ... 
arXiv:1805.03441v1 fatcat:bhd7mpl6lzaedbuy7iln4hntki

Learning Patterns of the Ageing Brain in MRI using Deep Convolutional Networks

Nicola K. Dinsdale, Emma Bluemke, Stephen M. Smith, Zobair Arya, Diego Vidaurre, Mark Jenkinson, Ana I.L. Namburete
2020 NeuroImage  
Due to the longitudinal aspect of the UK Biobank study, in the future it will be possible to explore whether the ΔBrainAge from models such as this network were predictive of any health outcomes.  ...  Furthermore, we show that the use of nonlinearly registered images to train CNNs can lead to the network being driven by artefacts of the registration process and missing subtle indicators of ageing, limiting  ...  UK Biobank contains data from participants' lifestyle, clinical measurements, genetic, and imaging data.  ... 
doi:10.1016/j.neuroimage.2020.117401 pmid:32979523 fatcat:5cikugg5wfhlvgb5eoorxxdx64

Machine learning for biochemical engineering: A review

Max Mowbray, Thomas Savage, Chufan Wu, Ziqi Song, Bovinille Anye Cho, Ehecatl A. Del Rio-Chanona, Dongda Zhang
2021 Biochemical engineering journal  
We review the use of machine learning within biochemical engineering over the last 20 years.  ...  Finally, core challenges into the application of machine learning in biochemical engineering are thoroughly discussed, and further insight into adoption of innovative hybrid modelling and transfer learning  ...  Broadly, predictions are made based on a weighted sum of the existing output data, weighted by the predictions distance from the existing data in input space.  ... 
doi:10.1016/j.bej.2021.108054 fatcat:jvbkblcoevghxm4swnormswt64
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