Massive datasets and machine learning for computational biomedicine: trends and challenges

This survey paper attempts to cover a broad range of topics related to computational biomedicine. The field has been attracting great attention due to a number of benefits it can provide the society with. New technological and theoretical advances have made it possible to progress considerably. Traditionally, problems emerging in this field are challenging from many perspectives. In this paper, we considered the influence of big data on the field, problems associated with massive datasets in biomedicine and ways to address these problems. We analyzed the most commonly used machine learning and feature mining tools and several new trends and tendencies such as deep learning and biological networks for computational biomedicine.

[1]  Yoav Freund,et al.  Experiments with a New Boosting Algorithm , 1996, ICML.

[2]  Brian Litt,et al.  Human and automated detection of high-frequency oscillations in clinical intracranial EEG recordings , 2007, Clinical Neurophysiology.

[3]  Carol S Ringelberg,et al.  Implication of the miR-184 and miR-204 Competitive RNA Network in Control of Mouse Secondary Cataract , 2012, Molecular medicine.

[4]  Efstathios D. Gennatas,et al.  Divergent network connectivity changes in behavioural variant frontotemporal dementia and Alzheimer's disease. , 2010, Brain : a journal of neurology.

[5]  Haibo He,et al.  Learning from Imbalanced Data , 2009, IEEE Transactions on Knowledge and Data Engineering.

[6]  Q. Cui,et al.  An Analysis of Human MicroRNA and Disease Associations , 2008, PloS one.

[7]  Ian Riley,et al.  Using verbal autopsy to measure causes of death: the comparative performance of existing methods , 2014, BMC Medicine.

[8]  Joachim Selbig,et al.  Non-linear PCA: a missing data approach , 2005, Bioinform..

[9]  Alexander J. Smola,et al.  Second Order Cone Programming Approaches for Handling Missing and Uncertain Data , 2006, J. Mach. Learn. Res..

[10]  W. Art Chaovalitwongse,et al.  Seizure warning algorithm based on optimization and nonlinear dynamics , 2004, Math. Program..

[11]  M. Newman Communities, modules and large-scale structure in networks , 2011, Nature Physics.

[12]  M. Helmstaedter Cellular-resolution connectomics: challenges of dense neural circuit reconstruction , 2013, Nature Methods.

[13]  Herna L. Viktor,et al.  Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach , 2004, SKDD.

[14]  Michael Eisenstein,et al.  Big data: The power of petabytes , 2015, Nature.

[15]  J. Friedman Multivariate adaptive regression splines , 1990 .

[16]  M. P. S. Chawla,et al.  PCA and ICA processing methods for removal of artifacts and noise in electrocardiograms: A survey and comparison , 2011, Appl. Soft Comput..

[17]  D. Picard Testing and estimating change-points in time series , 1985, Advances in Applied Probability.

[18]  Bart Vanrumste,et al.  Journal of Neuroengineering and Rehabilitation Open Access Review on Solving the Inverse Problem in Eeg Source Analysis , 2022 .

[19]  Kuo-Chen Chou,et al.  iPPI-Esml: An ensemble classifier for identifying the interactions of proteins by incorporating their physicochemical properties and wavelet transforms into PseAAC. , 2015, Journal of theoretical biology.

[20]  Arnon D. Cohen,et al.  Biomedical Signal Processing , 1986 .

[21]  M. Giger,et al.  Quantitative analysis of multiparametric prostate MR images: differentiation between prostate cancer and normal tissue and correlation with Gleason score--a computer-aided diagnosis development study. , 2013, Radiology.

[22]  Jürgen Schmidhuber,et al.  Multi-column deep neural network for traffic sign classification , 2012, Neural Networks.

[23]  O. Sporns,et al.  Identification and Classification of Hubs in Brain Networks , 2007, PloS one.

[24]  Gary Weiss,et al.  Does cost-sensitive learning beat sampling for classifying rare classes? , 2005, UBDM '05.

[25]  Teresa H. Y. Meng,et al.  HermesD: A High-Rate Long-Range Wireless Transmission System for Simultaneous Multichannel Neural Recording Applications , 2010, IEEE Transactions on Biomedical Circuits and Systems.

[26]  David S. Siscovick,et al.  A multiple-imputation analysis of a case-control study of the risk of primary cardiac arrest among pharmacologicallytreated hypertensives , 1996 .

[27]  Maarten de Rooij,et al.  Cost-effectiveness of magnetic resonance (MR) imaging and MR-guided targeted biopsy versus systematic transrectal ultrasound-guided biopsy in diagnosing prostate cancer: a modelling study from a health care perspective. , 2014, European urology.

[28]  AbdiHervé,et al.  Principal Component Analysis , 2010, Essentials of Pattern Recognition.

[29]  Robert C. Holte,et al.  C4.5, Class Imbalance, and Cost Sensitivity: Why Under-Sampling beats Over-Sampling , 2003 .

[30]  P L Nunez,et al.  The Spline‐Laplacian in Clinical Neurophysiology: A Method to Improve EEG Spatial Resolution , 1991, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[31]  G. Cecchi,et al.  Scale-free brain functional networks. , 2003, Physical review letters.

[32]  CireşAnDan,et al.  2012 Special Issue , 2012 .

[33]  Daniel S Berman,et al.  Coronary artery calcium as a measure of biologic age. , 2006, Atherosclerosis.

[34]  Peter Szolovits,et al.  MIMIC-III, a freely accessible critical care database , 2016, Scientific Data.

[35]  Albert-László Barabási,et al.  Internet: Diameter of the World-Wide Web , 1999, Nature.

[36]  Lei Zheng,et al.  Discrimination of transgenic soybean seeds by terahertz spectroscopy , 2016, Scientific Reports.

[37]  K. Chou,et al.  Recent progress in protein subcellular location prediction. , 2007, Analytical biochemistry.

[38]  Nassir Navab,et al.  AggNet: Deep Learning From Crowds for Mitosis Detection in Breast Cancer Histology Images , 2016, IEEE Trans. Medical Imaging.

[39]  Zhi-Hua Zhou,et al.  Exploratory Under-Sampling for Class-Imbalance Learning , 2006, Sixth International Conference on Data Mining (ICDM'06).

[40]  Keum-Shik Hong,et al.  Online binary decision decoding using functional near-infrared spectroscopy for the development of brain–computer interface , 2014, Experimental Brain Research.

[41]  Ronald M. Summers,et al.  Deep Learning in Medical Imaging: Overview and Future Promise of an Exciting New Technique , 2016 .

[42]  Yi Deng,et al.  Multiple Imputation for General Missing Data Patterns in the Presence of High-dimensional Data , 2016, Scientific Reports.

[43]  Nikolaus Kriegeskorte,et al.  Deep Supervised, but Not Unsupervised, Models May Explain IT Cortical Representation , 2014, PLoS Comput. Biol..

[44]  Bernhard E. Boser,et al.  A training algorithm for optimal margin classifiers , 1992, COLT '92.

[45]  Hui Han,et al.  Borderline-SMOTE: A New Over-Sampling Method in Imbalanced Data Sets Learning , 2005, ICIC.

[46]  Panos M. Pardalos,et al.  Connectivity brain networks based on wavelet correlation analysis in Parkinson fMRI data , 2011, Neuroscience Letters.

[47]  D. Moher,et al.  A comparison of direct vs. self‐report measures for assessing height, weight and body mass index: a systematic review , 2007, Obesity reviews : an official journal of the International Association for the Study of Obesity.

[48]  Bram van Ginneken,et al.  Fast Convolutional Neural Network Training Using Selective Data Sampling: Application to Hemorrhage Detection in Color Fundus Images , 2016, IEEE Transactions on Medical Imaging.

[49]  Frederico A. C. Azevedo,et al.  Equal numbers of neuronal and nonneuronal cells make the human brain an isometrically scaled‐up primate brain , 2009, The Journal of comparative neurology.

[50]  Mark D. McDonnell,et al.  Understanding Data Augmentation for Classification: When to Warp? , 2016, 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA).

[51]  Martin F. Kraus,et al.  Optical coherence tomography angiography of optic disc perfusion in glaucoma. , 2014, Ophthalmology.

[52]  Guoqi Zhang,et al.  More than Moore: Creating High Value Micro/Nanoelectronics Systems , 2009 .

[53]  M E J Newman,et al.  Finding and evaluating community structure in networks. , 2003, Physical review. E, Statistical, nonlinear, and soft matter physics.

[54]  Young Hwan Choi,et al.  The impact of laser Doppler imaging on the early decision-making process for surgical intervention in adults with indeterminate burns. , 2013, Burns : journal of the International Society for Burn Injuries.

[55]  Nitesh V. Chawla,et al.  SMOTE: Synthetic Minority Over-sampling Technique , 2002, J. Artif. Intell. Res..

[56]  Michael Vourkas,et al.  Mild traumatic brain injury: graph-model characterization of brain networks for episodic memory. , 2011, International journal of psychophysiology : official journal of the International Organization of Psychophysiology.

[57]  Teuvo Kohonen,et al.  The self-organizing map , 1990, Neurocomputing.

[58]  Jonathan J Halford,et al.  American Clinical Neurophysiology Society Guideline 1: Minimum Technical Requirements for Performing Clinical Electroencephalography , 2016, The Neurodiagnostic journal.

[59]  Nasser Ali,et al.  ECG parameter extraction algorithm using (DWTAE) algorithm , 2009 .

[60]  Jake Luo,et al.  Big Data Application in Biomedical Research and Health Care: A Literature Review , 2016, Biomedical informatics insights.

[61]  Richard A. Robb,et al.  Biomedical Imaging, Visualization, and Analysis , 1999 .

[62]  Panos M. Pardalos,et al.  On the time series support vector machine using dynamic time warping kernel for brain activity classification , 2008 .

[63]  D. Rubin Multiple imputation for nonresponse in surveys , 1989 .

[64]  Stef van Buuren,et al.  MICE: Multivariate Imputation by Chained Equations in R , 2011 .

[65]  R. Tibshirani,et al.  Least angle regression , 2004, math/0406456.

[66]  Panos M. Pardalos,et al.  Robust chance-constrained support vector machines with second-order moment information , 2018, Ann. Oper. Res..

[67]  Panos M. Pardalos,et al.  Small World Networks in Computational Neuroscience , 2013 .

[68]  Leo Breiman,et al.  Bagging Predictors , 1996, Machine Learning.

[69]  H. Adeli,et al.  Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis , 2015, Seizure.

[70]  Clayton W. Commander,et al.  Identifying Critical Nodes in Protein-Protein Interaction Networks , 2009 .

[71]  Jesper Andersson,et al.  A multi-modal parcellation of human cerebral cortex , 2016, Nature.

[72]  Wei-Yin Loh,et al.  Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..

[73]  Alan C. Evans,et al.  Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. , 2009, Cerebral cortex.

[74]  K. Hajian‐Tilaki,et al.  Receiver Operating Characteristic (ROC) Curve Analysis for Medical Diagnostic Test Evaluation. , 2013, Caspian journal of internal medicine.

[75]  James A. Purdy,et al.  Three-dimensional Conformal and Intensity Modulated Radiation Therapy: Physics and Clinical Applications , 2003 .

[76]  Zhi Jin,et al.  Improved relation classification by deep recurrent neural networks with data augmentation , 2016, COLING.

[77]  Adam M. Feist,et al.  A comprehensive genome-scale reconstruction of Escherichia coli metabolism—2011 , 2011, Molecular systems biology.

[78]  J. Friedman Stochastic gradient boosting , 2002 .

[79]  D. Baker,et al.  The coming of age of de novo protein design , 2016, Nature.

[80]  Zhi-Hua Zhou,et al.  The Influence of Class Imbalance on Cost-Sensitive Learning: An Empirical Study , 2006, Sixth International Conference on Data Mining (ICDM'06).

[81]  Celia Shahnaz,et al.  Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains , 2012, Biomed. Signal Process. Control..

[82]  Jonathan J Halford,et al.  American Clinical Neurophysiology Society Guideline 4: Recording Clinical EEG on Digital Media , 2016, The Neurodiagnostic journal.

[83]  Stan Matwin,et al.  Addressing the Curse of Imbalanced Training Sets: One-Sided Selection , 1997, ICML.

[84]  Enzo Grossi,et al.  Pregnancy risk factors in autism: a pilot study with artificial neural networks , 2016, Pediatric Research.

[85]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[86]  Joseph E. Burns,et al.  Note: This Copy Is for Your Personal Non-commercial Use Only. to Order Presentation-ready Copies for Distribution to Your Colleagues or Clients, Contact Us at Www.rsna.org/rsnarights. Distributed Human Intelligence for Colonic Polyp Classification in Computer-aided Detection for Ct Colonography 1 , 2022 .

[87]  D. Yao,et al.  A method to standardize a reference of scalp EEG recordings to a point at infinity , 2001, Physiological measurement.

[88]  Jeffrey P. Krischer,et al.  A comparison of the baseline metabolic profiles between Diabetes Prevention Trial‐Type 1 and TrialNet Natural History Study participants , 2011, Pediatric diabetes.

[89]  Jeff Gilchrist,et al.  Neonatal mortality prediction using real-time medical measurements , 2011, 2011 IEEE International Symposium on Medical Measurements and Applications.

[90]  Chandan Chakraborty,et al.  Application of Higher Order cumulant Features for Cardiac Health Diagnosis using ECG signals , 2013, Int. J. Neural Syst..

[91]  Craig K. Enders,et al.  Applied Missing Data Analysis , 2010 .

[92]  Salvatore J. Stolfo,et al.  AdaCost: Misclassification Cost-Sensitive Boosting , 1999, ICML.

[93]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[94]  Elaine R. Mardis,et al.  A decade’s perspective on DNA sequencing technology , 2011, Nature.

[95]  H. Abdi,et al.  Principal component analysis , 2010 .

[96]  Jason A. Papin,et al.  Applications of genome-scale metabolic reconstructions , 2009, Molecular systems biology.

[97]  Albert,et al.  Emergence of scaling in random networks , 1999, Science.

[98]  Luís B. Almeida,et al.  MISEP -- Linear and Nonlinear ICA Based on Mutual Information , 2003, J. Mach. Learn. Res..

[99]  Qiang Yang,et al.  Decision trees with minimal costs , 2004, ICML.

[100]  Krin A. Kay,et al.  The implications of human metabolic network topology for disease comorbidity , 2008, Proceedings of the National Academy of Sciences.

[101]  D. Donoho,et al.  Hessian eigenmaps: Locally linear embedding techniques for high-dimensional data , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[102]  Davide Heller,et al.  STRING v10: protein–protein interaction networks, integrated over the tree of life , 2014, Nucleic Acids Res..

[103]  Diogo Almeida,et al.  Resnet in Resnet: Generalizing Residual Architectures , 2016, ArXiv.

[104]  J. Ross Quinlan,et al.  Combining Instance-Based and Model-Based Learning , 1993, ICML.

[105]  Panos M. Pardalos,et al.  Sparse Proximal Support Vector Machines for feature selection in high dimensional datasets , 2015, Expert Syst. Appl..

[106]  R. Tibshirani Regression Shrinkage and Selection via the Lasso , 1996 .

[107]  Wei-Dong Dang,et al.  Multiscale limited penetrable horizontal visibility graph for analyzing nonlinear time series , 2016, Scientific Reports.

[108]  Charles Elkan,et al.  The Foundations of Cost-Sensitive Learning , 2001, IJCAI.

[109]  Xin Li,et al.  Protein classification with imbalanced data , 2007, Proteins.

[110]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[111]  Tobias Loddenkemper,et al.  Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy , 2014, Epilepsy & Behavior.

[112]  Marco Ferrari,et al.  A brief review on the history of human functional near-infrared spectroscopy (fNIRS) development and fields of application , 2012, NeuroImage.

[113]  Zhimin He,et al.  Comparative QSAR modeling of antitumor activity of ARC-111 analogues using stepwise MLR, PLS, and ANN techniques , 2010, Medicinal Chemistry Research.

[114]  Brian A. Nosek,et al.  Power failure: why small sample size undermines the reliability of neuroscience , 2013, Nature Reviews Neuroscience.

[115]  K. Tsakalis,et al.  Long-term prospective on-line real-time seizure prediction , 2005, Clinical Neurophysiology.

[116]  Cristina Nader Vasconcelos,et al.  Increasing Deep Learning Melanoma Classification by Classical And Expert Knowledge Based Image Transforms , 2017, ArXiv.

[117]  A. E. Hoerl,et al.  Ridge regression: biased estimation for nonorthogonal problems , 2000 .

[118]  R. Albert,et al.  The large-scale organization of metabolic networks , 2000, Nature.

[119]  K L Lam,et al.  Computer-aided detection of mammographic microcalcifications: pattern recognition with an artificial neural network. , 1995, Medical physics.

[120]  Ivor W. Tsang,et al.  Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets , 2010, ICML.

[121]  William R. Gray Roncal,et al.  Saturated Reconstruction of a Volume of Neocortex , 2015, Cell.

[122]  Gordon E. Moore,et al.  Progress in digital integrated electronics , 1975 .

[123]  Lisa Tang,et al.  Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation , 2016, IEEE Transactions on Medical Imaging.

[124]  SchmidhuberJürgen Deep learning in neural networks , 2015 .

[125]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[126]  Monica L. Mo,et al.  Global reconstruction of the human metabolic network based on genomic and bibliomic data , 2007, Proceedings of the National Academy of Sciences.

[127]  Lahcène Mitiche,et al.  Medical image denoising using dual tree complex thresholding wavelet transform and Wiener filter , 2015, J. King Saud Univ. Comput. Inf. Sci..

[128]  Pradeep Tomar,et al.  An Optimal Wavelet Approach for ECG Noise Cancellation , 2016 .

[129]  D J PRICE,et al.  NETWORKS OF SCIENTIFIC PAPERS. , 1965, Science.

[130]  P. Pardalos,et al.  Clustering challenges in biological networks , 2009 .

[131]  S. Brenner,et al.  The structure of the nervous system of the nematode Caenorhabditis elegans. , 1986, Philosophical transactions of the Royal Society of London. Series B, Biological sciences.

[132]  J. Ioannidis Why Most Published Research Findings Are False , 2019, CHANCE.

[133]  Elhanan Borenstein,et al.  The discovery of integrated gene networks for autism and related disorders , 2015, Genome research.

[134]  Ron Kohavi,et al.  Wrappers for Feature Subset Selection , 1997, Artif. Intell..

[135]  Ronald M. Summers,et al.  Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning , 2016, IEEE Transactions on Medical Imaging.

[136]  Charles X. Ling,et al.  Data Mining for Direct Marketing: Problems and Solutions , 1998, KDD.

[137]  J. Ross,et al.  Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. , 2006, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[138]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[139]  Christophe Lemetre,et al.  MicroRNA signatures predict oestrogen receptor, progesterone receptor and HER2/neu receptor status in breast cancer , 2009, Breast Cancer Research.

[140]  Haibo He,et al.  ADASYN: Adaptive synthetic sampling approach for imbalanced learning , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).

[141]  Arthur W. Wetzel,et al.  Network anatomy and in vivo physiology of visual cortical neurons , 2011, Nature.

[142]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[143]  Leo Breiman,et al.  Random Forests , 2001, Machine Learning.

[144]  U. Rajendra Acharya,et al.  Application of Non-Linear and Wavelet Based Features for the Automated Identification of Epileptic EEG signals , 2012, Int. J. Neural Syst..

[145]  Tezcan Ozrazgat-Baslanti,et al.  The Pattern of Longitudinal Change in Serum Creatinine and 90-Day Mortality After Major Surgery , 2016, Annals of surgery.

[146]  Geoffrey E. Hinton,et al.  Speech recognition with deep recurrent neural networks , 2013, 2013 IEEE International Conference on Acoustics, Speech and Signal Processing.

[147]  Xin Yao,et al.  MWMOTE--Majority Weighted Minority Oversampling Technique for Imbalanced Data Set Learning , 2014 .

[148]  G Tononi,et al.  Theoretical neuroanatomy: relating anatomical and functional connectivity in graphs and cortical connection matrices. , 2000, Cerebral cortex.

[149]  B. Scholkopf,et al.  Fisher discriminant analysis with kernels , 1999, Neural Networks for Signal Processing IX: Proceedings of the 1999 IEEE Signal Processing Society Workshop (Cat. No.98TH8468).

[150]  Mukund Balasubramanian,et al.  The Isomap Algorithm and Topological Stability , 2002, Science.

[151]  A. Webb,et al.  Introduction to biomedical imaging , 2002 .

[152]  Guodong Tang,et al.  ECG De-noising Based on Empirical Mode Decomposition , 2008, 2008 The 9th International Conference for Young Computer Scientists.

[153]  R.R. Harrison,et al.  Wireless Neural Recording With Single Low-Power Integrated Circuit , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[154]  Leonard M. Freeman,et al.  A set of measures of centrality based upon betweenness , 1977 .

[155]  M. Mitchell Waldrop,et al.  The chips are down for Moore’s law , 2016, Nature.

[156]  Ming Tang,et al.  Suppressing disease spreading by using information diffusion on multiplex networks , 2016, Scientific Reports.

[157]  Jason A. Papin,et al.  Metabolic network reconstruction of Chlamydomonas offers insight into light-driven algal metabolism , 2011, Molecular systems biology.

[158]  George M. Furnival,et al.  Regressions by leaps and bounds , 2000 .

[159]  Pierrick Coupé,et al.  MRI noise estimation and denoising using non-local PCA , 2015, Medical Image Anal..

[160]  Constantin F. Aliferis,et al.  A gentle introduction to support vector machines in biomedicine: Volume 1: Theory and methods , 2011 .

[161]  Yu Zhang,et al.  Deep Neural Networks for High Dimension, Low Sample Size Data , 2017, IJCAI.

[162]  F. Balloux,et al.  Discriminant analysis of principal components: a new method for the analysis of genetically structured populations , 2010, BMC Genetics.

[163]  Mohammad Khalilia,et al.  Predicting disease risks from highly imbalanced data using random forest , 2011, BMC Medical Informatics Decis. Mak..

[164]  K. Morris,et al.  A pseudogene long noncoding RNA network regulates PTEN transcription and translation in human cells , 2013, Nature Structural &Molecular Biology.

[165]  Daoqiang Zhang,et al.  Multimodal classification of Alzheimer's disease and mild cognitive impairment , 2011, NeuroImage.

[166]  Abdollah Dehzangi,et al.  A Combination of Feature Extraction Methods with an Ensemble of Different Classifiers for Protein Structural Class Prediction Problem , 2013, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[167]  U. Rajendra Acharya,et al.  Automated identification of normal and diabetes heart rate signals using nonlinear measures , 2013, Comput. Biol. Medicine.

[168]  Milos Hauskrecht,et al.  An efficient pattern mining approach for event detection in multivariate temporal data , 2015, Knowledge and Information Systems.

[169]  Aapo Hyvärinen,et al.  Nonlinear independent component analysis: Existence and uniqueness results , 1999, Neural Networks.

[170]  Lior Rokach,et al.  Ensemble-based classifiers , 2010, Artificial Intelligence Review.

[171]  Alan D. Lopez,et al.  Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013 , 2014, The Lancet.

[172]  Maria A. Kazandjieva,et al.  A high-resolution human contact network for infectious disease transmission , 2010, Proceedings of the National Academy of Sciences.

[173]  Dimitris Kanellopoulos,et al.  Handling imbalanced datasets: A review , 2006 .

[174]  J. Blasco,et al.  Recent Advances and Applications of Hyperspectral Imaging for Fruit and Vegetable Quality Assessment , 2012, Food and Bioprocess Technology.

[175]  G. Borghini,et al.  Neuroscience and Biobehavioral Reviews , 2022 .

[176]  A. Barabasi,et al.  Lethality and centrality in protein networks , 2001, Nature.

[177]  S. Mikula Progress Towards Mammalian Whole-Brain Cellular Connectomics , 2016, Front. Neuroanat..

[178]  A K Tun,et al.  RBF networks for source localization in quantitative electrophysiology. , 2000, Critical reviews in biomedical engineering.

[179]  Ruth Nussinov,et al.  Structure and dynamics of molecular networks: A novel paradigm of drug discovery. A comprehensive review , 2012, Pharmacology & therapeutics.

[180]  Byoung-Tak Zhang,et al.  Ensemble Learning with Active Example Selection for Imbalanced Biomedical Data Classification , 2011, IEEE/ACM Transactions on Computational Biology and Bioinformatics.

[181]  N. Clarke,et al.  Enhanced FTIR bench-top imaging of single biological cells. , 2015, The Analyst.

[182]  I Romero,et al.  PCA and ICA applied to noise reduction in multi-lead ECG , 2011, 2011 Computing in Cardiology.

[183]  Nicholas L. Crookston,et al.  yaImpute: An R Package for kNN Imputation , 2008 .

[184]  Fernando Lopes da Silva,et al.  Comprar Niedermeyer's Electroencephalography, 6/e (Basic Principles, Clinical Applications, and Related Fields ) | Fernando Lopes Da Silva | 9780781789424 | Lippincott Williams & Wilkins , 2010 .

[185]  Mohamad Sawan,et al.  A Novel Low-Power-Implantable Epileptic Seizure-Onset Detector , 2011, IEEE Transactions on Biomedical Circuits and Systems.

[186]  Terrence J. Sejnowski,et al.  Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis , 2007, NeuroImage.

[187]  Massimo Marchiori,et al.  Economic small-world behavior in weighted networks , 2003 .

[188]  Pierre Baldi,et al.  Autoencoders, Unsupervised Learning, and Deep Architectures , 2011, ICML Unsupervised and Transfer Learning.

[189]  Graham Ball,et al.  KI67 and DLX2 predict increased risk of metastasis formation in prostate cancer–a targeted molecular approach , 2016, British Journal of Cancer.

[190]  Pedro Larrañaga,et al.  A review of feature selection techniques in bioinformatics , 2007, Bioinform..

[191]  Olvi L. Mangasarian,et al.  Multisurface proximal support vector machine classification via generalized eigenvalues , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.