Machine learning and integrative analysis of biomedical big data

Deep learning

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Support-vector networks

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SMOTE synthetic minority over-sampling technique

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The Elements of Statistical Learning

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Regularization and Variable Selection Via the Elastic Net

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Reducing the dimensionality of data with neural networks

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Classification and Regression by randomForest

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MapReduce: simplified data processing on large clusters

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TensorFlow: A system for large-scale machine learningPreprint

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Integrating single-cell transcriptomic data across different conditions, technologies, and species

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Representation Learning: A Review and New Perspectives

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Learning from Imbalanced Data

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Gene selection for cancer classification using support vector machines

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Inference and missing data

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The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity

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An introduction to variable and feature selection

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Mass spectrometry-based proteomics

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Clustering by Passing Messages Between Data Points

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An Introduction to Variable and Feature Selection.

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Missing data analysis: making it work in the real world

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Analysis of Incomplete Multivariate Data

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Feature Selection with the <b>Boruta</b> Package

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Extreme Learning Machine for Regression and Multiclass Classification

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Statistical Modeling: The Two Cultures (with comments and a rejoinder by the author)

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Missing Value Estimation Methods for DNA Microarrays

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mixOmics: An R package for ‘omics feature selection and multiple data integration

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Real-Time Computing Without Stable States: A New Framework for Neural Computation Based on Perturbations

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The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2018 update

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Predicting the future - big data, machine learning, and clinical medicine

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MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis

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A few useful things to know about machine learning

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MINIMUM REDUNDANCY FEATURE SELECTION FROM MICROARRAY GENE EXPRESSION DATA

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<b>Amelia</b> II: A Program for Missing Data

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Exploratory Undersampling for Class-Imbalance Learning

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Apache Spark

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Privacy-preserving deep learning

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Stability Selection

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Mass spectrometry‐based metabolomics

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Learning from class-imbalanced data: Review of methods and applications

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Addressing the Curse of Imbalanced Training Sets: One-Sided Selection.

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Machine learning applications in genetics and genomics

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Missing Data

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Similarity network fusion for aggregating data types on a genomic scale

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A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks

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MLlib: Machine Learning in Apache SparkPreprint

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Single-cell RNA sequencing technologies and bioinformatics pipelines

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Multiple Kernel Learning Algorithms

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Deep learning in bioinformatics

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Multi-Omics Factor Analysis—a framework for unsupervised integration of multi-omics data sets

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Multiplatform Analysis of 12 Cancer Types Reveals Molecular Classification within and across Tissues of Origin

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Extreme Learning Machine for Multilayer Perceptron

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Efficient BackProp

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Fully conditional specification in multivariate imputation

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Incremental and Decremental Support Vector Machine Learning

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A survey of heterogeneous information network analysis

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Deep learning-based multi-omics integration robustly predicts survival in liver cancer

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DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays

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Multimodal Learning with Deep Boltzmann Machines

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Auto-encoder based dimensionality reduction

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Integrative single-cell analysis of transcriptional and epigenetic states in the human adult brain

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A Review of Feature Selection and Feature Extraction Methods Applied on Microarray Data

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Functional-link net computing: theory, system architecture, and functionalities

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Multiple Imputation and its Application

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Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis

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Understanding multicellular function and disease with human tissue-specific networks

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Comparison of Random Forest and Parametric Imputation Models for Imputing Missing Data Using MICE: A CALIBER Study

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The model organism as a system: integrating 'omics' data sets

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Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM

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A community effort to assess and improve drug sensitivity prediction algorithms

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Controlling the Sensitivity of Support Vector Machines

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Integrated omics: tools, advances and future approaches

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Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis

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Joint and individual variation explained (JIVE) for integrated analysis of multiple data types

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Piercing the dark matter: bioinformatics of long-range sequencing and mapping

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A Review of Ensemble Methods in Bioinformatics

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Pattern discovery and cancer gene identification in integrated cancer genomic data

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A review on neural networks with random weights

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A comprehensive evaluation of random vector functional link networks

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Enhancer-promoter interactions are encoded by complex genomic signatures on looping chromatin

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mixOmics : an R package for ‘omics feature selection and multiple data integrationPreprint

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Filter Methods for Feature Selection – A Comparative Study

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A wellness study of 108 individuals using personal, dense, dynamic data clouds

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A review on machine learning principles for multi-view biological data integration

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Handling Missing Data by Maximum Likelihood

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TIGRESS: Trustful Inference of Gene REgulation using Stability Selection

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Privacy-Preserving Generative Deep Neural Networks Support Clinical Data Sharing

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Dimension reduction techniques for the integrative analysis of multi-omics data

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Discovery of multi-dimensional modules by integrative analysis of cancer genomic data

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Late Fusion Incomplete Multi-View Clustering

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A survey of randomized algorithms for training neural networks

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A multimodal deep neural network for human breast cancer prognosis prediction by integrating multi-dimensional data

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Online Deep Learning: Learning Deep Neural Networks on the Fly

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Transcriptomic and metabolomic data integration

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Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data

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MIDA: Multiple Imputation Using Denoising Autoencoders

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Convex Optimization for Big Data: Scalable, randomized, and parallel algorithms for big data analytics

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Class-imbalanced classifiers for high-dimensional data

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Rapid genotype imputation from sequence without reference panels

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Combining Molecular, Imaging, and Clinical Data Analysis for Predicting Cancer Prognosis

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Frailty and Biological Age

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From Multi-Omics Approaches to Precision Medicine in Amyotrophic Lateral Sclerosis

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Artificial Intelligence Models and Tools for the Assessment of Drug–Herb Interactions

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Development and Internal Validation of Supervised Machine Learning Algorithms for Predicting Clinically Significant Functional Improvement in a Mixed Population of Primary Hip Arthroscopy

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ADHD/CD-NET: automated EEG-based characterization of ADHD and CD using explainable deep neural network technique

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Prospect of Using Machine Learning-Based Microwave Nondestructive Testing Technique for Corrosion Under Insulation: A Review

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Knowledge Generation with Rule Induction in Cancer Omics

Scala, G., Federico, A., Fortino, V., Greco, D., & Majello, B. (2019) · International Journal of Molecular Sciences · doi:10.3390/ijms21010018 · Cited by 29 · see citing works

Prediction and design of protease enzyme specificity using a structure-aware graph convolutional network

Lu, C., Lubin, J. H., Sarma, V., Stentz, S. Z., Wang, G., Wang, S., & Khare, S. D. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2303590120 · Cited by 28 · see citing works

A Brief Review of Machine Learning-Based Bioactive Compound Research

Park, J., Beck, B. R., Kim, H. H., Lee, S.-B., & Kang, K. (2022) · Applied Sciences · doi:10.3390/app12062906 · Cited by 30 · see citing works

Multi-omics approaches in psychoneuroimmunology and health research: Conceptual considerations and methodological recommendations

Mengelkoch, S., Rose, S. M. S., Lautman, Z., Alley, J. C., Roos, L. G., Ehlert, B., Moriarity, D. P., Lancaster, S. M., Snyder, M. P., & Slavich, G. M. (2023) · Brain Behavior and Immunity · doi:10.1016/j.bbi.2023.07.022 · Cited by 29 · see citing works

Analytics methods and tools for integration of biomedical data in medicine

Zhang, L., Karimzadeh, M., Welch, M. L., McIntosh, C., & Wang, B. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-821259-2.00007-7 · Cited by 3 · see citing works

Machine learning meets omics: applications and perspectives

Li, R., Li, L., Xu, Y., & Yang, J. (2021) · Briefings in Bioinformatics · doi:10.1093/bib/bbab460 · Cited by 180 · see citing works

Unlocking biological complexity: the role of machine learning in integrative multi-omics

Kumar, R., Ruhel, R., & van Wijnen, A. J. (2024) · Academia Biology · doi:10.20935/acadbiol7428 · Cited by 12 · see citing works

Using machine learning approaches for multi-omics data analysis: A review

Reel, P. S., Reel, S., Pearson, E., Trucco, E., & Jefferson, E. (2021) · Biotechnology Advances · doi:10.1016/j.biotechadv.2021.107739 · Cited by 903 · see citing works

Computational Techniques and Tools for Omics Data Analysis: State-of-the-Art, Challenges, and Future Directions

Kaur, P., Singh, A., & Chana, I. (2021) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-021-09547-0 · Cited by 111 · see citing works