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1. Variable selection and feature screening

   https://doi.org/10.1007/978-3-030-31150-6_10  

   Liu, W.; Li, R. (July 2020, Macroeconomic Forecasting in the Era of Big Data)

   null (Ed.)

   This chapter provides a selective review on feature screening methods for ultra-high dimensional data. The main idea of feature screening is reducing the ultrahigh dimensionality of the feature space to a moderate size in a fast and efficient way and meanwhile retaining all the important features in the reduced feature space. This is referred to as the sure screening property. After feature screening, more sophisticated methods can be applied to reduced feature space for further analysis such as parameter estimation and statistical inference. This chapter only focuses on the feature screening stage. From the perspective of different types of data, we review feature screening methods for independent and identically distributed data, longitudinal data and survival data. From the perspective of modeling, we review various models including linear model, generalized linear model, additive model, varying-coefficient model, Cox model, etc. We also cover some model-free feature screening procedures. 

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2. Robust Distance Correlation for Variable Screening

   https://doi.org/10.1002/sta4.70094  

   Ma, Tianzhou; Yang, Fan; Ke, Hongjie; Ren, Zhao (September 2025, Stat)

   ABSTRACT In modern statistical applications, identifying critical features in high‐dimensional data is essential for scientific discoveries. Traditional best subset selection methods face computational challenges, while regularization approaches such as Lasso, SCAD and their variants often exhibit poor performance with ultrahigh‐dimensional data. Sure screening methods, widely used for dimensionality reduction, have been developed as popular alternatives, but few target heavy‐tailed characteristics in modern big data. This paper introduces a new sure screening method, based on robust distance correlation (‘RDC’), designed for heavy‐tailed data. The proposed method inherits the benefits of the original model‐free distance correlation‐based screening while robustly estimating distance correlation in the presence of heavy‐tailed data. We further develop an FDR control procedure by incorporating the Reflection via Data Splitting (REDS) method. Extensive simulations demonstrate the method's advantage over existing screening procedures under different scenarios of heavy‐tailedness. Its application to high‐dimensional heavy‐tailed RNA‐seq data from The Cancer Genome Atlas (TCGA) pancreatic cancer cohort showcases superior performance in identifying biologically meaningful genes predictive of MAPK1 protein expression critical to pancreatic cancer. 

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3. Finding predictive models for singlet fission by machine learning

   https://doi.org/10.1038/s41524-022-00758-y  

   Liu, Xingyu; Wang, Xiaopeng; Gao, Siyu; Chang, Vincent; Tom, Rithwik; Yu, Maituo; Ghiringhelli, Luca M.; Marom, Noa (April 2022, npj Computational Materials)

   Abstract Singlet fission (SF), the conversion of one singlet exciton into two triplet excitons, could significantly enhance solar cell efficiency. Molecular crystals that undergo SF are scarce. Computational exploration may accelerate the discovery of SF materials. However, many-body perturbation theory (MBPT) calculations of the excitonic properties of molecular crystals are impractical for large-scale materials screening. We use the sure-independence-screening-and-sparsifying-operator (SISSO) machine-learning algorithm to generate computationally efficient models that can predict the MBPT thermodynamic driving force for SF for a dataset of 101 polycyclic aromatic hydrocarbons (PAH101). SISSO generates models by iteratively combining physical primary features. The best models are selected by linear regression with cross-validation. The SISSO models successfully predict the SF driving force with errors below 0.2 eV. Based on the cost, accuracy, and classification performance of SISSO models, we propose a hierarchical materials screening workflow. Three potential SF candidates are found in the PAH101 set. 

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4. Discovering and deciphering relationships across disparate data modalities

   https://doi.org/10.7554/eLife.41690  

   Vogelstein, Joshua T; Bridgeford, Eric W; Wang, Qing; Priebe, Carey E; Maggioni, Mauro; Shen, Cencheng (January 2019, eLife)

   If you want to estimate whether height is related to weight in humans, what would you do? You could measure the height and weight of a large number of people, and then run a statistical test. Such ‘independence tests’ can be thought of as a screening procedure: if the two properties (height and weight) are not related, then there is no point in proceeding with further analyses. In the last 100 years different independence tests have been developed. However, classical approaches often fail to accurately discern relationships in the large, complex datasets typical of modern biomedical research. For example, connectomics datasets include tens or hundreds of thousands of connections between neurons that collectively underlie how the brain performs certain tasks. Discovering and deciphering relationships from these data is currently the largest barrier to progress in these fields. Another drawback to currently used methods of independence testing is that they act as a ‘black box’, giving an answer without making it clear how it was calculated. This can make it difficult for researchers to reproduce their findings – a key part of confirming a scientific discovery. Vogelstein et al. therefore sought to develop a method of performing independence tests on large datasets that can easily be both applied and interpreted by practicing scientists. The method developed by Vogelstein et al., called Multiscale Graph Correlation (MGC, pronounced ‘magic’), combines recent developments in hypothesis testing, machine learning, and data science. The result is that MGC typically requires between one half to one third as big a sample size as previously proposed methods for analyzing large, complex datasets. Moreover, MGC also indicates the nature of the relationship between different properties; for example, whether it is a linear relationship or not. Testing MGC on real biological data, including a cancer dataset and a human brain imaging dataset, revealed that it is more effective at finding possible relationships than other commonly used independence methods. MGC was also the only method that explained how it found those relationships. MGC will enable relationships to be found in data across many fields of inquiry – and not only in biology. Scientists, policy analysts, data journalists, and corporate data scientists could all use MGC to learn about the relationships present in their data. To that extent, Vogelstein et al. have made the code open source in MATLAB, R, and Python. 

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5. Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

   https://doi.org/10.3390/pr8080951  

   He, Q. Peter; Wang, Jin (August 2020, Processes)

   null (Ed.)

   In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics. 

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