Era Of Data Science Research Articles

Building a computing infrastructure in the era of data science

Building a computing infrastructure in the era of data science

Medical Application of Big Data: Between Systematic Review and Randomized Controlled Trials

In terms of medical health, we are currently living in the era of data science, which has brought tremendous change. Big data related to healthcare includes medical data, genome data, and lifelog data. Among medical data, public medical data is very important for actual research and medical policy reflection because it has data on a large number of patients and is representative. However, there are many difficulties in actually using such public health big data and designing a study, and conducting systematic review (SR) on the research topic can help a lot in the methodology. In this review, in addition to the importance of research using big data for the public interest, we will introduce important public medical big data in Korea and show how SR can be specifically applied in research using public medical big data.

A historical overview of textbook presentations of statistical science

AbstractWe discuss the evolution in the presentation of statistical science in English‐language textbooks, focusing on the period 1900–1970 as the field became increasingly influenced by research contributions of R. A. Fisher and Jerzy Neyman. George Udny Yule authored an early popular book that had 14 editions. Methods books authored by Fisher and George Snedecor guided scientists in implementing modern statistical methods. In the World War 2 era, textbooks authored by Maurice Kendall, Samuel Wilks, and Harald Cramér presented a dramatically different “mathematical statistics” portrayal that centered on theoretical foundations. The textbook emergence of the Bayesian approach occurred later, influenced by books by Harold Jeffreys and Leonard J. Savage. The quarter century after World War 2 saw an explosion of books in mathematical statistics and in particular topic areas. In addition to his highly cited research contributions, Sir David Cox was a prolific author of books on a great variety of topics. Most were published after the 1900–1970 period considered in this article, but we also summarize them as part of this special issue to honor his memory. We conclude by discussing the future of textbooks on the foundations of statistical science in the emerging, ever‐broader, era of data science.

Re-Reading Deming’s 14 Points in the Era of Data Science: 14 Questions for Vibrant Management of Statistical Information Systems

This article is based on the Deming Lecture presented at the 2018 Joint Statistical Meetings and reconsiders the "14 Points" on management that were developed by W. Edwards Deming and suggests that these points reflect fundamental concepts and principles that can offer important insights for the statistical community as it responds to these opportunities and challenges related to improvement of the quality, integrity, and societal value of statistical information. The concepts un-derlying Deming's 14 Points can provide a useful lens for considering a wide range of management opportunities and challenges that arise in many complex organizations, especially those that produce or use statistical information.

An improved Artificial Neural Network for the direct prediction of fretting fatigue crack initiation lifetime

Fretting fatigue is a common type of problem in the aviation and other engineering fields. Due to its multiaxial characteristics, it leads to a shorter overall fatigue life compared to plain fatigue conditions. Fretting fatigue crack initiation lifetime is a crucial part of the total lifetime, and the currently dominant method for research on fatigue behavior is the combination of the theoretical and numerical models. With the advent of the era of data science, machine learning has been widely used to predict fatigue behavior, but there are no many applications in the field of fretting fatigue. This paper proposed an improved Artificial Neural Network (ANN) using Balancing Composite Motion Optimization (BCMO) for quick prediction of fretting fatigue crack initiation lifetime. Physical-mechanical reasoning parameters, axial stress amplitude, shear stress amplitude, half contact width, and half stick zone width are considered as input parameters, and fretting fatigue crack initiation lifetime is set as the output feature. The main aim of BCMO is to improve the robustness of the ANN based on the influential parameters, namely bias and weight. The provided results using ANN-BCMO are robust compared to ANN and traditional techniques from the literature. The Matlab code of improved ANN using BCMO can be found at https://github.com/Samir-Khatir/BCMO-ANN.git

From Data Science to Bioscience: Emerging era of bioinformatics applications, tools and challenges

With the gradual technological changes in the current era, data science using soft computing and machine learning methods has played a tremendous role in various areas, including the biomedical and health sectors. The etymology of bioinformatics with several types of omics, such as genomics, transcriptomics, proteomics, and metabolomics was coined in the late twentieth century in the direction of the investigation and finding solutions for informatics processing in biological decision-making systems. Similarly, the data science era began and played a vital role in solving a large domain, particularly in biological problem solutions. In this conceptual article, an integrated machine learning-based framework for the prediction of outbreaks is proposed, followed by omics biological data processing and dimension reduction. Subsequently, various prominent benchmark tools have been shown related to problem solutions in data science and bioinformatics applications. In this study, the role of data science and soft computing approaches in bioinformatics disciplines with various associated applications is explored. Various simple soft computing techniques such as fuzzy logic, artificial neural networks, support vector machines and evolutionary computation techniques have been explained. Furthermore, complex optimization and dimension reduction techniques, such as artificial bee colony, ant colony optimization, formal concept analysis, and principal component analysis have been presented. Finally, active research zones in bioinformatics, as well as their current trends and challenges have been highlighted which will lead to future research directions.

Active Data Science for Improving Clinical Risk Prediction

Clinical risk prediction models are commonly developed in a post-hoc and passive fashion, capitalizing on convenient data from completed clinical trials or retrospective cohorts. Impacts of the models often end at their publication rather than with the patients. The field of clinical risk prediction is rapidly improving in a progressively more transparent data science era. Based on collective experience over the past decade by the Prostate Biopsy Collaborative Group (PBCG), this paper proposes the following four data science-driven strategies for improving clinical risk prediction to the benefit of clinical practice and research. The first proposed strategy is to actively design prospective data collection, monitoring, analysis and validation of risk tools following the same standards as for clinical trials in order to elevate the quality of training data. The second suggestion is to make risk tools and model formulas available online. User-friendly risk tools will bring quantitative information to patients and their clinicians for improved knowledge-based decision-making. As past experience testifies, online tools expedite independent validation, providing helpful information as to whether the tools are generalizable to new populations. The third proposal is to dynamically update and localize risk tools to adapt to changing demographic and clinical landscapes. The fourth strategy is to accommodate systematic missing data patterns across cohorts in order to maximize the statistical power in model training, as well as to accommodate missing information on the end-user side too, in order to maximize utility for the public.

Optimal Integrating Learning for Split Questionnaire Design Type Data

In the era of data science, it is common to encounter data with different subsets of variables obtained for different cases. An example is the split questionnaire design (SQD), which is adopted to reduce respondent fatigue and improve response rates by assigning different subsets of the questionnaire to different sampled respondents. A general question then is how to estimate the regression function based on such block-wise observed data. Currently, this is often carried out with the aid of missing data methods, which may unfortunately suffer from intensive computational cost, high variability, and possible large modeling biases in real applications. In this article, we develop a novel approach for estimating the regression function for SQD-type data. We first construct a list of candidate models using available data-blocks separately, and then combine the estimates properly to make an efficient use of all the information. We show the resulting averaged model is asymptotically optimal in the sense that the squared loss and risk are asymptotically equivalent to those of the best but infeasible averaged estimator. Both simulated examples and an application to the SQD dataset from the European Social Survey show the promise of the proposed method. Supplementary materials for this article are available online.

A Tensor-EM Method for Large-Scale Latent Class Analysis with Binary Responses.

Latent class models are powerful statistical modeling tools widely used in psychological, behavioral, and social sciences. In the modern era of data science, researchers often have access to response data collected from large-scale surveys or assessments, featuring many items (large J) and many subjects (large N). This is in contrary to the traditional regime with fixed J and large N. To analyze such large-scale data, it is important to develop methods that are both computationally efficient and theoretically valid. In terms of computation, the conventional EM algorithm for latent class models tends to have a slow algorithmic convergence rate for large-scale data and may converge to some local optima instead of the maximum likelihood estimator (MLE). Motivated by this, we introduce the tensor decomposition perspective into latent class analysis with binary responses. Methodologically, we propose to use a moment-based tensor power method in the first step and then use the obtained estimates as initialization for the EM algorithm in the second step. Theoretically, we establish the clustering consistency of the MLE in assigning subjects into latent classes when N and J both go to infinity. Simulation studies suggest that the proposed tensor-EM pipeline enjoys both good accuracy and computational efficiency for large-scale data with binary responses. We also apply the proposed method to an educational assessment dataset as an illustration.

Visualization of frequent temporal patterns in single or two populations

Temporal knowledge discovery in clinical problems, is crucial to investigate problems in the data science era. Meaningful progress has been made computationally in the discovery of frequent temporal patterns, which may store potentially meaningful knowledge. However, for temporal knowledge discovery and acquisition, effective visualization is essential and still stores much room for contributions. While visualization of frequent temporal patterns was relatively under researched, it stores meaningful opportunities in facilitating usable ways to assist domain experts, or researchers, in exploring and acquiring temporal knowledge. In this paper, a novel approach for the visualization of an enumeration tree of frequent temporal patterns is introduced for, whether mined from a single population, or for the comparison of patterns that were discovered in two separate populations. While this approach is relevant to any sequence-based patterns, we demonstrate its use on the most complex scenario of time intervals related patterns (TIRPs). The interface enables users to browse an enumeration tree of frequent patterns, or search for specific patterns, as well as discover the most discriminating TIRPs among two populations. For that a novel visualization of the temporal patterns is introduced using a bubble chart, in which each bubble represents a temporal pattern, and the chart axes represent the various metrics of the patterns, such as their frequency, reoccurrence, and more, which provides a fast overview of the patterns as a whole, as well as access specific ones. We present a comprehensive and rigorous user study on two real-life datasets, demonstrating the usability advantages of the novel approaches.

Computational thinking in the era of data science

Incorporating data thinking into computer science education.

Recognition of Hand Gesture Using Electromyography Signal: Human-Robot Interaction

Recognition of hand gestures has been developed in various research domains and proven to have significant benefits in improving the necessity of human-robot interaction (HRI). The introduction of intelligent statistics knowledge methodologies, such as big data and machine learning, has ushered in a new era of data science and made it easier to classify hand motions accurately using electromyography (EMG) signals. However, the collecting and labelling of the vast dataset enforces a significant workload; resulting in implementations takes a long time. As a result, a unique strategy for combining the advantages of depth vision learning with EMG-based hand gesture detection was developed. It is accomplished of automatically categorizing the class of the obtained EMG data using ensemble learning without considering the hand motion sequence. The models were built and interpreted using the SVM with RBF kernel, Random Forest, and Catboost with the best hyperparameters. The resultant value states that Catboost produces the best accuracy of around 0.95 as compared with other models. This demonstrates that the suggested technique can recognize hand gestures with better performance rate.

Fairness & friends in the data science era

The data science era is characterized by data-driven automated decision systems (ADS) enabling, through data analytics and machine learning, automated decisions in many contexts, deeply impacting our lives. As such, their downsides and potential risks are becoming more and more evident: technical solutions, alone, are not sufficient and an interdisciplinary approach is needed. Consequently, ADS should evolve into data-informed ADS, which take humans in the loop in all the data processing steps. Data-informed ADS should deal with data responsibly, guaranteeing nondiscrimination with respect to protected groups of individuals. Nondiscrimination can be characterized in terms of different types of properties, like fairness and diversity. While fairness, i.e., absence of bias against minorities, has been widely investigated in machine learning, only more recently this issue has been tackled by considering all the steps of data processing pipelines at the basis of ADS, from data acquisition to analysis. Additionally, fairness is just one point of view of nondiscrimination to be considered for guaranteeing equity: other issues, like diversity, are raising interest from the scientific community due to their relevance in society. This paper aims at critically surveying how nondiscrimination has been investigated in the context of complex data science pipelines at the basis of data-informed ADS, by focusing on the specific data processing tasks for which nondiscrimination solutions have been proposed.

Big Code

Big data, the “new oil” of the modern data science era, has attracted much attention in the GIScience community. However, we have ignored the role of code in enabling the big data revolution in this modern gold rush. Instead, what attention code has received has focused on computational efficiency and scalability issues. In contrast, we have missed the opportunities that the more transformative aspects of code afford as ways to organize our science. These “big code” practices hold the potential for addressing some ill effects of big data that have been rightly criticized, such as algorithmic bias, lack of representation, gatekeeping, and issues of power imbalances in our communities. In this article, I consider areas where lessons from the open source community can help us evolve a more inclusive, generative, and expansive GIScience. These concern best practices for codes of conduct, data pipelines and reproducibility, refactoring our attribution and reward systems, and a reinvention of our pedagogy.

Special issue on advances in data, information and knowledge engineering in data science era

Special issue on advances in data, information and knowledge engineering in data science era

Machine learning approaches to optimize small-molecule inhibitors for RNA targeting

In the era of data science, data-driven algorithms have emerged as powerful platforms that can consolidate bioisosteric rules for preferential modifications on small molecules with a common molecular scaffold. Here we present complementary data-driven algorithms to minimize the search in chemical space for phenylthiazole-containing molecules that bind the RNA hairpin within the ribosomal peptidyl transferase center (PTC) of Mycobacterium tuberculosis. Our results indicate visual, geometrical, and chemical features that enhance the binding to the targeted RNA. Functional validation was conducted after synthesizing 10 small molecules pinpointed computationally. Four of the 10 were found to be potent inhibitors that target hairpin 91 in the ribosomal PTC of M. tuberculosis and, as a result, stop translation.Graphical

PyKVFinder: an efficient and integrable Python package for biomolecular cavity detection and characterization in data science

BackgroundBiomolecular interactions that modulate biological processes occur mainly in cavities throughout the surface of biomolecular structures. In the data science era, structural biology has benefited from the increasing availability of biostructural data due to advances in structural determination and computational methods. In this scenario, data-intensive cavity analysis demands efficient scripting routines built on easily manipulated data structures. To fulfill this need, we developed pyKVFinder, a Python package to detect and characterize cavities in biomolecular structures for data science and automated pipelines.ResultspyKVFinder efficiently detects cavities in biomolecular structures and computes their volume, area, depth and hydropathy, storing these cavity properties in NumPy arrays. Benefited from Python ecosystem interoperability and data structures, pyKVFinder can be integrated with third-party scientific packages and libraries for mathematical calculations, machine learning and 3D visualization in automated workflows. As proof of pyKVFinder’s capabilities, we successfully identified and compared ADRP substrate-binding site of SARS-CoV-2 and a set of homologous proteins with pyKVFinder, showing its integrability with data science packages such as matplotlib, NGL Viewer, SciPy and Jupyter notebook.ConclusionsWe introduce an efficient, highly versatile and easily integrable software for detecting and characterizing biomolecular cavities in data science applications and automated protocols. pyKVFinder facilitates biostructural data analysis with scripting routines in the Python ecosystem and can be building blocks for data science and drug design applications.

Statistical modeling and inference in the era of Data Science and Graphical Causal modeling

AbstractThe paper discusses four paradigm shifts in statistics since the 1920s with a view to compare their similarities and differences, and evaluate their effectiveness in giving rise to ‘learning from data' about phenomena of interest. The first is Fisher's 1922 recasting of Karl Pearson's descriptive statistics into a model‐based [] statistical induction that dominates current statistics (frequentist and Bayesian). A crucial departure was Fisher's replacing the curve‐fitting perspective guided by goodness‐of‐fit measures with a model‐based perspective guided by the statistical adequacy: the validity of the probabilistic assumptions comprising . Statistical adequacy is pivotal in securing trustworthy evidence since it underwrites the reliability of inference. The second is the nonparametric turn in the 1970s aiming to broaden by replacing its distribution assumption with weaker mathematical conditions relating to the unknown density function underlying . The third is a two‐pronged development initiated in Artificial Intelligence (AI) in the 1990s that gave rise to Data Science (DS) and Graphical Causal (GC) modeling. The primary objective of the paper is to compare and evaluate the other competing approaches with a refined/enhanced version of Fisher's model‐based approach in terms of their effectiveness in giving rise to genuine “learning from data;” excellent goodness‐of‐fit/prediction is neither necessary nor sufficient for statistical adequacy, or so it is argued.

Integrating Models and Fusing Data in a Deep Ensemble Learning Method for Predicting Epidemic Diseases Outbreak

Due to the continuous and growing spread of the novel corona virus (COVID-19) worldwide, it is urgent, especially in the data science era, to develop accurate data driven decision-aided methods to predict and early detect the outbreak of this epidemic disease and then to support healthcare decision makers. In this context, the main goal of this paper is to build an accurate and generic data driven method that can predict daily COVID-19 positive cases and therefore helps stakeholders to make and review their epidemic response plans. This method is based on the integration of three deep learning models: Long Short Term Memory (LSTM), Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN) and takes advantage of their complementarity. The proposed method is validated on two experimental scenarios where the first one aims to validate the method on China and Tunisia case studies and the second one is based on data fusion and transfer learning process where China data and models will be reused to predict Tunisia COVID-19 outbreak. Experiment results indicate that, compared with individual learners, the stacked-DNN meta-learner, whose inputs are results of LSTM, DNN and CNN learners, achieved the best results in terms of accuracy as well as RMSE and it required the lowest time for training as well as prediction for the two scenarios. The main outcomes of this paper are i) to adopt deep learning models combined to stacking ensemble learning to accurately forecast COVID-19 positive cases and ii) to merge data and to adopt transfer learning for the prediction of confirmed cases by reusing China data, learners and meat-learners to make prediction of the epidemic trend for other countries, with less facilities of collecting data, when preventive and control measures are similar.

Role of statistics in the era of data science

Statistics evolved as a science in an era when the amount of data available was small and efforts were on to extract maximum information from them. Are the techniques developed during those times relevant anymore in the era of data science? We will illustrate using examples that several statistical concepts developed over the last 150 years are as relevant in this era as they were then