Data Transformation Strategy Research Articles

GSFM: A genome-scale functional module transformation to represent drug efficacy for in silico drug discovery

Pharmacotranscriptomic profiles, which capture drug-induced changes in gene expression, offer vast potential for computational drug discovery and are widely used in modern medicine. However, current computational approaches neglected the associations within gene‒gene functional networks and unrevealed the systematic relationship between drug efficacy and the reversal effect. Here, we developed a new genome-scale functional module (GSFM) transformation framework to quantitatively evaluate drug efficacy for in silico drug discovery. GSFM employs four biologically interpretable quantifiers: GSFM_Up, GSFM_Down, GSFM_ssGSEA, and GSFM_TF to comprehensively evaluate the multi-dimension activities of each functional module (FM) at gene-level, pathway-level, and transcriptional regulatory network-level. Through a data transformation strategy, GSFM effectively converts noisy and potentially unreliable gene expression data into a more dependable FM active matrix, significantly outperforming other methods in terms of both robustness and accuracy. Besides, we found a positive correlation between RSGSFM and drug efficacy, suggesting that RSGSFM could serve as representative measure of drug efficacy. Furthermore, we identified WYE-354, perhexiline, and NTNCB as candidate therapeutic agents for the treatment of breast-invasive carcinoma, lung adenocarcinoma, and castration-resistant prostate cancer, respectively. The results from in vitro and in vivo experiments have validated that all identified compounds exhibit potent anti-tumor effects, providing proof-of-concept for our computational approach.

Self-efficacy of Data Literacy among Primary and Secondary School Teachers in Guangdong Province

The application of educational big data provides new basis for teachers to improve teaching and promote student development. However, there are still many challenges in using data to solve complex educational problems. Teacher data literacy has become the key to transforming educational data into effective teaching strategies. The study sought to gain a better understanding of the current state of primary and secondary school Teachers self-efficacy of data literacy and put forward some suggestions for teacher data literacy training. This study used a quantitative methodology. 813 primary and secondary school teachers from Guangdong Province, China, completed a questionnaire on self-efficacy of data literacy. (1) Primary and secondary school teachers reported significantly high levels of self-efficacy in data literacy (mean=3.624). Among the dimensions, the data awareness and ethics dimension had the highest self-efficacy (mean=3.738) and the data research dimension had a lower self-efficacy (mean=3.358). (2)There were significant differences in the primary and secondary school teachers’ self-efficacy of data literacy with different ages, educational backgrounds, and school locations. Primary and secondary school teachers generally recognized the importance of data literacy training. Older teachers who worked in rural schools and had low levels of education have the lowest level of self-efficacy for data literacy. For primary and secondary school teachers' low level of data analysis skills and weak data transformation strategy ability, teacher data literacy training adopts an embedded teamwork approach, integrating multiple resources to build a data team, integrating big data technology throughout the entire teaching process, and helping teachers to use new data and technologies to understand students' needs in a timely manner, to select teaching methods, and to solve real-world problems. It makes data literacy the foundation of teachers' teaching and promotes the development of all students.

Interpretable Multi-Modal Stacking-Based Ensemble Learning Method for Real Estate Appraisal

With the development of online real estate trading platforms, multi-modal housing trading data, including structural information, location, and interior image data, are being accumulated. The accurate appraisal of real estate makes sense for government officials, urban policymakers, real estate sellers, and personal purchasers. In this study, we propose an interpretable multi-modal stacking-based ensemble learning (IMSEL) method that deals with various modalities for real estate appraisals. We crawl the structural and image data of real estate in Chengdu city, China from the nation's largest real estate transaction platform with the location information, including public services, within 2 km of the real estate using Baidu map. We then compare the predictive results from IMSEL with those from previous state-of-art methods in the literature in terms of the root mean square error, mean absolute percentage error, mean absolute error, and coefficient of determination (R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> ). The comparison results show that IMSEL outperformed the other methods. We verified the improvement of introducing a data transformation strategy and deep visual features through a 10-fold cross-validation. We also discuss the managerial implications of our research findings.

A predictive analysis framework of heart disease using machine learning approaches

Heart diseaseis among the leading causes for death globally. Thus, early identification and treatment are indispensable to prevent the disease. In this work, we propose a framework based on machine learning algorithms to tackle such problems through the identification of risk variables associated to this disease. To ensure the success of our proposed model, influential data pre-processing and data transformation strategies are used to generate accurate data for the training model that utilizes the five most popular datasets (Hungarian, Stat log, Switzerland, Long Beach VA, and Cleveland) from UCI. The univariate feature selection technique is applied to identify essential features and during the training phase, classifiers, namely extreme gradient boosting (XGBoost), support vector machine (SVM), random forest (RF), gradient boosting (GB), and decision tree (DT), are deployed. Subsequently, various performance evaluations are measured to demonstrate accurate predictions using the introduced algorithms. The inclusion of Univariate results indicated that the DT classifier achieves a comparatively higher accuracy of around 97.75% than others. Thus, a machine learning approach is recognize, that can predict heart disease with high accuracy. Furthermore, the 10 attributes chosen are used to analyze the model's outcomes explainability, indicating which attributes are more significant in the model's outcome.

Sensitive assay design for detection of anti-drug antibodies to biotherapeutics that lack an immunoglobulin Fc domain

Today the evaluation of unwanted immunogenicity is a key component in the clinical safety evaluation of new biotherapeutic drugs and macromolecular delivery strategies. However, the evolving structural complexity in contemporary biotherapeutics creates a need for on-going innovation in assay designs for reliable detection of anti-drug antibodies, especially for biotherapeutics that may not be well-suited for testing by a bridging assay. We, therefore, initiated systematic optimization of the direct binding assay to adapt it for routine use in regulatory-compliant assays of serum anti-drug antibodies. Accordingly, we first prepared a SULFO-TAG labeled conjugate of recombinant Protein-A/G to create a sensitive electrochemiluminescent secondary detection reagent with broad reactivity to antibodies across many species. Secondly, we evaluated candidate blocker-diluents to identify ones producing the highest signal-to-noise response ratios. Lastly, we introduced use of the ratio of signal responses in biotherapeutic-coated and uncoated wells as a data transformation strategy to identify biological outliers. This alternative data normalization approach improved normality, reduced skewness, and facilitated application of a parametric screening cut point. We believe the optimized direct binding assay design employing SULFO-TAG labeled Protein-A/G represents a useful analytical design for detecting serum ADA to biotherapeutics that lack an immunoglobulin Fc domain.

Digital identity as a platform for improving refugee management

AbstractDigital platforms are restructuring how many companies and industries function, including humanitarian organisations that operate in complex environments and serve vulnerable populations. To date, however, there has been limited study of their use in humanitarian and particularly refugee contexts. This paper seeks to address this gap by drawing on the concept of platformisation to study the opportunities and challenges arising from UNHCR's transition from a closed transactional system to an open innovation platform focusing on core processes of identification, value creation and platform governance that are relevant for refugee management and protection. Our empirical study captures the perspectives of the UNHCR, organisational stakeholders and refugees in the world's largest refugee camp in Northern Uganda with regards to UNHCR's strategy towards platform openness. We find that UNHCR's data transformation strategy introduces the potential for increasing institutional value in the form of more effective service delivery to refugees. However, these technological opportunities do not necessarily translate to greater value if they do not mesh with current work practices, incentives and activities of service provider organisations and refugees. Our study helps identify opportunities to address these constraints, primarily through improving understanding of the emergent governance‐related tensions that exist for digital platforms for development and surfacing existing issues of exclusion and vulnerability. We conclude with insights for the broader theorisation of identification platforms and with recommendations for policies and practices that together might help realise the potential value creation introduced through the platformisation of identification systems.

Conceptual Thinking in Statistics and Data Science Education: Interactive Formative Assessment with Meaning Equivalence Reusable Learning Objects (MERLO)

Computer age statistics, machine learning, data science and in general, data analytics, are having an ubiquitous impact on industry, business and services. Deploying a data transformation strategy requires a workforce which is up to the job in terms of knowledge, experience and capabilities. The application of analytics needs to address organizational needs, invoke proper methods, build on adequate infrastructures and ensure availability of the right skills to the right people. such upskilling requires a focus on conceptual understanding affecting both the pedagogical approach and the complementary learning assessment tools, This paper is about the application of advanced educational concepts to the teaching and evaluation of statistical and data science related concepts. Two educational elements will be included in the discussion: i) the use of simulations to facilitate problem based experiential learning and ii) an emphasis on information quality, as the overall objective of statistics and data science activity. We begin with an introduction to conceptual thinking and meaning equivalence and the application of Meaning Equivalence Reusable Learning Objects (MERLO) in the classroom. We then describe how MERLO and concept science can be applied in the domain of Statistics and Data Science. Section 3 is about the use of simulations in statistical education. In section 4 we discuss practical aspects of an education program focused on conceptual understanding. Section 5 is on the conceptual mapping of information quality as a MERLO infrastructure. The paper concludes with a discussion.

Astronomical Data Preprocessing Implementation Based on FPGA and Data Transformation Strategy for the FAST Telescope as a Giant CPS

The emergence of cyber-physical-social systems (CPSS) as a novel paradigm has revolutionized the relationship between humans, computers and the physical environment. CPSS extend cyber-physical systems (CPS) to include the social domain, which introduces a challenge of massive data processing. As a typically giant CPS, the Five-hundred-meter Aperture Spherical radio Telescope (FAST), the world’s largest filled-aperture radio telescope, generates massive volume of data which poses a huge storage problem that CPSS face likewise and requires real-time data compressing to reduce data storage and movement overhead. The recently introduced Bitshuffle preprocessing algorithm is a novel approach towards exploiting spatial redundancy incorporation to improve the compression ratio with a specific compressor. However, the existing high-performance CPU-based solutions cannot satisfy the performance requirement and power budget requirement simultaneously. In the paper, we propose the implementation of this algorithm on Field Programmable Gate Array (FPGA) and present an unique data transformation strategy to turn raw FAST data in classic FITS format into another format to support huge file sizes, i.e. Hierarchical Data Format (HDF5). Evaluation results show that our implementation can achieve 3.2Gbyte/s throughput which can be equipped with LZ4 compressor to be high performance compressor. This makes Bitshuffle on FPGAs a candidate for meeting the computational and energy efficiency constraints of radio telescopes and provide reference for CPSS facing the same situation.

Privacy Preserving Data Mining for Ordinal Data using Correlation Based Transformation Strategy (CBTS)

Objectives: Preservation of privacy is a significant aspect of data mining. The main objective of PPDM is to hide or provide privacy to certain sensitive information so that they can be protected from unauthorized parties or intruders. Methods/ Statistical Analysis: Though privacy is achieved by hiding the sensitive or private data, it will affect the data mining algorithms in knowledge extraction, so an effective method or strategy is required to provide privacy to the data and simultaneously protecting the quality of data mining algorithms. Instead of removing or encrypting sensitive or private data, we make use of data transformation strategies that keep the statistical, semantic and heuristic nature of data while protecting the sensitive or private data. Findings: In this paper we studied the technical feasibility of realizing Privacy Preserving Data Mining. In the proposed work, Correlation Based Transformation Strategy for Privacy Preserving Data Mining is used for ordinal data. We apply the method on few datasets namely soybean, Breast Cancer, Nursery dataset and Car dataset. We tabulate the end results applying the proposed strategy on both the original and the transformed dataset and observe correlation difference, Information Entropy and Classification Accuracy with different machine learning algorithms and Clustering Quality. Application/Improvements: As an improvement, the proposed work can be extended by use of vector marking techniques where these techniques help in increasing the efficiency by avoiding unauthorised access to the information.

Genomic simulation for the study of data transformation efficiency in the genetic evaluation of cattle in the presence of heterogeneity

Efficiency of data transformation strategies is evaluated to correct heterogeneity among cattle in the genetic evaluation of bulls, cows and progenies. Four data structures of bovine weaning weights were simulated: herds with and without heterogeneity for means and variances, and herds with and without genetic connectedness. In the genetic evaluations, data were used in the original scale and transformed (Logarithmic, Square root, standardization and ratio by phenotypic standard deviation). The evaluated data transformation strategies were not efficient in eliminating the negative effects of heterogeneity among cattle in the genetic evaluation of bulls, cows and progenies.