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Asian hypertensive population: key insights from large-scale data analysis

HYPERTENSION RESEARCH 2024年第11期47卷 2979-2980页

作者： Hoshide, Satoshi Mogi, Masaki Kario, Kazuomi Jichi Med Univ Sch Med Div Cardiovasc Med Tochigi Japan Ehime Univ Grad Sch Med Dept Pharmacol Tochigi Japan

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IL-3 Changing Cancer Genomics and Cancer Genomic Medicine by Artificial Intelligence and large-scale data analysis

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Neuro-Oncology Advances 2021年第SUPPLEMENT_6期3卷 vi1-vi1页

作者： Miyano, Satoru M&D Data Science Center Tokyo Medical and Dental University

In MEXT Program for Scientific Research on Innovative Areas “Systems Cancer” and “Systems Cancer in Neo-Dimension” (2010-2019), we developed a large-scale genome data analysis pipeline called Genomon in collaboration with Professor Seiji Ogawa (Kyoto University). Our efforts successfully produced innovative results on cancer genomics. This system is implemented on the supercomputers SHIROKANE and FUGAKU. One of the contributions unraveled the overall picture of genetic abnormalities in malignant brain tumors (Mutational landscape and clonal architecture in grade II and III gliomas. Nat Genet 2015) that exploited Genomon on SHIROKANE. However, with the spread of new measurement technology and new computing environments, no one thinks that the future can be figured on this simple extension. On the other hand, for cancer genomic medicine, Institute of Medical Science University of Tokyo made a research team analyzing whole genome sequences. The challenge we faced was to transform thousands to millions of genomic aberrations per case into precision medicine. It is what we now call “digital transformation.” IBM’s Watson for Genomics was introduced for our research purpose. In the process, we identified the effectiveness of AI, the indispensability of specialist intervention, and bottlenecks. We recognized that natural language processing technology such as BERT and Google Knowledge Graph AI technology will open up the future. Automatic document creation is also a realistic issue. Cancer research is getting more difficult and larger in scale. For example, analysis of genomic data from 60, 954 cases revealed a new underlying mechanism in which multiple mutations within the same oncogene synergistically work (Nature 2021). AI with an accuracy of X% does not seem to be the goal. What is needed is not a black box, but explainable AI that explains “why” in a human-understandable way. We are currently conducting research with Fujitsu Laboratories for this direction.

关键词： cancer genomics artificial intelligence large-scale data analysis

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AFrame: Extending dataFrames for large-scale Modern data analysis

AFrame: Extending DataFrames for Large-Scale Modern Data Ana...

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IEEE International Conference on Big data (Big data)

作者： Sinthong, Phanwadee Carey, Michael J. Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA

ISBN: (纸本)9781728108582

Analyzing the increasingly large volumes of data that are available today, possibly including the application of custom machine learning models, requires the utilization of distributed frameworks. This can result in serious productivity issues for "normal" data scientists. This paper introduces AFrame, a new scalable data analysis package powered by a Big data management system that extends the data scientists' familiar dataFrame operations to efficiently operate on managed data at scale. AFrame is implemented as a layer on top of Apache AsterixDB, transparently scaling out the execution of dataFrame operations and machine learning model invocation through a parallel, shared-nothing big data management system. AFrame incrementally constructs SQL++ queries and leverages AsterixDB's semistructured data management facilities, user-defined function support, and live data ingestion support. In order to evaluate the proposed approach, this paper also introduces an extensible micro-benchmark for use in evaluating dataFrame performance in both single-node and distributed settings via a collection of representative analytic operations. This paper presents the architecture of AFrame, describes the underlying capabilities of AsterixDB that efficiently support modern data analytic operations, and utilizes the proposed benchmark to evaluate and compare the performance and support for largescale data analyses provided by alternative dataFrame libraries.

关键词： dataFrames distributed data management large-scale data analysis data science benchmark

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A New Efficient Resource Management Framework for Iterative MapReduce Processing in large-scale data analysis

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2017年第4期E100D卷 704-717页

作者： Hong, Seungtae Park, Kyongseok Lim, Chae-Deok Chang, Jae-Woo Elect & Telecommun Res Inst Daejeon South Korea Korea Inst Sci & Technol Informat Sci Data Technol Lab Daejeon South Korea Chonbuk Natl Univ Dept Informat Technol Jeonju South Korea

To analyze large-scale data efficiently, studies on Hadoop, one of the most popular MapReduce frameworks, have been actively done. Meanwhile, most of the large-scale data analysis applications, e.g., data clustering, are required to do the same map and reduce functions repeatedly. However, Hadoop cannot provide an optimal performance for iterative MapReduce jobs because it derives a result by doing one phase of map and reduce functions. To solve the problems, in this paper, we propose a new efficient resource management framework for iterative MapReduce processing in large-scale data analysis. For this, we first design an iterative job state-machine for managing the iterative MapReduce jobs. Secondly, we propose an invariant data caching mechanism for reducing the I/O costs of data accesses. Thirdly, we propose an iterative resource management technique for efficiently managing the resources of a Hadoop cluster. Fourthly, we devise a stop condition check mechanism for preventing unnecessary computation. Finally, we show the performance superiority of the proposed framework by comparing it with the existing frameworks.

关键词： large-scale data analysis iterative data processing framework MapReduce Hadoop

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High-Order Tensor Decomposition for large-scale data analysis 4

High-Order Tensor Decomposition for Large-Scale Data Analysi...

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IEEE International Congress on Big data

作者： Li, Longzhuang Boulware, Douglas Texas A&M Univ Sch Engn & Comp Sci Corpus Christi TX 78413 USA Air Force Res Lab Griffiss AFB NY 13441 USA

ISBN: (纸本)9781467372787

Higher-order tensor decomposition is a basis for many important data mining tasks and the efficient large-scale tensor decomposition algorithms will have positive impact on clustering, trend detection, and anomaly detection. In the paper, we develop a scalable and distributed version of the Tucker tensor decomposition, MR-T, using the Hadoop MapReduce framework. We avoid large matrix-matrix multiplication and exploit the sparsity of large data sets to minimize intermediate data and flops by sequentially computing the intermediate matrices and generating the intermediate tensor vector-wise.

关键词： tensor decomposition large-scale data analysis MapReduce-based Tucker decomposition (MR-T)

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data-driven large-scale genomic analysis reveals an intricate phylogenetic and functional landscape in J-domain proteins

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2023年第32期120卷 e2218217120-e2218217120页

作者： Malinverni, Duccio Zamuner, Stefano Rebeaud, Mathieu E. Barducci, Alessandro Nillegoda, Nadinath B. De Los Rios, Paolo St Jude Childrens Res Hosp Dept Struct Biol Memphis TN 38105 USA St Jude Childrens Res Hosp Ctr Data Driven Discovery Memphis TN 38105 USA Ecole Polytech Fed Lausanne Inst Phys Sch Basic Sci CH-1015 Lausanne Switzerland Univ Montpellier CNRS Ctr Biol Struct INSERM Montpellier France Monash Univ Australian Regenerat Med Inst Melbourne Vic 3800 Australia Monash Univ Australian Regenerat Med Inst Ctr Dementia & Brain Repair Melbourne Vic 3800 Australia Ecole Polytech Fed Lausanne Inst Bioengn Sch Life Sci CH-1015 Lausanne Switzerland

The 70-kD heat shock protein (Hsp70) chaperone system is a central hub of the proteostasis network that helps maintain protein homeostasis in all organisms. The recruitment of Hsp70 to perform different and specific cellular functions is regulated by the J-domain protein (JDP) co-chaperone family carrying the small namesake J-domain, required to interact and drive the ATPase cycle of Hsp70s. Besides the J-domain, prokaryotic and eukaryotic JDPs display a staggering diversity in domain architecture, function, and cellular localization. Very little is known about the overall JDP family, despite their essential role in cellular proteostasis, development, and its link to a broad range of human diseases. In this work, we leverage the exponentially increasing number of JDP gene sequences identified across all kingdoms owing to the advancements in sequencing technology and provide a broad overview of the JDP repertoire. Using an automated classification scheme based on artificial neural networks (ANNs), we demonstrate that the sequences of J -domains carry sufficient discriminatory information to reliably recover the phylogeny, localization, and domain composition of the corresponding full-length JDP. By harnessing the interpretability of the ANNs, we find that many of the discriminatory sequence positions match residues that form the interaction interface between the J-domain and Hsp70. This reveals that key residues within the J-domains have coevolved with their obligatory Hsp70 partners to build chaperone circuits for specific functions in cells.

关键词： J-domain proteins protein homeostasis large-scale data analysis Hsp40 co-chaperones artificial neural networks

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Scalable Machine Learning: Development of Efficient Clustering Algorithms for High-Volume Temporal data analysis 2

Scalable Machine Learning: Development of Efficient Clusteri...

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2nd IEEE International Conference on Communication, Security and Artificial Intelligence, ICCSAI 2023

作者： Taneja, Madhur Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350369960

In the era of big data, the analysis of large-scale time series data has gained significant importance across various applications such as finance and healthcare. However, traditional clustering approaches, often encounter accuracy and scalability issues when applied to extensive data sets. This paper introduces innovative clustering method specifically tailored for time series data to enter these challenges. These methods leverage advanced data structures for computational efficiency, optimal distance measurements to improve, improve accuracy, and parallel computing framework for enhanced Scalability. Rig testing involving millions of time series occurrences from real world. data sets was conducted to evaluate the effectiveness of the proposed methods. The results indicate that these novel clustering algorithms can deliver high-quality outcomes even when handling massive data sets. The exhibit verity in accommodating different time series lengths and resilience in handling noise. These characteristics contribute to effective and scalable time series clustering, entering the proposed approaches suitable for real-time analytics in data intensive environments. Consequently, this research facilitates the extraction of actionable insights in real time, offering valuable contributions for both academia and industry practitioners. Additionally, it broadens the spectrum of tools available for the analysis and interpretation of large-scale time series data. © 2023 IEEE.

关键词： Distance Metrics large-scale data analysis Parallel Computing Scalability Time Series Clustering

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Deep Optimized Broad Learning System for Applications in Tabular data Recognition

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IEEE TRANSACTIONS ON CYBERNETICS 2024年第12期54卷 7119-7132页

作者： Zhang, Wandong Yang, Yimin Wu, Q. M. Jonathan Liu, Tianlong Western Univ Dept Elect & Comp Engn London ON N6A 3K7 Canada Vector Inst Artificial Intelligence Toronto ON M5G 1M1 Canada Univ Windsor Dept Elect & Comp Engn Windsor ON N9B 3P4 Canada Western Univ Dept Chem & Biochem Engn London ON N6A 3K7 Canada

The broad learning system (BLS) is a versatile and effective tool for analyzing tabular data. However, the rapid expansion of big data has resulted in an overwhelming amount of tabular data, necessitating the development of specialized tools for effective management and analysis. This article introduces an optimized BLS (OBLS) specifically tailored for big data analysis. In addition, a deep-optimized BLS (DOBLS) network is developed further to enhance the performance and efficiency of the OBLS. The main contributions of this article are: 1) by retracing the network's error from the output space to the latent space, the OBLS adjusts parameters in the feature and enhancement node layers. This process aims to achieve more resilient representations, resulting in improved performance;2) the DOBLS is a multilayered structure consisting of multiple OBLSs, wherein each OBLS connects to the input and output layers, enabling direct data propagation. This design helps reduce information loss between layers, ensuring an efficient flow of information throughout the network;and 3) the proposed methods demonstrate robustness across various applications, including multiview feature embedding, one-class classification (OCC), camera model identification, electroencephalogram (EEG) signal processing, and radar signal analysis. Experimental results validate the effectiveness of the proposed models. To ensure reproducibility, the source code is available at https://***/1027051515/OBLS_DOBLS.

关键词： Training data analysis Neurons Decision trees Representation learning Feature extraction Signal processing algorithms Brain modeling Backpropagation Analytical models Broad learning system (BLS) deep learning (DL) large-scale data analysis tabular data analysis

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Inverted Index Automata Frequent Itemset Mining for large dataset Frequent Itemset Mining

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IEEE ACCESS 2024年 12卷 195111-195130页

作者： Dai, Xin Hamed, Haza Nuzly Abdull Su, Qichen Hao, Xue Univ Teknol Malaysia UTM Fac Comp Johor Baharu 81310 Johor Malaysia

Frequent itemset mining (FIM) faces significant challenges with the expansion of large-scale datasets. Traditional algorithms such as Apriori, FP-Growth, and Eclat suffer from poor scalability and low efficiency when applied to modern datasets characterized by high dimensionality and high-density features. These algorithms demand substantial memory resources and multiple database scans, which diminishes their practicality for rapid data processing. To address these challenges, this study proposes the Inverted Index Automata Frequent Itemset Mining (IA-FIM) algorithm. IA-FIM integrates the swift retrieval of an inverted index with the robust pattern recognition of finite automata, enabling efficient processing of extensive datasets. Distinct from conventional FIM algorithms, IA-FIM utilizes an inverted index automata to efficiently reduce the search space and memory footprint, eliminating repetitive database scans and multiple tree constructions. The proposed algorithm employs a single-pass scan strategy, constructing a dynamic and adjustable inverted index for a streamlined and compact representation of data. IA-FIM demonstrates superior performance in processing large sparse dataset, enhancing the processing speed of large dataset and fulfilling the demands of the big data era. At the same time, it improves the efficiency and practicality of FIM by reducing repeated scans and large memory dependencies, making it more feasible when processing large dataset.

关键词： Automata data mining Itemsets Memory management Computational efficiency Complexity theory Real-time systems Heuristic algorithms Indexing Distributed databases Frequent itemset mining inverted index finite automata depth-first search large-scale data analysis

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Integrated single-cell and bulk transcriptomic analysis identifies a novel macrophage subtype associated with poor prognosis in breast cancer

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CANCER CELL INTERNATIONAL 2025年第1期25卷 1-14页

作者： Wang, Qing Yu, Yushuai Ruan, Liqiong Huang, Mingyao Chen, Wei Sun, Xiaomei Liu, Jun Jiang, Zirong Ningde Normal Univ Ningde Municipal Hosp Dept Thyroid & Breast Surg Ningde 352100 Peoples R China Fujian Med Univ Ningde Clin Med Coll Ningde 352100 Peoples R China Fujian Med Univ Fuzhou 350011 Peoples R China Ningde Normal Univ Ningde Municipal Hosp Dept Clin Lab Ningde 352100 Peoples R China Fujian Med Univ Affiliated Prov Hosp Fujian Prov Hosp Dept Breast SurgShengli Clin Med CollFuzhou Univ Fuzhou 350001 Peoples R China Ningde Normal Univ Ningde Municipal Hosp Dept Pathol Ningde 352100 Peoples R China Shanghai Gen Hosp Dept Breast Thyroid Surg Shanghai 200000 Peoples R China

BackgroundTumor-associated macrophages (TAMs) are pivotal components of the breast cancer (BC) tumor microenvironment (TME), significantly influencing tumor progression and response to therapy. However, the heterogeneity and specific roles of TAM subpopulations in BC remain inadequately *** performed an integrated analysis of single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (RNA-seq) data from BC patients to comprehensively characterize TAM heterogeneity. Utilizing the MetaTiME computational framework and consensus clustering, we identified distinct TAM subtypes and assessed their associations with clinical outcomes and treatment responses. A machine learning-based predictive model was developed to evaluate the prognostic significance of TAM-related gene expression *** analysis revealed three distinct TAM subgroups. Notably, we identified a novel macrophage subtype, M_Macrophage-SPP1-C1Q, characterized by high expression of SPP1 and C1QA, representing an intermediate differentiation state with unique proliferative and oncogenic properties. High infiltration of M_Macrophage-SPP1-C1Q was significantly associated with poor overall survival (OS) and chemotherapy resistance in BC patients. We developed a Random Forest (RF)-based predictive model, ***, which accurately stratified patients based on survival outcomes and chemotherapy responses, independent of established prognostic *** study uncovers a previously unrecognized TAM subtype that drives poor prognosis in BC. The identification of M_Macrophage-SPP1-C1Q enhances our understanding of TAM heterogeneity within the TME and offers a novel prognostic biomarker. The *** model provides a robust tool for predicting clinical outcomes and guiding personalized treatment strategies in BC patients.

关键词： Macrophages large-scale data analysis Tumor microenvironment Prognosis

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