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Article
Moreau-Yoshida variational transport: a general framework for solving regularized distributional optimization problems
We address a general optimization problem involving the minimization of a composite objective functional defined over a class of probability distributions. The objective function consists of two components: on...
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Chapter
Numerical Analysis for Data Relationship
In recent years, a vast amount of data has been accumulated across various fields in industry and academia, and with the rise of artificial intelligence and machine learning technologies, knowledge discovery a...
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Article
Open AccessDC-SHAP Method for Consistent Explainability in Privacy-Preserving Distributed Machine Learning
Ensuring the transparency of machine learning models is vital for their ethical application in various industries. There has been a concurrent trend of distributed machine learning designed to limit access to ...
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Article
Mirror variational transport: a particle-based algorithm for distributional optimization on constrained domains
We consider the optimization problem of minimizing an objective functional, which admits a variational form and is defined over probability distributions on a constrained domain, which poses challenges to both th...
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Article
Open AccessData collaboration analysis in predicting diabetes from a small amount of health checkup data
Recent studies showed that machine learning models such as gradient-boosting decision tree (GBDT) can predict diabetes with high accuracy from big data. In this study, we asked whether highly accurate predicti...
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Article
Fully sequencing the cassava full-length cDNA library reveals unannotated transcript structures and alternative splicing events in regions with a high density of single nucleotide variations, insertions–deletions, and heterozygous sequences
The primary transcript structure provides critical insights into protein diversity, transcriptional modification, and functions. Cassava transcript structures are highly diverse because of alternative splicing...
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Article
Open Accessm5U-SVM: identification of RNA 5-methyluridine modification sites based on multi-view features of physicochemical features and distributed representation
RNA 5-methyluridine (m5U) modifications are obtained by methylation at the C5 position of uridine catalyzed by pyrimidine methylation transferase, which is related to the development of human diseases. Accurate i...
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Article
Open AccessDeep generative model for drug design from protein target sequence
Drug discovery for a protein target is a laborious and costly process. Deep learning (DL) methods have been applied to drug discovery and successfully generated novel molecular structures, and they can substan...
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Article
Distortion-free PCA on sample space for highly variable gene detection from single-cell RNA-seq data
Single-cell RNA-seq (scRNA-seq) allows the analysis of gene expression in each cell, which enables the detection of highly variable genes (HVG) that contribute to cell-to-cell variation within a homogeneous ce...
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Article
Open AccessExtracellular vesicles derived from Wharton’s Jelly mesenchymal stem cells inhibit the tumor environment via the miR-125b/HIF1α signaling pathway
Triple negative breast cancer (TNBC) is associated with worse outcomes and results in high mortality; therefore, great efforts are required to find effective treatment. In the present study, we suggested a nov...
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Chapter and Conference Paper
Image Data Recoverability Against Data Collaboration and Its Countermeasure
The development machine learning and related techniques has accelerated the use of data in a variety of fields, including medicine, finance, and advertising. Because the amount of data is increasing extremely ...
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Article
Open AccessDecentralized Learning with Virtual Patients for Medical Diagnosis of Diabetes
Machine learning, applied to medical data, can uncover new knowledge and support medical practices. However, analyzing medical data by machine learning methods presents a trade-off between accuracy and privacy...
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Chapter and Conference Paper
Accelerating the Backpropagation Algorithm by Using NMF-Based Method on Deep Neural Networks
Backpropagation (BP) is the most widely used algorithm for the training of deep neural networks (DNN) and is also considered a de facto standard algorithm. However, the BP algorithm often requires a lot of com...
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Chapter and Conference Paper
Collaborative Data Analysis: Non-model Sharing-Type Machine Learning for Distributed Data
This paper proposes a novel non-model sharing-type collaborative learning method for distributed data analysis, in which data are partitioned in both samples and features. Analyzing these types of distributed ...
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Article
Open AccessA genome resource for green millet Setaria viridis enables discovery of agronomically valuable loci
Wild and weedy relatives of domesticated crops harbor genetic variants that can advance agricultural biotechnology. Here we provide a genome resource for the wild plant green millet (Setaria viridis), a model spe...
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Chapter and Conference Paper
Parameter Evolution Self-Adaptive Strategy and Its Application for Cuckoo Search
Cuckoo Search (CS) is a simple yet efficient swarm intelligence algorithm based on Lévy Flight. However, its performance can depend heavily on the parameter settings. Though many studies have designed control ...
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Chapter and Conference Paper
ESSEX: Equip** Sparse Solvers For Exascale
The ESSEX project has investigated programming concepts, data structures, and numerical algorithms for scalable, efficient, and robust sparse eigenvalue solvers on future heterogeneous exascale systems. Starti...
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Article
Open AccessLocalization-specific distributions of protein pI in human proteome are governed by local pH and membrane charge
Whole-proteome distributions of protein isoelectric point (pI) values in different organisms are bi- or trimodal with some variations. It was suggested that the observed multimodality of the proteome-wide pI d...
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Article
International workshop on eigenvalue problems: algorithms; software and applications, in petascale computing (EPASA2018)
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Article
Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects
We first briefly report on the status and recent achievements of the ELPA-AEO (Eigen value Solvers for Petaflop Applications—Algorithmic Extensions and Optimizations) and ESSEX II (Equip** Sparse Solvers for...
