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Making Canonical Workflow Building Blocks Interoperable across Workflow Languages

data Intelligence 2022年第2期4卷 342-357页

作者： Stian Soiland-Reyes Genis Bayarri Pau Andrio Robin Long Douglas Lowe Ania Niewielska Adam Hospital Paul Groth Department of Computer Science The University of ManchesterManchester M139PLUK Informatics Institute University of AmsterdamAmsterdam 1098 XHThe Netherlands Institute for Research in Biomedicine(IRB Barcelona) The Barcelona Institute of Science and Technology(BIST)Barcelona O8028Spain The Spanish National Bioinformatics Institute(INB) Barcelona Supercomputing Center(BSC)Barcelona 08034Spain Data Science Institute Lancaster UniversityLancasterLancashire LA14YWUK Research IT IT ServicesThe University of ManchesterManchester M139PLUK European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeshire CB101SDUK

We introduce the concept of Canonical Workflow Building Blocks(CWBB),a methodology of describing and wrapping computational tools,in order for them to be utilised in a reproducible manner from multiple workflow languages and execution *** concept is implemented and demonstrated with the BioExcel Building Blocks library(BioBB),a collection of tool wrappers in the field of computational biomolecular *** across different workflow languages is showcased through a protein Molecular Dynamics setup transversal workflow,built using this library and run with 5 different Workflow Manager Systems(WfMS).We argue such practice is a necessary requirement for FAIR Computational Workflows and an element of Canonical Workflow Frameworks for Research(CWFR)in order to improve widespread adoption and reuse of computational methods across workflow language barriers.

关键词： Scientific workflows Interoperable FAIR Computational tools Containers Software packaging FAIR digital object(FDO),BioExcel Building Blocks library(BioBB),Canonical Workflow Frameworks for Research(CWFR)

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A Privacy-Preserving Framework for Collaborative Machine Learning with Kernel Methods

A Privacy-Preserving Framework for Collaborative Machine Lea...

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IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA)

作者： Anika Hannemann Ali Burak Ünal Arjhun Swaminathan Erik Buchmann Mete Akgün Dept. of Computer Science Leipzig University Center for Scalable Data Analytics and Artificial Intelligence (ScaDS.AI) Dresden/Leipzig Germany Medical Data Privacy and Privacy-preserving Machine Learning (MDPPML) University of Tübingen Institute for Bioinformatics and Medical Informatics (IBMI) University of Tübingen Germany

It is challenging to implement Kernel methods, if the data sources are distributed and cannot be joined at a trusted third party for privacy reasons. It is even more challenging, if the use case rules out privacy-preserving approaches that introduce noise or entail significant computational overhead. An example for such a use case is machine learning on clinical data. To realize exact and efficient privacy preserving computation of kernel methods, we propose FLAKE, a Framework for Learning with Anonymized KErnels on horizontally distributed data. With our method, the data sources mask their data so that a Gram matrix can be computed without compromising privacy or utility. The Gram matrix allows to calculate many kernel matrices, which can be used to train kernel-based machine learning algorithms such as Support Vector Machines. We prove that our framework prevents an adversary from learning the input data or the number of input features under a semi-honest threat model. The conducted experiments on clinical, genomic, and image data provide confirmation that our approach is applicable across a wide range of settings. Additionally, our method outperforms comparable approaches in both computational efficiency and accuracy. Thus, FLAKE is a lightweight, applicable approach suitable for various use cases.

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Back to Science in Searching for SARS-CoV-2 Origins

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China CDC weekly 2023年第14期5卷 315-317页

作者： William J Liu Wenwen Lei Xiaozhou He Peipei Liu Qihui Wang Zhiqiang Wu Yun Tan Shuhui Song Gary Wong Jian Lu Jingkun Jiang Qiang Wei Mingkun Li Juncai Ma Xiaozhong Peng Yixue Li Baoxu Huang Yigang Tong Jun Han Guizhen Wu NHC Key Laboratory of Biosafety National Institute for Viral Disease Control and PreventionChinese Center for Disease Control and PreventionBeijingChina CAS Key Laboratory of Pathogen Microbiology and Immunology Institute of MicrobiologyChinese Academy of SciencesBeijingChina NHC Key Laboratory of Systems Biology of Pathogens Institute of Pathogen BiologyChinese Academy of Medical Sciences&Peking Union Medical CollegeBeijingChina Shanghai Institute of Hematology State Key Laboratory of Medical GenomicsNational Research Center for Translational MedicineRuijin Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghaiChina Beijing Institute of Genomics Chinese Academy of Sciencesand China National Center for BioinformationBeijingChina Laboratory of Molecular Virology and Immunology Institut Pasteur of ShanghaiChinese Academy of SciencesShanghaiChina State Key Laboratory of Protein and Plant Gene Research Center for BioinformaticsSchool of Life SciencesPeking UniversityBeijingChina School of Environment Tsinghua UniversityBeijingChina National Pathogen Resource Center Chinese Center for Disease Control and Prevention(China CDC)BeijingChina Microbial Resource and Big Data Center Chinese National Microbiology Data Center(NMDC)Institute of MicrobiologyChinese Academy of SciencesBeijingChina State Key Laboratory of Medical Molecular Biology Department of Molecular Biology and BiochemistryInstitute of Basic Medical SciencesMedical Primate Research CenterNeuroscience CenterChinese Academy of Medical SciencesSchool of Basic MedicinePeking Union Medical CollegeBeijingChina School of Life Science and Technology ShanghaiTech UniversityShanghaiChina Bio-Med Big Data Center Key Laboratory of Computational BiologyCAS-MPG Partner Institute for Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghaiChina China Animal Health and Epidemiology Center Qingdao CityShandong ProvinceChi

In recent decades,emerging and re-emerging human-infecting pathogens have been represented as huge threats to public health and have become a global concern(1).After outbreaks of two coronaviruses(CoVs),severe acute respiratory syndrome coronavirus(SARS-CoV)and Middle East respiratory syndrome coronavirus(MERS-CoV),severe acute respiratory syndrome coronavirus 2(SARS-CoV-2)became the first-known pandemic hastening CoV with tremendous wrecking to the world(2).The origin tracing of these emerging pathogens is of great significance in infectious disease prevention and control(3–4).The origin of SARS-CoV-2 remains elusive after the more than 3-year pandemic,though scientists around the world are making great *** the experience of studying many other infectious pathogens,origin tracing is systematic and time-consuming *** supposed origins of many infectious pathogens are still in debate,including SARS-CoV and human immunodeficiency virus,etc(5).

关键词： prevention respiratory acute

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Predicting the physiological effects of multiple drugs using electronic health record

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Computers in Biology and Medicine 2025年 184卷 109485-109485页

作者： Jeon, Junhyeok Hong, Eujin Kim, Jong-Yeup Lee, Suehyun Kim, Hyun Uk Daejeon34141 Korea Republic of Department of Biomedical Informatics College of Medicine Konyang University Daejeon35365 Korea Republic of Healthcare Data Science Center Konyang University Hospital Daejeon35365 Korea Republic of Department of Computer Engineering Gachon University Seongnam13120 Korea Republic of Graduate School of Engineering Biology KAIST Daejeon34141 Korea Republic of BioProcess Engineering Research Center and BioInformatics Research Center KAIST Daejeon34141 Korea Republic of

Various computational models have been developed to understand the physiological effects of drug-drug interactions, which can contribute to more effective drug treatments. However, they mostly focus on interactions of only two drugs, and do not consider the patient information. To address this challenge, we use publicly available electronic health record (EHR), MIMIC-IV, to develop machine learning models that predict the physiological effects of two or more drugs. This study involves extensive preprocessing of laboratory measurement data, prescription data and patient data. The resulting machine learning models predict potential abnormalities across 20 selected measurement items (e.g., concentrations of metabolites and blood cells) in the form of a sentence. Analysis of the model predictions showed that age, specific active pharmaceutical ingredients, and male/female appeared to be the most influential features. The model development process showcased in this study can be extended to other measurement items for a target EHR. © 2024 Elsevier Ltd

关键词： Electronic health record

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Development of artificial intelligence for variable rate application based oil palm fertilization recommendation system 6

Development of artificial intelligence for variable rate app...

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6th International Conference on Information Management and Technology, ICIMTech 2021

作者： Firmansyah, Erick Pardamean, Bens Ginting, Candra Mawandha, Hangger Gahara Pratama Putra, Dian Suparyanto, Teddy Institute of Agriculture STIPER Faculty of Agriculture Yogyakarta55282 Indonesia Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia Institute of Agriculture STIPER Faculty of Agriculture Yogyakarta55282 Indonesia

ISBN: (纸本)9781665449373

The need for fertilization knowledge that meets good cultivation principles is the background for the need for a platform that can help provide advice on fertilization implementation. The conventional method through leaf and soil analysis to determine the dose of fertilization has many obstacles in its implementation. The availability of experts to provide advice is also not available at all times. One way to transfer knowledge to non-experts is to use Artificial Intelligence (AI) in the form of a dynamic expert system. The purpose of this research is to create a dynamic expert system that can provide advice and apply fertilization quickly, cheaply, accurately, and available at all times. In building this AI system, a knowledge base, working memory, inference engine, and interface are needed. The implementation of dynamic expert system development consists of four stages, namely (1) literature study, (2) laboratory analysis, (3) construction of an inference engine, and (4) interface creation. Based on the literature study that has been carried out, it is known that there are three domains of knowledge of oil palm fertilization, namely the domain of soil, plants and climate. Each of these knowledge domains consists of attributes, sub-attributes, and facts of knowledge which are then arranged in the form of mathematical rules. The relationship between the three knowledge domains is used as the basis for making fertilization rules. The result of this research is an application with Artificial Intelligence to provide information on nutritional needs according to plant age, population, production, land area and location. The application provides recommendations for the type, frequency, amount of fertilizer, and time of application. © 2021 IEEE.

关键词： Inference engines

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LSD 4.0:an improved database for comparative studies of leaf senescence

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Molecular Horticulture 2022年第1期2卷 303-306页

作者： Jie Cao Yang Zhang Shuya Tan Qi Yang Hou‑Ling Wang Xinli Xia Jingchu Luo Hongwei Guo Zhang Zhang Zhonghai Li National Engineering Research Center for Tree Breeding and Ecological Restoration College of Biological Sciences and TechnologyBeijing Forestry UniversityBeijing 100083China National Genomics Data Center Beijing 100101China China National Center for Bioinformation Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China College of Life Sciences Peking UniversityBeijing 100871China Center for Bioinformatics Peking UniversityBeijing 100871China Department of Biology Institute of Plant and Food ScienceSouthern University of Science and Technology(SUSTech)Shenzhen 518055GuangdongChina

Leaf senescence is the final stage of leaf development and involves the active degradation and dynamic transfer of its cellular components to newly growing and storage tissues,which contributes to plant fitness(Gan and Amasino,1997;Lim et al.,2007).The genetic modification of leaf senescence has emerged as a promising strategy for improving nutritional traits and stress tolerance in plants(Rivero et al.,2007).Efforts to dissect the molecular mechanisms underpinning leaf senescence reveal that it is a highly coordinated process regulated by a large number of senescence-associated genes(SAGs)(Guo and Gan,2005;Lim et al.,2007).Functional studies of these SAG genes through reverse genetics strategies and identification of senescence-altered mutants through forward genetic screening have deepened the understanding of leaf senescence(Guo et al.,2021).

关键词： traits senescence emerged

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Comparative Analysis of CNN Models for Multiclass Detection on Colon Disease

Comparative Analysis of CNN Models for Multiclass Detection ...

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Information Technology and Digital Applications (ICITDA), IEEE International Conference on

作者： Jason Sebastian Sulistyawan Kuncahyo Setyo Nugroho Mahmud Isnan Bens Pardamean Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331510732

ISBN: (纸本)9798331510749

Gastrointestinal diseases are significant health concerns that mostly affect the digestive and biliary tracts. These can only be observed internally through endoscopy, or a modern approach named Wireless Capsule Endoscopy. Though the approach has proven to work, the process relies heavily on medical experts, which is prone to error when given a considerably large load. The use of deep learning in detecting diseases with images has been proven to work in various cases and can be implemented for gastrointestinal diseases. This study compared four deep learning models based on Convolutional Neural Network architectures consisting of the VGG16, ResNet152V2, InceptionV3, and Xception for classifying colon disease images. The experiment showed that the ResNet152V2 performed the best, compared to the other three with a testing accuracy score of 0.9837. On the other hand, the VGG16 had the lowest performance with around accuracy score of 0.89 while the Xception and InceptionV3models yielded similar accuracy scores of 0.9587 and 0.9549, respectively. The results highlight the effectiveness of ResNet152V2 in handling the complexity of colon disease detection

关键词： Hands Deep learning Accuracy Endoscopes Real-time systems Gastrointestinal tract data models Colon Medical diagnostic imaging Diseases

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Synthetic Control Removes Spurious Discoveries from Double Dipping in Single-Cell and Spatial Transcriptomics data Analyses 29th

Synthetic Control Removes Spurious Discoveries from Double ...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Song, Dongyuan Chen, Siqi Lee, Christy Li, Kexin Ge, Xinzhou Li, Jingyi Jessica Department of Genetics and Genome Sciences University of Connecticut Health Center FarmingtonCT06032 United States Interdepartmental Program of Bioinformatics University of California Los Angeles Los AngelesCA90095-7246 United States Department of Statistics and Data Science University of California Los Angeles Los AngelesCA90095-1554 United States Department of Statistics Oregon State University CorvallisOR97331-4606 United States

ISBN: (纸本)9783031902512

Double dipping is a common analytical pitfall in single-cell and spatial transcriptomics data analysis: after a clustering algorithm finds clusters as putative cell types or spatial domains, statistical tests are applied to the same data to identify differentially expressed (DE) genes as potential cell-type or spatial-domain markers. Because the genes that contribute to clustering are inherently likely to be identified as DE genes, double dipping can result in false-positive markers, especially when clusters are spurious, leading to ambiguously defined cell types or spatial domains. To address this challenge, we propose ClusterDE, a statistical method designed to identify post-clustering DE genes as reliable markers of cell types and spatial domains, while controlling the false discovery rate (FDR) regardless of clustering quality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Biomolecules

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Dual decoder UNet with Contrastive learning for Brain Image Registration

Dual decoder UNet with Contrastive learning for Brain Image ...

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IEEE International Conference on bioinformatics and Biomedicine (BIBM)

作者： Ying Zhang Shuai Guo Dantong Shi Jinhai Xiang College of Informatics Huazhong Agricultural University Wuhan China Agricultural Bioinformatics Key Laboratory of Hubei Province Key Laboratory of Smart Farming for Agricultural Animals Ministry of Agriculture Hubei Engineering Technology Research Center of Agricultural Big Data Engineering Research Center of Intelligent Technology for Agriculture Ministry of Education Huazhong Agricultural University Wuhan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The core of the image registration task is to accurately extract and compare the spatial feature information between moving and fixed images. The model must not only be able to capture features inside an image, but also be able to distinguish similarities and differences between moving image and fixed image. Although UNet and its variant networks are widely used in the field of registration, they still present challenges in terms of fine-grained and multi-level feature extraction. In order to address these issues, we propose a dual-decoder UNet network based on contrastive learning. For enhancing the discriminant ability of the network at the feature level, the proposed model applies contrastive learning to the encoder stage of UNet, which strengthens the differential learning in the process of feature extraction. In addition, a multi-level feature fusion method is introduced in the encoder to consider the feature differences at each level more comprehensively. Experimental results show that the proposed method has achieved a significant improvement in the registration accuracy. On the LPBA40 dataset, compared with Affine, VoxelMorph and CLMorph, the Dice scores were increased by 15.2%, 2.5% and 1.9%, respectively, which verified the effectiveness of the proposed method. Our code implementation is publicly available at https://***/LaneAzur/DD-CLUNet.

关键词： Learning systems Image registration Accuracy Deformation Contrastive learning Feature extraction Transformers Decoding data mining Biomedical imaging

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Decision-Level Scheme for Fusing Multi-Omics and Histology Slide Image Using Deep Neural Network for Tumor Prognosis Prediction

SSRN

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SSRN 2023年

作者： Kong, Yan Lu, Hui Ren, Yongyong Department of Bioinformatics and Biostatistics School of Life Sciences and Biotechnology Shanghai Jiao Tong University 800 Dong Chuan Road Shanghai200240 China SJTU-Yale Joint Center for Biostatistics and Data Science National Center for Translational Medicine Shanghai Jiao Tong University Shanghai China Center for Biomedical Informatics Shanghai Engineering Research Center for Big Data in Pediatric Precision Medicine Shanghai Children's Hospital Shanghai200240 China School of Life Sciences and Biotechnology India

Molecular bio-statistic workflows in oncology often rely on predictive models from multimodal data. Multimodal data fusion is becoming a fundamental but challenging task. Advances in deep learning (DL), and artificial intelligence technology (AI), have enabled the multimodal fusion from huge volume of data. Here, we present a decision level multimodal data fusion framework for integrating multi-omics and pathological tissue slide images for prognosis prediction. Basic convolution neural networks aim at extracting representation features from the perspective of perception and not fully explore the spatial information between nuclei instances. Our approach establish the spatial map of instances by connecting the neighboring nuclei in space and calculated the characteristic tensor via graph convolution layers for the input pathological tissue slide image. The subsequent Global Average Pooling calculation behind, we eliminated the heterogeneity between the feature tensors from pathological images and the multi-omics. We performed our proposed approach using beast cancer and non-small cell lung cancer datasets from the Cancer Genome Atlas (TCGA), which contains paired whole-slide images, transcriptome data, genotype, epienetic, and survival information. In a 10-fold cross-validation, the comparison results demonstrate that the multimodal fusion paradigm improves outcome predictions from single modal data alone with the average C-Index increasing from 0.61 and 0.52 to 0.75 and 0.67 for Breast cancer and Non-small cell lung cancer cohort separately. The proposed decision-level multimodal data fusion framework is hoped to provide insights and technically methodologies for the follow-up studies that seed to merge heterogeneous data in disease mechanisms. © 2023, The Authors. All rights reserved.

关键词： Tensors

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