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Towards robust results in Multi-Criteria Decision Analysis: ranking reversal free methods case study

Procedia computer science 2022年 207卷 4584-4592页

作者： Jakub Więckowski Robert Król Jarosław Wątróbski Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland Doctoral School of University of Szczecin ul. Mickiewicza 16 70-383 Szczecin Poland Institute of Management University of Szczecin ul. Cukrowa 8 71-004 Szczecin Poland

The practical capabilities of decision support systems are beneficial in many complex problems. They aim to equip the decision-maker with knowledge about the preferred choices from the set under consideration. Often, there are minor differences between the alternatives analyzed, making it necessary to determine whether changes in the parameters characterizing them can influence their final position. For this purpose, a sensitivity analysis of the solutions can be used to examine the resistance of the results to changes. In this paper, we address the problem of performing the sensitivity analysis using MCDA methods. Most studies focus on testing the sensitivity of rankings of popular methods. However, their main disadvantage is their susceptibility to the phenomenon of ranking reversal. It may distort the results obtained in a sensitivity analysis. Therefore, it is worth using methods resistant to the ranking reversal phenomenon for this purpose. The SPOTIS and COMET methods were used in a theoretical multi-criteria problem, where the calculated results were then subjected to sensitivity analysis. The study showed that the SPOTIS method tends to be more robust to the computed results.

关键词： MCDA SPOTIS COMET Sensitivity Analysis

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Survival analysis of Kasturi tobacco plants in Jember district using the stratified cox and extended cox models.

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MethodsX 2025年 14卷

作者： Angga Iryanto Pratama Dwi Rantini Ratih Ardiati Ningrum Arip Ramadan Fazidah Othman Data Science Technology Study Program Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Research Group of Data-Driven Decision Support System Faculty of Advanced Technology and Multidiscipline Universitas Airlangga Surabaya 60115 Indonesia Information System Study Program School of Industrial and System Engineering Telkom University Surabaya Campus Surabaya 60231 Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaya Malaysia

In 2022, tobacco was the top export commodity, generating $106.3 million in sales. East Java Province contributes significantly to Indonesia's tobacco production, with an annual output of 188.6 thousand tons. Jember Regency is the area in East Java Province which is a Kasturi tobacco cultivation area. The productivity of Kasturi tobacco continues to decrease due to several factors that can affect the survival of the tobacco. This study aims to analyze the survival of Kasturi tobacco by creating stratified Cox and extended Cox models in order to handle factors that cannot fulfill the proportional hazard assumption. Based on the results of the analysis, the stratified Cox model is the best model with AIC and BIC values of 798.108 and 805.5748 respectively, while the extended Cox has AIC and BIC values of 840.2186 and 850.1732 respectively. Variables that can significantly affect the survival of Kasturi tobacco are the variable concentration of ZA fertilizer, pesticide concentration. The addition of ZA fertilizer must be appropriate, because if excessive it can cause poisoning of tobacco plants. Likewise with pesticides, if excessive it will cause damage to the leaves. This policy can increase the productivity of Kasturi tobacco. Then, Jember Regency contributes greater export capacity. • This paper aims to determine the factors that affect the survival of Kasturi tobacco plants in Jember • By using the extended Cox model, the best one is obtained using the Heaviside function with AIC and BIC values of 840.2186 and 850.1732, respectively • The stratified Cox model is better than the extended Cox model, which has AIC and BIC values of 798.108 and 805.5748, respectively

关键词： Productive agriculture Cox proportional hazard Stratified cox Extended cox Kasturi tobacco Cox Proportional Hazard Model, Stratified Cox Model, and Extended Cox Model

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Accuracy of the TOPSIS Method with Different Input Data

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Procedia computer science 2022年 207卷 4574-4583页

作者： Robert Król Jakub Więckowski Jarosław Wątróbski Doctoral School of University of Szczecin ul. Mickiewicza 16 70-383 Szczecin Poland Institute of Management University of Szczecin ul. Cukrowa 8 71-004 Szczecin PolandS Research Team on Intelligent Decision Support Systems Department of Artificial Intelligence Methods and Applied Mathematics Faculty of Computer Science and Information Technology West Pomeranian University of Technology Szczecin ul. Żołnierska 49 71-210 Szczecin Poland

Using fuzzy logic in multi-criteria problems allows real-world problems to be modeled with greater accuracy. In addition, it is possible to solve problems in which all data are incomplete. The difficulty may be the choice of an appropriate technique leading to a ranking calculation. Methods fully integrated with fuzzy logic can be used. It is also possible to convert fuzzy data to crisp values and then apply traditional MCDA methods. In this paper, the performance of the TOPSIS method in a crisp and fuzzy environment was analyzed. The approaches of operating fully on uncertain data when calculating preference values were compared with converting data to crisp values using selected membership functions. The research has shown that significant differences and discrepancies in how the alternatives are classified are noticeable in the rankings.

关键词： MCDA TOPSIS Fuzzy TOPSIS Comparative analysis Power plants

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Supervised Contrastive Learning Enhances MHC-II Peptide Binding Affinity Prediction

SSRN

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SSRN 2024年

作者： Shen, Long-Chen Liu, Yan Liu, Zi Zhang, Yumeng Wang, Zhikang Guo, Yuming Rossjohn, Jamie Song, Jiangning Yu, Dong-Jun School of Computer Science and Engineering Nanjing University of Science and Technology 200 Xiaolingwei Nanjing210094 China Department of Computer Science Yangzhou University Yangzhou225100 China School of Information Enginnering Jingdezhen Ceramic University Jingdezhen333403 China Monash Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVIC3800 Australia Monash Data Futures Institute Monash University MelbourneVIC3800 Australia School of Life Sciences and Biotechnology Shanghai Jiao Tong University Shanghai200240 China Department of Epidemiology and Preventive Medicine Xiangya Hospital China Infection and Immunity Program Netherlands

Accurate prediction of major histocompatibility complex (MHC)-peptide binding affinity can improve our understanding of cellular immune responses and guide personalized immunotherapies. Nevertheless, the existing deep learning-based approaches for predicting MHC-II peptide interactions fall short of satisfactory performance and offer restricted model interpretability. In this study, we propose a novel deep neural network, termed ConBoTNet, to address the above issues by introducing the designed supervised contrastive learning and bottleneck transformer extractors. Specifically, the supervised contrastive learning pre-training enhances the model’s representative and generalizable capabilities on MHC-II peptides by pulling positive pairs closer and pushing negative pairs further in the feature space, while the bottleneck transformer module focuses on MHC-II peptide interactions to precisely identify binding cores and anchor positions in an unsupervised manner. Extensive experiments on benchmark datasets under 5-fold cross-validation, leave-one-molecule-out validation, independent testing, and binding core prediction settings highlighted the superiority of our proposed ConBoTNet over current state-of-the-art methods. Data distribution analysis in the latent feature space demonstrated that supervised contrastive learning can aggregate MHC-II-peptide samples with similar affinity labels and learn common features of similar affinity. Additionally, we interpreted the trained neural network by associating the attention weights with peptides and innovatively find both well-established and potential peptide motifs. This work not only introduces an innovative tool for accurately predicting MHC-II peptide affinity, but also provides new insights into a new paradigm for modeling essential biological interactions, advancing data-driven discovery in biomedicine. © 2024, The Authors. All rights reserved.

关键词： Peptides

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Bayesian multinomial logistic normal models through marginally latent matrix-T processes

The Journal of Machine Learning Research

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The Journal of Machine Learning Research 2022年第1期23卷 255-296页

作者： Justin D. Silverman Kimberly Roche Zachary C. Holmes Lawrence A. David Sayan Mukherjee College of Information Science and Technology Department of Statistics and Institute for Computational and Data Science Penn State University University Park PA Program in Computational Biology and Bioinformatics Duke University Durham NC Department of Molecular Genetics and Microbiology Duke University Durham NC Department of Molecular Genetics and Microbiology and Center for Genomic and Computational Biology Duke University Durham NC Departments of Statistical Science Mathematics Computer Science Biostatistics & Bioinformatics Duke University Durham NC

Bayesian multinomial logistic-normal (MLN) models are popular for the analysis of sequence count data (e.g., microbiome or gene expression data) due to their ability to model multivariate count data with complex covariance structure. However, existing implementations of MLN models are limited to small datasets due to the non-conjugacy of the multinomial and logistic-normal distributions. Motivated by the need to develop efficient inference for Bayesian MLN models, we develop two key ideas. First, we develop the class of Marginally Latent Matrix-T Process (Marginally LTP) models. We demonstrate that many popular MLN models, including those with latent linear, non-linear, and dynamic linear structure are special cases of this class. Second, we develop an efficient inference scheme for Marginally LTP models with specific accelerations for the MLN subclass. Through application to MLN models, we demonstrate that our inference scheme are both highly accurate and often 4-5 orders of magnitude faster than MCMC.

关键词： Bayesian statistics multivariate analysis count data microbiome gene expression

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Improving quantitation accuracy in isobaric-labeling mass spectrometry experiments with spectral library searching and feature-based peptide-spectrum match filter

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Scientific Reports 2023年第1期13卷 1-10页

作者： Kuo, Tzu-Yun Wang, Jen-Hung Huang, Yung-Wen Sung, Ting-Yi Chen, Ching-Tai Department of Biochemical Science and Technology National Taiwan University Taipei 10617 Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Statistical Science Academia Sinica Taipei 11529 Taiwan Institute of Information Science Academia Sinica Taipei 11529 Taiwan Institute of Biomedical Informatics National Yang Ming Chiao Tung University Taipei 11221 Taiwan Department of Computer Science and Information Engineering National Taiwan University Taipei 10617 Taiwan Department of Bioinformatics and Biomedical Engineering Asia University Taichung 41354 Taiwan Center for Precision Health Research Asia University Taichung 41354 Taiwan

Isobaric labeling relative quantitation is one of the dominating proteomic quantitation technologies. Traditional quantitation pipelines for isobaric-labeled mass spectrometry data are based on sequence database searching. In this study, we present a novel quantitation pipeline that integrates sequence database searching, spectral library searching, and a feature-based peptide-spectrum-match (PSM) filter using various spectral features for filtering. The combined database and spectral library searching results in larger quantitation coverage, and the filter removes PSMs with larger quantitation errors, retaining those with higher quantitation accuracy. Quantitation results show that the proposed pipeline can improve the overall quantitation accuracy at the PSM and protein levels. To our knowledge, this is the first study that utilizes spectral library searching to improve isobaric labeling-based quantitation. For users to conveniently perform the proposed pipeline, we have implemented the feature-based filter being executable on both Windows and Linux platforms;its executable files, user manual, and sample data sets are freely available at https://***/comics/Software_*** . Furthermore, with the developed filter, the proposed pipeline is fully compatible with the Trans-Proteomic Pipeline. © 2023, Springer Nature Limited.

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Economics of Semantic Communication System: An Auction Approach

arXiv

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arXiv 2022年

作者： Liew, Zi Qin Du, Hongyang Yang, Wei Lim, Bryan Xiong, Zehui Niyato, Dusit Miao, Chunyan Kim, Dong In Alibaba-NTU Singapore Joint Research Institute Nanyang Technological Univerity Singapore The School of Computer Science and Engineering The Energy Research Institute NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore Nanyang Technological University Singapore639798 Singapore The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

Semantic communication technologies enable wireless edge devices to communicate effectively by transmitting semantic meaning of data. Edge components, such as vehicles in next-generation intelligent transport systems, use well-trained semantic models to encode and decode semantic information extracted from raw and sensor data. However, the limitation in computing resources makes it difficult to support the training process of accurate semantic models on edge devices. As such, edge devices can buy the pretrained semantic models from semantic model providers, which is called "semantic model trading". Upon collecting semantic information with the semantic models, the edge devices can then sell the extracted semantic information, e.g., information about urban road conditions or traffic signs, to the interested buyers for profit, which is called "semantic information trading". To facilitate both types of the trades, effective incentive mechanisms should be designed. Thus, in this paper, we propose a hierarchical trading system to support both semantic model trading and semantic information trading jointly. The proposed incentive mechanism helps to maximize the revenue of semantic model providers in the semantic model trading, and effectively incentivizes model providers to participate in the development of semantic communication systems. For semantic information trading, our designed auction approach can support the trading between multiple semantic information sellers and buyers, while ensuring individual rationality, incentive compatibility, and budget balance, and moreover, allowing them achieve higher utilities than the baseline method. Copyright © 2022, The Authors. All rights reserved.

关键词： Commerce

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High-Order Cardiomyopathy Human Heart Model and Mesh Generation

High-Order Cardiomyopathy Human Heart Model and Mesh Generat...

引用

2021 Computing in Cardiology, CinC 2021

作者： Mohammadi, Fariba Shontz, Suzanne M Linte, Cristian A Department of Mechanical Engineering University of Kansas LawrenceKS United States Department of Electrical Engineering and Computer Science University of Kansas LawrenceKS United States Bioengineering Program University of Kansas LawrenceKS United States Information and Telecommunication Technology Center University of Kansas LawrenceKS United States Chester F Carlson Center for Imaging Science Rochester Institute of Technology RochesterNY United States Department of Biomedical Engineering Rochester Institute of Technology RochesterNY United States

ISBN: (纸本)9781665479165

Faithful, accurate, and successful cardiac biomechanics and electrophysiological simulations require patient-specific geometric models of the heart. Since the cardiac geometry consists of highly-curved boundaries, the use of high-order meshes with curved elements would ensure that the various curves and features present in the cardiac geometry are well-captured and preserved in the corresponding mesh. Most other existing mesh generation techniques require computer-aided design files to represent the geometric boundary, which are often not available for biomedical applications. Unlike such methods, our technique takes a high-order surface mesh, generated from patient medical images, as input and generates a high-order volume mesh directly from the curved surface mesh. In this paper, we use our direct high-order curvilinear tetrahedral mesh generation method [1] to generate several second-order cardiac meshes. Our meshes include the left ventricle myocardia of a healthy heart and hearts with dilated and hypertrophic cardiomyopathy. We show that our high-order cardiac meshes do not contain inverted elements and are of sufficiently high quality for use in cardiac finite element simulations. © 2021 Creative Commons.

关键词： Heart

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NTRK3 exhibits a pro-oncogenic function in upper tract urothelial carcinomas

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Kaohsiung Journal of Medical sciences 2024年第5期40卷 445-455页

作者： Lim, Lee-Moay Lee, Yi-Chen Lin, Ting-Wei Hong, Zi-Xuan Hsu, Wei-Chi Ke, Hung-Lung Hwang, Daw-Yang Chung, Wen-Yu Li, Wei-Ming Lin, Hui-Hui Kuo, Hung-Tien Huang, A-Mei Graduate Institute of Clinical Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Division of Nephrology Department of Internal Medicine Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Graduate Institute of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Anatomy School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Urology School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Department of Urology Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Department of Urology Kaohsiung Municipal Ta-Tung Hospital Kaohsiung Medical University Kaohsiung Taiwan National Institute of Cancer Research National Health Research Institute Tainan Taiwan Department of Computer Science and Information Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan Department of Urology Ministry of Health and Welfare Pingtung Hospital Pingtung Taiwan Department of Medical Research Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung Taiwan Doctoral Degree Program in Toxicology College of Pharmacy Kaohsiung Medical University Kaohsiung Taiwan Department of Biochemistry School of Medicine College of Medicine Kaohsiung Medical University Kaohsiung Taiwan Research Center for Environmental Medicine Kaohsiung Medical University Kaohsiung Taiwan

Neurotrophic receptor tyrosine kinase 3 (NTRK3) has pleiotropic functions: it acts not only as an oncogene in breast and gastric cancers but also as a dependence receptor in tumor suppressor genes in colon cancer and neuroblastomas. However, the role of NTRK3 in upper tract urothelial carcinoma (UTUC) is not well documented. This study investigated the association between NTRK3 expression and outcomes in UTUC patients and validated the results in tests on UTUC cell lines. A total of 118 UTUC cancer tissue samples were examined to evaluate the expression of NTRK3. Survival curves were generated using Kaplan–Meier estimates, and Cox regression models were used for investigating survival outcomes. Higher NTRK3 expression was correlated with worse progression-free survival, cancer-specific survival, and overall survival. Moreover, the results of an Ingenuity Pathway Analysis suggested that NTRK3 may interact with the PI3K-AKT-mTOR signaling pathway to promote cancer. NTRK3 downregulation in BFTC909 cells through shRNA reduced cellular migration, invasion, and activity in the AKT-mTOR pathway. Furthermore, the overexpression of NTRK3 in UM-UC-14 cells promoted AKT-mTOR pathway activity, cellular migration, and cell invasion. From these observations, we concluded that NTRK3 may contribute to aggressive behaviors in UTUC by facilitating cell migration and invasion through its interaction with the AKT-mTOR pathway and the expression of NTRK3 is a potential predictor of clinical outcomes in cases of UTUC. © 2024 The Authors. The Kaohsiung Journal of Medical sciences published by John Wiley & Sons Australia, Ltd on behalf of Kaohsiung Medical University.

关键词： AKT-mTOR pathway neurotrophic receptor tyrosine kinase 3 (NTRK3) prognosis tropomyosin-related kinase (TrK) upper tract urothelial carcinoma (UTUC)

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Developing a medical artificial intelligence course for high school students

Developing a medical artificial intelligence course for high...

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International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Huang, Chao-Jung Wu, Tinghui Lu, Jui-Ting Lin, Beatrice Chang, Dawei Wang, Pochuan Wang, Mei-Chi Lee, Peijung Wang, Weichung National Taiwan University-Stanford Joint Program Office of AI in Biotechnology Ministry of Science and Technology Joint Research Center for Artificial Intelligence Technology and All Vista Healthcare Taipei Taiwan Institute of Applied Mathematical Sciences National Taiwan University Taipei Taiwan Department of Mathematics Ecole Normale Supérieure de Lyon Lyon France Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei Taiwan Department of Mathematics National Taiwan University Taipei Taiwan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Artificial intelligence (AI) training courses often require prerequisites such as calculus or statistics. It is hence challenging to design and develop an introductory AI course for students of secondary education. This research intends to develop a medical AI course, provide high school students with an overview of deep learning applications in medical image analysis, and inspire them to pursue careers in the field of medical AI. We designed a 20-hour course, including lectures and two hands-on projects based on medical image classification. The proposed courses provided medical AI disciplines and built up their knowledge from basic to advanced levels. During the ten-day online courses, all the students were fully engaged and gave us positive feedback. The students endeavored to complete the experimental study in training, testing, and hypothesis of medical images application in the course. Their performance exceeded all expectations, for they did further analysis by tuning different hyperparameters. We designed a course evaluation form, which suggested that the students found it essential and expected to interact with the instructors. The results indicate that combining lectures with hands-on sessions would lead to evidently better achievement in terms of high school students' medical AI knowledge and positive attitudes while addressing real-world problems in the projects. Through this innovative education model, high school students regained their enthusiasm and were encouraged to cultivate their medical AI skills through self-learning while finishing the project. We conclude that this course could be successfully applied to interdisciplinary education in high school. © 2021 SPIE.

关键词： Students

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