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machine learning-Driven Digital Technologies for Educational Innovation Workshop

作者： Nadia N. Sánchez-Pozo Juan S. Mejía-Ordóñez Diana C. Chamorro Dagoberto Mayorca-Torres Diego H. Peluffo-Ordóñez Machine learning Research Program SDAS Research Group Ben Guerir Morocco Carrera de Ingeniería Química Faculted de Ciencias Químicas y de la Salud Universidad Técnica de Machala Machala Ecuador Programa de Mecatrónica Facultad de Ingeniería Universidad Mariana Pasto Colombia Modeling Simulation and Data Analysis (MSDA) Research Program Mohamed VI Polytechnique University Ben Guerir Morocco

ISBN: (纸本)9781665427647

The proliferation of mobile devices and the rapid development of information and communication technologies have revolutionized education. Educational data has evolved to be voluminously massive, broadly various, and produced at high velocity. Therefore, computerized techniques for integrating, processing, and transforming data into valuable knowledge have become necessary to improve internal academic processes. Specifically, educational data mining is an emerging discipline concerned with analyzing the massive amounts of academic data generated and stored by educational institutions. In this sense, machine learning algorithms aid decision-makers who are establishing strategies to improve students' learning experience and institutional effectiveness by revealing hidden patterns in academic performance. Thus, this paper describes our comparative study of machine learning techniques to predict academic performance. We selected the features that best fit the discovery of patterns in the academic performance of high school students, resulting in a balance between accuracy and interpretability. We implemented six supervised learning algorithms for pattern recognition: Light Gradient Boosting machine, Gradient Boosting, AdaBoost, Logistic Regression, Random Forest, and K-nearest Neighbors. The experimental results showed that the Gradient Boosting (Gbc) algorithm achieved the highest accuracy (96.77%), superior to other classification techniques considered.

关键词： Technological innovation machine learning algorithms Social networking (online) Predictive models Boosting Prediction algorithms Classification algorithms

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Posterior Fusiform and Midfusiform Contribute to Distinct Stages of Facial Expression Processing

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CEREBRAL CORTEX 2019年第7期29卷 3209-3219页

作者： Li, Yuanning Richardson, R. Mark Ghuman, Avniel Singh Carnegie Mellon Univ Ctr Neural Basis Cognit Pittsburgh PA 15213 USA Univ Pittsburgh Pittsburgh PA 15213 USA Carnegie Mellon Univ Program Neural Computat & Machine Learning Pittsburgh PA 15213 USA Univ Pittsburgh Dept Neurol Surg Pittsburgh PA 15261 USA

Though the fusiform is well-established as a key node in the face perception network, its role in facial expression processing remains unclear, due to competing models and discrepant findings. To help resolve this debate, we recorded from 17 subjects with intracranial electrodes implanted in face sensitive patches of the fusiform. Multivariate classification analysis showed that facial expression information is represented in fusiform activity and in the same regions that represent identity, though with a smaller effect size. Examination of the spatiotemporal dynamics revealed a functional distinction between posterior fusiform and midfusiform expression coding, with posterior fusiform showing an early peak of facial expression sensitivity at around 180 ms after subjects viewed a face and midfusiform showing a later and extended peak between 230 and 460 ms. These results support the hypothesis that the fusiform plays a role in facial expression perception and highlight a qualitative functional distinction between processing in posterior fusiform and midfusiform, with each contributing to temporally segregated stages of expression perception.

关键词： face perception facial expressions fusiform intracranial electroencephalography multivariate temporal pattern analysis

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A multi-level consensus function clustering ensemble

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SOFT COMPUTING 2021年第21期25卷 13147-13165页

作者： Pho, Kim-Hung Akbarzadeh, Hamidreza Parvin, Hamid Nejatian, Samad Alinejad-Rokny, Hamid Ton Duc Thang Univ Fac Math & Stat Fract Calculus Optimizat & Algebra Res Grp Ho Chi Minh City Vietnam Islamic Azad Univ Yasooj Branch Dept Comp Engn Yasuj Iran Islamic Azad Univ Nourabad Mamasani Branch Dept Comp Engn Nourabad Mamasani Iran Islamic Azad Univ Nourabad Mamasani Branch Young Researchers & Elite Club Nourabad Mamasani Iran Islamic Azad Univ Yasooj Branch Dept Elect Engn Yasuj Iran Islamic Azad Univ Yasooj Branch Young Researchers & Elite Club Yasuj Iran UNSW Sydney Grad Sch Biomed Engn BioMed Machine Learning Lab BML Sydney NSW 2052 Australia Univ New South Wales UNSW Sydney UNSW Data Sci Hub Sydney NSW 2052 Australia Macquarie Univ AI Enabled Proc AIP Res Ctr Hlth Data Analyt Program Sydney NSW 2109 Australia

In order to improve the performance of a clustering on a data set, a number of primary partitions are generated and stored in an ensemble and their aggregated consensus partition is used as their clustering. It is widely accepted that the consensus partition outperforms the primary partitions. In this paper, an ensemble clustering method called multi-level consensus clustering (MLCC) is proposed. To construct the MLCC, a cluster-cluster similarity matrix which is achieved by an innovative similarity metric is first generated. The mentioned cluster-cluster similarity matrix is based on a multi-level similarity metric. In fact, it can be computed in a new defined multi-level space. Then, a point-point similarity matrix which is boosted using the mentioned cluster-cluster similarity matrix is generated. The new consensus function applies an average linkage hierarchical clusterer algorithm on the mentioned point-point similarity matrix to make consensus partition. MLCC is better than traditional clustering ensembles and simple versions of clustering ensembles on traditional cluster-cluster similarity matrix. Its computational cost is not very bad too. Accuracy and robustness of the proposed method are compared with those of the modern clustering algorithms through the experimental tests. Also, time analysis is presented in the experimental results.

关键词： Consensus partition Multi-level similarity metric Ensemble learning

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The Cell Tracking Challenge: 10 years of objective benchmarking

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NATURE METHODS 2023年第7期20卷 1010-+页

作者： Maska, Martin Ulman, Vladimir Delgado-Rodriguez, Pablo Gomez-de-Mariscal, Estibaliz Necasova, Tereza Pena, Fidel A. Guerrero Ren, Tsang Ing Meyerowitz, Elliot M. Scherr, Tim Loeffler, Katharina Mikut, Ralf Guo, Tianqi Wang, Yin Allebach, Jan P. Bao, Rina Al-Shakarji, Noor M. Rahmon, Gani Toubal, Imad Eddine Palaniappan, Kannappan Lux, Filip Matula, Petr Sugawara, Ko Magnusson, Klas E. G. Aho, Layton Cohen, Andrew R. Arbelle, Assaf Ben-Haim, Tal Raviv, Tammy Riklin Isensee, Fabian Jaeger, Paul F. Maier-Hein, Klaus H. Zhu, Yanming Ederra, Cristina Urbiola, Ainhoa Meijering, Erik Cunha, Alexandre Munoz-Barrutia, Arrate Kozubek, Michal Ortiz-de-Solorzano, Carlos Masaryk Univ Fac Informat Ctr Biomed Image Anal Brno Czech Republic VSB Tech Univ Ostrava IT4Innovat Natl Supercomp Ctr Ostrava Czech Republic Univ Carlos III Madrid Bioengn Dept Madrid Spain Inst Invest Sanitaria Gregorio Maranon Madrid Spain Inst Gulbenkian Ciencias Opt Cell Biol Oeiras Portugal Univ Fed Pernambuco Ctr Informat Recife PE Brazil CALTECH Beckman Inst Ctr Adv Methods Biol Image Anal Pasadena CA USA CALTECH Div Biol & Biol Engn Pasadena CA USA CALTECH Howard Hughes Med Inst Pasadena CA USA Karlsruhe Inst Technol Inst Automat & Appl Informat Eggenstein Leopoldshafen Germany Purdue Univ Elmore Family Sch Elect & Comp Engn W Lafayette IN USA Boston Childrens Hosp Boston MA USA Harvard Med Sch Boston MA USA Univ Missouri Dept Elect Engn & Comp Sci CIVA Lab Columbia MO USA Ecole Normale Super Lyon Inst Genom Fonct Lyon IGFL Lyon France Ctr Natl Rech Sci CNRS Paris France Raysearch Labs AB Stockholm Sweden Drexel Univ Dept Elect & Comp Engn Philadelphia PA USA Ben Gurion Univ Negev Sch Elect & Comp Engn Beer Sheva Israel German Canc Res Ctr Div Med Image Comp Heidelberg Germany German Canc Res Ctr Helmholtz Imaging Heidelberg Germany German Canc Res Ctr Interact Machine Learning Grp Heidelberg Germany Heidelberg Univ Hosp Dept Radiat Oncol Pattern Anal & Learning Grp Heidelberg Germany Univ New South Wales Sch Comp Sci & Engn Sydney NSW Australia Griffith Univ Nathan Qld Australia Univ Navarra Ctr Appl Med Res Biomed Engn Program Pamplona Spain Univ Navarra Ctr Appl Med Res Ciberonc Pamplona Spain

The Cell Tracking Challenge is an ongoing benchmarking initiative that has become a reference in cell segmentation and tracking algorithm development. Here, we present a significant number of improvements introduced in the challenge since our 2017 report. These include the creation of a new segmentation-only benchmark, the enrichment of the dataset repository with new datasets that increase its diversity and complexity, and the creation of a silver standard reference corpus based on the most competitive results, which will be of particular interest for data-hungry deep learning-based strategies. Furthermore, we present the up-to-date cell segmentation and tracking leaderboards, an in-depth analysis of the relationship between the performance of the state-of-the-art methods and the properties of the datasets and annotations, and two novel, insightful studies about the generalizability and the reusability of top-performing methods. These studies provide critical practical conclusions for both developers and users of traditional and machine learning-based cell segmentation and tracking algorithms. This updated analysis of the Cell Tracking Challenge explores how algorithms for cell segmentation and tracking in both 2D and 3D have advanced in recent years, pointing users to high-performing tools and developers to open challenges.

关键词： Computational platforms and environments Image processing

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A Deep learning Approach to Antibiotic Discovery

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CELL 2020年第4期180卷 688-+页

作者： Stokes, Jonathan M. Yang, Kevin Swanson, Kyle Jin, Wengong Cubillos-Ruiz, Andres Donghia, Nina M. MacNair, Craig R. French, Shawn Carfrae, Lindsey A. Bloom-Ackerman, Zohar Tran, Victoria M. Chiappino-Pepe, Anush Badran, Ahmed H. Andrews, Ian W. Chory, Emma J. Church, George M. Brown, Eric D. Jaakkola, Tommi S. Barzilay, Regina Collins, James J. MIT Inst Med Engn & Sci Synthet Biol Ctr Dept Biol Engn 77 Massachusetts Ave Cambridge MA 02139 USA Broad Inst MIT & Harvard Cambridge MA 02142 USA MIT Machine Learning Pharmaceut Discovery & Synth Con 77 Massachusetts Ave Cambridge MA 02139 USA MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA Harvard Univ Wyss Inst Biol Inspired Engn Boston MA 02115 USA McMaster Univ Michael G DeGroote Inst Infect Dis Res Dept Biochem & Biomed Sci Hamilton ON L8N 3Z5 Canada Harvard Med Sch Dept Genet Boston MA 02115 USA Harvard MIT Program Hlth Sci & Technol Cambridge MA 02139 USA MIT Abdul Latif Jameel Clin Machine Learning Hlth 77 Massachusetts Ave Cambridge MA 02139 USA

Due to the rapid emergence of antibiotic-resistant bacteria, there is a growing need to discover new antibiotics. To address this challenge, we trained a deep neural network capable of predicting molecules with antibacterial activity. We performed predictions on multiple chemical libraries and discovered a molecule from the Drug Repurposing Hub-halicin-that is structurally divergent from conventional antibiotics and displays bactericidal activity against a wide phylogenetic spectrum of pathogens including Mycobacterium tuberculosis and carbapenem-resistant Enterobacteriaceae. Halicin also effectively treated Clostridioides difficile and pan-resistant Acinetobacter baumannii infections in murine models. Additionally, from a discrete set of 23 empirically tested predictions from >107 million molecules curated from the ZINC15 database, our model identified eight antibacterial compounds that are structurally distant from known antibiotics. This work highlights the utility of deep learning approaches to expand our antibiotic arsenal through the discovery of structurally distinct antibacterial molecules.

关键词： antibiotics antibiotic resistance antibiotic tolerance machine learning drug discovery

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Quantitative neurogenetics: applications in understanding disease

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BIOCHEMICAL SOCIETY TRANSACTIONS 2021年第4期49卷 1621-1631页

作者： Afrasiabi, Ali Keane, Jeremy T. Heng, Julian Ik-Tsen Palmer, Elizabeth E. Lovell, Nigel H. Alinejad-Rokny, Hamid UNSW SYDNEY BioMed Machine Learning Lab BML Grad Sch Biomed Engn Sydney NSW 2052 Australia Univ Sydney Ctr Immunol & Allergy Res Westmead Inst Med Res Sydney NSW Australia Curtin Univ Curtin Hlth Innovat Res Inst Bentley WA 6845 Australia Curtin Univ Sch Pharm & Biomed Sci Bentley WA 6845 Australia Sydney Childrens Hosp Randwick NSW 2031 Australia Univ New South Wales Sch Womens & Childrens Hlth Randwick NSW 2031 Australia UNSW SYDNEY Grad Sch Biomed Engn Sydney NSW 2052 Australia Macquarie Univ AI Enabled Proc AIP Res Ctr Hlth Data Analyt Program Leader Sydney NSW 2109 Australia Univ New South Wales UNSW SYDNEY UNSW Data Sci Hub Sydney NSW 2052 Australia

Neurodevelopmental and neurodegenerative disorders (NNDs) are a group of conditions with a broad range of core and co-morbidities, associated with dysfunction of the central nervous system. Improvements in high throughput sequencing have led to the detection of putative risk genetic loci for NNDs, however, quantitative neurogenetic approaches need to be further developed in order to establish causality and underlying molecular genetic mechanisms of pathogenesis. Here, we discuss an approach for prioritizing the contribution of genetic risk loci to complex-NND pathogenesis by estimating the possible impacts of these loci on gene regulation. Furthermore, we highlight the use of a tissue-specificity gene expression index and the application of artificial intelligence (AI) to improve the interpretation of the role of genetic risk elements in NND pathogenesis. Given that NND symptoms are associated with brain dysfunction, risk loci with direct, causative actions would comprise genes with essential functions in neural cells that are highly expressed in the brain. Indeed, NND risk genes implicated in brain dysfunction are disproportionately enriched in the brain compared with other tissues, which we refer to as brain-specific expressed genes. In addition, the tissue-specificity gene expression index can be used as a handle to identify non-brain contexts that are involved in NND pathogenesis. Lastly, we discuss how using an AI approach provides the opportunity to integrate the biological impacts of risk loci to identify those putative combinations of causative relationships through which genetic factors contribute to NND pathogenesis.

关键词： artificial intelligence functional analysis genomic variants health data analytics neurogenetics disorders tissue-specific gene expression

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CNN-KCL: Automatic myocarditis diagnosis using convolutional neural network combined with k-means clustering

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MATHEMATICAL BIOSCIENCES AND ENGINEERING 2022年第3期19卷 2381-2402页

作者： Sharifrazi, Danial Alizadehsani, Roohallah Joloudari, Javad Hassannataj Band, Shahab S. Hussain, Sadiq Sani, Zahra Alizadeh Hasanzadeh, Fereshteh Shoeibi, Afshin Dehzangi, Abdollah Sookhak, Mehdi Alinejad-Rokny, Hamid Islamic Azad Univ Sch Tech & Engn Dept Comp Engn Shiraz Branch Shiraz Iran Deakin Univ Inst Intelligent Syst Res & Innovat Geelong Vic 3216 Australia Univ Birjand Fac Engn Dept Comp Engn Birjand Iran Natl Yunlin Univ Sci & Technol Coll Future Future Technol Res Ctr 123 Univ RdSect 3 Touliu 64002 Yunlin Taiwan Dibrugarh Univ Syst Administrator Dibrugarh 786004 Assam India Iran Univ Med Sci Rajaie Cardiovasc Med & Res Ctr Tehran Iran Iran Univ Med Sci Omid Hosp Tehran Iran KN Toosi Univ Technol Fac Elect Engn FPGA Lab Tehran Iran Rutgers State Univ Dept Comp Sci Camden NJ 08102 USA Rutgers State Univ Ctr Computat & Integrat Biol Camden NJ 08102 USA Texas A&M Univ Dept Comp Sci Corpus Christi TX 78412 USA UNSW Sydney Grad Sch Biomed Engn BioMed Machine Learning Lab BML Sydney NSW 2052 Australia Macquarie Univ AI Enabled Proc AIP Res Ctr Hlth Data Analyt Program Sydney NSW 2109 Australia

Myocarditis is the form of an inflammation of the middle layer of the heart wall which is caused by a viral infection and can affect the heart muscle and its electrical system. It has remained one of the most challenging diagnoses in cardiology. Myocardial is the prime cause of unexpected death in approximately 20% of adults less than 40 years of age. Cardiac MRI (CMR) has been considered a noninvasive and golden standard diagnostic tool for suspected myocarditis and plays an indispensable role in diagnosing various cardiac diseases. However, the performance of CMR depends heavily on the clinical presentation and features such as chest pain, arrhythmia, and heart failure. Besides, other imaging factors like artifacts, technical errors, pulse sequence, acquisition parameters, contrast agent dose, and more importantly qualitatively visual interpretation can affect the result of the diagnosis. This paper introduces a new deep learning-based model called Convolutional Neural Network-Clustering (CNN-KCL) to diagnose Myocarditis. In this study, we used 47 subjects with a total number of 98,898 images to diagnose myocarditis disease. Our results demonstrate that the proposed method achieves an accuracy of 97.41% based on 10 fold-cross validation technique with 4 clusters for diagnosis of Myocarditis. To the best of our knowledge, this research is the first to use deep learning algorithms for the diagnosis of myocarditis.

关键词： myocarditis diagnosis convolutional neural network biomedical machine learning cardiac MRI prediction

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Integrating an Association Rule Mining Agent in an ERP System: A Proposal and a Computational Scalability Analysis 11

Integrating an Association Rule Mining Agent in an ERP Syste...

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11th International Conference on Agents and Artificial Intelligence (ICAART)

作者： Machado de Souza, Rafael Marin Vilasboas, Fabricio Gomes Notargiacomo, Pollyana de Castro, Leandro Nunes Univ Prebiteriana Mackenzie Grad Program Elect Engn & Comp Games Syst & Signals Lab JAS3 Rua Consolacao 930 Sao Paulo Brazil Univ Prebiteriana Mackenzie Grad Program Elect Engn & Comp Nat Comp & Machine Learning Lab LCoN Rua Consolacao 930 Sao Paulo Brazil

ISBN: (纸本)9789897583506

Deployment flexibility, low development cost, and value-adding tools are some of the features that developers are looking for in ERP systems. Modularization through software agents is one way of achieving these objectives. In this sense, the present paper proposes the planning, implementation and integration of a software agent for association rule mining into an ERP system. The development and use of tools for all Knowledge Discovery in Databases (KDD) phases (pre-processing, data mining and post-processing), will be presented. This includes input data, file loading for the agent processing, use of the Apriori association rule mining algorithm, generation of output files with association rules, use of agent outputs for database storage and use of the stored data by the item recommendation tool. Experiments were carried out focusing the assessment of the running profile for databases of different sizes and using different computational architectures.

关键词： Software Agents Data Mining Association Rule Mining Apriori Erp Systems Computational Performance

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Front Cover: Generation of Molecular Counterfactuals for Explainable machine learning Based on Core-Substituent Recombination (ChemMedChem 3/2024)

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ChemMedChem 2024年第3期19卷

作者： Alec Lamens Prof. Jürgen Bajorath Department of Life Science Informatics and Data Science B-IT LIMES Program Unit Chemical Biology and Medicinal Chemistry Rheinische Friedrich-Wilhelms-Universität Friedrich-Hirzebruch-Allee 5/6 53115 Bonn Germany Lamarr Institute for Machine Learning and Artificial Intelligence Rheinische Friedrich-Wilhelms-Universität Bonn Friedrich-Hirzebruch-Allee 5/6 53115 Bonn Germany

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Application of artificial intelligence methods in vital signs analysis of hospitalized patients: A systematic literature review

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APPLIED SOFT COMPUTING 2020年 96卷 106612-106612页

作者： Kaieski, Naira da Costa, Cristiano Andre Righi, Rodrigo da Rosa Lora, Priscila Schmidt Eskofier, Bjoern Univ Vale Rio Sinos UNISINOS Software Innovat Lab SOFTWARELAB Appl Comp Grad Program BR-93022750 Sao Leopoldo Brazil Univ Vale Rio Sinos UNISINOS Grad Program Nursing BR-93022750 Sao Leopoldo Brazil Friedrich Alexander Univ Erlangen Nurnberg FAU Comp Sci Dept Machine Learning & Data Analyt Lab D-91058 Erlangen Germany

In a hospital environment, patients are monitored continuously by electronic devices and health professionals. Therefore, a large amount of data is collected and stored in electronic health records systems for each patient. Among such data, vital signs are one of the most common and relevant types of information monitored to assess a patient's health status. Artificial intelligence techniques can be used to analyze and learn useful standards from clinical datasets to provide better evidence to support the decisions of health professionals and thus help to improve patient health outcomes in hospitals. This systematic literature review aims to provide an updated computational perspective of how artificial intelligence has been applied to analyze the vital signs of adult hospitalized patients and the outcomes obtained. To this end, we reviewed 2899 scientific articles published between 2008 and 2018 and selected 78 articles that met our inclusion criteria to answer the research questions. Moreover, we used the information found in the reviewed articles to propose a taxonomy and identified the main concerns, challenges, and opportunities in this field. Our findings demonstrate that many researchers are exploring the use of artificial intelligence methods in tasks related to improving the health outcomes of hospitalized patients in distinct units. Additionally, although vital signs are significant predictors of clinical deterioration, they are not analyzed in isolation to predict or identify a clinical outcome. Our taxonomy and discussion contribute to the achievement of a significant degree of coverage regarding the aspects related to using machine learning to improve health outcomes in hospital environments, while highlighting gaps in the literature for future research. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Artificial intelligence machine learning Health informatics Vital signs

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