检索结果-内蒙古大学图书馆

*** as digital platform for vegetation analysis and estimation of carbon stock in KEHATI aqua park

AIP Conference Proceedings 2024年第1期3026卷

作者： Nanda Satya Nugraha Dian Pratama Putra Mohammad Prasanto Bimantio Amallia Ferhat Rama Zakaria Rudi Nirwantono Teddy Suparyanto Bens Pardamean Faculty of Forestry Institute of Agriculture STIPER Yogyakarta Indonesia 55282 Faculty of Agriculture Institute of Agriculture STIPER Yogyakarta Indonesia 55282 Faculty of Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55282 PT. Tirta Investama Pabrik Klaten Klaten Central Java Indonesia 57474 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 1148 Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

KEHATI AQUA Park is one of the green areas managed by PT. Tirta Investama Klaten (Klaten Factory). This area is located in Wangen and Ponggok Villages, Polanharjo District, Klaten Regency, Central Java, and is an integral part of the company’s seriousness in carbon reduction movement. When maintenance of the area is important for the sustainability of the park, there is however no data on the trees inventory in the area. Therefore, this study aims to invent the trees at KEHATI AQUA Park and evaluate the potential of the area to become a carbon sink using *** application. This study was conducted at the KEHATI AQUA Park. The census method was performed to collect tree data, and the result was recorded in *** application. The carbon stock calculation was estimated using the application according to the tree data submitted in the application. The study included a total of 1048 plantation spots in 51 tree species. From those numbers, about 738 trees existed, and the remaining spots were blank spaces. Based on the condition of the trees, those were 660 good trees, 17 dry trees, and 61 under pest infection. The carbon stock estimation at KEHATI AQUA Park Klaten in the next five years (2026) is around 40 tons of carbon. Therefore, it can be concluded that the park is valuable as an area to preserve biodiversity and as a carbon sink.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Prediction of oral food challenge outcomes via ensemble learning

引用

Informatics in Medicine Unlocked 2023年 36卷

作者： Zhang, Justin Lee, Deborah Jungles, Kylie Shaltis, Diane Najarian, Kayvan Ravikumar, Rajan Sanders, Georgiana Gryak, Jonathan Department of Electrical and Computer Engineering University of Michigan Ann Arbor MI United States Department of Internal Medicine University of Michigan Ann Arbor MI United States Department of Pediatrics University of Michigan Ann Arbor MI United States Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI United States Michigan Institute for Data Science University of Michigan Ann Arbor MI United States Department of Emergency Medicine University of Michigan Ann Arbor MI United States Department of Computer Science and Engineering University of Michigan Ann Arbor MI United States Max Harry Weil Institute for Critical Care Research and Innovation University of Michigan Ann Arbor MI United States Mary H. Weiser Food Allergy Center University of Michigan Ann Arbor MI United States Department of Computer Science Queens College City University of New York New York NY United States

Oral Food Challenges (OFCs) are essential to accurately diagnosing food allergy due to the limitations of existing clinical testing. However, some patients are hesitant to undergo OFCs, while those willing suffer from limited access to allergists in rural/community healthcare settings. Despite its success in predicting patient outcomes in other clinical settings, few applications of machine learning to food allergy have been developed. Thus, in this study, we seek to leverage machine learning methodologies for OFC outcome prediction. Retrospective data was gathered from 1,112 patients who collectively underwent a total of 1,284 OFCs, and consisted of clinical factors including serum-specific Immunoglobulin E (IgE), total IgE, skin prick tests (SPTs), comorbidities, sex, and age. Using these features, multiple machine learning models were constructed to predict OFC outcomes for three common allergens: peanut, egg, and milk. The best performing model for each allergen was an ensemble of random forest (egg) or Learning Using Concave and Convex Kernels (LUCCK) (peanut, milk) models, which achieved an Area under the Curve (AUC) of 0.91, 0.96, and 0.94, in predicting OFC outcomes for peanut, egg, and milk, respectively. Moreover, all such models had sensitivity and specificity values ≥89%. Model interpretation via SHapley Additive exPlanations (SHAP) indicates that specific IgE, along with wheal and flare values from SPTs, are highly predictive of OFC outcomes. The results of this analysis suggest that ensemble learning has the potential to predict OFC outcomes and reveal relevant clinical factors for further study. © 2022 The Author(s)

关键词： Ensemble learning Learning Using Concave and Convex Kernels Machine learning Oral food challenges SHAP

来源：评论

学校读者我要写书评

暂无评论

Progression subtypes in Parkinson's disease: Results of an AI-based multi-cohort analysis

引用

Clinical Neurophysiology 2024年 159卷 e39-e39页

作者： Hähnel, T. Raschka, T. Sapienza, S. Klucken, J. Glaab, E. Corvol, J. Falkenburger, B. Fröhlich, H. Department of Neurology University Hospital and Faculty of Medicine Carl Gustav Carus TUD Dresden University of Technology Dresden Germany Department of Bioinformatics Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) Sankt Augustin Germany Bonn-Aachen International Center for IT University of Bonn Bonn Germany Biomedical Data Science Luxembourg Centre for Systems Biomedicine (LCSB) University of Luxembourg Esch-sur-Alzette Luxembourg Luxembourg Institute of Health (LIH) Strassen Luxembourg Centre Hospitalier de Luxembourg (CHL) Luxembourg Sorbonne Université Paris Brain Institute – ICM Inserm CNRS Assistance Publique Hôpitaux de Paris Pitié-Salpêtrière Hospital Department of Neurology Paris France German Center for Neurodegenerative Diseases (DZNE) Tatzberg 41 01307 Dresden Germany

来源：评论

学校读者我要写书评

暂无评论

Improved Risk Stratification of Smoldering Multiple Myeloma (SMM) Using Trajectory data in the Pangea 2.0 Model: A Multicenter Study in 1,431 Participants

引用

Blood 2024年 144卷 1017-1017页

作者： Floris Chabrun Daniel Schwartz Susanna Gentile Tulika R Gupta Noelia Collado Gisbert Esperanza Martín-Sánchez Rosalinda Termini Jacqueline F Perry Annie Cowan Federico Ferrari Samuel Freeman Robert A. Redd Vidhi Patel Patrick Costello Catherine Tobia Romanos Sklavenitis-Pistofidis Habib El-Khoury Michael Timonian David J Lee Elizabeth D. Lightbody Irene Ghobrial Dana-Farber Cancer Institute Boston MA Harvard T.H. Chan School of Public Health Boston MA Cancer Center Clinica Universidad de Navarra (CCUN) Centro de Investigacion Medica Aplicada (CIMA) Instituto de Investigacion Sanitaria de Navarra (IdiSNA) CIBER-ONC numbers CB16/12/00369 and CB16/12/00489 Pamplona Spain Biostatistics and Research Decision Sciences Merck & Co Rahway NJ Bioinformatics Program Broad Institute of MIT and Harvard Cambridge MA Department of Data Science Dana-Farber Cancer Institute Boston MA Broad Institute of MIT and Harvard Cambridge MA University of Chicago Medical Center Chicago IL Harvard Medical School Boston MA

Background The 20/2/20 model is the current gold standard to stratify smoldering multiple myeloma (SMM) patients at baseline into three subgroups (low, intermediate, and high) according to the risk of progression based on the free light chain ratio (FLCr), M-protein concentration, and percentage of bone marrow (BM) plasma cells (PC). Evolving patterns that may alter the risk of progression are not considered in this static model. We previously proposed the PANGEA model that allows for personalized risk prediction using FLCr, M-protein, creatinine, age, hemoglobin trajectory, and optionally BM PC. We developed an improved PANGEA 2.0 model that includes trajectory modeling of these biomarkers to capture evolving patterns and improve predictions of MM progression. Methods We conducted a retrospective review of clinical data from 1,431 participants diagnosed with SMM at 4 international sites (Dana-Farber Cancer Institute, Boston, US, n = 737; National and Kapodistrian University of Athens, Greece, n = 379; University College London, UK, n = 97; and University of Navarra, Spain, n = 218). Dana-Farber participants comprised a training cohort to identify biomarker trajectories and to develop the PANGEA 2.0 model. The model was validated on two international cohorts: validation cohort 1 included patients from Greece and the UK (n = 476) and validation cohort 2 included patients from Spain (n = 218). The longitudinal data collected from 2018-2024 included current values and historical trajectories of age, M-protein, FLCr, creatinine, and hemoglobin, as well as BM PC (optional). We used a systematic grid search with 5-fold cross-validation to determine optimal trajectory definitions for M-protein, FLCr, creatinine, and hemoglobin. For each, we evaluated seven binary trajectory definitions based on average increase over time (slopes) or recent increase from the previous visit on absolute or relative (%) increase scales, with varying thresholds and time periods. We used Cox reg

关键词：

来源：评论

学校读者我要写书评

暂无评论

Ten quick tips for deep learning in biology

arXiv

引用

arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Android-Based oil palm farmer’s income analysis to support precision agriculture

引用

AIP Conference Proceedings 2023年第1期2594卷

作者： M. P. Bimantio A. Ferhat L. Isnaini D. P. Putra T. Suparyanto B. Pardamean Department of Food and Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Department of Agricultural Socio-Economics Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Department of Agricultural Socio-Economics Gadjah Mada University Yogyakarta Indonesia 55281 Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia 55281 Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

The purpose of this research is to create a simulator application in calculating farmer's income from the sale of fresh fruit bunches. Farmers can easily find out the outcomes based on how many kilograms of crops are sold through this application. This research was conducted in Mesuji Regency, Lampung Province, using primary data and descriptive analysis. The sampling technique utilized a proportional random sampling method and consisted of 88 oil palm farmers. The results indicated that the application made it simpler for farmers to find out their income. Farmers only needed to input the land area, the harvest number obtained on the harvest day that had been carried out, and the price of fresh fruit bunches so that they would know the estimated monthly and annual income calculations. These calculations can be used by farmers as a predicting and planning tool to avoid losses.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Convergent evidence linking neonatal vitamin D status and risk of neurodevelopmental disorders: a Danish case-cohort study

引用

The Lancet Psychiatry 2025年第6期12卷 410-420页

作者： Horsdal, Henriette Thisted Albiñana, Clara Zhu, Zhihong Boelt, Sanne Grundvad Borbye-Lorenzen, Nis Cohen, Arieh S Skogstrand, Kristin Melgaard, Lars MacSween, Nadia Jensen Thorbek, Marta Jadwiga Plana-Ripoll, Oleguer Petersen, Liselotte Vogdrup Bulik, Cynthia M B⊘rglum, Anders D Mors, Ole Nordentoft, Merete Werge, Thomas Moen, Gunn-Helen Wray, Naomi R Vilhjálmsson, Bjarni J Agerbo, Esben Pedersen, Carsten B⊘cker Mortensen, Preben Bo McGrath, John J National Centre for Register-Based Research Aarhus University Aarhus Denmark Centre for Integrated Register-based Research at Aarhus University Aarhus University Aarhus Denmark Big Data Centre for Environment and Health Aarhus University Aarhus Denmark Department of Biomedicine Aarhus University Aarhus Denmark Department of Clinical Medicine Aarhus University Aarhus Denmark Bioinformatics Research Centre Aarhus University Aarhus Denmark Department of Psychiatry University of Oxford Oxford United Kingdom Institute for Molecular Bioscience University of Queensland Brisbane QLD Australia University of Queensland Frazer Institute University of Queensland Brisbane QLD Australia Queensland Brain Institute University of Queensland Brisbane QLD Australia Novo Nordisk Foundation Center for Genomic Mechanisms of Disease Broad Institute Cambridge MA United States Danish Center for Neonatal Screening Department of Congenital Disorders Statens Serum Institut Copenhagen Denmark Department of Clinical Genetics Pathology and Molecular Diagnostics Office for Medical Services Lund Region Skåne Sweden Department of Clinical Epidemiology Aarhus University and Aarhus University Hospital Aarhus Denmark Department of Psychiatry University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Nutrition University of North Carolina at Chapel Hill Chapel Hill NC United States Department of Medical Epidemiology and Biostatistics Karolinska Institutet Stockholm Sweden The Lundbeck Foundation Initiative for Integrative Psychiatric Research Aarhus Denmark Department of Biomedicine Center for Genomics and Personalized Medicine Aarhus Denmark Psychosis Research Unit Department of Psychiatry Aarhus University Hospital Aarhus Denmark Copenhagen Research Centre for Mental Health Mental Health Services in the Capital Region of Denmark University of Copenhagen Copenhagen Denmark Department of Clinical Medicine Faculty of Health Sciences University of Cop

Background: There is growing evidence linking neonatal vitamin D deficiency to an increased risk of schizophrenia, ADHD, and autism spectrum disorder (ASD). The aim of this study was to examine the association between two vitamin D biomarkers (25 hydroxyvitamin D [25(OH)D] and vitamin D-binding protein [DBP], and their related genetic correlates) and the risk of six mental disorders. Methods: We used a population-based, case-cohort sample of all individuals born in Denmark between 1981 and 2005. Using Danish health registers with follow-up to Dec 31, 2012, we identified individuals diagnosed with major depressive disorder, bipolar disorder, schizophrenia, ADHD, ASD, and anorexia nervosa based on ICD-10 criteria. Additionally, a random subcohort from the general population was selected. Based on neonatal dried blood spots, we measured concentrations of 25(OH)D and DBP. Our primary analyses were based on hazard ratios (HR) with 95% CI and absolute risks for the six mental disorders according to measured concentrations of 25(OH)D and DBP. As secondary analyses, we examined the association between genetic predictors of 25(OH)D and DBP, and the six mental disorders, and Mendelian randomisation analyses based on published summary statistics for 25(OH)D, DBP, and the six mental disorders. People with lived experience contributed to the development of the guiding hypothesis. Findings: We used the total population from the iPSYCH2012 design (n=88 764), which included individuals who developed the six mental disorders, major depressive disorder (n=24 240), bipolar disorder (n=1928), schizophrenia (n=3540), ADHD (n=18 726), ASD (n=16 146), anorexia nervosa (n=3643), and the randomly sampled subcohort (n=30 000). Among those who met a range of inclusion criteria (eg, measured 25[OH]D, DBP or genotype, and predominantly European ancestry), we measured 25(OH)D or DBP in 71 793 individuals (38 118 [53·1%] male and 33 675 [46·9%] female);65 952 had 25(OH)D and 66 797 the DBP measur

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Learning for Imbalance data Classification using Class Expert Generative Adversarial Network 3

Deep Learning for Imbalance Data Classification using Class ...

引用

3rd International Conference on Computer Science and Computational Intelligence, ICCSCI 2018

作者： Fanny Cenggoro, Tjeng Wawan Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia

Without any specific way for imbalance data classification, artificial intelligence algorithm cannot recognize data from minority classes easily. In general, modifying the existing algorithm by assuming that the training data is imbalanced, is the only way to handle imbalance data. However, for a normal data handling, this way mostly produces a deficient result. In this research, we propose a class expert generative adversarial network (CE-GAN) as the solution for imbalance data classification. CE-GAN is a modification in deep learning algorithm architecture that does not have an assumption that the training data is imbalance data. Moreover, CE-GAN is designed to identify more detail about the character of each class before classification step. CE-GAN has been proved in this research to give a good performance for imbalance data classification. © 2018 The Authors. Published by Elsevier Ltd.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

A novel tropical geometry-based interpretable machine learning method: Application in prognosis of advanced heart failure

arXiv

引用

arXiv 2021年

作者： Yao, Heming Derksen, Harm Golbus, Jessica R. Zhang, Justin Aaronson, Keith D. Gryak, Jonathan Najarian, Kayvan Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Department of Mathematics Northeastern University BostonMA United States Division of Cardiovascular Medicine Department of Internal Medicine University of Michigan Ann ArborMI United States Electrical and Computer Engineering College of Engineering University of Michigan Ann ArborMI United States Michigan Institute for Data Science University of Michigan Ann ArborMI United States Department of Emergency Medicine University of Michigan Ann ArborMI United States Michigan Center for Integrative Research in Critical Care University of Michigan Ann ArborMI United States

A model's interpretability is essential to many practical applications such as clinical decision support systems. In this paper, a novel interpretable machine learning method is presented, which can model the relationship between input variables and responses in humanly understandable rules. The method is built by applying tropical geometry to fuzzy inference systems, wherein variable encoding functions and salient rules can be discovered by supervised learning. Experiments using synthetic datasets were conducted to investigate the performance and capacity of the proposed algorithm in classification and rule discovery. Furthermore, the proposed method was applied to a clinical application that identified heart failure patients that would benefit from advanced therapies such as heart transplant or durable mechanical circulatory support. Experimental results show that the proposed network achieved great performance on the classification tasks. In addition to learning humanly understandable rules from the dataset, existing fuzzy domain knowledge can be easily transferred into the network and used to facilitate model training. From our results, the proposed model and the ability of learning existing domain knowledge can significantly improve the model generalizability. The characteristics of the proposed network make it promising in applications requiring model reliability and justification. Copyright © 2021, The Authors. All rights reserved.

关键词： Domain Knowledge

来源：评论

学校读者我要写书评

暂无评论

Impact of the protein data bank across scientific disciplines

引用

data Science Journal 2020年第1期19卷 1-14页

作者： Feng, Zukang Verdiguel, Natalie Di Costanzo, Luigi Goodsell, David S. Westbrook, John D. Burley, Stephen K. Zardecki, Christine Research Collaboratory for Structural Bioinformatics Protein Data Bank Rutgers The State University of New Jersey PiscatawayNJ United States Institute for Quantitative Biomedicine Rutgers The State University of New Jersey PiscatawayNJ United States University of Central Florida OrlandoFL United States Department of Agricultural Sciences University of Naples Federico II Portici Italy Department of Integrative Structural and Computational Biology The Scripps Research Institute La JollaCA United States Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California San Diego La JollaCA United States Rutgers Cancer Institute of New Jersey Rutgers The State University of New Jersey New BrunswickNJ United States Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La JollaCA United States

The Protein data Bank archive (PDB) was established in 1971 as the 1st open access digital data resource for biology and medicine. Today, the PDB contains >160,000 atomic-level, experimentally-determined 3D biomolecular structures. PDB data are freely and publicly available for download, without restrictions. Each entry contains summary information about the structure and experiment, atomic coordinates, and in most cases, a citation to a corresponding scientific publication. Individually and in bulk, PDB structures can be downloaded and/or analyzed and visualized online using tools at ***. As such, it is challenging to understand and monitor reuse of data. Citations of the scientific publications describing PDB structures provide one way of understanding which structures are being used, and in which research areas. Our analysis highlights frequently-cited structures and identifies milestone structures that have demonstrated impact across scientific fields. © 2020 The Author(s).

关键词： Proteins

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：