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SSRN 2022年

作者： Cenggoro, Tjeng Wawan Elwirehardja, Gregorius Natanael Dominic, Nicholas Setiawan, Karli Eka Rahutomo, Reza Djuana, Endang Gunawan, Fergyanto E. Budiman, Arief S. Romeli, Sugiarto Pardamean, Bens Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Program Bina Nusantara University Jakarta11480 Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta11480 Indonesia Electrical Engineering Department Faculty of Industrial Technology Universitas Trisakti Jakarta11440 Indonesia Industrial Engineering Department BINUS Graduate Program Master of Industrial Engineering Bina Nusantara University Jakarta11480 Indonesia Department of Manufacturing and Mechanical Engineering and Technology Oregon Institute of Technology Klamath FallsOR97601 United States PT. Impack Pratama Industri Jakarta Indonesia

Drying has been an eco-friendly and cost-efficient method to reduce post-harvest losses of agricultural crops. In various countries, the technique has been widely utilized in the form of Solar Dryer Dome (SDD) buildings. Accurate indoor microclimate forecasting is very important to implement smart control systems in SDDs that can maintain the ideal conditions, as the availability of solar energy is limited only at daytimes and the temperature may drop significantly during nighttimes in remote areas. The microclimate variables that play an important role in maintaining ideal environmental conditions include temperature and relative humidity. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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IDEA: Integrating Divisive and Ensemble-Agglomerate hierarchical clustering framework for arbitrary shape data

IDEA: Integrating Divisive and Ensemble-Agglomerate hierarch...

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IEEE International Conference on Big Data

作者： Hongryul Ahn Inuk Jung Heejoon Chae Minsik Oh Inyoung Kim Sun Kim Division of Data Science DS&ML Center The University of Suwon Hwaseong Republic of Korea School of Computer Science and Engineering Kyungpook National University Daegu Republic of Korea Division of Computer Science Sookmyung Women's University Seoul Republic of Korea BK21 Four Intelligence Computing Seoul National University Seoul Republic of Korea Artificial Intelligence Institute Seoul National University Seoul Republic of Korea Interdisciplinary Program in Bioinformatics and Bioinformatics Institute Seoul National University Seoul Republic of Korea

ISBN: (纸本)9781665445993

Hierarchical clustering, a traditional clustering method, has been getting attention again. Among several reasons, a credit goes to a recent paper by Dasgupta in 2016 that proposed a cost function that quantitatively evaluates hierarchical clustering trees. An important question is how to combine this recent advance with existing successful clustering methods. In this paper, we propose a hierarchical clustering method to minimize the cost function of clustering tree by incorporating existing clustering techniques. First, we developed an ensemble tree-search method that finds an integrated tree with reduced cost by integrating multiple existing hierarchical clustering methods. Second, to operate on large and arbitrary shape data, we designed an efficient hierarchical clustering framework, called integrating divisive and ensemble-agglomerate (IDEA) by combining it with advanced clustering techniques such as nearest neighbor graph construction, divisive-agglomerate hybridization, and dynamic cut tree. The IDEA clustering method showed better performance in minimizing Dasgupta's cost and improving accuracy (adjusted rand index) over existing cost-minimization-based, and density-based hierarchical clustering methods in experiments using arbitrary shape datasets and complex biology-domain datasets.

关键词： Costs Shape Clustering methods Conferences Big Data Cost function Minimization

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An atlas of single-cell eQTLs dissects autoimmune disease genes and identifies novel drug classes for treatment

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Cell Genomics 2025年第4期5卷 100820-100820页

作者： Wang, Lida Markus, Havell Chen, Dieyi Chen, Siyuan Zhang, Fan Gao, Shuang Khunsriraksakul, Chachrit Chen, Fang Olsen, Nancy Foulke, Galen Jiang, Bibo Carrel, Laura Liu, Dajiang J. Department of Public Health Sciences Pennsylvania State University College of Medicine Hershey 17033 PA United States Bioinformatics and Genomics PhD Program Pennsylvania State University College of Medicine Hershey 17033 PA United States Institute for Personalized Medicine Pennsylvania State University College of Medicine Hershey 17033 PA United States Department of Biochemistry and Molecular Biology Pennsylvania State University College of Medicine Hershey 17033 PA United States Department of Medicine Penn State University College of Medicine Hershey 17033 PA United States Department of Dermatology Penn State University College of Medicine Hershey 17033 PA United States

Most variants identified from genome-wide association studies (GWASs) are non-coding and regulate gene expression. However, many risk loci fail to colocalize with expression quantitative trait loci (eQTLs), potentially due to limited GWAS and eQTL analysis power or cellular heterogeneity. Population-scale single-cell RNA-sequencing (scRNA-seq) datasets are emerging, enabling mapping of eQTLs in different cell types (sc-eQTLs). Compared to eQTL data from bulk tissues (bk-eQTLs), sc-eQTL datasets are smaller. We propose a joint model of bk-eQTLs as a weighted sum of sc-eQTLs (JOBS) from constituent cell types to improve power. Applying JOBS to One1K1K and eQTLGen data, we identify 586% more eQTLs, matching the power of 4× the sample sizes of OneK1K. Integrating sc-eQTLs with GWAS data creates an atlas for 14 immune-mediated disorders, colocalizing 29.9% or 32.2% more loci than using sc-eQTL or bk-eQTL alone. Extending JOBS, we develop a drug-repurposing pipeline and identify novel drugs validated by real-world data. © 2025 The Author(s)

关键词： autoimmune disease bulk eQTL colocalization drug repurposing GWAS scRNAseq sequencing design single cell eQTL transcriptome

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Digitizing Farmers' Land Data Collection Systems in Indonesia with the Use of Tani Millenial Apps

Digitizing Farmers' Land Data Collection Systems in Indonesi...

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Mohammad Prasanto Bimantio Dian Pratama Putra Teddy Suparyanto Amallia Ferhat Nanda Satya Nugraha Alam Ahmad Hidayat Bens Pardamean Department of Food and Agricultural Technology Institute of Agriculture STIPER Yogyakarta Indonesia Department of Agrotechnology Institute of Agriculture STIPER Yogyakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Department of Agricultural Socio-Economics Institute of Agriculture STIPER Yogyakarta Indonesia Department of Forestry Institute of Agriculture STIPER Yogyakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

In this era of digital world information, farmers face challenges about information management of huge data and the complexity of processes in precision farming. The data collection carried out so far can cause several difficulties, especially if the data is needed by the government or academics to support decision-making. Agricultural data collection applications only cover 12% of the market share of Android-based agriculture-based applications. In this study, Tani Milenial application is present as an alternative to support agricultural data collection activities in Indonesia. This application encourages farmers to be active and contribute independently to their role as subjects of agricultural development and is accustomed to digitally recording their agricultural activities. In this developed application system, the community, especially farmers and farmer groups, will find it easier to get information about agricultural potential data around where they carry out agricultural activities and can access this information online. Tani Milenial works as a guide to help farmers and transform agriculture but still have some gaps which should be checked and removed. The data collected can be used by various parties for research and decision-making. The app can be further improved to develop new features related to education, market information, and goods.

关键词： Decision making Education Government Transforms Data collection Agriculture Recording

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METTL4 is an snRNA m6 Am methyltransferase that regulates RNA splicing

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细胞研究（英文版） 2020年第6期30卷 544-547页

作者： Hao Chen Lei Gu Esteban A.Orellana Yuanyuan Wang Jiaojiao Guo Qi Liu Longfei Wang Zhangfei Shen Hao Wu Richard I.Gregory Yi Xing Yang Shi Division of Newborn Medicine and Program in Epigenetics Boston Children's Hospital Harvard Medical School 300 Longwood AvenueBoston MA 02115 USA Stem Cell Program Division of Hematology/Oncology Boston Children's Hospital Department of Biological Chemistry and Molecular Pharmacology Harvard Medical SchoolBoston MA 02115 USA Bioinformatics Interdepartmental Graduate Program University of California Los Angeles Los Angeles CA 90095USA College of Science Northeastern University 360 Huntington Ave. Boston MA 02115 USA Program in Cellular and Molecular Medicine Boston Children's Hospital Boston MA 02115 USA Department of Biological Chemistry and Molecular Pharmacology Harvard Medical School Boston MA 02115 USA Division of Newborn Medicine and Program in Epigenetics Boston Children's Hospital Harvard Medical School 300 Longwood AvenueBoston MA 02115 USA Program in Cellular and Molecular Medicine Boston Children's Hospital Boston MA 02115 USA Department of Biological Chemistry and Molecular Pharmacology Harvard Medical School Boston MA 02115 USA Center for Computational and Genomic Medicine The Children's Hospital of Philadelphia Philadelphia PA 19104 USA Department of Pathology and Laboratory Medicine University of PennsylvaniaPhiladelphia PA 19104 USA

Dear Editor, Protein coding mRNAs and non-coding RNAs are extensively modified by chemical modifications,1 including methylation of the N6 position of adenosine (m6A).2 While mRNA internal m6A methylation is mediated by METTL3 and METTL14, which are members of the MT-A70 family,2 N6-methylation of cap-adjacent N6,2'-O-dimethyladenosine (m6Am) is catalyzed by PCIF1.3-6 Non-coding RNAs are also m6Am methylated internally,7 but the corresponding enzyme remains *** we provide evidence that METTL4 mediates internal m6Am methylation of U2 small nuclear RNA (snRNA).

关键词： Epigenetics RNA modification RNA splicing

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Graph AI in Medicine

arXiv

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arXiv 2023年

作者： Johnson, Ruth Li, Michelle M. Noori, Ayush Queen, Owen Zitnik, Marinka Department of Biomedical Informatics Harvard Medical School BostonMA02115 United States Berkowitz Family Living Laboratory Harvard Medical School BostonMA02115 United States Bioinformatics and Integrative Genomics Program Harvard Medical School BostonMA02115 United States Harvard College CambridgeMA02138 United States Broad Institute of MIT and Harvard CambridgeMA02142 United States Harvard Data Science Initiative CambridgeMA02138 United States Kempner Institute for the Study of Natural and Artificial Intelligence Harvard University AllstonMA United States

In clinical artificial intelligence (AI), graph representation learning, mainly through graph neural networks and graph transformer architectures, stands out for its capability to capture intricate relationships and structures within clinical datasets. With diverse data-from patient records to imaging-graph AI models process data holistically by viewing modalities and entities within them as nodes interconnected by their relationships. Graph AI facilitates model transfer across clinical tasks, enabling models to generalize across patient populations without additional parameters or minimal to no re-training. However, the importance of human-centered design and model interpretability in clinical decision-making cannot be overstated. Since graph AI models capture information through localized neural transformations defined on relational datasets, they offer both an opportunity and a challenge in elucidating model rationale. Knowledge graphs can enhance interpretability by aligning model-driven insights with medical knowledge. Emerging graph AI models integrate diverse data modalities through pre-training, facilitate interactive feedback loops, and foster human-AI collaboration, paving the way to clinically meaningful predictions. © 2023, CC BY.

关键词： Knowledge graph

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Fetal Hemoglobin Mediates the Effect of Beta Globin Gene Polymorphisms on Neurocognitive Functioning in Sickle Cell Disease

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BLOOD 2020年 136卷 23-24页

作者： Heitzer, Andrew M. Longoria, Jennifer Rampersaud, Evadnie Rashkin, Sara Estepp, Jeremie H. Okhomina, Victoria Wang, Winfred Raches, Darcy Potter, Brian King, Allison A. Kang, Guolian Hankins, Jane S. Department of Psychology St. Jude Children's Research Hospital Memphis TN Center for Applied Bioinformatics St. Jude Children's Research Hospital Memphis TN Department of Hematology St. Jude Children's Research Hospital Memphis TN Department of Biostatistics St. Jude Children's Research Hospital Memphis TN Program in Occupational Therapy Washington University in St. Louis School of Medicine Clayton MO Associate Member Director of the Sickle Cell Program St. Jude Children's Research Hospital Memphis TN

Background: Fetal hemoglobin (HbF) is the most influential modifier of the clinical and hematologic phenotype of sickle cell disease (SCD) and is highly heritable. Low HbF is independently associated with increased white matter changes on brain imaging and poorer performance on neurocognitive measures (Ruffiuex, Child Neuropsychology, 2013). Our previous work has shown that 11 SNPs in three genes ( BCL11A , MYB , and β-globin), contribute over 20% of the variance in HbF (Rampersaud, Kang. et al., 2020, under review). Clinically, these HbF-associated SNPs are associated with disease severity and frequency of pain events. However, the neurocognitive implications of these SNPs have yet to be explored. As part of a prospective longitudinal cohort study, the Sickle Cell Clinical Research and Intervention program (SCCRIP), we evaluated the relationship between HbF-associated SNPs and neurocognitive functioning in SCD patients. Methods: We included 257 patients with SCD (69% HbSS/HbSβ 0 -thalassemia). The median age of participants was 13 years (range 4 - 25). We extracted genotypes for the 11 HbF-associated SNPs from whole genome sequencing data and analyzed them based on an additive genetic model. Following informed consent, patients completed a battery of gold-standard neurocognitive measures, supervised by a psychologist, assessing IQ, verbal and perceptual reasoning, working memory, processing speed, sustained attention, and executive functioning skills. HbF was the average value of measurements collected within 3 months of neurocognitive assessment, or the closest value. In univariate analyses, Kruskal-Wallis rank sum test was used to assess the associations of the 11 HbF-associated SNPs with neurocognitive measures. Linear regression was used to examine these associations adjusting for age, sickle genotype, hydroxyurea exposure, socioeconomic status (index of social vulnerability) and 5 principal components to adjust for population stratification. Benjamini and Hoch

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Development of a respiratory sound labeling software for training a deep learning-based respiratory sound analysis model

Development of a respiratory sound labeling software for tra...

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International Forum on Medical Imaging in Asia 2021, IFMIA 2021

作者： Hsu, Fu-Shun Huang, Chao-Jung Kuo, Chen-Yi Huang, Shang-Ran Cheng, Yuan-Ren Wang, Jia-Horng Wu, Yi-Lin Tzeng, Tzu-Ling Lai, Feipei Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University Taipei Taiwan Heroic Faith Medical Science Corporation Limited New Taipei Taiwan 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 Department of Life Science College of Life Science National Taiwan University Taipei Taiwan Institute of Biomedical Sciences Academia Sinica Taipei Taiwan Department of Critical Care Far Eastern Memorial Hospital New Taipei City Taiwan

ISBN: (数字)9781510644212

ISBN: (纸本)9781510644205

Respiratory auscultation can help healthcare professionals detect abnormal respiratory conditions if adventitious lung sounds are heard. The state-of-the-art artificial intelligence technologies based on deep learning show great potential in the development of automated respiratory sound analysis. To train a deep learning-based model, a huge number of accurate labels of normal breath sounds and adventitious sounds are needed. In this paper, we demonstrate the work of developing a respiratory sound labeling software to help annotators identify and label the inhalation, exhalation, and adventitious respiratory sound more accurately and quickly. Our labeling software integrates six features from MATLAB Audio Labeler, and one commercial audio editor, RX7. As of October, 2019, we have labeled 9,765 15- second-long audio files of breathing lung sounds, and accrued 34,095 inhalation labels,18,349 exhalation labels, 13,883 continuous adventitious sounds (CASs) labels and 15,606 discontinuous adventitious sounds (DASs) labels, which are significantly larger than previously published studies. The trained convolutional recurrent neural networks based on these labels showed good performance with F1-scores of 86.0% on inhalation event detection, 51.6% on CASs event detection and 71.4% on DASs event detection. In conclusion, our results show that our proposed respiratory sound labeling software could easily pre-define a label, perform one-click labeling, and overall facilitate the process of accurately labeling. This software helps develop deep learning-based models that require a huge amount of labeled acoustic data. © 2021 SPIE.

关键词： Recurrent neural networks

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Integrative modeling of transmitted and de novo variants identifies novel risk genes for congenital heart disease

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Quantitative Biology 2021年第2期9卷 216-227页

作者： Mo Li Xue Zeng Chentian Jin Sheng Chih Jin Weilai Dong Martina Brueckner Richard Lifton Qiongshi Lu Hongyu Zhao Department of Biostatistics Yale School of Public HealthNew HavenCT 06510USA Department of Genetics Yale UniversityNew HavenCT 06510USA Department of Molecular Cellular&Developmental BiologyYale UniversityCT 06510USA Department of Genetics Washington University School of MedicineSt LouisMO 63110USA Department of Pediatrics Yale UniversityNew HavenCT 06510USA Laboratory of Human Genetics and Genomics Rockefeller UniversityNew YorkNY 10065USA Department of Biostatistics and Medical Informatics University of Wisconsin-MadisonMadisonWl 53792USA Program of Computational Biology and Bioinformatics Yale UniversityNew HavenCT 06510USA

Background:Whole-exome sequencing(WES)studies have identified multiple genes enriched for de novo mutations(DNMs)in congenital heart disease(CHD)***,risk gene identification based on DNMs alone remains statistically challenging due to heterogenous etiology of CHD and low mutation rate in each ***:In this manuscript,we introduce a hierarchical Bayesian framework for gene-level association test which jointly analyzes de novo and rare transmitted *** integrative modeling of multiple types of genetic variants,gene-level annotations,and reference data from large population cohorts,our method accurately characterizes the expected frequencies of both de novo and transmitted variants and shows improved statistical power compared to analyses based on DNMs ***:Applied to WES data of 2,645 CHD proband-parent trios,our method identified 15 significant genes,half of which are novel,leading to new insights into the genetic bases of ***:These results showcase the power of integrative analysis of transmitted and de novo variants for disease gene discovery.

关键词： rare variants gene-level association test congenital heart disease de novo mutation

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QTL analysis for ascorbic acid content in strawberry fruit reveals a complex genetic architecture and association with GDP-L-galactose phosphorylase

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Horticulture Research 2023年第3期10卷 136-150页

作者： Pilar Muñoz Cristina Castillejo JoséAntonio Gómez Luis Miranda Silke Lesemann Klaus Olbricht Aurélie Petit Philippe Chartier Annika Haugeneder Johanna Trinkl Luca Mazzoni Agnieszka Masny Edward Zurawicz Freya Maria Rosemarie Ziegler Björn Usadel Wilfried Schwab Béatrice Denoyes Bruno Mezzetti Sonia Osorio JoséFSánchez-Sevilla Iraida Amaya Centro IFAPA de Málaga Instituto Andaluz de Investigación y Formación Agraria y Pesquera(IFAPA)29140MálagaSpain PhD program in Advanced Biotechnology Universidad de Málaga29071MálagaSpain Finca el Cebollar Centro IFAPA las Torres04745HuelvaSpain Hansabred GmbH&Co.KG 01108DresdenGermany INVENIO 33800BordeauxFrance Biotechnology of Natural Products Technische Universität München85354FreisingGermany Dipartimento di Scienze Agrarie Alimentari e AmbientaliUniversitàPolitecnica delle Marche60131AnconaItaly Department of Horticultural Crop Breeding the National Institute of Horticultural ResearchKonstytucji 3 Maja 1/396-100SkierniewicePoland Institute of Bio-and Geosciences Bioinformatics(IBG-4)Forschungszentrum Jülich GmbH52428JülichGermany Univ.Bordeaux INRAEBiologie du Fruit et PathologieUMR 1332F-33140France Departamento de Biología Molecular y Bioquímica Instituto de Hortofruticultura Subtropical y Mediterránea"La Mayora"Universidad de Málaga-Consejo Superior de Investigaciones CientíficasCampus de Teatinos29071 MálagaSpain Unidad Asociada de I+D+i IFAPA-CSIC Biotecnología y Mejora en Fresa 29010MálagaSpain.

Strawberry(Fragaria×ananassa)fruits are an excellent source of L-ascorbic acid(AsA),a powerful antioxidant for plants and *** the genetic components underlying AsA accumulation is crucial for enhancing strawberry nutritional ***,we unravel the genetic architecture of AsA accumulation using an F1 population derived from parental lines‘Candonga’and‘Senga Sengana’,adapted to distinct Southern and Northern European *** account for environmental effects,the F1 and parental lines were grown and phenotyped in five locations across Europe(France,Germany,Italy,Poland and Spain).Fruit AsA content displayed normal distribution typical of quantitative traits and ranged five-fold,with significant differences among genotypes and *** content in each country and the average in all of them was used in combination with 6,974 markers for quantitative trait locus(QTL)*** stable QTLs for AsA content were detected in linkage group(LG)3A,LG 5A,LG 5B,LG 6B and LG *** genes were identified within stable QTL intervals and expression analysis in lines with contrasting AsA content suggested that GDP-L-Galactose Phosphorylase FaGGP(3A),and the chloroplast-located AsA transporter gene FaPHT4;4(7C)might be the underlying genetic factors for QTLs on LG 3A and 7C,*** show that recessive alleles of FaGGP(3A)inherited from both parental lines increase fruit AsA ***,expression of FaGGP(3A)was two-fold higher in lines with high *** here identified represent a useful resource for efficient selection of new strawberry cultivars with increased AsA content.

关键词： straw cultivar content

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