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An Evaluation of Deep Neural Network Performance on Limited Protein Phosphorylation Site Prediction data

Procedia Computer science 2019年 157卷 25-30页

作者： Favorisen Rosyking Lumbanraja Bharuno Mahesworo Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Department of Computer Science Faculty of Mathematics and Natural Science University of Lampung Jalan Prof. Dr. Sumantri Brojonegoro No.17 35145 Bandar Lampung Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

One of the common and important post-translational modification (PTM) types is phosphorylation. Protein phosphorylation is used to regulate various enzyme and receptor activations which include signal pathways. There have been many significant studies conducted to predict phosphorylation sites using various machine learning methods. Recently, several researchers claimed deep learning based methods as the best methods for phosphorylation sited prediction. However, the performance of these methods were backed up with the massive training data used in the researches. In this paper, we study the performance of simple deep neural network on the limited data generally used prior to deep learning employment. The result shows that a deep neural network can still achieve comparable performance in the limited data settings.

关键词： Phosphorylation Site Prediction Protein Phosphorylation Deep Learning Deep Neural Network

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Geospatial data and scholia 3

Geospatial data and scholia

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3rd International Workshop on Geospatial Linked data and the 2nd Workshop on Querying the Web of data, GeoLD-QuWeDa 2018

作者： Nielsen, Finn Årup Mietchen, Daniel Willighagen, Egon Cognitive Systems DTU Compute Technical University of Denmark Denmark Data Science Institute University of Virginia CharlottesvilleVA United States Dept of Bioinformatics - BiGCaT NUTRIM Maastricht University Netherlands

Scholia is a website that displays information about scientific works represented in Wikidata. It extracts the information via queries to the extended SPARQL endpoint Wikidata Query Service (WDQS). Scholia handles geospatial data that may be rendered on maps with the default map output format available in WDQS. We describe the use of geospatial data in Scholia, how we combine it with other data-e.g., on topics, events and affiliations-and present a set of user stories to illustrate its current capabilities and its potential in light of the ongoing expansion of Wikidata and its integration with research-related resources. © CEUR-WS. All rights reserved.

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Racial disparities in lung function by pulmonary function testing among lung transplant candidates and race-specific reference equations

JHLT Open

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JHLT Open 2025年 8卷 100252-100252页

作者： Daniel M. Guidot Mackenzie Wood Emily Poehlein Scott Palmer Lisa McElroy Geriatrics Research Education and Care Center Durham VA Medical Center Durham North Carolina Division of Pulmonary Allergy and Critical Care Duke University Medical Center Durham North Carolina Department of Biostatistics and Data Science Wake Forest School of Medicine Winston-Salem North Carolina Department of Biostatistics and Bioinformatics Duke University School of Medicine Durham North Carolina Duke Clinical Research Institute Duke University Medical Center Durham North Carolina Division of Abdominal Transplant Department of Surgery Duke University Medical Center Durham North Carolina Department of Population Health Sciences Duke University Medical Center Durham North Carolina

Non-White patients with interstitial lung disease (ILD) experience racial disparities in lung transplant waitlist mortality. Race-specific equations for spirometry may contribute by underestimating restriction severity in non-White candidates. We analyzed US lung transplant candidates to assess for disparities in forced vital capacity (FVC) at listing, comparing absolute and adjusted values using race-specific and race-neutral equations. We identified 17,457 adults with ILD listed May 4, 2005 to September 31, 2023. At listing, mean absolute FVC was higher for White patients (2.03 ± 0.80 liters) than Black patients (1.61 ± 0.67 liters) and Asian patients (1.49 ± 0.86 liters). Differences were attenuated after applying race-specific equations (White patients 50.0 ± 17.5%, Black patients 47.7 ± 17.9%, Asian patients 46.2 ± 24.2%). Compared with race-neutral equations, race-specific equations had higher odds of classifying FVC as severe (≤40%) requiring listing in White patients (OR 1.37, 95% CI 1.28–1.40) but lower odds in Black patients (OR 0.82, 95% CI 0.74–0.90). Using race-neutral equations might help improve racial disparities for lung transplant candidates with ILD.

关键词： BMI Body mass index CI Confidence interval cm Centimeter FVC Forced vital capacity GLI Global lung initiative IPF Idiopathic pulmonary fibrosis ILD Interstitial lung disease kg/m 2 Kilogram per meter squared L Liter OR Odds ratio Sep September STAR Standard Transplant Analysis Research UNOS United Network of Organ Sharing interstitial lung disease lung transplantation forced vital capacity race-specific reference equations racial disparity referral United Network for Organ Sharing

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Epitopedia: identifying molecular mimicry between pathogens and known immune epitopes

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ImmunoInformatics 2023年 9卷

作者： Christian A Balbin Janelle Nunez-Castilla Vitalii Stebliankin Prabin Baral Masrur Sobhan Trevor Cickovski Ananda Mohan Mondal Giri Narasimhan Prem Chapagain Kalai Mathee Jessica Siltberg-Liberles Department of Biological Sciences College of Arts Science and Education Florida International University Miami United States Bioinformatics Research Group (BioRG) Knight Foundation School of Computing and Information Sciences Florida International University Miami United States Department of Physics College of Arts Science and Education Florida International University Miami United States Machine Learning and Data Analytics Group (MLDAG) Knight Foundation School of Computing and Information Sciences Florida International University Miami United States Biomolecular Sciences Institute Florida International University Miami United States Department of Human and Molecular Genetics Herbert Wertheim College of Medicine Florida International University Miami United States

Upon infection, foreign antigenic proteins stimulate the host's immune system to produce antibodies targeting the pathogen. These antibodies bind to regions on the antigen called epitopes. Structural similarity (molecular mimicry) of epitopes between an infecting pathogen and host proteins or other pathogenic proteins the host has previously encountered can impact the host immune response to the pathogen and may lead to cross-reactive antibodies. The ability to identify potential regions of molecular mimicry in a pathogen can illuminate immune effects which are especially important to pathogen treatment and vaccine design. Here we present Epitopedia, a software pipeline that facilitates the identification of regions that may exhibit potential three-dimensional molecular mimicry between an antigenic pathogen protein and known immune epitopes as catalogued by the Immune Epitope database (IEDB). Epitopedia is open-source software released under the MIT license and is freely available on GitHub, including a Docker container with all other software dependencies preinstalled. We performed an analysis describing how various secondary structure states, identity between pentapeptide pairs, and identity between the parent sequences of pentapeptide pairs affects RMSD. We found that pentapeptides pairs in a helical conformation had considerably lower RMSD values than those in extended or coil conformations. We also found that RMSD is significantly increased when pentapeptide pairs are from non-homologous sequences.

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Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data:a multi-phased study of prostate cancer

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Cancer Communications 2021年第12期41卷 1387-1397页

作者： Chong Wu Jingjing Zhu Austin King Xiaoran Tong Qing Lu Jong Y.Park Liang Wang Guimin Gao Hong-Wen Deng Yaohua Yang Karen E.Knudsen Timothy R.Rebbeck Jirong Long Wei Zheng Wei Pan David V.Conti Christopher A Haiman Lang Wu Department of Statistics Florida State UniversityTallahasseeFL 32304USA Cancer Epidemiology Division Population Sciences in the Pacific ProgramUniversity of Hawaii Cancer CenterUniversity of Hawaii at ManoaHonoluluHI 96813USA Department of Epidemiology and Biostatistics Michigan State UniversityEast LansingMI 48824USA Department of Biostatistics University of FloridaGainesvilleFL 32603USA Department of Cancer Epidemiology H.Lee Moffitt Cancer Center and Research InstituteTampaFL 33612USA Department of Tumor Biology H.Lee Moffitt Cancer Center and Research InstituteTampaFL 33612USA Department of Public Health Sciences University of ChicagoChicagoIL 60637USA Center of Bioinformatics and Genomics Department of Global Biostatistics and Data ScienceTulane UniversityNew OrleansLA 70112USA Division of Epidemiology Department of MedicineVanderbilt Epidemiology CenterVanderbilt-Ingram Cancer CenterVanderbilt University Medical CenterNashvilleTN 37203USA Department of Cancer Biology Sidney Kimmel Cancer CenterThomas Jefferson UniversityPhiladelphiaPA 19107USA Department of Medical Oncology Dana-Farber Cancer InstituteBostonMA 02215USA Department of Epidemiology Harvard TH Chan School of Public HealthBostonMA 02115USA Division of Biostatistics University of MinnesotaMinneapolisMN 55455USA Department of Preventive Medicine Keck School of MedicineUniversity of Southern California/Norris Comprehensive Cancer CenterLos AngelesCA 90033USA

Background:DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer(PCa).However,it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores(PRSs).Here,we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation,and other genomic information using an integrative ***:Using data from the PRACTICAL consortium,we derived multiple sets of genetic scores,including those based on available single-nucleotide polymorphisms through widely used methods of pruning and thresholding,LDpred,LDpred-funt,AnnoPred,and EBPRS,as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding *** the tuning step,using the UK Biobank data(1458 prevalent cases and 1467 controls),we selected PRSs with the best *** an independent set of data from the UK Biobank,we developed an integrative PRS combining information from individual ***,in the testing step,we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and ***:Our constructed PRS had improved performance(C statistics:76.1%)over PRSs constructed by individual benchmark methods(from 69.6%to 74.7%).Furthermore,our new PRS had much higher risk assessment power than family *** overall net reclassification improvement was 69.0%by adding PRS to the baseline model compared with 12.5%by adding family ***:We developed and validated a new PRS which may improve the utility in predicting the risk of developing *** innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes.

关键词： risk prediction polygenic risk scores predicted gene expression predicted DNA methylation integrative models prostate cancer

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Trends and determinants of health facility childbirth service utilization among mothers in urban slums of Nairobi, Kenya

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Global Epidemiology 2020年 2卷

作者： Atahigwa, Catherine Kadengye, Damazo T. Iddi, Samuel Abrams, Steven Van Rie, Annelies University of Antwerp Global Health Institute Campus Drie Eiken Universiteitsplein 1 Wilrijk 2610 Belgium African Population and Health Research Center APHRC Campus Manga Close Off Kirawa Road P.O. Box 10787-00100 Nairobi Kenya Center for Statistics Interuniversity Institute for Biostatistics and statistical Bioinformatics Data Science Institute UHasselt Agoralaan Gebouw D Diepenbeek Belgium Department of Statistics and Actuarial Science Accra Ghana

High maternal mortality remains a challenge for the attainment of the third Sustainable Development Goal in Sub-Saharan Africa. In Kenya, maternal mortality ratio remains high at 362 deaths per 100,000 live births. Utilization of health facility childbirth services ensures safe birth and is vital for the reduction of maternal mortality. However, this can be greatly affected by socioeconomic and geographical inequalities. In this study, we assess the trends and determinants of health facility childbirth service utilization among women giving birth in the urban slums of Nairobi, Kenya. data were obtained from the Nairobi Urban Health and Demographic Surveillance System (NUHDSS) comprising 19,469 births observed between 2003 and [19]. A logistic regression model, with parameter estimation using a generalized estimating equations (GEE) approach, was used to assess factors associated with health facility childbirth. About 81% of the births occurred at health facilities while 19% were occurring at home or outside a health facility. The results further indicated that, education, parity, and relationship to head of households were associated with health facility childbirth. Increasing awareness of the mothers about the benefits of health facility childbirth service utilization and the risks of home childbirth should be given extra attention by health practitioners during antenatal care visits. © 2020 The Authors

关键词： Healthcare childbirth Maternal mortality Nairobi NUHDSS Slums

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Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence

arXiv

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arXiv 2025年

作者： Sun, Yingying Jun, A. Liu, Zhiwei Sun, Rui Qian, Liujia Payne, Samuel H. Bittremieux, Wout Ralser, Markus Li, Chen Chen, Yi Dong, Zhen Perez-Riverol, Yasset Khan, Asif Sander, Chris Aebersold, Ruedi Vizcaíno, Juan Antonio Krieger, Jonathan R. Yao, Jianhua Wen, Han Zhang, Linfeng Zhu, Yunping Xuan, Yue Sun, Benjamin Boyang Qiao, Liang Hermjakob, Henning Tang, Haixu Gao, Huanhuan Deng, Yamin Zhong, Qing Chang, Cheng Bandeira, Nuno Li, Ming Weinan, E. Sun, Siqi Yang, Yuedong Omenn, Gilbert S. Zhang, Yue Xu, Ping Fu, Yan Liu, Xiaowen Overall, Christopher M. Wang, Yu Deutsch, Eric W. Chen, Luonan Cox, Jürgen Demichev, Vadim He, Fuchu Huang, Jiaxing Jin, Huilin Liu, Chao Li, Nan Luan, Zhongzhi Song, Jiangning Yu, Kaicheng Wan, Wanggen Wang, Tai Zhang, Kang Zhang, Le Bell, Peter A. Mann, Matthias Zhang, Bing Guo, Tiannan Affiliated Hangzhou First People’s Hospital State Key Laboratory of Medical Proteomics School of Medicine Westlake University Zhejiang Province Hangzhou China Westlake Center for Intelligent Proteomics Westlake Laboratory of Life Sciences and Biomedicine Zhejiang Province Hangzhou China Biology Department Brigham Young University ProvoUT84602 United States Department of Computer Science University of Antwerp Antwerp2020 Belgium Department of Biochemistry CharitéUniversitätsmedizin Berlin Berlin Germany Biomedicine Discovery Institute Department of Biochemistry and Molecular Biology Monash University MelbourneVICVIC 3800 Australia Wellcome Genome Campus Hinxton CambridgeCB10 1SD United Kingdom Harvard Medical School Ludwig Center at Harvard United States Harvard Medical School Broad Institute Ludwig Center at Harvard Dana-Farber Cancer Institute United States Department of Biology Institute of Molecular Systems Biology ETH Zürich Zürich Switzerland Bruker Ltd. MiltonONL9T 6P4 Canada AI for Life Sciences Lab Tencent Shenzhen518057 China State Key Laboratory of Medical Proteomics AI for Science Institute Beijing100080 China Beijing Institute of Lifeomics Beijing102206 China Thermo Fisher Scientific GmbH Hanna-Kunath Str. 11 Bremen28199 Germany Informatics and Predictive Sciences Research Bristol Myers Squibb United States Department of Chemistry Fudan University Songhu Road 2005 Shanghai200438 China Department of Computer Science Luddy School of Informatics Computing and Engineering Indiana University IN47408 United States ProCan® Children’s Medical Research Institute Faculty of Medicine and Health The University of Sydney WestmeadNSW Australia La Jolla CA United States Central China Institute of Artificial Intelligence University of Waterloo Canada AI for Science Institute Center for Machine Learning Research School of Mathematical Sciences Peking University China Research Institute of Intelligent Complex Systems Fudan U

Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, combined with groundbreaking AI techniques, are unlocking new challenges and opportunities in biological discovery. Here, we highlight key areas where AI is driving innovation, from data analysis to new biological insights. These include developing an AI-friendly ecosystem for proteomics data generation, sharing, and analysis;improving peptide and protein identification and quantification;characterizing protein-protein interactions and protein complexes;advancing spatial and perturbation proteomics;integrating multi-omics data;and ultimately enabling AI-empowered virtual cells. © 2025, CC BY.

关键词： Biotic

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Improvement of Jakarta's air quality during large scale social restriction

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IOP Conference Series: Earth and Environmental science 2021年第1期729卷

作者： R Rahutomo K Purwandari J W C Sigalingging B Pardamean Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Database Center Division of BMKG Meteorological Climatological and Geophysical Agency Jakarta Indonesia 10720 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

After Large-Scale Social Restriction (PSBB) established in Jakarta, a change of air quality was indicated by the citizens. Representatives of Indonesia's Agency for Meteorology, Climatology, and Geophysics (BMKG) supported as the fact that mobility of Jakarta's citizen and commuters were reduced. With utilizing Weather data Acquisition System managed by BMKG, this research compared fluctuations of Jakarta's air quality during two periods of time: Pre-PSBB and During-PSBB. This study proved that Jakarta's air quality has positively improved compared to the previous year, although in the lower range of unhealthy level.

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Cross-platform transcriptomic profiling of the response to recombinant human erythropoietin

Research Square

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Research Square 2021年

作者： Wang, Guan Kitaoka, Traci Crawford, Ali Mao, Qian Hesketh, Andrew Guppy, Fergus M. Ash, Garrett I. Liu, Jason Gerstein, Mark B. Pitsiladis, Yannis P. Sport and Exercise Science and Sports Medicine Research and Enterprise Group University of Brighton Brighton United Kingdom Centre for Regenerative Medicine and Devices University of Brighton Brighton United Kingdom Illumina San DiegoCA United States BGI Shenzhen China School of Pharmacy and Biomolecular Sciences University of Brighton Brighton United Kingdom Centre for Stress and Age-related Disease University of Brighton Brighton United Kingdom Veterans Affairs Connecticut Healthcare System West HavenCT United States Center for Medical Informatics Yale University New HavenCT United States Program in Computational Biology & Bioinformatics Yale University New HavenCT United States Department of Molecular Biophysics & Biochemistry Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Statistics and Data Science Yale University New HavenCT United States

RNA-seq has matured and become an important tool for studying RNA biology. Here we compared two RNA-seq (Illumina sequencing by synthesis and MGI DNBSEQTM) and two microarray platforms (Illumina Expression BeadChip and GeneChipTM Human Transcriptome Array 2.0) in healthy individuals administered recombinant human erythropoietin for transcriptome-wide quantification of differential gene expression. The results show that total RNA sequencing combined with DNB-seq produced a multitude of genes of biological relevance and significance in response to recombinant human erythropoietin, in contrast to other platforms. Through data triangulation linking genes to functions, genes representing the processes of erythropoiesis as well as non-erythropoietic functions of erythropoietin were unveiled. This study provides a knowledge base of genes characterising the responses to recombinant human erythropoietin through cross-platform comparison and validation. © 2021, CC BY.

关键词： RNA

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Motion-based camera localization system in colonoscopy videos

arXiv

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arXiv 2020年

作者： Yao, Heming Stidham, Ryan W. Gao, Zijun Gryak, Jonathan Najarian, Kayvan Department of Computational Medicine and Bioinformatics University of Michigan Ann ArborMI United States Department of Gastroenterology University of Michigan Ann ArborMI United States Michigan Institute for Data Science University of Michigan Ann ArborMI United States Michigan Center for Integrative Research in Critical Care University of Michigan Ann ArborMI United States Department of Emergency Medicine University of Michigan Ann ArborMI United States

Optical colonoscopy is an essential diagnostic and prognostic tool for many gastrointestinal diseases, including cancer screening and staging, intestinal bleeding, diarrhea, abdominal symptom evaluation, and inflammatory bowel disease assessment. However, the evaluation, classification, and quantification of findings from colonoscopy are subject to inter-observer variation. Automated assessment of colonoscopy is of interest considering the subjectivity present in qualitative human interpretations of colonoscopy findings. Localization of the camera is essential to interpreting the meaning and context of findings for diseases evaluated by colonoscopy. In this study, we propose a camera localization system to estimate the relative location of the camera and classify the colon into anatomical segments. The camera localization system begins with non-informative frame detection and removal. Then a self-training end-to-end convolutional neural network is built to estimate the camera motion, where several strategies are proposed to improve its robustness and generalization on endoscopic videos. Using the estimated camera motion a camera trajectory can be derived and a relative location index calculated. Based on the estimated location index, anatomical colon segment classification is performed by constructing a colon template. The proposed motion estimation algorithm was evaluated on an external dataset containing the ground truth for camera pose. The experimental results show that the performance of the proposed method is superior to other published methods. The relative location index estimation and anatomical region classification were further validated using colonoscopy videos collected from routine clinical practice. This validation yielded an average accuracy in classification of 0.754, which is substantially higher than the performances obtained using location indices built from other methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Endoscopy

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