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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Kians Azizatikarna Deby Erina Parung Dian Amirullah Alam Ahmad Hidayat Tjeng Wawan Cenggoro Arif Budiarto Simon Bens Pardamean Genetics Indonesia Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

As the cost of genetic testing becomes more affordable each year, direct-to-customer (DTC) genetic testing services witness rapid market growth. This has encouraged the development of an easy-to-use website application to optimize potential customers to obtain informed choice regarding the offered DTC genetic testing as well as purchasing information. We present a wireframing process as a part of the agile software development process to build a web portal prototype for an Indonesia-based genetic testing service called DNAku. The approach in building the prototype used in this case is a part of the Agile Software Development Method. In brief, the wireframing stage resulted in this work consists of three main important outcomes. The first one is the use case diagram as a blueprint of this web-based system. A site map is then proposed as an extended version of the use case diagram which visualizes the whole pages with the links. Finally, based on this site map a series of page mock-ups are designed using Hyper Text Marking Language (HTML) and Cascading Style Sheets (CSS). The result of the prototype can be used as the basis for the next development stage, which is the coding stage based on the collected feedback from users. Considering the continuous application development via the wireframing method by taking into account the cycle of feedbacks from involved parties, this approach can strengthen the infrastructure to sustain DTC genetic business model, which has a niche market in Indonesia.

关键词： Visualization Costs Buildings Prototypes Agile software development Genetics Encoding

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Bernstein-based estimation of the cross ratio function

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Series Statistics 2024年第1期58卷

作者： Steven Abrams Ömer Sercik Noël Veraverbeke a Data Science Institute Interuniversity Institute for Biostatistics and statistical Bioinformatics Hasselt University Diepenbeek Belgiumb Global Health Institute Department of Family Medicine and Population Health University of Antwerp Wilrijk Belgium a Data Science Institute Interuniversity Institute for Biostatistics and statistical Bioinformatics Hasselt University Diepenbeek Belgium a Data Science Institute Interuniversity Institute for Biostatistics and statistical Bioinformatics Hasselt University Diepenbeek Belgiumc School of Computer Science Statistics and Mathematics North-West University Potchefstroom South Africa

Local association measures provide useful insights in time-varying changes in association, especially between time-to-event variables. Such local dependence between two correlated random variables can be measured using the cross ratio function. The cross ratio function is defined as the ratio of conditional hazard functions which have been estimated using Bernstein polynomials before. Alternatively, the cross ratio function can be expressed in terms of (derivatives of) the joint survival function of the two random variables. In this paper, we discuss an alternative Bernstein-based plug-in estimator of the cross ratio function in which each of the ingredients is estimated separately. Next to asymptotic normality of the nonparametric estimator, a simulation study is used to assess its finite-sample performance. Finally, the novel estimator is applied to a real-life data application.

关键词： Bernstein polynomials copula functions local association time-to-event data survival functions

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Classification of Breast Thermal Images into Healthy/Cancer Group Using Pre-Trained Deep Learning Schemes

Classification of Breast Thermal Images into Healthy/Cancer ...

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2022 International Conference on Machine Learning and data Engineering, ICMLDE 2022

作者： Kadry, Seifedine Crespo, Rubén González Herrera-Viedma, Enrique Krishnamoorthy, Sujatha Rajinikanth, Venkatesan Faculty of Applied Computing and Technology Noroff University College Kristiansand94612 Norway Computer Science Department School of Engineering and Technology Universidad Internacional de la Rioja Andalusia26006 Spain Research Institute in Data Science and Computational Intelligence University of Granada Granada Spain Zhejiang Bioinformatics International Science and Technology Cooperation Center Wenzhou-Kean University Zhejiang Province China Wenzhou Municipal Key Lab of Applied Biomedical and Biopharmaceutical Informatics Wenzhou-Kean University Zhejiang Province China Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai602105 India

In the women's community, Breast Cancer (BC) is a severe disease. The World Health Organization reported in 2020 that 2.26 million deaths occur due to BC. BC is curable if detected early. Since thermal imaging is non-invasive and supports disease detection, it is commonly used in clinics. Compared to other methods, it keeps BC early and accurate. The proposed work aims to evaluate the performance of the Pretrained Deep-Learning Methods (PDLM) in detecting BC using the thermal images collected from the benchmark dataset. It includes the following stages: primary image processing, deep feature mining, handcrafted feature mining, feature optimization using Firefly-Algorithm (FA), classification and validation. Visual Lab thermal images were used in the study. The investigational outcome of this study authenticates that the VGG16, along with the DT, provides better detection accuracy (95.5%) compared to other classifiers used in this study. To justify the significance of the implemented technique, the proposed work not only improved accuracy, but also improved precision, sensitivity, specificity, and F1-Scores. © 2023 The Authors. Published by Elsevier B.V.

关键词： Classification (of information)

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Identification of Meteorological Parameters Affecting Dengue Hemorrhagic Fever Incidences

Identification of Meteorological Parameters Affecting Dengue...

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International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS)

作者： Faisal Asadi Joko Pebrianto Trinugroho Alam Ahmad Hidayat Reza Rahutomo Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665473286

Tropical disease is one of the infectious diseases that affect Indonesia. Many people die because of tropical diseases, such as dengue hemorrhagic fever (DHF), chikungunya, leprosy, lymphatic, and filariasis. The Indonesian government's lack of public policy is the main problem in treating and preventing tropical diseases. The paper aims to describe the analysis results of the incidence of DHF in Jakarta, Indonesia. The analysis used the DHF incidences data from the Jakarta Health service surveillance office representative and combined it with the meteorological parameters data. The meteorological parameters were collected from the Bureau of Meteorological Climatology and Geophysics (BMKG). The statistical analysis of this research used Pearson's correlation. The results show that the meteorological parameters affecting DHF incidences are temperature, humidity, and wind speed.

关键词： Correlation Statistical analysis Wind speed Surveillance Multimedia systems Sociology Humidity

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A Design of Childhood Stunting Assessment Feature with Agile UX Approach

A Design of Childhood Stunting Assessment Feature with Agile...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Reza Rahutomo Anzaludin Samsinga Perbangsa Faisal Asadi Rudi Nirwantono Bens Pardamean Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665476652

multidisciplinary collaboration between public health, system engineering, and UX is able to generate a solution in healthcare problem like stunting. The principle of Agile UX gathers requirements to generate an application design. Currently, various website and mobile applications are available to be utilized for stunting research. StuntingDB is one of many web-based applications that specialized in stunting data management. The application required several types of users to operate hierarchically. This research focused on Public Health Officers’ workflow that require a solution to in the form of assessment module for stunting DBMS by using Agile UX principle. The method collaborates agile team and UX team to solve Public Health Officer's difficulty in changing stunting status at pre-saved child patient's profile. Collaboration of both agile team and UX team successfully proposed an application design that system design.

关键词： Human computer interaction Pediatrics Collaboration Companies Mobile applications Public healthcare System analysis and design

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A Short Note on Relevant Cuts

arXiv

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arXiv 2024年

作者： Domschke, Nico Gatter, Thomas Golnik, Richard Stadler, Peter F. Bioinformatics Group Department of Computer Science Leipzig University Härtelstraße 16–18 LeipzigD-04107 Germany Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics Center for Scalable Data Analytics and Artificial Intelligence Dresden/Leipzig School of Embedded Composite Artificial Intelligence Leipzig University Härtelstraße 16–18 LeipzigD-04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 LeipzigD-04103 Germany Department of Theoretical Chemistry University of Vienna Währingerstraße 17 WienA-1090 Austria Facultad de Ciencias Universidad National de Colombia Bogotá Colombia Santa Fe Institute 1399 Hyde Park Rd. Santa FeNM87501 United States

The set of relevant cuts in a graph is the union of all minimum weight bases of the cut space. We show that a cut is relevant if and only if it is the a minimum weight cut between two distinct vertices. Moreover, we give a characterization in terms of Picard-Queyranne DAGs that can be used to accelerate the enumeration of the relevant cuts. © 2024, CC BY-NC-ND.

关键词： Trees (mathematics)

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Skew-Probabilistic Neural Networks for Learning from Imbalanced data

arXiv

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arXiv 2023年

作者： Naik, Shraddha M. Chakraborty, Tanujit Panja, Madhurima Hadid, Abdenour Chakraborty, Bibhas Khalifa University of Science and Technology Abu Dhabi United Arab Emirates Sorbonne University Abu Dhabi SAFIR Abu Dhabi United Arab Emirates Duke-NUS Medical School National University of Singapore Singapore Department of Statistics and Data Science National University of Singapore Singapore Department of Biostatistics and Bioinformatics Duke University United States

Real-world datasets often exhibit imbalanced data distribution, where certain class levels are severely underrepresented. In such cases, traditional pattern classifiers have shown a bias towards the majority class, impeding accurate predictions for the minority class. This paper introduces an imbalanced data-oriented classifier using probabilistic neural networks (PNN) with a skew-normal kernel function to address this major challenge. PNN is known for providing probabilistic outputs, enabling quantification of prediction confidence, interpretability, and the ability to handle limited data. By leveraging the skew-normal distribution, which offers increased flexibility, particularly for imbalanced and non-symmetric data, our proposed Skew-Probabilistic Neural Networks (SkewPNN) can better represent underlying class densities. Hyperparameter fine-tuning is imperative to optimize the performance of the proposed approach on imbalanced datasets. To this end, we employ a population-based heuristic algorithm, the Bat optimization algorithm, to explore the hyperparameter space effectively. We also prove the statistical consistency of the density estimates, suggesting that the true distribution will be approached smoothly as the sample size increases. Theoretical analysis of the computational complexity of the proposed SkewPNN and BA-SkewPNN is also provided. Numerical simulations have been conducted on different synthetic datasets, comparing various benchmark-imbalanced learners. Real-data analysis on several datasets shows that SkewPNN and BA-SkewPNN substantially outperform most state-of-the-art machine-learning methods for both balanced and imbalanced datasets (binary and multi-class categories) in most experimental settings. © 2023, CC BY.

关键词： Adversarial machine learning

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PDB-IHM: A System for Deposition, Curation, Validation, and Dissemination of Integrative Structures

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Journal of Molecular Biology 2025年第15期437卷 168963页

作者： Vallat, Brinda Webb, Benjamin M. Zalevsky, Arthur Tangmunarunkit, Hongsuda Sekharan, Monica R. Voinea, Serban Shafaeibejestan, Aref Sagendorf, Jared Hoch, Jeffrey C. Kurisu, Genji Morris, Kyle L. Velankar, Sameer Kesselman, Carl Burley, Stephen K. Berman, Helen M. Sali, Andrej Research Collaboratory for Structural Bioinformatics Protein Data Bank and the Institute for Quantitative Biomedicine Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Cancer Institute Rutgers The State University of New Jersey New Brunswick 08901 NJ United States Research Collaboratory for Structural Bioinformatics Protein Data Bank Department of Bioengineering and Therapeutic Sciences Quantitative Biosciences Institute (QBI) Department of Pharmaceutical Chemistry University of California San Francisco. San Francisco 94157 CA United States Information Sciences Institute Viterbi School of Engineering University of Southern California Los Angeles CA United States Biological Magnetic Resonance Data Bank Department of Molecular Biology and Biophysics University of Connecticut Farmington 06030-3305 CT United States Protein Data Bank Japan Institute for Protein Research Osaka University Osaka Suita 565-0871 Japan Electron Microscopy Data Bank European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Protein Data Bank in Europe European Molecular Biology Laboratory European Bioinformatics Institute Cambridge Hinxton CB10 1SD United Kingdom Research Collaboratory for Structural Bioinformatics Protein Data Bank San Diego Supercomputer Center University of California La Jolla 92093 CA United States Department of Chemistry and Chemical Biology Rutgers The State University of New Jersey Piscataway 08854 NJ United States Rutgers Artificial Intelligence and Data Science (RAD) Collaboratory Rutgers The State University of New Jersey Piscataway 08854 NJ United States Department of Quantitative and Computational Biology University of Southern California Los Angeles 90089 CA United States

Structures of many large biomolecular assemblies are now being determined using integrative approaches. In these approaches, information derived from multiple experimental and computational methods is combined to compute three-dimensional structures of multi-protein complexes and other macromolecular machines. A standalone prototype data resource for integrative structures called PDB-Dev was built, based on recommendations of the Integrative and Hybrid Methods (IHM) Task Force of the Worldwide Protein data Bank (wwPDB). This effort included developing data standards and software tools for collecting, curating, validating, visualizing, archiving, and disseminating integrative structures that span diverse spatiotemporal scales and conformational states. Mechanisms have been created to validate integrative structures based on the experimental data underpinning them. Building upon this foundational framework, PDB-Dev has been further expanded to handle large dynamic macromolecular systems and integrative structures that combine, for example, experimental restraints with atomic coordinates computed by machine learning algorithms. data standards and supporting tools have also been extended to capture information about biomolecular dynamics, such as conformational transitions and related kinetic data derived from biophysical methods. Recently, PDB-Dev was unified with the PDB archive and rebranded as PDB-IHM (***), further promoting FAIR (Findable, Accessible, Interoperable, and Reusable) principles of data stewardship for integrative structural biology. © 2025 The Author(s)

关键词： IHMCIF integrative modeling PDB PDBx/mmCIF structure validation

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APIR:Aggregating Universal Proteomics database Search Algorithms for Peptide Identification with FDR Control

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Genomics, Proteomics & bioinformatics 2024年第2期22卷 171-187页

作者： Yiling Elaine Chen Xinzhou Ge Kyla Woyshner MeiLu McDermott Antigoni Manousopoulou Scott B.Ficarro Jarrod A.Marto Kexin Li Leo David Wang Jingyi Jessica Li Department of Statistics and Data Science University of CaliforniaLos AngelesCA 90095USA Department of Immuno-Oncology Beckman Research InstituteCity of Hope National Medical CenterDuarteCA 91010USA Department of Quantitative and Computational Biology University of Southern CaliforniaLos AngelesCA 90089USA Department of Cancer Biology and Blais Proteomics Center Dana-Farber Cancer InstituteDepartment of PathologyBrigham and Women’s Hospital and Harvard Medical SchoolBostonMA 02215USA Department of Pediatrics City of Hope National Medical CenterDuarteCA 91010USA Bioinformatics Interdepartmental Program University of CaliforniaLos AngelesCA 90095USA Department of Human Genetics University of CaliforniaLos AngelesCA 90095USA Department of Computational Medicine University of CaliforniaLos AngelesCA 90095USA Department of Biostatistics University of CaliforniaLos AngelesCA 90095USA

Advances in mass spectrometry(MS)have enabled high-throughput analysis of proteomes in biological *** state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide–spectrum matches(PSMs),which convert mass spectra to peptide *** database search algorithms use distinct search strategies and thus may identify unique ***,no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate(FDR).To fill in this gap,we proposed a statistical framework,Aggregation of Peptide Identification Results(APIR),that is universally compatible with all database search ***,under an FDR threshold,APIR is guaranteed to identify at least as many,if not more,peptides as individual database search algorithms *** of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the *** data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search *** APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis,e.g.,differential gene expression analysis on RNA sequencing *** APIR R package is available at https://***/yiling0210/APIR.

关键词： Shotgun proteomics Peptide–spectrum match Peptide identification Aggregation of lists FDR control

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The Technology Behind Genomic database

The Technology Behind Genomic Database

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International Conference on Cybernetics and Intelligent System (ICORIS)

作者： Joko Pebrianto Trinugroho Anzaludin Samsinga Perbangsa Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9781665453967

In this digital era, we are exposed to a large amount of data. This includes biological data, which stores information about living organisms, including Deoxyribonucleic acid (DNA), genes, and proteins. With the development of information technology and information system, most of available biological data are stored in an online public database. Many of the databases are free-access and easily used, which helps the users, especially researchers, to make use of the data. Among the known public biological databases are the University of California Santa Cruz (UCSC) Genome Browser database and the Rat Genome database (RGD). These two databases provide access to the biological data from different organisms. This paper aims to describe the technology of public biological databases. Also elucidated in this paper are the differences features between UCSC Genome Browser database and the RGD. Our results showed that the UCSC contains much more biological data and features than the RGD. However, the genome browser of UCSC has a complex display, while the RGD has a simple display. Overall, both databases give the users the option to choose the most suitable database for them.

关键词： Proteins databases Genomics DNA Rats Biology Organisms

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