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8th International Conference on Systems Biology, ISB 2014

作者： Hayashida, Morihiro Koyano, Hitoshi Akutsu, Tatsuya Bioinformatics Center Institute for Chemical Research Kyoto University Kyoto611-0011 Japan Department of Data Science Graduate School of Medicine Kyoto University Kyoto606-8507 Japan

ISBN: (纸本)9781479972944

Understanding of protein structures is important to find their functions. Many methods such as structural alignment, alignment-free similarity, and use of structural fragments have been developed for finding similar protein structures. In our previous study, we transformed protein structures into images each pixel of which represents the distance between the corresponding Cαatoms, and proposed similarity measures between two protein structures based on Kolmogorov complexity using image compression algorithms. In this paper, we examine efficient and effective image recognition techniques, SIFT (Scale Invariant Feature Transform) and SURF (Speeded Up Robust Features), which are invariant to image scaling, translation, and rotation, and partially invariant to affine or three-dimensional projection. We propose similarity based on SIFT and SURF, and apply it to classification of several protein structures. The results suggest that the similarity based on SURF outperforms several existing similarity measures including the compression-based similarity measures in our previous study, and that SIFT and SURF are useful for recognizing protein structures as well as objects in images. © 2014 IEEE.

关键词： Image recognition

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Parallel genome-wide analysis with central and graphic processing units

Parallel genome-wide analysis with central and graphic proce...

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International Conference on Computer and Communications (ICCC)

作者： Muhamad Fitra Kacamarga James W. Baurley Bens Pardamean Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia

The Indonesia Colorectal Cancer Consortium (IC3), the first cancer biobank repository in Indonesia, is faced with computational challenges in analyzing large quantities of genetic and phenotypic data. To overcome this challenge, we explore and compare performance of two parallel computing platforms that use central and graphic processing units. We present the design and implementation of a genome-wide association analysis using the MapReduce and Compute Unified Device Architecture (CUDA) frameworks and evaluate performance (speedup) using simulated case/control status on 1000 Genomes, Phase 3, chromosome 22 data (1,103,547 Single Nucleotide Polymorphisms). We demonstrated speedup on a server with Intel Xeon E5-2620 (6 cores) and NVIDIA Tesla K20 over sequential processing.

关键词： Graphics processing units Genomics Instruction sets Cancer Kernel bioinformatics Computer architecture

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Sentiment Analysis: Indonesia Netflix User's Comment Using Multiple Lexicon-Based Dictionaries 4

Sentiment Analysis: Indonesia Netflix User's Comment Using M...

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4th International Conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Mola, Sebastianus Adi Santoso Widiastuti, Tiwuk Roma, Rosa V. K. Isabela Ohe Karnyoto, Andrea Stevens Pardamean, Bens University of Nusa Cendana Faculty of Science and Engineering Kupang Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331506490

A subscription movie streaming service from the United States, Netflix, has been present in Indonesia since 2016 and provides a wide variety of films without showing any single advertisements that can be viewed from anywhere, as long as there is access to the internet. Despite these advantages, many users have complained through the Google Play Store's comments column. Some of the common complaints include frequent buffering and connectivity issues, dissatisfaction with the limited selection of Indonesian movies, lack of subtitles for specific languages, or pricing concerns have also been raised. In this study developed two combined scenario methods using the InSet and SentiStrength-id dictionary to obtain better performance and compare them against independent InSet and SentiStrength-id. This study collected users' comments for the Netflix app in the Google Play Store as a dataset using web scraping techniques through the Google Collaboration tool. The dataset contains 3250 rows spanning the period from January 22, 2024, through June 6, 2024. To ensure proper processing of the text, data cleaning, lowercasing, normalization, tokenization, stemming, and stopwords removal are conducted. The results show that most user opinions are negative. The InSet dictionary has an accuracy of 92%, SentiStrength-id 78%, Combined scenario 1 is 87%, and scenario 2 reaches the highest among others which is 92.52%. © 2024 IEEE.

关键词： Video streaming

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Embedding Model Design for Producing Book Recommendation 4

Embedding Model Design for Producing Book Recommendation

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4th International Conference on Information Management and Technology, ICIMTech 2019

作者： Rahutomo, Reza Perbangsa, Anzaludin Samsinga Soeparno, Haryono Pardamean, Bens Bioinformatics Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Information System Department Bina Nusantara University Jakarta11480 Indonesia Computer Science Department Bina Nusantara University Jakarta11480 Indonesia

ISBN: (纸本)9781728133331

Internet services often recommend contents to users in order to maintain the interaction. Recommendation system plays a major role to formulate and produce a series of recommendation based on users' behavior. Surprisingly, user-generated scoring or known as ratings are the main raw materials to learn the pattern of favorable contents of each user. As a part of collaborative filtering strategy, rating is considerable to be included in formulating recommended contents. In this research, the basic formulation to recommend books is discovered. The recommendation system has been tested on one random-picked user behavior and successfully has generated five recommended books by analyzing prior activities. The embedding model produced the recommended books with 59% of accuracy. This research is done to provide an insightful experience for developing content recommendation system in Binus University's corporate learning system. © 2019 IEEE.

关键词： Recommender systems

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Lightweight Pedestrian Detection through Guided Filtering and Deep Learning 10

Lightweight Pedestrian Detection through Guided Filtering an...

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10th IEEE Global Conference on Consumer Electronics, GCCE 2021

作者： Muchtar, Kahlil Saddami, Khairun Bintang, Akhyar Cenggoro, Tjeng Wawan Pardamean, Bens Lin, Chih-Yang Ernita, Tia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Universitas Syiah Kuala Department of Electrical And Computer Engineering Aceh Indonesia Bina Nusantara University School of Computer Science Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Bina Nusantara University BINUS Graduate Program-Master of Computer Science Program Bina Nusantara University Bioinformatics And Data Science Research Center Computer Science Department Jakarta Indonesia Yuan-Ze University Department of Electrical And Engineering Taoyuan Taiwan

ISBN: (纸本)9781665436762

In this paper, we propose a novel approach to locate and detect moving pedestrians in a video. Our proposed method first locates the region of interest (ROI) using a background subtraction algorithm based on guided filtering. This novel background subtraction algorithm allows our method to also filter unexpected noises at the same time, which could benefit the performance of our proposed method. Subsequently, the pedestrians are detected using YOLOv3 within the provided ROI. Our experiments showed that the proposed method has a competitive performance in the CDNET2014 dataset with a fast-processing time. © 2021 IEEE.

关键词： Convolutional neural networks

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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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Cancer Prediction using Clinical and Genomic data Fusion: A Systematic Review 6

Cancer Prediction using Clinical and Genomic Data Fusion: A ...

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6th International Conference on Cybernetics and Intelligent System, ICORIS 2024

作者： Julian, Kelvin Nugroho, Kuncahyo Setyo Nirwantono, Rudi Pardamean, Bens Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Biotechnology Department Faculty of Engineering Bina Nusantara University Jakarta Indonesia

ISBN: (纸本)9798331539603

This systematic review provides a comprehensive overview of the methods used to integrate genomic and clinical data in cancer prediction. The review includes 19 studies across various cancers, including breast, colorectal, melanoma, lung, pancreatic, and thyroid. The studies employed different methods to combine genomic and clinical data, including weighted polygenic risk scores, genetic and non-genetic risk scores, and different machine learning algorithms. The results show significant improvements in model prediction performance accuracy across multiple studies. The review highlights the potential benefits of integrating genetic and phenotypic information to improve disease risk prediction models and inform personalized healthcare strategies. © 2024 IEEE.

关键词： Lung cancer

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STATISTICAL DESIGN AND RATIONALE OF THE BIOMARKERS FOR EVALUATING SPINE TREATMENTS (BEST) TRIAL

arXiv

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arXiv 2025年

作者： Sperger, John Kidwell, Kelley M. Mauck, Matthew C. Zhao, Beibo Anstrom, Kevin J. Batorsky, Anna Carey, Timothy S. Clauw, Daniel J. Freeman, Nikki L.B. Greco, Carol M. Ivanova, Anastasia Berkeley, Sara Jones McLean, Samuel A. Psioda, Matthew A. Rowland, Bryce Sowa, Gwendolyn A. Wasan, Ajay D. Zitovsky, Joshua P. Kosorok, Michael R. Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Biostatistics School of Public Health University of Michigan United States Department of Anesthesiology University of North Carolina Chapel Hill United States Collaborative Studies Coordinating Center Department of Biostatistics Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Medicine University of North Carolina Chapel Hill United States Department of Anesthesiology Chronic Pain & Fatigue Research Center University of Michigan Ann Arbor United States Department of Biostatistics and Bioinformatics Duke University United States Department of Psychiatry University of Pittsburgh School of Medicine United States Department of Epidemiology Gillings School of Global Public Health University of North Carolina Chapel Hill United States Department of Psychiatry University of North Carolina Chapel Hill United States Statistics and Data Science Innovation Hub Biostatistics GSK Department of Physical Medicine and Rehabilitation University of Pittsburgh School of Medicine United States Departments of Anesthesiology and Perioperative Medicine and Psychiatry University of Pittsburgh School of Medicine United States Amazon Postdoctoral Scientist United States Ferguson Laboratory for Orthopaedic and Spine Research Bethel Musculoskeletal Research Center Department of Orthopaedic Surgery University of Pittsburgh School of Medicine United States Department of Bioengineering University of Pittsburgh Swanson School of Engineering United States Clinical and Translational Science Institute University of Pittsburgh United States Carolina Pragmatic Trials Laboratory North Carolina Translational and Clinical Sciences Institute UNC School of Medicine United States Duke Clinical Research Institute United States

Background/Aims: Chronic low back pain (cLBP) is a widely prevalent condition with potentially severe impacts on day-to-day functioning and quality of life. A variety of evidence-based treatment modalities exist, but all approaches have modest average treatment effects—potentially due to individual variation in treatment response and the diverse etiologies of cLBP. The Biomarkers for Evaluating Spine Treatments (BEST) trial was designed to investigate the potential for using patient features including biomarkers and phenotypic measurements to inform cLBP treatment. Methods: We provide an overview of the BEST trial and detail the statistical rationale for key study design decisions. We discuss the salient features of the scientific and clinical context of cLBP treatment that motivated the study design and planned analysis, describe modifications to existing methods that were needed because of the setting, and present simulation studies that were used to inform the study design. Results: The BEST trial was designed as a multi-site sequential, multiple-assignment randomized trial (SMART) of four cLBP treatment modalities. The primary objective was to inform a precision medicine approach to the treatment of cLBP by estimating the optimal treatment or combination of treatments based on patient features and individual patient’s response to the initial treatment. The primary analysis includes estimating an interpretable dynamic treatment regime for recommending treatments based on patient features and investigating the subgroups that respond best to each one of the treatment modalities. The trial aimed to enroll 630 protocol completers. Broad eligibility criteria were used to improve the generalizability of study results. Notably, participants could be excluded from one of the four interventions and still be eligible for the study. We modified the minimization method for reducing covariate imbalance to allow for these exclusions. Conclusion: The BEST trial may pave the way

关键词： Patient treatment

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Dysregulation of microRNA in cholangiocarcinoma identified through a meta-analysis of microRNA profiling

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World Journal of Gastroenterology 2020年第29期26卷 4356-4371页

作者： Somsak Likhitrattanapisal Supeecha Kumkate Pravech Ajawatanawong Kanokpan Wongprasert Rutaiwan Tohtong Tavan Janvilisri National Center for Genetic Engineering and Biotechnology Pathumthani 12120Thailand Department of Biology Faculty of ScienceMahidol UniversityBangkok 10400Thailand Division of Bioinformatics and Data Management for Research Faculty of Medicine Siriraj HospitalMahidol UniversityBangkok 10700Thailand Department of Anatomy Faculty of ScienceMahidol UniversityBangkok 10400Thailand Department of Biochemistry Faculty of ScienceMahidol UniversityBangkok 10400Thailand

BACKGROUND In the past decades,the potential of microRNA(miRNA)in cancer diagnostics and prognostics has gained a lot of *** this study,a meta-analysis was conducted upon the pooled miRNA microarray data of cholangiocarcinoma(CCA).AIM To identify differentially expressed(DE)miRNAs and perform functional analyses in order to gain insights to understanding miRNA-target interactions involved in tumorigenesis pathways of *** Raw data from 8 CCA miRNA microarray datasets,consisting of 443 samples in total,were integrated and statistically analyzed to identify DE miRNAs via comparison of levels of miRNA expression between CCA and normal bile duct samples using t-tests(P<0.001).The 10-fold cross validation was performed in order to increase the robustness of the t-test *** data showed 70 up-regulated and 48 down-regulated miRNAs in CCA. GeneOntology and pathway enrichment analyses revealed that mRNA targets of DEmiRNAs were significantly involved in several biological processes. The mostprominent dysregulated pathways included phosphatidylinositol-3 kinases/Akt,mitogen-activated protein kinase and Ras signaling *** miRNAs found in our meta-analysis revealed dysregulation in major cancerpathways involved in the development of CCA. These results indicated thenecessity of understanding the miRNA-target interactions and the significance ofdysregulated miRNAs in terms of diagnostics and prognostics of cancers.

关键词： Cholangiocarcinoma Microarray MicroRNA Meta-analysis

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Utilizing Semi-supervised Method in Predicting BRCA1 Pathogenicity Variants 8

Utilizing Semi-supervised Method in Predicting BRCA1 Pathoge...

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8th International Conference on Computer science and Computational Intelligence, ICCSCI 2023

作者： Hidayat, Alam Ahmad Trinugroho, Joko Pebrianto Nirwantono, Rudi Sudigyo, Digdo Pardamean, Bens Department of Mathematics School of Computer Science Bina Nusantara University 11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

Quantifying the effect of mutations in the BRCA1 gene is useful for understanding their clinical consequences on breast cancer. Machine learning models can be applied to predict the landscape of protein variant effects that might not be always accessible by experiments. In this work, we propose a simple semi-supervised learning method using a Gaussian mixture model to predict ~90% of the unlabeled missense variants of the BRCA1 gene collected from the ClinVar database. High-quality embeddings are used as a feature of the protein sequences, extracted using the latest pre-trained transformer-based language protein model. A statistical test show that the protein embeddings are effective and robust for predicting pathogenicity. Further, the lower representations of the features are then fed into the semi-supervised model. The prediction performance of the model only for the labeled testing data achieves an AUC score and an accuracy of 79.27% and 71.58%, respectively. Using our defined pathogenic probability score, we find that ~94% of variants in our unlabeled dataset are well-separated into either benign or pathogenic classes according to that scoring. Our scores obtain a moderate Spearman rank correlation with the results of established unsupervised variant effect models. Finally, our approach can potentially be developed for more accurate and biologically reliable predictions of the variant effects. © 2023 The Authors. Published by Elsevier B.V.

关键词： embeddings language model mutation effect protein sequence semi-supervised learning

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