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Unique,Persistent,Resolvable:Identifiers as the Foundation of FAIR

data Intelligence 2020年第1期2卷 30-39,302页

作者： Nick Juty Sarala M.Wimalaratne Stian Soiland-Reyes John Kunze Carole A.Goble Tim Clark Department of Computer Science The University of ManchesterOxford RoadManchester M139PLUK European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeCB101SDUK California Digital Library OaklandCalifornia 94612-2901USA Data Science Institute University of VirginiaCharlottesvilleVA 22903-1738USA

The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ***,globally unique identifiers,resolvable on the Web,and associated with a set of additional descriptive metadata,are foundational to FAIR *** we describe some basic principles and exemplars for their design,use and orchestration with other system elements to achieve FAIRness for digital research objects.

关键词： Identifiers metadata findability FAIR data data infrastructures

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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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Comparative Analysis of CNN Models for Multiclass Detection on Colon Disease 9

Comparative Analysis of CNN Models for Multiclass Detection ...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Sulistyawan, Jason Sebastian Nugroho, Kuncahyo Setyo Isnan, Mahmud Pardamean, Bens Binus Graduate Program Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Gastrointestinal diseases are significant health concerns that mostly affect the digestive and biliary tracts. These can only be observed internally through endoscopy, or a modern approach named Wireless Capsule Endoscopy. Though the approach has proven to work, the process relies heavily on medical experts, which is prone to error when given a considerably large load. The use of deep learning in detecting diseases with images has been proven to work in various cases and can be implemented for gastrointestinal diseases. This study compared four deep learning models based on Convolutional Neural Network architectures consisting of the VGG16, ResNet152V2, InceptionV3, and Xception for classifying colon disease images. The experiment showed that the ResNet152V2 performed the best, compared to the other three with a testing accuracy score of 0.9837. On the other hand, the VGG16 had the lowest performance with around accuracy score of 0.89 while the Xception and InceptionV3models yielded similar accuracy scores of 0.9587 and 0.9549, respectively. The results highlight the effectiveness of ResNet152V2 in handling the complexity of colon disease detection © 2024 IEEE.

关键词： Convolutional neural networks

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Automatic Multiple Choice Question Generation: A Systematic Literature Review 9

Automatic Multiple Choice Question Generation: A Systematic ...

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9th International Conference on Information Technology and Digital Applications, ICITDA 2024

作者： Henry, Matthew Martianus Heryanto, Nur Adhianti Isnan, Mahmud Nugroho, Kuncahyo Setyo Pardamean, Bens Bina Nusantara University Bioinformatics and Data Science Research Center Jakarta11480 Indonesia School of Computer Science Bina Nusantara University Computer Science Department Jakarta11480 Indonesia Bina Nusantara University Binus Graduate Program Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9798331510732

Despite their drawbacks, multiple-choice questions (MCQ) have been widely used to assess the students' understanding of lectures through examinations. The development of automatic MCQ generation is beneficial, especially for educators. As a starting point for further development, a Systematic Literature Review (SLR) is conducted to uncover current trends, future challenges, and opportunities in automatic MCQ generation. Previously, an SLR was conducted, but it lacks coverage of the utilization of transformer-based models. This SLR covers the development of automatic MCQ generation using either traditional or advanced approaches such as Transformers. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses framework was used to gather the data from Scopus, IEEE Xplore, SpringerLink, arXiv, and Semantic Scholar. The included articles must be open-access computer science conference papers or journal articles and written in English less than five years ago. Four independent reviewers analyzed the research workflow, evaluation metric, and dataset used in each study. There are 18 included studies, where 17% (n = 3) studies are from 2024, 33% (n = 6) studies are from 2023, 22% (n = 4) studies are from 2022, 11% (n = 2) studies are from 2021, and 17% (n = 3) studies are from 2020. There are 33% (n = 6) of the studies used the traditional feature-based engineering approach, 39% (n = 7) of the studies used the Transformer-based model fine-tuning approach, and the remaining used novel approaches. The study found that BERT variants are the most utilized Transformer-based model in automatic MCQ. The research notes some challenges, but also open various opportunities for further research, including Large Language Model (LLM) utilization for automatic MCQ generation, the utilization BERT-based models for standardized machine-learned evaluation metrics, and the initiative for the creation of an MCQ dataset benchmark. © 2024 IEEE.

关键词： Semantics

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Automatic question generation for bahasa indonesia examination using copynet 9th

Automatic question generation for bahasa indonesia examinati...

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9th International Conference on Computer science and Computational Intelligence, ICCSCI 2024

作者： Henry, Matthew Martianus Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program Bina Nusantara University Jakarta11480 Indonesia

In educational institutions, an educator is responsible for assessing the student's knowledge grasp through examination. Creating exam questions, even the low-level factoid questions, is time-consuming, especially for inexperienced educators. Therefore, this study aims to create a sequence-to-sequence model using CopyNet by exploiting its copying mechanism advantage to automatically generate Bahasa Indonesia factoid questions to ease the educator's burden. Indonesian records in the TyDi QA dataset are used as the model input. GRU and Bi-GRU are employed as the CopyNet encoder, while LSTM is used as the CopyNet decoder. The model that utilizes GRU as the encoder achieves BLEU1, BLEU2, BLEU3, BLEU4, and ROUGE-L scores of 0.28, 0.19, 0.14, 0.1, and 0.32, respectively. Bi-GRU utilization as the model encoder achieves BLEU1, BLEU2, BLEU3, BLEU4, and ROUGE-L scores of 0.26, 0.17, 0.12, 0.09, and 0.30, respectively. Models using either encoder still achieve low scores. However, compared with the previous work, the result is still on par regarding the BLEU score. Further examination found that the generated questions do not adhere to semantic and syntactical correctness. Adding more records to the dataset and utilizing a more advanced architecture like CopyBERT are encouraged to improve the model performance in future work. Despite the result, this study has shown that CopyNet, primarily designed for text summarization or single-turn dialogue, can be tailored for factoid question generation. © 2024 The Authors.

关键词： Semantics

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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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A review: data pre-processing techniques used for diabetes prediction 9th

A review: Data pre-processing techniques used for diabetes p...

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9th International Conference on Computer science and Computational Intelligence, ICCSCI 2024

作者： Isnan, Mahmud Elwirehardja, Gregorius Natanael Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Cmputer Science Department BINUS Graduate Program Computer Science Program Bina Nusantara University Jakarta11480 Indonesia

When processing datasets in diabetes classification, common problems included a large number of missing values, outliers, and dataset imbalance. To deal with those issues, this study analyzed 18 studies on diabetes classification with machine learning algorithms over the past 5 years. This revealed the important role of data pre-processing in creating effective classification models, as it was found that by using different data pre-processing techniques, the same model can provide different performance. The study identified K-Nearest Neighbor (KNN) and support vector machine (SVM) as superior methods for filling in missing values, achieving an accuracy of 98.49% and 94.89%, respectively. These approaches outperformed traditional methods such as median or mean replacement. However, the challenge of imbalanced data sets remains in all studies reviewed. The common evaluation metrics used to evaluate the created models in previous studies included accuracy, precision, specificity, sensitivity/recall, and F1 Score. Overall, this review showed that the role of data pre-processing is no less important than algorithm selection to improve the performance of machine learning models in diabetes classification. © 2024 The Authors.

关键词： Adversarial machine learning

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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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Systematic Literature Review on Statistics and Machine Learning Predictive Models for Rice Phenotypes 8

Systematic Literature Review on Statistics and Machine Learn...

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

作者： Dominic, Nicholas Cenggoro, Tjeng Wawan Pardamean, Bens Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta11480 Indonesia Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta11480 Indonesia

Predicting the best-quality of rice phenotypes is the priority among agricultural researchers to fulfill worldwide food security. Trend development of predictive models from statistics to machine learning is the subject of this review. Gathered from the Google Scholar database, 14 appropriate papers (2016-2020) related to the rice phenotypes prediction were selected through title and abstract content filtering. The outputs show that Support Vector Machine, Multi-layer Perceptron, and regression are the most used models, while yield is the priority prediction point besides tiller, panicle, and 1000-grain weight of rice. However, finding the accurate predictor is invariably challenging due to distinct rice varieties in the world and high confounding factors. Thus, developing an advanced deep learning model that accommodates these needs is worth considering further. © 2023 The Authors. Published by Elsevier B.V.

关键词： machine learning rice phenotypes prediction statistics

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A theoretic study of a distance-based regression model

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science China Mathematics 2019年第5期62卷 979-998页

作者： Jialu Li Wei Zhang Sanguo Zhang Qizhai Li School of Mathematics and Statistics Beijing Institute of Technology Biostatistics and Bioinformatics Branch National Institute of Child Health and Human Development School of Mathematical Sciences University of Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences LSC NCMIS Academy of Mathematics and Systems Science Chinese Academy of Sciences

The distance-based regression model has many applications in analysis of multivariate response regression in various ?elds, such as ecology, genomics, genetics, human microbiomics, and neuroimaging. It yields a pseudo F test statistic that assesses the relation between the distance(dissimilarity) of the subjects and the predictors of interest. Despite its popularity in recent decades, the statistical properties of the pseudo F test statistic have not been revealed to our knowledge. This study derives the asymptotic properties of the pseudo F test statistic using spectral decomposition under the matrix normal assumption, when the utilized dissimilarity measure is the Euclidean or Mahalanobis distance. The pseudo F test statistic with the Euclidean distance has the same distribution as the quotient of two Chi-squared-type mixtures. The denominator and numerator of the quotient are approximated using a random variable of the form ξχ_d^2+ η, and the approximate error bound is given. The pseudo F test statistic with the Mahalanobis distance follows an F *** simulation studies, the approximated distribution well matched the "exact" distribution obtained by the permutation procedure. The obtained distribution was further validated on H1N1 in?uenza data, aging human brain data, and embryonic imprint data.

关键词： distance-based regression Euclidean pseudo F test statistic Mahalanobis

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