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Convolutional Neural Networks for Scops Owl Sound Classification

Procedia Computer science 2021年 179卷 81-87页

作者： Alam Ahmad Hidayat Tjeng Wawan Cenggoro Bens Pardamean 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 Bina Nusantara University Jakarta Indonesia 11480

Adopting a deep learning model into bird sound classification tasks becomes a common practice in order to construct a robust automated bird sound detection system. In this paper, we employ a four-layer Convolutional Neural Network (CNN) formulated to classify different species of Indonesia scops owls based on their vocal sounds. Two widely used representations of an acoustic signal: log-scaled mel-spectrogram and Mel Frequency Cepstral Coefficient (MFCC) are extracted from each sound file and fed into the network separately to compare the model performance with different inputs. A more complex CNN that can simultaneously process the two extracted acoustic representations is proposed to provide a direct comparison with the baseline model. The dual-input network is the well-performing model in our experiment that achieves 97.55% Mean Average Precision (MAP). Meanwhile, the baseline model achieves a MAP score of 94.36% for the mel-spectrogram input and 96.08% for the MFCC input.

关键词： acoustic features bird sound classification convolutional neural network mean average precision scops owl

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Systematic Evaluation of Cross Population Polygenic Risk Score on Colorectal Cancer

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Procedia Computer science 2021年 179卷 344-351页

作者： Bharuno Mahesworo Arif Budiarto Bens Pardamean 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 Bina Nusantara University Jakarta Indonesia 11480

The number of findings in cancer genomics research has grown rapidly in the last decade due to the decline in the cost of human sequencing and genotyping. However, the majority of the reported significant marker associated with cancer traits are based on European and East Asian population. Large population such as South Asian and South-East Asian population are under-represented in genomics research. In this study, we explored the possibility of computing a Polygenic Risk Score (PRS) of colorectal cancer on our test sample based on reported significant Single Nucleotide Polymorphism (SNP). The SNPs used to compute the risk score were collected from GWAS Central and GWAS Catalog. Significant SNPs from IC3 study were used as a benchmark. The result shows that calculating colorectal cancer risk score using reported significant marker from different population group is possible. The p-value of our statistic model shows significant differences between case and control group risk score.

关键词： Polygenic Risk Score Cross Population Colorectal Cancer

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Leveraging Anatomical Constraints with Uncertainty for Pneumothorax Segmentation

arXiv

引用

arXiv 2023年

作者： Yuan, Han Hong, Chuan Tran, Nguyen Tuan Anh Xu, Xinxing Liu, Nan Centre for Quantitative Medicine Duke-NUS Medical School Singapore Department of Biostatistics and Bioinformatics Duke University United States Department of Diagnostic Radiology Singapore General Hospital Singapore Institute of High Performance Computing Agency for Science Technology and Research Singapore Programme in Health Services and Systems Research Duke-NUS Medical School Singapore Institute of Data Science National University of Singapore Singapore

Background Pneumothorax is a medical emergency caused by abnormal accumulation of air in the pleural space – the potential space between the lungs and chest wall. On 2D chest radiographs, pneumothorax occurs within the thoracic cavity and outside of the mediastinum and we refer to this area as "lung+ space". While deep learning (DL) has increasingly been utilized to segment pneumothorax lesions in chest radiographs, many existing DL models employ an end-to-end approach. These models directly map chest radiographs to clinician-annotated lesion areas, often neglecting the vital domain knowledge that pneumothorax is inherently location-sensitive. Methods We propose a novel approach that incorporates the lung+ space as a constraint during DL model training for pneumothorax segmentation on 2D chest radiographs. To circumvent the need for additional annotations and to prevent potential label leakage on the target task, our method utilizes external datasets and an auxiliary task of lung segmentation. This approach generates a specific constraint of lung+ space for each chest radiograph. Furthermore, we have incorporated a discriminator to eliminate unreliable constraints caused by the domain shift between the auxiliary and target datasets. Results Our results demonstrated significant improvements, with average performance gains of 4.6%, 3.6%, and 3.3% regarding Intersection over Union (IoU), Dice Similarity Coefficient (DSC), and Hausdorff Distance (HD). These results were consistent across six baseline models built on three architectures (U-Net, LinkNet, or PSPNet) and two backbones (VGG-11 or MobileOne-S0). We further conducted an ablation study to evaluate the contribution of each component in the proposed method and undertook several robustness studies on hyperparameter selection to validate the stability of our method. Conclusion The integration of domain knowledge in DL models for medical applications has often been underemphasized. Our research underscores the signi

关键词： Domain Knowledge

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Human movement decisions during Coronavirus Disease 2019

arXiv

引用

arXiv 2023年

作者： Omori, Ryosuke Ito, Koichi Kanemitsu, Shunsuke Kimura, Ryusuke Iwasa, Yoh Division of Bioinformatics International Institute for Zoonosis Control Hokkaido University Hokkaido Sapporo001-0020 Japan Faculty of Environmental Earth Science Hokkaido University Hokkaido Sapporo060-0810 Japan Data Management & Planning Office Product Development Management Office Recruit Co. Ltd Chiyoda-ku Tokyo100-6640 Japan SaaS Data Solution Unit Data Management & Planning Office Product Development Management Office Recruit Co. Ltd Chiyoda-ku Tokyo100-6640 Japan Department of Biology Faculty of Science Kyushu University 744 Motooka Nishi-ku Fukuoka819-0395 Japan

To predict epidemics’ future course in changing situations, understanding human mobility patterns is important, notwithstanding decision-making process uncertainties owing to difficulties in quantifying people’s mobility change decision timings, which make the mobility-epidemic causal relationship unclear. We used the ‘mobility avoidance index’ to investigate time-series changes during Japan’s Coronavirus Disease 2019 (eight waves until February 2023) as a previous study, which measured this index using accommodation reservation data—booking/cancellation timings—was able to quantify the timing of decision-making for mobility changes. Our analyses revealed two general patterns: 1) the index increased/decreased proportional to logarithms of reported cases during the first wave, conforming with Weber–Fechner’s psychophysics law;2) its slope against the change in the number of reported cases had similar values among the waves, but its intercepts changed as the waves passed, suggesting that people neglected reported cases lower than a certain threshold for behavioural decision-making. We shifted the threshold level as the waves passed, and named this pattern ‘shift of negligible epidemic’ rule. It is the first pattern quantitatively observed, that possesses decision making tendencies for future mobility avoidance. Our findings contribute to constructing a mathematical model, which simultaneously considers epidemics and human mobility dynamics. © 2023, CC BY-NC-ND.

关键词： Coronavirus

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Predicting Microsatellite Instability from Histology Images with Dilated Neighborhood Attention Transformer in Colorectal Cancer

Predicting Microsatellite Instability from Histology Images ...

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IEEE Symposium on bioinformatics and Bioengineering (BIBE)

作者： Yun-Chi Chen Tung Chao Wimaya Nitya Phandita Tzu-Yu Sun Huai-Zhi Wang Yi-Hsien Hsieh Le-Yin Hsu Che Lin Graduate Institute of Biomedical Electronics and Bioinformatics National Taiwan University (NTU) Taipei Taiwan College of Bioresources and Agriculture NTU Taipei Taiwan Department of Computer Science and Information Engineering National Taiwan University of Science and Technology Taipei Taiwan Graduate Institute of Communication Engineering NTU Taipei Taiwan Data Science Degree Program NTU Taipei Taiwan Department of Electrical Engineering NTU Taipei Taiwan Center for Advanced Computing and Imaging in Biomedicine NTU Taipei Taiwan Smart Medicine and Health Informatics Program NTU Taipei Taiwan

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Microsatellite instability (MSI) is a pivotal genetic marker influencing the efficacy of immunotherapy in colorectal cancer. Traditional MSI examination often requires additional genetic or immunohistochemical tests, whereas histology images, widely available in colorectal cancer diagnosis, offer a valuable alternative for MSI prediction. Although Transformer-based models have demonstrated promising outcomes in predicting MSI from histology images, they are hampered by traditional local attention mechanisms that struggle to capture long-range interdependencies and establish a comprehensive global receptive field. In this study, we introduce DiNAT-MSI, a novel framework for histology-based MSI prediction that incorporates the Dilated Neighborhood Attention Transformer (DiNAT). This model enhances global context recognition and substantially expands receptive fields, all without additional computational burden. Our results demonstrate that DiNAT-MSI achieves a superior patientwise AUROC compared to ResNet18 and Swin Transformer, along with commendable explainability. Our work not only illustrates a more accessible diagnostic tool for leveraging histological data but also underscores the potential of Transformerbased models with sophisticated attention designs in advancing precision medicine for colorectal cancer patients.

关键词： Attention mechanisms Histopathology Computational modeling Precision medicine Small satellites Immunotherapy Colorectal cancer Predictive models Transformers Genetics

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ProHap Explorer: Visualizing Haplotypes in Proteogenomic datasets

arXiv

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arXiv 2025年

作者： Vašíček, Jakub Skiadopoulou, Dafni Kuznetsova, Ksenia G. Käll, Lukas Vaudel, Marc Bruckner, Stefan Mohn Center for Diabetes Precision Medicine Department of Clinical Science University of Bergen Bergen Norway Computational Biology Unit Department of Informatics University of Bergen Bergen Norway Metabolism Program Broad Institute of MIT and Harvard CambridgeMA United States Science for Life Laboratory Department of Gene Technology KTH - Royal Institute of Technology Stockholm Sweden Department of Genetics and Bioinformatics Health Data and Digitalization Norwegian Institute of Public Health Oslo Norway Institute for Visual and Analytic Computing University of Rostock Rostock Germany

In mass spectrometry-based proteomics, experts usually project data onto a single set of reference sequences, overlooking the influence of common haplotypes (combinations of genetic variants inherited together from a parent). We recently introduced ProHap, a tool for generating customized protein haplotype databases. Here, we present ProHap Explorer, a visualization interface designed to investigate the influence of common haplotypes on the human proteome. It enables users to explore haplotypes, their effects on protein sequences, and the identification of non-canonical peptides in public mass spectrometry datasets. The design builds on well-established representations in biological sequence analysis, ensuring familiarity for domain experts while integrating novel interactive elements tailored to proteogenomic data exploration. User interviews with proteomics experts confirmed the tool's utility, highlighting its ability to reveal whether haplotypes affect proteins of interest. By facilitating the intuitive exploration of proteogenomic variation, ProHap Explorer supports research in personalized medicine and the development of targeted therapies. © 2025, CC BY.

关键词： Proteomics

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A Design of Polygenic Risk Model with Deep Learning for Colorectal Cancer in Multiethnic Indonesians

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Procedia Computer science 2021年 179卷 632-639页

作者： Steven Amadeus Tjeng Wawan Cenggoro Arif Budiarto Bens Pardamean Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Recently, health management is emerging and attract attention to how to provide better prognostication and health management systems. The challenges in the prognostication are how to develop a model that can self-learn the prognostication features and how to get a high accuracy prediction. Prognostication in health disease involves SNPs which is a genetic marker. In this paper, we propose a polygenic risk model using deep learning: Transformer with self-attention mechanism and DeepLIFT. The use of these deep learning model allows us to predict the risk of colorectal cancer and see the correlation between SNPs.

关键词： Prognostication Polygenic Risk Model Transformer Model DeepLIFT Colorectal Cancer

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The microcirculation: master in normal pregnancy, puppet in preeclampsia

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American Journal of Obstetrics and Gynecology 2025年

作者： Thevissen, Kristof Cornette, Jerome Bruckers, Liesbeth Gyselaers, Wilfried Department of Rheumatology Ziekenhuis Oost-Limburg Genk Belgium Faculty of Medicine and Life Sciences Hasselt University Diepenbeek Belgium Department of Obstetrics and Fetal Medicine Erasmus Medical Center Rotterdam Netherlands Data Science Institute Interuniversity Institute for Biostatistics and Statistical Bioinformatics Hasselt University Diepenbeek Belgium Department of Obstetrics and Gynaecology Ziekenhuis Oost-Limburg Genk Belgium

Background: The microcirculation is studied sparsely in the field of maternal hemodynamics. With nailfold video capillaroscopy, further insight is possible in this interesting field within maternal hemodynamics. Objective: This study aimed to investigate the association between functional parameters of the microcirculation and the systemic cardiovascular system in pregnant women at risk for gestational hypertension disorders. Study Design: For this observational study, women with high cardiovascular risk according to maternal anthropometrics and obstetrical and medical history were recruited at random gestational ages, depending on the time of referral to the outpatient clinic for high-risk prenatal care at Ziekenhuis Oost-Limburg, Genk, Belgium. After birth, data on maternal and neonatal outcomes were obtained from hospital records, and only women with normal pregnancy (n=142) and preeclampsia (n=34) were included in this analysis. Nailfold video capillaroscopy measurements were performed in the first, second, and/or third trimesters. Video magnification of 200× was used for all fingers except the thumbs, and the stored images were analyzed offline. Capillary density was quantified (n/mm²), mean capillary diameter measured (μm), and capillary bed surface calculated as density × diameter. Cardiac output and total peripheral resistance were measured using impedance cardiography, together with sphygmomanometric blood pressure measurement. A linear mixed model for repeated measures was used to investigate the association between the microvascular and macrovascular parameters. No corrections for multiple testing were applied. Results: In normal pregnancies, a positive association was observed between the capillary bed surface and total vascular resistance (1.807;P=.01) and a negative association between capillary density and cardiac output (−0.269;P=.037). In preeclampsia, a negative association was observed between capillary density and mean arterial pressur

关键词： capillary density capillary diameter capillary perfusion cardiac output gestational hypertension disorder hypertension maternal hemodynamics microcirculation nailfold video capillaroscopy preeclampsia systemic circulation vascular resistance

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Impact of CLAHE-based image enhancement for diabetic retinopathy classification through deep learning

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Procedia Computer science 2023年 216卷 57-66页

作者： Mira Hayati Kahlil Muchtar Roslidar Novi Maulina Irfan Syamsuddin Gregorius Natanael Elwirehardja Bens Pardamean Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh 23111 Indonesia Telematics Research Center (TRC) Universitas Syiah Kuala Banda Aceh 23111 Indonesia Faculty of Medicine Universitas Syiah Kuala Banda Aceh 23111 Indonesia Department of Computer and Networking Engineering Politeknik Negeri Ujung Pandang Makassar 90245 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta 11480 Indonesia

Diabetic retinopathy (DR) is a type of diabetes mellitus that attacks the retina of the eye. DR will cause patients to experience blindness slowly. Generally, DR can be detected by using a special instrument called an ophthalmoscope to view the inside of the eyeball. However, in conditions where there is a very small difference between the normal image and the DR image, computer-based assistance is needed for maximizing image reading value. In this research, a method of image quality improvement will be carried out which will then be integrated with a classification algorithm based on deep learning. The results of image improvement using Contrast Limited Adaptive Histogram Equalization (CLAHE) shows that the average accuracy of the method on several models is very competitive, 91% for the VGG16 model, 95% for InceptionV3, and 97% for EfficientNet compared to the results original image which only has an accuracy of 87% for VGG16 model, 90% for InceptionV3 model, and 95% for EfficientNet. However, in ResNet34 better accuracy is obtained in the original image with an accuracy of 95% while in the CLAHE image the accuracy value is only 84%. The results of this comprehensive evaluation and recommendation of famous backbone networks can be useful in the computer-aided diagnosis of diabetic retinopathy.

关键词： Diabetic retinopathy Deep Learning CNN Image Enhancement CLAHE

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Deep Learning as a Vector Embedding Model for Customer Churn

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Procedia Computer science 2021年 179卷 624-631页

作者： Tjeng Wawan Cenggoro Raditya Ayu Wirastari Edy Rudianto Mochamad Ilham Mohadi Dinne Ratj Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program – Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480

To face the tight competition in the telecommunication industry, it is important to minimize the rate of customers stopping their service subscription, which is known as customer churn. For that goal, an explainable predictive customer churn model is an essential tool to be owned by a telecommunication provider. In this paper, we developed the explainable model by utilizing the concept of vector embedding in Deep Learning. We show that the model can reveal churning customers that can potentially be converted back to use the previous telecommunication service. The generated vectors are also highly discriminative between the churning and loyal customers, which enable the developed models to be highly predictive for determining whether a customer would cease his/her service subscription or not. The best model in our experiment achieved a predictive performance of 81.16%, measured by the F1 Score. Further analysis on the clusters similarity and t-SNE plot also confirmed that the generated vectors are discriminative for churn prediction.

关键词： customer churn customer behavior deep learning vector embedding representation learning

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