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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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Adjustment of Conditional Bias in Hazard Ratios for Group Sequential Testing of Progression-Free Survival and Overall Survival

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Statistics in Medicine 2025年第10-12期44.0卷 e70112页

作者： Izumi, Shoki Nomura, Shogo Matsuyama, Yutaka Department of Biostatistics Division of Health Sciences and Nursing Graduate School of Medicine The University of Tokyo Tokyo Japan Section of Biostatistics Department of Clinical Data Science Clinical Research & Education Promotion Division National Center of Neurology and Psychiatry Tokyo Japan Department of Biostatistics and Bioinformatics Graduate School of Medicine The University of Tokyo Tokyo Japan Department of Biostatistics School of Public Health Graduate School of Medicine The University of Tokyo Tokyo Japan

In confirmatory randomized controlled trials of patients with metastatic cancer, progression-free survival (PFS) and overall survival (OS) are often used as multiple primary endpoints. The overall hierarchical strategy is a typical multiplicity adjustment method that analyzes PFS once and performs an interim OS analysis at the time of PFS analysis using an alpha-spending function—only if the statistical significance of PFS is demonstrated. A subsequent final OS analysis is conducted if the interim OS analysis does not result in early stopping for efficacy. In this study, we focused on the adjustment of conditional bias (CB) in hazard ratio estimates for OS in both interim and final analyses when a trial applied the overall hierarchical strategy. As CB-adjusting estimators for a single primary endpoint may have limited performance, we extended the conditional mean-adjusted estimator to the case of an overall hierarchical strategy. Motivated by an actual oncology trial, we evaluated the performance of the proposed estimators through a simulation study. In the case of early stopping for efficacy, the CB of the proposed estimator was smaller than that of the existing methods with comparable root mean squared error. © 2025 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.

关键词： conditional estimation group sequential design hierarchical procedure

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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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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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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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scCASE: accurate and interpretable enhancement for single-cell chromatin accessibility sequencing data (vol 15, 1629, 2024)

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NATURE COMMUNICATIONS 2024年第1期15卷 1629页

作者： Tang, Songming Cui, Xuejian Wang, Rongxiang Li, Sijie Li, Siyu Huang, Xin Chen, Shengquan Nankai University School of Mathematical Sciences and LPMC Tianjin China (GRID:grid.216938.7) (ISNI:0000 0000 9878 7032) Tsinghua University MOE Key Laboratory of Bioinformatics and Bioinformatics Division of BNRIST Department of Automation Beijing China (GRID:grid.12527.33) (ISNI:0000 0001 0662 3178) University of Virginia Department of Computer Science Charlottesville USA (GRID:grid.27755.32) (ISNI:0000 0000 9136 933X) Nankai University School of Statistics and Data Science Tianjin China (GRID:grid.216938.7) (ISNI:0000 0000 9878 7032) Beijing Institute of Radiation Medicine Beijing Key Laboratory for Radiobiology Department of Radiation Biology Beijing China (GRID:grid.506261.6) (ISNI:0000 0001 0706 7839)

Single-cell chromatin accessibility sequencing (scCAS) has emerged as a valuable tool for interrogating and elucidating epigenomic heterogeneity and gene regulation. However, scCAS data inherently suffers from limitations such as high sparsity and dimensionality, which pose significant challenges for downstream analyses. Although several methods are proposed to enhance scCAS data, there are still challenges and limitations that hinder the effectiveness of these methods. Here, we propose scCASE, a scCAS data enhancement method based on non-negative matrix factorization which incorporates an iteratively updating cell-to-cell similarity matrix. Through comprehensive experiments on multiple datasets, we demonstrate the advantages of scCASE over existing methods for scCAS data enhancement. The interpretable cell type-specific peaks identified by scCASE can provide valuable biological insights into cell subpopulations. Moreover, to leverage the large compendia of available omics data as a reference, we further expand scCASE to scCASER, which enables the incorporation of external reference data to improve enhancement ***-cell chromatin accessibility sequencing (scCAS) data suffers from high sparsity and dimensionality. Here, authors propose an accurate and interpretable computational framework for enhancing scCAS data that considers cell-to-cell similarity.

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Deep and handcrafted feature supported diabetic retinopathy detection: A study

Deep and handcrafted feature supported diabetic retinopathy ...

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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 Electronics and Instrumentation Engineering St. Joseph's College of Engineering OMR Tamil Nadu Chennai600119 India

The eye is the prime sensory organ in physiology, and the abnormality in the eye severely influences the vision system. Therefore, eye irregularity is commonly assessed using imaging schemes, and Fundus Retinal Image (FRI) supported eye screening is one of the ophthalmological practices. This work proposed a Deep-Learning Procedure (DLP) to recognize Diabetic Retinopathy (DR) in FI. The proposed work presents the experimental work with different DLP methods found in the literature. This work is executed with two modes;(i) DR detection using conventional deep-features and (ii) DR discovery using deep ensemble features. To demonstrate this work, 1800 fundus images (900 regular and 900 DR class) are considered for the assessment, and the advantage of proposed plan is confirmed using various performance metrics. The experimental outcome of this study confirms that the AlexNet-based detection provides a better detection (>96%), and the deep ensemble features of AlexNet, VGG16, and ResNet18 provide a detection accuracy of >98% on the chosen FRI database. © 2022 Elsevier B.V.. All rights reserved.

关键词： Deep learning

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Crack Catcher AI - Enabling Smart Fracture Mechanics Approach for Damage Control in Thin Silicon Wafers for 3D Semiconductor Integrated Devices/Packages

Crack Catcher AI - Enabling Smart Fracture Mechanics Approac...

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2025 IEEE International Reliability Physics Symposium, IRPS 2025

作者： Preston, Gabriel Gonzolez, Greer Karnyoto, Andrea S. Mozar, Fitya S. Henry, Matthew M. Isnan, Mahmud Nugroho, Kuncahyo S. Candra, Henry Doris, Muhamad Putri, Dianing N. N. Sari, Tyas K Pardamean, Bens Djuana, Endang Speaks, Derrric Budiman, Arief S. Oregon Renewable Energy Center (OREC) Oregon Institute of Technology (Oregon Tech) Klamath Falls 97601 OR United States Dept. of Manufacturing & Mechanical Eng. & Tech. (MMET) Oregon Institute of Technology Klamath Falls 97601 OR United States Bioinformatics & Data Science Research Center (BDSRC) Bina Nusantara University Jakarta 11480 Indonesia Center for Artificial Intelligence and Advanced Technology (CAPTIVATE) Universitas Trisakti Jakarta 11440 Indonesia Department of Electrical Engineering Faculty of Industrial Technology Universitas Trisakti Jakarta 11440 Indonesia Industrial Eng. Dept. Bina Nusantara University Jakarta 11480 Indonesia Computer Sci. Dept. Bina Nusantara University Jakarta 11480 Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta 11480 Indonesia

ISBN: (纸本)9798331504779

The importance of silicon cannot be undermined - in photovoltaics (PV) as well as in semiconductor industries. However, silicon is very brittle. Silicon cells/wafers crack easily during manufacturing assembly and/or during device operations. Crack Catcher AI uses novel smart fracture mechanics approach with Artificial Intelligence (AI) methodologies to predict and control crack/damage evolution in thin silicon cells/wafers. This is critical as semiconductor 3D integration technology calls for wafer-to-wafer bonding with utmost alignment accuracy and yield/reliability. © 2025 IEEE.

关键词： 3D Integrated Devices/Packages Fracture Mechanics Machine Learning Approaches Silicon Crack

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A Novel Method for Quantitative Evaluation of Anti-Infection Immune Defense Function and its Application

SSRN

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SSRN 2024年

作者： Tan, Zhen-Lin Luo, Tao Wu, Xiao-Jun Lei, Qian Chen, Jie Guo, An-Yuan Department of Laboratory Medicine West China Biomedical Big Data Center West China Hospital Sichuan University Chengdu610041 China Hubei Key Laboratory of Bioinformatics and Molecular-imaging College of Life Science and Technology Huazhong University of Science and Technology Hubei Wuhan430074 China College of Life Sciences University of Chinese Academy of Sciences Beijing101408 China BGI Education Center University of Chinese Academy of Sciences Shenzhen518083 China

ImmuDef is a novel algorithm for precise, quantitative assessment of anti-infection immune defense based on RNA-seq data. Using enrichment scores of immunologic signature gene sets, we defined three feature sets for bacteria, viruses, and fungi infection. Three normalized scores based on modified Manhattan distances were defined as immune scores (IS) to evaluate immunity against these infections. Tested on 3,202 samples across 39 datasets, ImmuDef distinguished well between healthy individuals and patients with AIDS, COVID-19, sepsis, tuberculosis (AUC: 0.71-1.00). It accurately predicted survival and death outcomes in critical sepsis (AUC = 0.89) and severe COVID-19 (AUC = 0.84). In hepatitis B patients, IS was significantly lower in those with active HBV replication and negatively correlated with ALT levels. ImmuDef offers a novel approach for precise measurement of anti-infection immunity, aiding in disease prognosis prediction and individual health status ***: Financial support was provided by the National Natural science Foundation of China (32370717) and the 1.3.5 project for disciplines of excellence from West China Hospital of Sichuan University (ZYYC23007).Declaration of Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this *** Approval: This study was approved by Ethics Committee on Biomedical Research West China Hospital of Sichuan University (Reference: HX-IRB-AF-21-V3.0). © 2024, The Authors. All rights reserved.

关键词： COVID-19

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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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