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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Mikyung Je Myeongji Cho Hyeon S. Son Interdisciplinary Graduate Program in Bioinformatics College of Natural Science Seoul National University Seoul Republic of Korea Lab. of Computational Biology & Bioinformatics Graduate School of Public Health Seoul National University Seoul Republic of Korea Institute of Health and Environment Seoul National University Seoul Republic of Korea

Viruses possess specific conserved regions known as RNA recognition motifs, which are shared within taxonomic groups. Applying these to the Baltimore virus classification system, there are genes/proteins that serve as biomarkers for each of the seven groups. Mutations in conserved sequence regions can have a significant impact on the function of a virus, especially if these conserved regions are part of a major protein. In this study, we analyzed the RdRp gene, known to be a representative RRM of RNA viruses, from viruses of five families (Alternaviridae, Chrysoviridae, Totiviridae, Sedoreoviridae, and Spinareoviridae) within the phylum Duplornaviricota. Data were retrieved from the NCBI non-redundant (nr) database (https://***/protein) applying a cut-off of 300 amino acids in length. RdRp protein sequences were aligned using PROMALS3D, and PhyML (v3.0) was used for phylogenetic analysis. In the next step, we utilized the officially provided ICTV taxonomy and bioinformatics tools to assign the phylogenetic position of RdRp. Although this analysis focused solely on RdRp, it can be concluded that using this gene as a marker may be sufficient for taxonomic assignment. Subsequent analyses will target DNA viruses and RT viruses to perform a more comprehensive assessment. This extensive analysis aims to validate the usefulness of marker genes in the overall viral taxonomy.

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CoxFNN: Interpretable machine learning method for survival analysis

CoxFNN: Interpretable machine learning method for survival a...

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Annual International conference of the ieee Engineering in Medicine and biology Society (EMBC)

作者： Yufeng Zhang Emily Wittrup Kayvan Najarian Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Survival analysis plays a pivotal role in healthcare, particularly in analyzing time-to-event data such as in disease progression, treatment efficacy, and drug development. Traditional methods in survival analysis often face a trade-off: they either make linear assumptions, which are interpretable but may be overly simplistic, or they capture complex, non-linear relationships but lack clarity and ease of understanding. To overcome these challenges, we develop a novel machine-learning approach. This method is an extension of the Cox proportional hazards model. Unlike its traditional version, our method can model non-linear relationships between various features and the associated risks without relying on specific distribution assumptions. More importantly, it also adds the ability to learn rules from the dataset that are comprehensible to humans. Our proposed model demonstrated comparable performance in our experimental evaluations relative to other existing survival analysis models while also successfully identifying potential high-risk factors and clinical rules, demonstrating its practical utility in real-world healthcare settings. This balance of sophisticated analysis and interpretability makes it promising in the field of survival analysis.

关键词： Drugs Analytical models Medical services Machine learning Hazards Faces Diseases

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BP-DDI: Drug-drug interaction prediction based on biological information and pharmacological text

BP-DDI: Drug-drug interaction prediction based on biological...

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2022 ieee International conference on bioinformatics and Biomedicine, BIBM 2022

作者： Dou, Mingliang Han, Han Chen, Genlang Guo, Fei Tang, Jijun Tianjin University College of Intelligence and Computing Tianjin China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China NingboTech University School of Computer and Data Engineering Ningbo China Central South University School of Computer Science and Engineering Changsha China University of South Carolina College of Engineering and Computing United States

ISBN: (纸本)9781665468190

In the treatment of many diseases, combination drug therapy has been widely used and achieved good clinical efficacy. However, drug-drug interaction (DDI) may occur between multiple drugs and pose a huge threat to the health of patients. Therefore, predicting the presence or absence of DDI among multiple drugs is an important part of pharmacovigilance. Currently, various computational methods for DDI prediction usually use biological information such as molecular structures, targets and enzymes of drugs, or construct heterogeneous networks about drugs, diseases, and genes, so as to obtain abundant information related to drugs. In addition to biological data, pharmacology texts also contain a wealth of information about drug properties, but these texts have not yet been applied to DDI predictions. In this study, we first collect six types of pharmacology texts from DrugBank that can reflect properties of drugs, and propose a novel method named BP-DDI which can combine biological information and pharmacological text to realize DDI event prediction. BP-DDI first extracts biological features (chemical substructure features and target features) from biological data, and then extracts specific types of text features from the collected pharmacology text data. Finally, the biological features are fused with different types of pharmacological text features in order to predict DDI events. Our experiments demonstrate that BP-DDI outperforms existing methods on all three types of prediction tasks. BP-DDI achieves 0.9052 on ACC, and achieves 0.9612 on AUPR. © 2022 ieee.

关键词： Drug interactions

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ERINS: Novel Sequence Insertion Detection by Constructing an Extended Reference

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1893-1901页

作者： Yuan, Xiguo Xu, Xiangyan Zhao, Haiyong Duan, Junbo Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Liaocheng Univ Sch Comp Sci & Technol Liaocheng 252000 Shandong Peoples R China Xi An Jiao Tong Univ Sch Life Sci & Technol Key Lab Biomed Informat Engn Minist EducDept Biomed Engn Xian 710049 Peoples R China

Next generation sequencing technology has led to the development of methods for the detection of novel sequence insertions (nsINS). Multiple signatures from short reads are usually extracted to improve nsINS detection performance. However, characterization of nsINSs larger than the mean insert size is still challenging. This article presents a new method, ERINS, to detect nsINS contents and genotypes of full spectrum range size. It integrates the features of structural variations and mapping states of split reads to find nsINS breakpoints, and then adopts a left-most mapping strategy to infer nsINS content by iteratively extending the standard reference at each breakpoint. Finally, it realigns all reads to the extended reference and infers nsINS genotypes through statistical testing on read counts. We test and validate the performance of ERINS on simulation and real sequencing datasets. The simulation experimental results demonstrate that it outperforms several peer methods with respect to sensitivity and precision. The real data application indicates that ERINS obtains high consistent results with those of previously reported and detects nsINSs over 200 base pairs that many other methods fail. In conclusion, ERINS can be used as a supplement to existing tools and will become a routine approach for characterizing nsINSs.

关键词： Genomics bioinformatics Sequential analysis Feature extraction Computer science Sensitivity Tools Novel sequence insertions insertion contents genotypes next-generation sequencing extended reference

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FEniCSx-Based Effective Scheme for the Cahn-Hilliard Equation 5

FEniCSx-Based Effective Scheme for the Cahn-Hilliard Equatio...

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5th ieee International conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2025

作者： Bhutra, Harsh Thomas, Bettina Ann Shri, K. Shreya Smitha, T.V. Neelima, Nizampatnam Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Bengaluru India Amrita School of Engineering Amrita Vishwa Vidyapeetham Computational Science Lab Bengaluru India

ISBN: (纸本)9798350356236

The Cahn-Hilliard equation is a fourth-order parabolic partial differential equation that is challenging and time-intensive to solve using conventional methods. Although the equation is difficult to solve, it finds its use cases in many aspects of science including but not limited to the study of phase separation in the binary, isotropic and isothermal mixtures of materials such as molten alloys. Some of the other applications are bioinformatics, computational biology, Biomedical informatics, human activity recognition, Healthcare, computer vision, Climate Science and Natural Language Processing. Many researchers have come up with a way to deal with partial differential equations, but very few works and methods deal with different orders of partial differential equations. In this article, a novel and effective scheme for solving the Cahn-Hilliard equation is proposed using FEniCS. The time of execution and relative tolerance of the solutions of this equation are computed from methods of direct computation and parallel processing which is carried out for the equation with higher-order finite element method for various boundary conditions to demonstrate the efficiency of the proposed approach. The solution thus obtained helps in understanding the non-linear behaviour of the equation with the help of a three-dimensional heatmap plot. This work solves the Cahn Hilliard equation by using the in-built libraries, Dolphin an open-source software called FEniCS and Message Passing Interface (MPI). The MPI is responsible for achieving parallelism in high-performance computing environments and breaks down the problem into four smaller parts making it faster to get to the solution. The obtained results demonstrate that this approach outperforms the existing literature by achieving a forty percent faster computational speed. The approach will be very useful in solving many similar problems arising in the field of science and engineering. © 2025 ieee.

关键词： Open source software

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RNASeqR: An R Package for Automated Two-Group RNA-Seq Analysis Workflow

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 2023-2031页

作者： Chao, Kuan-Hao Hsiao, Yi-Wen Lee, Yi-Fang Lee, Chien-Yueh Lai, Liang-Chuan Tsai, Mong-Hsun Lu, Tzu-Pin Chuang, Eric Y. Natl Taiwan Univ Dept Elect Engn Taipei 10617 Taiwan Natl Taiwan Univ Ctr Genom & Precis Med Bioinformat & Biostat Core Taipei 10617 Taiwan Natl Taiwan Univ Grad Inst Biomed Elect & Bioinformat Taipei 10617 Taiwan Natl Taiwan Univ Grad Inst Physiol Taipei 10617 Taiwan Natl Taiwan Univ Inst Biotechnol Taipei 10617 Taiwan Natl Taiwan Univ Inst Epidemiol & Prevent Med Dept Publ Hlth Taipei 10617 Taiwan Ind Technol Res Inst Biomed Technol & Device Res Lab Hsinchu Taiwan

RNA-Seq analysis has revolutionized researchers' understanding of the transcriptome in biological research. Assessing the differences in transcriptomic profiles between tissue samples or patient groups enables researchers to explore the underlying biological impact of transcription. RNA-Seq analysis requires multiple processing steps and huge computational capabilities. There are many well-developed R packages for individual steps;however, there are few R/Bioconductor packages that integrate existing software tools into a comprehensive RNA-Seq analysis and provide fundamental end-to-end results in pure R environment so that researchers can quickly and easily get fundamental information in big sequencing data. To address this need, we have developed the open source R/Bioconductor package, RNASeqR. It allows users to run an automated RNA-Seq analysis with only six steps, producing essential tabular and graphical results for further biological interpretation. The features of RNASeqR include: six-step analysis, comprehensive visualization, background execution version, and the integration of both R and command-line software. RNASeqR provides fast, light-weight, and easy-to-run RNA-Seq analysis pipeline in pure R environment. It allows users to efficiently utilize popular software tools, including both R/Bioconductor and command-line tools, without predefining the resources or environments. RNASeqR is freely available for Linux and macOS operating systems from Bioconductor (https://***/packages/release/bioc/html/***).

关键词： bioinformatics Genomics Quality assessment Pipelines Electronic mail RNA RNA-Seq analysis workflow pipeline R bioconductor transcriptome assembly differential expression analysis gene expression statistical analysis visualization

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A Novel Cox Proportional Hazards Model for High-Dimensional Genomic Data in Cancer Prognosis

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1821-1830页

作者： Huang, Hai-Hui Liang, Yong Macau Univ Sci & Technol Fac Informat Technol Macau 999078 Peoples R China Macau Univ Sci & Technol State Key Lab Qual Res Chinese Med Macau 999078 Peoples R China

The Cox proportional hazards model is a popular method to study the connection between feature and survival time. Because of the high-dimensionality of genomic data, existing Cox models trained on any specific dataset often generalize poorly to other independent datasets. In this paper, we suggest a novel strategy for the Cox model. This strategy is included a new learning technique, self-paced learning (SPL), and a new gene selection method, SCAD-Net penalty. The SPL method is adopted to aid to build a more accurate prediction with its built-in mechanism of learning from easy samples first and adaptively learning from hard samples. The SCAD-Net penalty has fixed the problem of the SCAD method without an inherent mechanism to fuse the prior graphical information. We combined the SPL with the SCAD-Net penalty to the Cox model (SSNC). The simulation shows that the SSNC outperforms the benchmark in terms of prediction and gene selection. The analysis of a large-scale experiment across several cancer datasets shows that the SSNC method not only results in higher prediction accuracies but also identifies markers that satisfactory stability across another validation dataset. The demo code for the proposed method is provided in supplemental file.

关键词： Biological system modeling Predictive models Hazards Mathematical model Adaptation models computational modeling Genomics Cox model regularization variable selection gene expression

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DMFMDA: Prediction of Microbe-Disease Associations Based on Deep Matrix Factorization Using Bayesian Personalized Ranking

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1763-1772页

作者： Liu, Yue Wang, Shu-Lin Zhang, Jun-Feng Zhang, Wei Zhou, Su Li, Wen Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Hunan Peoples R China Changsha Univ Coll Comp Engn & Appl Math Changsha 410003 Hunan Peoples R China

Identifying the microbe-disease associations is conducive to understanding the pathogenesis of disease from the perspective of microbe. In this paper, we propose a deep matrix factorization prediction model (DMFMDA) based on deep neural network. First, the disease one-hot encoding is fed into neural network, which is transformed into a low-dimensional dense vector in implicit semantic space via embedding layer, and so is microbe. Then, matrix factorization is realized by neural network with embedding layer. Furthermore, our model synthesizes the non-linear modeling advantages of multi-layer perceptron based on the linear modeling advantages of matrix factorization. Finally, different from other methods using square error loss function, Bayesian Personalized Ranking optimizes the model from a ranking perspective to obtain the optimal model parameters, which makes full use of the unobserved data. Experiments show that DMFMDA reaches average AUCs of 0.9091 and 0.9103 in the framework of 5-fold cross validation and Leave-one-out cross validation, which is superior to three the-state-of-art methods. In case studies, 10, 9 and 9 out of top-10 candidate microbes are verified by recently published literature for asthma, inflammatory bowel disease and colon cancer, respectively. In conclusion, DMFMDA is successful application of deep learning in the prediction of microbe-disease association.

关键词： Diseases computational modeling Predictive models Neural networks Databases Computer science Encoding Microbe-disease associations neural network pairwise loss Bayesian personalized ranking

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PAN: Personalized Annotation-Based Networks for the Prediction of Breast Cancer Relapse

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2841-2847页

作者： Nguyen, Thin Lee, Samuel C. Quinn, Thomas P. Truong, Buu Li, Xiaomei Tran, Truyen Venkatesh, Svetha Le, Thuc Duy Deakin Univ Appl Artificial Intelligence Inst Burwood Vic 3125 Australia Univ South Australia Sch Informat Technol & Math Sci Adelaide SA 5001 Australia

The classification of clinical samples based on gene expression data is an important part of precision medicine. In this manuscript, we show how transforming gene expression data into a set of personalized (sample-specific) networks can allow us to harness existing graph-based methods to improve classifier performance. Existing approaches to personalized gene networks have the limitation that they depend on other samples in the data and must get re-computed whenever a new sample is introduced. Here, we propose a novel method, called Personalized Annotation-based Networks (PAN), that avoids this limitation by using curated annotation databases to transform gene expression data into a graph. Unlike competing methods, PANs are calculated for each sample independent of the population, making it a more efficient way to obtain single-sample networks. Using three breast cancer datasets as a case study, we show that PAN classifiers not only predict cancer relapse better than gene features alone, but also outperform PPI (protein-protein interactions) and population-level graph-based classifiers. This work demonstrates the practical advantages of graph-based classification for high-dimensional genomic data, while offering a new approach to making sample-specific networks. Supplementary information: PAN and the baselines are implemented in Python. Source code and data are available at https://***/thinng/PAN.

关键词： Annotations Gene expression Databases Breast cancer Ontologies Genomics Sociology Personalized medicine annotation-based networks gene expression breast cancer

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Ultra-fast and High-quality Mapping of Error-prone Long Reads

Ultra-fast and High-quality Mapping of Error-prone Long Read...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Boyuan Sun Department of Computational Biology and Medical Sciences Graduate School of Frontier Sciences The University of Tokyo Chiba Japan

To accelerate the mapping process of vast amounts of long reads to references, a novel mapper mapquik has an over 30 times speedup compared with the de facto standard minimap2 but maintains a comparable mapping quality on the human genome. However, the mapper is only available to accurately map PacBio HiFi reads with sequencing errors lower than 1%. Since the 3rd generation long reads with modest error rates higher than 1% are still widely used, like reads from Nanopore DNA sequencing technology, a versatile long-read mapper should consider more long-read cases. This paper adopts a mapping-friendly sequence reduction idea to compress sequencing errors from different sequencing technologies to boost seed sensitivity of mapquik. For reads with relatively high error rates, we combine an error-sensitive order from a deep learning algorithm to replace the random universe minimizer order in mapquik. An improved ultra-fast long read mapper named mapquikPLUS is verified to handle most 3rd generation read mapping tasks efficiently and a pipeline of mapquikPLUS followed by minimap2 shows better performance against other approximate mappers for ANI evaluation.

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