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17th International conference on Practical Applications of computational biology and bioinformatics, PACBB 2023

作者： Tejera-Nevado, Paloma Serrano, Emilio González-Herrero, Ana Bermejo-Moreno, Rodrigo Rodríguez-González, Alejandro ETS Ingenieros Informáticos Universidad Politécnica de Madrid Madrid Spain Centro de Tecnología Biomédica Universidad Politécnica de Madrid Madrid Pozuelo de Alarcón Spain Spanish National Research Council Madrid Spain

ISBN: (纸本)9783031380785

The determination of protein structure has been facilitated using deep learning models, which can predict protein folding from protein sequences. In some cases, the predicted structure can be compared to the already-known distribution if there is information from classic methods such as nuclear magnetic resonance (NMR) spectroscopy, X-ray crystallography, or electron microscopy (EM). However, challenges arise when the proteins are not abundant, their structure is heterogeneous, and protein sample preparation is difficult. To determine the level of confidence that supports the prediction, different metrics are provided. These values are important in two ways: they offer information about the strength of the result and can supply an overall picture of the structure when different models are combined. This work provides an overview of the different deep-learning methods used to predict protein folding and the metrics that support their outputs. The confidence of the model is evaluated in detail using two proteins that contain four domains of unknown function. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Forecasting

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FLR: A Revolutionary Alignment-Free Similarity Analysis Methodology for DNA-Sequences

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1924-1936页

作者： Medhat, Belal Shawish, Ahmed British Univ Egypt Dept Comp Sci Fac Informat & Comp Sci Cairo 11837 Egypt German Univ Cairo Dept Comp Sci Fac Media Engn & Technol Cairo 11837 Egypt Arab Open Univ Fac Comp Studies Headquarters Kuwait Kuwait Ain Shams Univ Fac Comp & Informat Sci Cairo 11566 Egypt

This paper introduces a novel alignment-free sequence analysis methodology. Its main idea is based on introducing a new representation of the DNA-Sequence. This representation breaks the dependency between the DNA bases that exist in the traditional string presentation. We called it the Four-Lists-Representation (FLR). Based on the FLR, a series of revolutionary algorithms for searching, map-discovery, similarity-score analysis, and similarity-visualization have been developed. They are combined in what we call the FLR Methodology. The paper also studies most of the available similarity analysis techniques in a comprehensive state-of-art review. The conducted extensive simulation and theoretical studies confirm the outperformance of the whole set of FLR-based algorithms in terms of speed and memory consumption in comparison to a long list of available similarity analysis algorithms. The ability to provide a similarity-map, similarity-score, and similarity-graph as a set of evidence-based rationales makes the quality of results provided by the proposed methodology presents a new edge in this field and promises a new area of genome-based research.

关键词： DNA Sequences Genomics bioinformatics Acceleration Organisms Diseases Sequence analysis similarity-score similarity analysis similarity measurements FLR alignment-free sequence analysis

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Lossy Compression of Quality Values in Sequencing Data

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1958-1969页

作者： Morales, Veronica Suaste Houghten, Sheridan CONABIO Mexico City 14010 DF Mexico Brock Univ Dept Comp Sci St Catharines ON L2S 3A1 Canada

The dropping cost of sequencing human DNA has allowed for fast development of several projects around the world generating huge amounts of DNA sequencing data. This deluge of data has run up against limited storage space, a problem that researchers are trying to solve through compression techniques. In this study we address the compression of SAM files, the standard output files for DNA alignment. We specifically study lossy compression techniques used for quality values reported in the SAM file and analyze the impact of such lossy techniques on the CRAM format. We present a series of experiments using a data set corresponding to individual NA12878 with three different fold coverages. We introduce a new lossy model, dynamic binning, and compare its performance to other lossy techniques, namely Illumina binning, LEON and QVZ. We analyze the compression ratio when using CRAM and also study the impact of the lossy techniques on SNP calling. Our results show that lossy techniques allow a better CRAM compression ratio. Furthermore, we show that SNP calling performance is not negatively affected and may even be boosted.

关键词： Sequential analysis DNA Standards Genomics bioinformatics Data models Compression algorithms Compression next-generation sequencing quality values SNP calling performance

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Multi-rater Prompting for Ambiguous Medical Image Segmentation

Multi-rater Prompting for Ambiguous Medical Image Segmentati...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Jinhong Wang Yi Cheng Jintai Chen Hongxia Xu Danny Chen Jian Wu College of Computer Science and Technology Zhejiang University Hangzhou China Alibaba Group Hangzhou China University of Illinois at Urbana-Champaign Urbana-Champaign USA Liangzhu Laboratory and WeDoctor Cloud and Zhejiang Key Laboratory of Medical Imaging Artificial Intelligence Hangzhou China University of Notre Dame South Bend USA State Key Laboratory of Transvascular Implantation Devices of The Second Affiliated Hospital School of Medicine and School of Public Health Zhejiang University Hangzhou China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Multi-rater annotations commonly occur when medical images are independently annotated by multiple experts (raters). In this paper, we tackle two challenges arisen in multi-rater annotations for medical image segmentation (called ambiguous medical image segmentation): (1) How to train a deep learning model when a group of raters produces a set of diverse but plausible annotations, and (2) how to fine-tune the model efficiently when computation resources are not available for retraining the entire model on a different dataset domain. We propose a multi-rater prompt-based approach to address these two challenges altogether. Specifically, we introduce a series of rater-aware prompts that can be plugged into the U-Net model for uncertainty estimation to handle multi-annotation cases. During the prompt-based fine-tuning process, only 0.3% of learnable parameters are required to be updated comparing to training the entire model. Further, in order to integrate expert consensus and disagreement, we explore different multi-rater incorporation strategies and design a mix-training strategy for comprehensive insight learning. Extensive experiments verify the effectiveness of our new approach for ambiguous medical image segmentation on two public datasets while alleviating the heavy burden of model re-training. Code will be made available.

关键词： Training Deep learning Image segmentation Uncertainty Codes Annotations computational modeling Estimation computational efficiency Biomedical imaging

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Identifying Drug Resistant miRNAs Using Entropy Based Ranking

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第3期18卷 973-984页

作者： Pal, Jayanta Kumar Ray, Shubhra Sankar Pal, Sankar K. Indian Stat Inst Ctr Soft Comp Res Kolkata 700108 India Univ Calcutta Dept Comp Sci & Engn Kolkata 700073 India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

MicroRNAs play an important role in controlling drug sensitivity and resistance in cancer. Identification of responsible miRNAs for drug resistance can enhance the effectiveness of treatment. A new set theoretic entropy measure (SPEM) is defined to determine the relevance and level of confidence of miRNAs in deciding their drug resistant nature. Here, a pattern is represented by a pair of values. One of them implies the degree of its belongingness (fuzzy membership) to a class and the other represents the actual class of origin (crisp membership). A measure, called granular probability, is defined that determines the confidence level of having a particular pair of membership values. The granules used to compute the said probability are formed by a histogram based method where each bin of a histogram is considered as one granule. The width and number of the bins are automatically determined by the algorithm. The set thus defined, comprising a pair of membership values and the confidence level for having them, is used for the computation of SPEM and thereby identifying the drug resistant miRNAs. The efficiency of SPEM is demonstrated extensively on six data sets. While the achieved F-score in classifying sensitive and resistant samples ranges between 0.31 & 0.50 using all the miRNAs by SVM classifier, the same score varies from 0.67 to 0.94 using only the top 1 percent drug resistant miRNAs. Superiority of the proposed method as compared to some existing ones is established in terms of F-score. The significance of the top 1 percent miRNAs in corresponding cancer is also verified by the different articles based on biological investigations. Source code of SPEM is available at http://***/***

关键词： miRNA cancer drug resistance fuzzy set Z(+) number entropy histogram granular probability bioinformatics

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MPGNN-DSA: A Meta-path-based Graph Neural Network for drug-side effect association prediction

MPGNN-DSA: A Meta-path-based Graph Neural Network for drug-s...

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2022 ieee International conference on bioinformatics and Biomedicine, BIBM 2022

作者： Yao, Wenjie Zhao, Weizhong Jiang, Xingpeng Shen, Xianjun He, Tingting Central China Normal University Hubei Provincial Key Laboratory of Artificial Intelligence and Smart Learning Hubei Wuhan430079 China Central China Normal University Wuhan School of Computer Hubei Hubei430079 China Central China Normal University National Language Resources Monitoring and Research Center for Network Media Hubei Wuhan430079 China

ISBN: (纸本)9781665468190

Drug side effect is an important entity in the biomedical field, and identifying the association of the drug-side effects is a very important issue in pharmacological studies and drug risk-benefit. Traditional side effect discovery methods are mainly based on pharmacological experiments. These methods can detect the side effects of some drugs, but the identification process is time-consuming, expensive, and fails to identify some rare side effects. In recent years, with the expansion of massive biomedical data, computational-based methods are widely developed and applied for the task of drug-side effect association(DSA) prediction. However, existing methods cannot fully utilize public biomedical databases, and the complex semantic associations between drugs and side effects are not effectively captured, which leads to suboptimal model prediction performance. In this study, we develop a novel meta-path-based graph neural network model for drug-side effect association prediction. In the proposed model, we first construct a heterogeneous information network(HIN) by fusing multiple biological datasets. And then, a novel meta-path-based feature learning module is designed to learn high-quality representations of drugs and side effects. Finally, with the learned features, the prediction module utilizes a fully connected neural network to make prediction. In addition, comprehensive experiments is conducted, the results demonstrate the effectiveness of our model, indicating that the method will be a viable approach for DSA prediction tasks. © 2022 ieee.

关键词： Forecasting

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Algorithms for computational biology: Sixth Edition

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第1期18卷 1-1页

作者： Martin-Vide, Carlos Vega-Rodriguez, Miguel A. Rovira & Virgili Univ Res Grp Math Linguist Tarragona 43002 Spain Univ Extremadura ARCO Res Grp Caceres 10003 Spain

The papers in this special section were presented at the Sixth International conference on Algorithms for computational biology, AlCoB 2019, that was held in Berkeley on May 28-30, 2019.

关键词： Special Issues And Sections bioinformatics Meetings computational biology

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Guest Editorial for the 29th International conference on Genome Informatics (GIW 2018)

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2020年第3期17卷 726-727页

作者： Zheng, Jie Li, Jinyan Zheng, Yun ShanghaiTech Univ Sch Informat Sci & Technol Shanghai 201210 Peoples R China Univ Technol Sydney Fac Engn & Informat Technol Adv Analyt Inst Ultimo NSW 2007 Australia Kunming Univ Sci & Technol Inst Primate Translat Med Kunming 650500 Yunnan Peoples R China Kunming Univ Sci & Technol Fac Informat Engn & Automat Kunming 650500 Yunnan Peoples R China

The six papers in this special section were presented at the 29th International conference on Genome Informatics (GIW 2018) that was held at Kunming University of Science and Technology, Kunming, China on December 3-5... 详细信息

关键词： Special Issues And Sections bioinformatics Meetings Genomics computational biology

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Estimating Functional Connectivity by Integration of Inherent Brain Function Activity Pattern Priors

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2420-2430页

作者： Zhu, Zhiyuan Zhen, Zonglei Wu, Xia Li, Shuo Beijing Normal Univ Sch Artificial Intelligence Beijing 100875 Peoples R China Beijing Normal Univ Engn Res Ctr Intelligent Technol & Educ Applicat Minist Educ Beijing 100875 Peoples R China Beijing Normal Univ Fac Psychol Beijing 100875 Peoples R China Western Univ Dept Med Imaging London ON N6A 3K7 Canada Digital Image Grp DIG London ON N6A 3K7 Canada

Brain functional connectivity (FC) has shown great potential in becoming biomarkers of brain status. However, the problem of accurately estimating FC from complex-noisy fMRI time series remains unsolved. Usually, a regularization function is more appropriate in fitting the real inherent properties of the brain function activity pattern, which can further limit noise interference to improve the accuracy of the estimated result. Recently, the neuroscientists widely suggested that the inherent brain function activity pattern indicates sparse, modular and overlapping topology. However, previous studies have never considered this factual characteristic. Thus, we propose a novel method by integration of these inherent brain function activity pattern priors to estimate FC. Extensive experiments on synthetic data demonstrate that our method can more accurately estimate the FC than previous. Then, we applied the estimated FC to predict the symptom severity of depressed patients, the symptom severity is related to subtle abnormal changes in the brain function activity, a more accurate FC can more effectively capture the subtle abnormal brain function activity changes. As results, our method better than others with a higher correlation coefficient of 0.4201. Moreover, the overlapping probability of each brain region can be further explored by the proposed method.

关键词： Brain modeling Functional magnetic resonance imaging Correlation Estimation Diseases Covariance matrices computational modeling Functional connectivity sparse inverse covariance estimation sparse overlapping modularized priors depression

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Evolving Transcriptomic Profiles From Single-Cell RNA-Seq Data Using Nature-Inspired Multiobjective Optimization

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2445-2458页

作者： Li, Xiangtao Zhang, Shixiong Wong, Ka-Chun Jilin Univ Sch Artificial Intelligence Jilin 132000 Jilin Peoples R China City Univ Hong Kong Dept Comp Sci Hong Kong Peoples R China

Transcriptomic profiling plays an important role in post-genomic analysis. Especially, the single-cell RNA-seq technology has advanced our understanding of gene expression from cell population level into individual cell level. Many computational methods have been proposed to decipher transcriptomic profiles from those RNA-seq data. However, most of the related algorithms suffer from realistic restrictions such as high dimensionality and premature convergence. In this paper, we propose and formulate an evolutionary multiobjective blind compressed sensing (EMOBCS) to address those problems for evolving transcriptomic profiles from single-cell RNA-seq data. In the proposed framework, to characterize various gene expression profile models, two objective functions including chi-squared kernel score and euclidean distance of different gene expression profiles are formulated. After that, multiobjective blind compressed sensing based on artificial bee colony is designed to optimize the two objective functions on single-cell RNA-seq data by proposing a rank probability model and two new search strategies into the cooperative convolution framework in an unbiased manner. To demonstrate its effectiveness, extensive experiments have been conducted, comparing the proposed algorithm with 14 algorithms including eight state-of-the-art algorithms and six different EMOBCS algorithms under different search strategies on 10 single-cell RNA-seq datasets and one case study. The experimental results reveal that the proposed algorithm is better than or comparable with those compared algorithms. Furthermore, we also conduct the time complexity analysis, convergence analysis, and parameter analysis to demonstrate various properties of EMOBCS.

关键词： Transcriptomic profiling compressed sensing RNA-seq multi-objective optimization

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