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

作者： Kanishka Ranaweera Pubudu N. Pathirana School of Engineering Deakin University Australia

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Medical image analysis has witnessed a paradigm shift with the advent of artificial intelligence, particularly the application of Vision Transformers (ViTs). In this study, we leverage the unique attention mechanisms of ViTs to enhance the representation learning of pneumonia-related patterns in medical images. We propose a novel framework that integrates ViTs into the diagnostic pipeline, aiming to capitalize on their ability to capture long-range dependencies and contextual information. The Vision Transformer’s unique ability to capture intricate patterns within radiological images illuminates previously unseen insights, establishing it as a promising tool for revolutionizing pneumonia diagnosis in clinical settings. The experimental evaluation conducted on a comprehensive dataset demonstrates superior performance compared to traditional approaches. The results highlight the potential of ViTs as a valuable tool in advancing the accuracy of pneumonia detection from medical imaging data, contributing to the ongoing efforts in improving diagnostic capabilities for respiratory diseases.

关键词： Representation learning Computer vision Pneumonia Accuracy Image analysis computational modeling Transformers Medical diagnostic imaging X-ray imaging Residual neural networks

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Automatic Fracture Image Recognition Based on an Improved Multi-Stream Residual Convolutional Neural Network

Automatic Fracture Image Recognition Based on an Improved Mu...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Yutong Xia Dawei Qiu Cheng Zhang Liang Jiang Jing Liu School of Medical Information Engineering Shandong University of Traditional Chinese Medicine Jinan China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

This study proposes an automatic fracture image recognition method based on an improved Multi-Stream Residual Convolutional Neural Network (MSR-CNN). Fractures, typically caused by external forces or diseases, are often accompanied by severe pain, swelling, and functional impairment. This is particularly concerning for the elderly, as fractures can lead to prolonged bed rest and multiple complications, significantly affecting their quality of life. Imaging techniques such as Xray, CT, MRI, and ultrasound play a crucial role in fracture diagnosis. However, traditional diagnostic methods heavily rely on the subjective judgment of radiologists, which has inherent limitations. The application of computer-aided diagnosis (CAD) and artificial intelligence technologies has brought about new breakthroughs in medical image recognition. The proposed MSRCNN model combines multi-stream network structures with residual network modules, effectively enhancing the accuracy and efficiency of fracture image recognition. Experimental results demonstrate that the model achieves excellent performance in terms of accuracy, precision, recall, and F1 scores in fracture detection tasks, highlighting its potential for broader applications in medical image analysis.

关键词： Solid modeling Image recognition Accuracy Image analysis computational modeling Magnetic resonance imaging Computed tomography Convolutional neural networks Medical diagnostic imaging Residual neural networks

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Biophysical Modeling of Excitable Cells - A New Approach to Undergraduate computational biology Curriculum Development 1

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21st International conference on computational Science, ICCS 2021

作者： Oprisan, Sorinel A. Department of Physics and Astronomy College of Charleston CharlestonSC United States

ISBN: (数字)9783030779801

ISBN: (纸本)9783030779795

As part of a broader effort of developing a comprehensive neuroscience curriculum, we implemented an interdisciplinary, one-semester, upper-level course called Biophysical Modeling of Excitable Cells (BMEC). The course exposes undergraduate students to broad areas of computational biology. It focuses on computational neuroscience (CNS), develops scientific literacy, promotes teamwork between biology, psychology, physics, and mathematics-oriented undergraduate students. This course also provides pedagogical experience for senior Ph.D. students from the Neuroscience Department at the Medical University of South Carolina (MUSC). BMEC is a three contact hours per week lecture-based course that includes a set of computer-based activities designed to gradually increase the undergraduates’ ability to apply mathematics and computational concepts to solving biologically-relevant problems. The class brings together two different groups of students with very dissimilar and complementary backgrounds, i.e., biology or psychology and physics or mathematics oriented. The teamwork allows students with more substantial biology or psychology background to explain to physics or mathematics students the biological implications and instill realism into the computer modeling project they completed for this class. Simultaneously, students with substantial physics and mathematics backgrounds can apply techniques learned in specialized mathematics, physics, or computer science classes to generate mathematical hypotheses and implement them in computer codes. © 2021, Springer Nature Switzerland AG.

关键词： Students

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Disease gene prediction with privileged information and heteroscedastic dropout

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bioinformatics 2021年第Sup1期37卷 I410-I417页

作者： Shu, Juan Li, Yu Wang, Sheng Xi, Bowei Ma, Jianzhu Purdue Univ Dept Stat W Lafayette IN 47906 USA Chinese Univ HongKong Dept Comp Sci & Engn Hong Kong 999077 Peoples R China Univ Washington Paul G Allen Sch Comp Sci & Engn Seattle WA 98195 USA Peking Univ Inst Artificial Intelligence Beijing 100871 Peoples R China

Motivation: Recently, machine learning models have achieved tremendous success in prioritizing candidate genes for genetic diseases. These models are able to accurately quantify the similarity among disease and genes based on the intuition that similar genes are more likely to be associated with similar diseases. However, the genetic features these methods rely on are often hard to collect due to high experimental cost and various other technical limitations. Existing solutions of this problem significantly increase the risk of overfitting and decrease the generalizability of the models. Results: In this work, we propose a graph neural network (GNN) version of the Learning under Privileged Information paradigm to predict new disease gene associations. Unlike previous gene prioritization approaches, our model does not require the genetic features to be the same at training and test stages. If a genetic feature is hard to measure and therefore missing at the test stage, our model could still efficiently incorporate its information during the training process. To implement this, we develop a Heteroscedastic Gaussian Dropout algorithm, where the dropout probability of the GNN model is determined by another GNN model with a mirrored GNN architecture. To evaluate our method, we compared our method with four state-of-the-art methods on the Online Mendelian Inheritance in Man dataset to prioritize candidate disease genes. Extensive evaluations show that our model could improve the prediction accuracy when all the features are available compared to other methods. More importantly, our model could make very accurate predictions when >90% of the features are missing at the test stage.

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Bayesian information sharing enhances detection of regulatory associations in rare cell types

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bioinformatics 2021年第Sup1期37卷 I349-I357页

作者： Wu, Alexander P. Peng, Jian Berger, Bonnie Cho, Hyunghoon MIT Comp Sci & Artificial Intelligence Lab 77 Massachusetts Ave Cambridge MA 02139 USA Univ Illinois Dept Comp Sci Champaign IL 61801 USA MIT Dept Math Cambridge MA 02139 USA Broad Inst MIT & Harvard Cambridge MA 02142 USA

Motivation: Recent advances in single-cell RNA-sequencing (scRNA-seq) technologies promise to enable the study of gene regulatory associations at unprecedented resolution in diverse cellular contexts. However, identifying unique regulatory associations observed only in specific cell types or conditions remains a key challenge;this is particularly so for rare transcriptional states whose sample sizes are too small for existing gene regulatory network inference methods to be effective. Results: We present ShareNet, a Bayesian framework for boosting the accuracy of cell type-specific gene regulatory networks by propagating information across related cell types via an information sharing structure that is adaptively optimized for a given single-cell dataset. The techniques we introduce can be used with a range of general network inference algorithms to enhance the output for each cell type. We demonstrate the enhanced accuracy of our approach on three benchmark scRNA-seq datasets. We find that our inferred cell type-specific networks also uncover key changes in gene associations that underpin the complex rewiring of regulatory networks across cell types, tissues and dynamic biological processes. Our work presents a path toward extracting deeper insights about cell type-specific gene regulation in the rapidly growing compendium of scRNA-seq datasets.

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TransProtFam: A Transformer Derived Deep Learning Model for Hierarchical Protein Family Annotation

TransProtFam: A Transformer Derived Deep Learning Model for ...

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Computer Vision and Machine intelligence (CVMI), International conference on

作者： Chinju John Jayakrushna Sahoo Department of Computer Science and Engineering Indian Institute of Information Technology Kottayam Pala Kerala India

ISBN: (数字)9798350376876

ISBN: (纸本)9798350376883

Transformers are knowledge reservoirs trained on vast and heterogeneous data sets with the potential for cross-functional tasks. These large corpus-trained models can be leveraged for cumbersome tasks like Protein family annotations which are pivotal for discerning the features of a protein sequence and its functional roles. Here we have harnessed the knowledge derived from such protein sequence-based transformers to fine-tune them for protein family *** performance of such models was compared on benchmark datasets like GPCR and PoG to annotate protein sequences at hierarchical levels. Parameter Efficient Fine Tuning was used to optimise the number of trainable parameters and to perform less computationally expensive training. Our proposed model -TransProtFam could come up with accuracy scores of 99.43%,90.85% and 84.49% at family, sub-family and sub-subfamily levels, which exceeds the state-of-the-art neural network models. Our findings and the finetuning process we employed may provide insights into utilising large models trained on protein sequence corpora for specific tasks in bioinformatics and related domains.

关键词： Deep learning Training Accuracy Annotations computational modeling Biological system modeling Neural networks Transformers Protein sequence Tuning

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Protein Crystallization Identification via Fuzzy Model on Linear Neighborhood Representation

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1986-1995页

作者： Ding, Yijie Tang, Jijun Guo, Fei Suzhou Univ Sci & Technol Sch Elect & Informat Engn Suzhou 215009 Jiangsu Peoples R China Tianjin Univ Sch Comp Sci & Technol Coll Intelligence & Comp Tianjin 300072 Peoples R China Univ South Carolina Dept Comp Sci & Engn Columbia SC 29208 USA

X-ray crystallography is the most popular approach for analyzing protein 3D structure. However, the success rate of protein crystallization is very low (2-10 percent). To reduce the cost of time and resources, lots of computation-based methods are developed to detect the protein crystallization. Improving the accuracy of predicting protein crystallization is very important for the determination of protein structure by X-ray crystallography. At present, many machine learning methods are used to predict protein crystallization. In this article, we propose a Fuzzy Support Vector Machine based on Linear Neighborhood Representation (FSVM-LNR) to predict the crystallization propensity of proteins. Proteins are represented by three types of features (PsePSSM, PSSM-DWT, MMI-PS), and these features are serially combined and fed into FSVM-LNR. FSVM-LNR can filter outliers by membership score, which is calculated via reconstruction residuals of k nearest samples. To evaluate the performance of our predictive model, we test FSVM-LNR on the datasets of TRAIN3587, TEST3585 and TEST500. Our method achieves better Mathew's correlation coefficient (MCC) on TRAIN3587 (MCC: 0.56) and TEST3585 (MCC: 0.58). Although the performance of independent test is not the best on TEST500, FSVM-LNR also has a certain predictability (MCC: 0.70) in the identification of protein crystallization. The good performance on the datasets proves the effectiveness of our method and the better performance on large datasets further demonstrates the stability and superiority of our method.

关键词： Protein crystallization prediction fuzzy support vector machine sparse representation membership function protein feature extraction

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Guest Editorial for Selected Papers from BIOKDD 2018 and DMBIH 2018

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2020年第6期17卷 1832-1834页

作者： Yan, Da Gao, Xin Fodeh, Samah J. Chen, Jake Y. Univ Alabama Birmingham Dept Comp Sci 4105 Univ Hall1402 10th Ave South Birmingham AL 35294 USA King Abdullah Univ Sci & Technol KAUST Computat Biosci Res Ctr Comp Elect & Math Sci & Engn Div Thuwal 23955 Saudi Arabia Jarad Yale Sch Med Ctr Healthcare Innovat Redesign & Learning CHIRAL New Haven CT 06510 USA Univ Alabama Birmingham PhD Informat Inst 1900 Univ BlvdTHT 1st Floor Birmingham AL 35233 USA

The papers in this special issue were presented at the 2018 17th International Workshop on Data Mining in bioinformatics (BIOKDD), held in conjunction with the ACM SIGKDD International conference on Knowledge Discover... 详细信息

关键词： Special Issues And Sections bioinformatics Data Mining computational biology Meetings

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Co-linear Chaining with Overlaps and Gap Costs 26th

Co-linear Chaining with Overlaps and Gap Costs

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26th International conference on Research in computational Molecular biology, RECOMB 2022

作者： Jain, Chirag Gibney, Daniel Thankachan, Sharma V. Department of Computational and Data Sciences Indian Institute of Science Bangalore India School of Computer Science and Engineering Georgia Institute of Technology Atlanta United States Department of Computer Science University of Central Florida Orlando United States

ISBN: (纸本)9783031047480

Co-linear chaining has proven to be a powerful heuristic for finding near-optimal alignments of long DNA sequences (e.g., long reads or a genome assembly) to a reference. It is used as an intermediate step in several alignment tools that employ a seed-chain-extend strategy. Despite this popularity, efficient subquadratic-time algorithms for the general case where chains support anchor overlaps and gap costs are not currently known. We present algorithms to solve the co-linear chaining problem with anchor overlaps and gap costs in O~ (n) time, where n denotes the count of anchors. We also establish the first theoretical connection between co-linear chaining cost and edit distance. Specifically, we prove that for a fixed set of anchors under a carefully designed chaining cost function, the optimal ‘anchored’ edit distance equals the optimal co-linear chaining cost. Finally, we demonstrate experimentally that optimal co-linear chaining cost under the proposed cost function can be computed orders of magnitude faster than edit distance, and achieves correlation coefficient above 0.9 with edit distance for closely as well as distantly related sequences. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Anchors

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New Distance-Based approach for Genome-Wide Association Studies

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第5期19卷 2938-2949页

作者： Irigoien, Itziar Cormand, Bru Soler-Artigas, Maria Sanchez-Mora, Cristina Ramos-Quiroga, Josep-Antoni Arenas, Concepcion Univ Basque Country UPV EHU Dept Comp Sci & Artificial Intelligence Leioa 48940 Biscay Spain Univ Barcelona Fac Biol Dept Genet Microbiol & Estadist Barcelona 08007 Catalonia Spain Inst Salud Carlos III Ctr Invest Biomed Red Enfermedades Raras CIBERER Madrid 28220 Spain Inst Biomed Univ Barcelona IBUB Barcelona 08028 Catalonia Spain Inst Recerca St Joan de Diu Barcelona 08950 Catalonia Spain Univ Autonoma Barcelona Vall dHebron Res Inst Psychiat Genet Unit Grp Psychiat Mental Hlth & Addict Barcelona 08193 Spain Hosp Univ Vall dHebron Dept Psychiat Barcelona 08035 Spain Inst Salud Carlos III Biomed Network Res Ctr Mental Hlth CIBERSAM Madrid 28220 Spain Univ Barcelona Fac Biol Dept Genet Microbiol & Stat Barcelona 08007 Catalonia Spain Univ Autonoma Barcelona Dept Psychiat & Legal Med Barcelona 08193 Spain Univ Barcelona Dept Genet Microbiol & Stat Stat Sect Barcelona 08028 Spain

With the rise of genome-wide association studies (GWAS), the analysis of typical GWAS data sets with thousands of single-nucleotide polymorphisms (SNPs) has become crucial in biomedicine research. Here, we propose a new method to identify SNPs related to disease in case-control studies. The method, based on genetic distances between individuals, takes into account the possible population substructure, and avoids the issues of multiple testing. The method provides two ordered lists of SNPs;one with SNPs which minor alleles can be considered risk alleles for the disease, and another one with SNPs which minor alleles can be considered as protective. These two lists provide a useful tool to help the researcher to decide where to focus attention in a first stage.

关键词： Diseases Genomics bioinformatics Psychiatry Tools Sociology Mental health Distances NN-nearest neighbours DB-discriminant genome-wide association studies ADHD

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