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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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Molecular Connectivity Index-Based Data Augmentation for Molecular Property Prediction

IEEE Transactions on Computational Biology and Bioinformatic...

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ieee Transactions on computational biology and bioinformatics 2025年第3期22卷 1234-1247页

作者： Zeyu Wang Tianyi Jiang Jinhuan Wang Jiafei Shao Bin Wei Qi Xuan Hong Wang Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology Hangzhou China Binjiang Institute of Artificial Intelligence Zhejiang University of Technology Hangzhou China College of Pharmaceutical Science & Collaborative Innovation Center of Yangtze River Delta Region Green Pharmaceuticals Zhejiang University of Technology Hangzhou China

Recent years have seen a rapid growth of machine learning in cheminformatics problems. In order to tackle the problem of insufficient training data in reality, more and more researchers pay attention to data augmentation technology. However, few researchers pay attention to the problem of construction rules and domain information of data, which will directly impact the quality of augmented data and the augmentation performance. While in graph-based molecular research, the molecular connectivity index, as a critical topological index, can directly or indirectly reflect the topology-based physicochemical properties and biological activities. In this paper, we propose a novel data augmentation technique that modifies the topology of the molecular graph to generate augmented data with the same molecular connectivity index as the original data. The molecular connectivity index combined with data augmentation technology helps to retain more topology-based molecular properties information and generate more reliable data. Furthermore, we adopt five benchmark datasets to test our proposed models, and the results indicate that the augmented data generated based on important molecular topology features can effectively improve the prediction accuracy of molecular properties, which also provides a new perspective on data augmentation in cheminformatics studies.

关键词： Indexes Data augmentation Data models Training Data mining Graph neural networks Chemicals Topology Reliability Predictive models

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AI-Driven Decision Support System for Heart Failure Diagnosis: INTELHEART Approach Towards Personalized Treatment Strategies

AI-Driven Decision Support System for Heart Failure Diagnosi...

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ieee Symposium on bioinformatics and Bioengineering (BIBE)

作者： Smiljana Tomasevic Andjela Blagojevic Tijana Geroski Gordana Jovicic Bogdan Milicevic Momcilo Prodanovic Ilija Kamenko Bojana Bajic Stefan Simovic Goran Davidovic Dragana Ignjatovic Ristic Andrej Preveden Lazar Velicki Arsen Ristic Svetlana Apostolovic Edin Dolicanin Nenad Filipovic Faculty of Engineering University of Kragujevac Serbia Institute for Information Technologies Kragujevac Serbia Institute for Artificial Intelligence Research and Development of Serbia Novi Sad Serbia Faculty of Medical Sciences University of Kragujevac Serbia Faculty of Medicine University of Novi Sad Serbia Faculty of Medicine (School of Medicine) University of Belgrade Serbia Faculty of Medicine University of Nis Serbia State University of Novi Pazar Serbia

ISBN: (数字)9798331518622

ISBN: (纸本)9798331518639

Heart failure is recognized as a modern epidemic and despite advances in therapy and research, heart failure still carries an ominous prognosis and a significant socioeconomic burden. The main aim of this paper is to demonstrate how novel Decision Support System (DSS) and computational platform like INTELHEART can transform the future of healthcare and early diagnosis of heart failure. The main idea is integration of patient-specific data (i.e. demographic and physical characteristics, medical history, symptoms and signs) and results obtained using existing and novel diagnostic technologies into the cloud environment. Data will be used by different tools for machine learning and computational modelling, developing virtual patient population. Moreover, voice as a biomarker will be collected among participating patients, in order to create a VoiceHeart mobile app. INTELHEART represents a transformative advancement in heart failure care, aiming to make treatment more personalized, and proactive. This initiative centers on precision medicine, using AI-driven analysis and a powerful DSS alongside the cloud-based platform and VoiceHeart mobile app to assist both clinicians and patients. Additionally, it incorporates assessments of psychological resilience and emotional well-being, addressing the oftenoverlooked mental health factors essential to comprehensive heart failure management.

关键词： Decision support systems Cloud computing Precision medicine Transforms Mobile applications Cardiovascular diseases Socioeconomics Prognostics and health management Medical diagnostic imaging Resilience

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Plant Leaf Disease Detection Using EfficientNetB7 and L1-Regularized Neural Networks

Plant Leaf Disease Detection Using EfficientNetB7 and L1-Reg...

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Applied intelligence and Computing (AIC), ieee World conference on

作者： B Suvarna R Vidhya Sagar T Havish B Sridevi A Sampath Department of CSE VFSTR Guntur AP India Department of CSE Guntur AP India Department of CSE GST GITAM (Deemed to be University) Guntur AP India

ISBN: (数字)9798350384598

ISBN: (纸本)9798350384604

The Indian economy heavily relies on agriculture, making it crucial to address plant diseases promptly to prevent significant losses in production and economic setbacks. Manual monitoring of plant health is labor-intensive and time-consuming, hence utilizing image processing techniques, specifically Convolutional Neural Networks, presents a viable solution. In this work, a pre trained model called EfficientNetB7 used for feature extraction and a neural network with L1 regularization is developed for finding the disease. The suggested model underwent training using a dataset of 4000 images depicting both diseased and healthy plant leaves. The objective was to build a precise technique for diagnosing plant illnesses by analyzing leaf data. This approach aims to streamline disease detection, enabling timely intervention to mitigate losses and enhance agricultural sustainability in India.

关键词： computational modeling Plants (biology) Neural networks Crops Production Feature extraction Loss measurement Sustainable development Monitoring Diseases

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Identifying Systematic Variation at the Single-Cell Level by Leveraging Low-Resolution Population-Level Data 26th

Identifying Systematic Variation at the Single-Cell Level ...

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

作者： Rahmani, Elior Jordan, Michael I. Yosef, Nir Department of Electrical Engineering and Computer Science University of California Berkeley BerkeleyCA United States Department of Statistics University of California Berkeley BerkeleyCA United States Center for Computational Biology University of California Berkeley BerkeleyCA United States Chan-Zuckerberg Biohub San FranciscoCA United States Ragon Institute of MGH MIT and Harvard CambridgeMA United States

ISBN: (纸本)9783031047480

A major limitation in single-cell genomics is a lack of ability to conduct cost-effective population-level studies. As a result, much of the current research in single-cell genomics focuses on biological processes that are broadly conserved across individuals, such as cellular organization and tissue development. This limitation prevents us from studying the etiology of experimental or clinical conditions that may be inconsistent across individuals owing to molecular variation and a wide range of effects in the population. In order to address this gap, we developed "kernel of integrated single cells" (Keris), a novel model-based framework to inform the analysis of single-cell gene expression data with population-level effects of a condition of interest. By inferring cell-type-specific moments and their variation across conditions using large tissue-level bulk data representing a population, Keris allows us to generate testable hypotheses at the single-cell level that would otherwise require collecting single-cell data from a large number of donors. Within the Keris framework, we show how the combination of low-resolution, large bulk data with small but high-resolution single-cell data enables the identification of changes in cell-subtype compositions and the characterization of subpopulations of cells that are affected by a condition of interest. Using Keris we estimate linear and non-linear age-associated changes in cell-type expression in large bulk peripheral blood mononuclear cells (PBMC) data. Combining with three independent single-cell PBMC datasets, we demonstrate that Keris can identify changes in cell-subtype composition with age and capture cell-type-specific subpopulations of senescent cells. This demonstrates the promise of enhancing single-cell data with population-level information to study compositional changes and to profile condition-affected subpopulations of cells, and provides a potential resource of targets for future clinical interventions. ©

关键词： Cost effectiveness

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A Hierarchical Discriminative Sparse Representation Classifier for EEG Signal Detection

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1679-1687页

作者： Gu, Xiaoqing Zhang, Cong Ni, Tongguang Changzhou Univ Sch Informat Sci & Engn Changzhou 213164 Jiangsu Peoples R China Changzhou Univ Sch Comp & Artificial Intelligence Changzhou 213164 Jiangsu Peoples R China

Classification of electroencephalogram (EEG) signal data plays a vital role in epilepsy detection. Recently sparse representation-based classification (SRC) methods have achieved the good performance in EEG signal automatic detection, by which the EEG signals are sparsely represented using a few active coefficients in the dictionary and classified according to the reconstruction criteria. However, most of SRC learn a linear dictionary for encoding, and cannot extract enough information and nonlinear relationship of data for classification. To solve this problem, a hierarchical discriminative sparse representation classification model (called HD-SRC) for EEG signal detection is proposed. Based on the framework of neural network, HD-SRC learns the hierarchical nonlinear transformation and maps the signal data into the nonlinear transformed space. Through incorporating this idea into label consistent K singular value decomposition (LC-KSVD) at the top layer of neural network, HD-SRC seeks discriminative representation together with dictionary, while minimizing errors of classification, reconstruction and discriminative sparse-code for pattern classification. By learning the hierarchical feature mapping and discriminative dictionary simultaneously, more discriminative information of data can be exploited. In the experiment the proposed model is evaluated on the Bonn EEG database, and the results show it obtains satisfactory classification performance in multiple EEG signal detection tasks.

关键词： Electroencephalography Feature extraction Dictionaries Epilepsy Classification algorithms Signal detection Brain modeling Electroencephalogram sparse representation-based classification deep neural network discriminative dictionary learning

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C2RG: Parameter-efficient Adaptation of 3D Vision and Language Foundation Model for Coronary CTA Report Generation

C2RG: Parameter-efficient Adaptation of 3D Vision and Langua...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Zhiyu Ye Yue Sun Wei Shi Bang Yang Shibin Wu Hancong Wang Cheng Xu Hairong Zheng Yining Wang Tong Zhang Department of Network Intelligence Pengcheng Laboratory Paul C.Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Shenzhen China University of Chinese Academy of Sciences Beijing China Department of Radiology Peking Union Medical College Hospital Chinese Academy of Medical Sciences and Peking Union Medical College Beijing China School of Computer Science Sun Yat-sen University Guangzhou China Department of Network Intelligence Pengcheng Laboratory Shenzhen China Department of Network Intelligence Pengcheng Laboratory ADSPLAB School of Electronic and Computer Engineering Peking University Shenzhen China Department of Network Intelligence Pengcheng Laboratory Shenzhen International Graduate School Tsinghua University Shenzhen China Paul C.Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Shenzhen China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Medical report generation (MRG) is a challenging yet highly demanding task in the application of multi-modal artificial intelligence in medicine. Typically, training an MRG model requires tens of thousands of labelled radiology images and reports datasets, which could be impractical for most clinical research groups. In this study, we present C2RG, a novel 3D vision and language foundation model tailored for Coronary Computed Tomography Angiography (CTA) Report Generation. Inspired by BLIP-2’s architecture, our method integrates a self-supervised pre-trained 3D cardiac vision model (ViT-B) and a general-purpose bilingual foundation model (ChatGLM-6B), with a lightweight querying Transformer (Q-Former). We also introduce a parallel high-resolution feature extractor module and a coronary calcification evaluation loss to simultaneously encode fine-grained 3D features and constrain the accuracy of report generation. We compared our model with six state-of-the-art MRG methods on a clinical dataset with 118 subjects, comprising 453 paired 3D CTA images and radiology reports. Experimental results with extensive ablations show the efficacy of our C2RG. Codes will be open-sourced after the conference.

关键词： Training Solid modeling Adaptation models Three-dimensional displays Accuracy Foundation models computational modeling Radiology Feature extraction Transformers

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PMDC: Pancreas Segmentation with Mamba Block and Deformable 3D Convolution

PMDC: Pancreas Segmentation with Mamba Block and Deformable ...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Ziyao Meng Yiming Qin Tianyi Wang Hao Shen Yang Liu Jie Dong Haitao Song Computer Science and Engineering Shanghai Jiao Tong University China Shanghai Artificial Intelligence Research Institute China Xiangfu Laboratory China Department of Critical Care Medicine Nanjing Jinling Hospital Medical School of Nanjing University Nanjing China National Center for Translational Medicine Shanghai Jiao Tong University China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Pancreas segmentation is a task traditionally hindered by the morphological complexity and variability of the pancreas. Transformers have excelled in capturing context information in medical images but are limited by computational resources. Recently, state space models (SSMs), especially Mamba, have demonstrated the ability to linearly scale with input sequences, surpassing transformers in long-sequence data analysis tasks and showing potential in visual tasks as well. Inspired by this, we propose a novel approach for pancreas segmentation using Mamba Block and deformable 3D convolution (PMDC). PMDC integrates Convolutional Neural Networks (CNN) and SSM, capturing both local fine-grained features and long-range dependencies through a Mamba-based U-Net architecture and leveraging deformable 3D convolutions (3DCN) to dynamically adapt to pancreatic shape variations. PMDC also ensures scalability and generalizability by self-configuring across different datasets. Experiments on NIH, MSD, and PancCT datasets demonstrate that PMDC outperforms existing CNNs and transformers, offering a robust solution for pancreas segmentation.

关键词： Image segmentation Visualization Solid modeling Three-dimensional displays Convolution Computer architecture Transformers Feature extraction Pancreas Convolutional neural networks

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HierMRL: Hierarchical Structure-Aware Molecular Representation Learning for Property Prediction

HierMRL: Hierarchical Structure-Aware Molecular Representati...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Maotao Liu Yifan Yang Xu Gong Li Liu Qun Liu Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing China Tencent Shenzhen China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Deep graph neural networks have recently demonstrated powerful representation learning capabilities in bioinformatics. It is still crucial and challenging to design a representation model fusing the fundamental chemistry and biology knowledge. However, most existing representation models not only ignore domain knowledge but are also built on labeled datasets. To address this issue, this paper proposed a hierarchical structure-aware pre-training model that used contrastive learning to improve molecular representations with unlabeled datasets. We conducted comprehensive experiments on 13 molecular benchmark datasets from different application domains. The results demonstrate that our hierarchical structure-aware pre-trained model achieves superior performance against state-of-the-art baselines.

关键词： Representation learning Drugs Chemistry Biological system modeling Benchmark testing Predictive models Graph neural networks

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GraphConvNet: A Dual Network Utilizing Local Features Coupled with Structural Information for Predicting Knee Osteoarthritis

GraphConvNet: A Dual Network Utilizing Local Features Couple...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Pengju Tang Dawei Dai Kai Zou Xionghui Yang Guoyin Wang Chongqing Key Laboratory of Computational Intelligence Key Laboratory of Big Data Intelligent Computing Chongqing University of Posts and Telecommunications Chongqing China College of Computer and Information Science Chongqing Normal University Chongqing China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Knee Osteoarthritis (KOA) is a common joint disease that severely affects the normal lives of patients. In clinical practice, the severity of KOA is commonly evaluated by observing radiographs of the knee joint However, this approach heavily relies on a doctor’s clinical experience and exhibits a certain degree of subjectivity. In previous studies, various advanced deep convolutional neural network (CNN) models have been used to diagnose KOA. As known, CNN models often focus on learning the local detailed features for decision-making and lack attention to global structural information. In this study, we propose a dual network called GraphConvNet that integrates a visual graph neural network with a deep CNN to enhance representation learning by leveraging both local detailed features and global structural information. Our proposed method was evaluated using an Osteoarthritis Initiative (OAI) dataset and achieved an overall accuracy, recall, precision, and mean absolute error (MAE) of 75.24%, 77.75%, 74.33% and 0.283, respectively. Experiments demonstrate that our proposed method significantly improves the performance and achieves state-of-the-art performance. All codes are available at https://***/ddw2AIGROUP2CQUPT/GraphConvNet.

关键词： Representation learning Visualization Decision making Medical services Feature extraction Graph neural networks Convolutional neural networks Osteoarthritis Diagnostic radiography Diseases

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