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CNN-RNN Based Intelligent Recommendation for Online Medical Pre-Diagnosis Support

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第3期18卷 912-921页

作者： Zhou, Xiaokang Li, Yue Liang, Wei Shiga Univ Fac Data Sci Hikone Shiga 5228522 Japan RIKEN Ctr Adv Intelligence Project Tokyo 1030027 Japan South China Univ Technol Sch Comp Sci & Engn Guangzhou 510006 Guangdong Peoples R China Hunan Univ Technol & Business Key Lab Hunan Prov New Retail Virtual Real Techno Changsha 410083 Peoples R China Cent South Univ Business Sch Changsha 410083 Peoples R China

The rapidly developed Health 2.0 technology has provided people with more opportunities to conduct online medical consultation than ever before. Understanding contexts within different online medical communications and activities becomes a significant issue to facilitate patients' medical decision making process. As a subcategory of machine learning, neural networks have drawn increasing attentions in natural language processing applications. In this article, we focus on modeling and analyzing the patient-physician-generated data based on an integrated CNN-RNN framework, in order to deal with the situation that patients' online inquiries are usually not very long. A so-called DP-CRNN algorithm is developed with a newly designed neural network structure, to extract and highlight the combination of semantic and sequential features in terms of patient's inquiries. An intelligent recommendation method is then proposed to provide patients with automatic clinic guidance and pre-diagnosis suggestions, in which a clustering mechanism is utilized to refine the learning process with more precise diagnosis scope and more representative features. Experiments based on the collected real world data demonstrate the effectiveness of our proposed model and method for intelligent pre-diagnosis service in online medical environments.

关键词： Medical diagnostic imaging Recurrent neural networks Feature extraction Convolutional neural networks Machine learning Task analysis Convolutional neural network recurrent neural network natural language processing online medical diagnosis online medical inquiry intelligent recommendation

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Improved Classification of Alzheimer’s Disease With Convolutional Neural Networks

Improved Classification of Alzheimer’s Disease With Convolu...

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ieee Signal Processing in Medicine and biology symposium (SPMB)

作者： S. Muhammed J. Upadhya S. Poudel M. Hasan K. Donthula J. Vargas J. Ranganathan K. Poudel Computational and Data Science Middle Tennessee State University Murfreesboro TN USA Computer Science Middle Tennessee State University Murfreesboro TN USA Optum Inc Murfreesboro TN USA Engineering Technology Middle Tennessee State University Murfreesboro TN USA

This work focuses on classifying MRI images using machine learning models to identify Alzheimer’s disease (AD), the most common form of dementia, at an early stage [1]. It is now possible to identify and forecast the onset of AD by analyzing brain scans collected through Magnetic Resonance Imaging (MRI) and using artificial intelligence (AI) technologies, classifying patients as either at risk or not. The main goal is to make precise predictions. Improved prediction and detection tools for radiologists, physicians, and caregivers will be made available as a result of the study’s determination of the likelihood that individuals would develop AD and proper categorization of them. Using machine learning methods such as Convolutional Neural Network (CNN), Support Vector Machine (SVM), and fastai—a user-friendly deep learning library and framework—we developed models using a dataset of 6400 MRI images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) 4 class for the early detection and classification of AD [2]. The models achieve an impressive accuracy rate of 84.4%, with the addition of a pre-trained Visual Geometry Group (VGG) layer to the sequential model, outperforming other algorithms. This study demonstrates a viable method for classifying and diagnosing Alzheimer’s disease early on, utilizing MRI images and machine learning models. The acquired accuracy rates show the models’ potential to assist doctors and other caregivers in AD diagnosis and management, thereby maximizing overall effectiveness.

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MGCT: Mutual-Guided Cross-Modality Transformer for Survival Outcome Prediction using Integrative Histopathology-Genomic Features

MGCT: Mutual-Guided Cross-Modality Transformer for Survival ...

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ieee International Conference on bioinformatics and Biomedicine (BIBM)

作者： Mingxin Liu Yunzan Liu Hui Cui Chunquan Li Jiquan Ma Department of Computer Science and Technology Heilongjiang University Harbin China Department of Computer Science and Information Technology La Trobe University Melbourne Australia The First Affiliated Hospital Institute of Cardiovascular Disease Hengyang Medical School University of South China Hengyang China The First Affiliated Hospital Cardiovascular Lab of Big Data and Imaging Artificial Intelligence Hengyang Medical School University of South China Hengyang China Department of Biochemistry and Molecular Biology School of Basic Medical Sciences Hengyang Medical School University of South China Hengyang China

The rapidly emerging field of deep learning-based computational pathology has shown promising results in utilizing whole slide images (WSIs) to objectively prognosticate cancer patients. However, most prognostic methods are currently limited to either histopathology or genomics alone, which inevitably reduces their potential to accurately predict patient prognosis. Whereas integrating WSIs and genomic features presents three main challenges: (1) the enormous heterogeneity of gigapixel WSIs which can reach sizes as large as 150,000×150,000 pixels; (2) the absence of a spatially corresponding relationship between histopathology images and genomic molecular data; and (3) the existing early, late, and intermediate multimodal feature fusion strategies struggle to capture the explicit interactions between WSIs and genomics. To ameliorate these issues, we propose the Mutual-Guided Cross-Modality Transformer (MGCT), a weakly-supervised, attention-based multimodal learning framework that can combine histology features and genomic features to model the genotype-phenotype interactions within the tumor microenvironment. To validate the effectiveness of MGCT, we conduct experiments using nearly 3,600 gigapixel WSIs across five different cancer types sourced from The Cancer Genome Atlas (TCGA). Extensive experimental results consistently emphasize that MGCT outperforms the state-of-the-art (SOTA) methods.

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Prospecting epilepsy surgery outcome using virtual resection paradigm. computational-model validation

Prospecting epilepsy surgery outcome using virtual resection...

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ieee Conference on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Kalitzin, Stiliyan Petkov, George Demuru, Matteo Widman, Guido Stichting Epilepsie Instellingen Nederland SEIN Dept Med Technol Zwolle Netherlands Stichting Epilepsie Instellingen Nederland SEIN Dept Res Zwolle Netherlands Stichting Epilepsie Instellingen Nederland SEIN Dept Clin Neurophysiol Zwolle Netherlands

ISBN: (纸本)9781728194684

Epilepsy surgery still lacks an operational method for finding the epileptogenic zone (EZ) or the minimal amount of tissue that has to be resected in order to leave the patient seizure-free. Here we propose a method for predicting the result of a resection from data collected before the eventual resection, therefore allowing an optimal surgical planning. Our major hypothesis is that focal generalized epilepsies are caused by sub-systems with excessive afferent connectivity to and from the rest of the neuronal tissue. To address the issue of delineating the EZ before the actual resection, we propose a paradigm performing "virtual surgery" on the matrix of connectivity between local EEG measurements. The virtual resection removes not only the nodes covered by the suspected EZ and their connections but also subtracts the influence of these nodes on the rest of connectivity. The residual connectivity is then compared to the original one and a significant decrease indicate that the resection contains the EZ or at least large part of it. We tested this approach on a computational model of spatially distributed bi-stable units that provides a generic model of focal epileptic neuronal system. In the modelled cases of epileptic system with spreading seizures, we found that the removal of the EZ can be predicted by significant decrease of the residual connectivity after performing virtual resection. This decrease commensurate with the increase of the epileptic threshold (the ground truth). The method also predicts the actual change of connectivity after removing the nodes from the model dynamics. In addition we tested our techniques against the "naive" virtual resection which is based on simply removing the corresponding nodes from the connectivity measure. The findings in this work can be exploited to increase the efficiency and accuracy of pre-surgical epileptogenic zone localization in cases of focal epileptic seizure onsets.

关键词： Epilepsy Surgery computational Models Connectivity

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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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Artificial intelligence in Biological Activity Prediction 13th

Artificial Intelligence in Biological Activity Prediction

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13th International Conference on Practical Applications of computational biology and bioinformatics (PACBB)

作者： Correia, Joao Resende, Tiago Baptista, Delora Rocha, Miguel Univ Minho CEB Ctr Biol Engn Campus Gualtar Braga Portugal

ISBN: (纸本)9783030238735;9783030238728

Artificial intelligence has become an indispensable resource in chemoinformatics. Numerous machine learning algorithms for activity prediction recently emerged, becoming an indispensable approach to mine chemical information from large compound datasets. These approaches enable the automation of compound discovery to find biologically active molecules with important properties. Here, we present a review of some of the main machine learning studies in biological activity prediction of compounds, in particular for sweetness prediction. We discuss some of the most used compound featurization techniques and the major databases of chemical compounds relevant to these tasks.

关键词： Machine learning Deep learning Biological activity prediction Sweetness prediction Compound featurization

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Epilepsy Signal Recognition Using Online Transfer TSK Fuzzy Classifier Underlying Classification Error and Joint Distribution Consensus Regularization

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第5期18卷 1667-1678页

作者： Zhang, Yuanpeng Zhou, Ziyuan Pan, Wenjie Bai, Heming Liu, Wei Wang, Li Lin, Chuang Nantong Univ Dept Med Informat Med Nursing Sch Nantong 226001 Peoples R China Hong Kong Polytech Univ Dept Hlth Technol Informat Hong Kong Peoples R China Nantong Univ Res Ctr Intelligence Informat Technol Nantong 226001 Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen 518000 Peoples R China

In this study, an online transfer TSK fuzzy classifier O-T-TSK-FC is proposed for recognizing epilepsy signals. Compared with most of the existing transfer learning models, O-T-TSK-FC enjoys its merits from the following three aspects: 1) Since different patients often response to the same neuronal firing stimulation in different neural manners, the labeled data in the source domain cannot accurately represent the primary EEG data in the target domain. Therefore, we design an objective function which can integrate with subject-specific data in the target domain to induce the target predictive function. 2) A new regularization used for knowledge transfer is proposed from the perspective of error consensus, and its rationality is explained from the perspective of probability density estimation. 3) Clustering is used to partition source domains so as to reduce the computation of O-T-TSK-FC without affecting its performance. Based on the EEG signals collected from Bonn University, six different online scenarios for transfer learning are constructed. Experimental results on them show that O-T-TSK-FC performs better than benchmarking algorithms and robustly.

关键词： Feature extraction Electroencephalography Brain modeling Calibration Epilepsy Training Probability distribution TSK fuzzy classifier transfer learning classification error consensus joint distribution consensus epilepsy signals recognition

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GrantExtractor: Accurate Grant Support Information Extraction from Biomedical Fulltext Based on Bi-LSTM-CRF

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第1期18卷 205-215页

作者： Dai, Suyang Ding, Yuxia Zhang, Zihan Zuo, Wenxuan Huang, Xiaodi Zhu, Shanfeng Fudan Univ Sch Comp Sci Shanghai 200433 Peoples R China Fudan Univ Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China Fudan Univ Sch Data Sci Shanghai 200433 Peoples R China Charles Sturt Univ Dept Comp & Math Albury NSW 2640 Australia Fudan Univ Inst Sci & Technol Brain Inspired Intelligence Shanghai 200433 Peoples R China Fudan Univ Key Lab Computat Neurosci & Brain Inspired Intell Minist Educ Shanghai 200433 Peoples R China

Grant support (GS) in the MEDLINE database refers to funding agencies and contract numbers. It is important for funding organizations to track their funcing outcomes from the GS information. As such, how to accurately and automatically extract funding information from biomedical literature is challenging. In this paper, we present a pipeline system called GrantExtractor that is able to accurately extract GS information from fulltext biomedical literature. GrantExtractor effectively integrates several advanced machine learning techniques. In particular, we use a sentence classifier to identify funcing sentences from articles first. A bi-directional LSTM and the CRF layer (BiLSTM-CRF), and pattern matching are then used to extract entities of grant numbers and agencies from these identified funcing sentences. After removing noisy numbers by a multi-class model, we finally match each grant number with its corresponding agency. Experimental results on benchmark datasets have demonstrated that GrantExtractor clearly outperforms all baseline methods. It is further evident that GrantExtractor won the first place in Task 5C of 2017 BioASQ challenge, with achieving the Micro-recall of 0.9526 for 22,610 articles. Moreover, GrantExtractor has achieved the Micro F-measure score as high as 0.90 in extracting grant pairs.

关键词： Grant support BiLSTM-CRF biomedical text mining information extraction biomedical fulltext

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From Nano to Macro: Overview of the ieee Bio Image and Signal Processing Technical Committee

arXiv

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arXiv 2022年

作者： Aviyente, Selin Frangi, Alejandro Meijering, Erik Muñoz-Barrutia, Arrate Liebling, Michael De Ville, Dimitri Van Olivo-Marin, Jean-Christophe Kovačević, Jelena Unser, Michael The Department of Electrical and Computer Engineering Michigan State University East LansingMI48824 United States Schools of Computing and Medicine University of Leeds LeedsLS2 9JT United Kingdom The Alan Turing Institute London United Kingdom and Cardiovascular Sciences KU Leuven Leuven Belgium The School of Computer Science and Engineering University of New South Wales SydneyNSW2052 Australia The Bioengineering Department Universidad Carlos III de Madrid Madrid Leganés Spain The Idiap Research Institute Martigny Switzerland The Neuro-X Institute School of Engineering EPFL Geneva Faculty of Medicine University of Geneva Switzerland The Cell Biology and Infection Department Institut Pasteur Paris France The New York University Tandon School of Engineering BrooklynNY United States The School of Engineering EPFL Lausanne Switzerland

The Bio Image and Signal Processing (BISP) Technical Committee (TC) of the ieee Signal Processing Society (SPS) promotes activities within the broad technical field of biomedical image and signal processing. Areas of interest include medical and biological imaging, digital pathology, molecular imaging, microscopy, and associated computational imaging, image analysis, and image-guided treatment, alongside physiological signal processing, computational biology, and bioinformatics. BISP has 40 members and covers a wide range of EDICS, including CIS-MI: Medical Imaging, BIO-MIA: Medical Image Analysis, BIO-BI: Biological Imaging, BIO: Biomedical Signal Processing, BIO-BCI: Brain/Human-Computer Interfaces, and BIO-INFR: bioinformatics. BISP plays a central role in the organization of the ieee International symposium on Biomedical Imaging (ISBI) and contributes to the technical sessions at the ieee International Conference on Acoustics, Speech and Signal Processing (ICASSP), and the ieee International Conference on Image Processing (ICIP). In this paper, we provide a brief history of the TC, review the technological and methodological contributions its community delivered, and highlight promising new directions we anticipate. © 2022, CC BY.

关键词： bioinformatics

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A computational Model to Analyse E/I (Excitation/Inhibition) Dynamics for Neural Network Integrated with Astrocyte

A Computational Model to Analyse E/I (Excitation/Inhibition)...

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2020 ieee Conference on computational intelligence in bioinformatics and computational biology, CIBCB 2020

作者： Bandyopadhyay, Anirban Sharma, Gaurav Roy Chowdhury, Shubhajit Biomedical Systems Laboratory Multimedia Analytics Networks and Systems Group School of Basic Science United States Biomedical Systems Laboratory Multimedia Analytics Networks and Systems Group School of Computing and Electrical Engineering Indian Institute of Technology Mandi India

ISBN: (纸本)9781728194684

Excitatory glutamate and inhibitory GABA dynamics homeostasis, which is often designated as E/I ratio, is regulated by different types of neural inhibitory circuit system. Gliotransmitter released from astrocyte coupled with two main types of the neural network system, feedback and feedforward inhibitory network can render alteration in E/I dynamics. The recent study provides a new model to study the E/I dynamics by integrating the neural circuit with astrocyte, aiming to understand the effect of input synaptic current ranging 20 -40μA/cm2, the effect of two different types of neural inhibition networks on the E/I dynamics and the effect of information transmission from astrocyte to the neural network with different coupling parameters on the E/I dynamics. The simulated result reveals that the frequency of E/I peak and the maximum peak height is increased with increasing the input current. The maximum E/I peak is diminished in the case of feedback inhibition system than the feedforward system with the constant input current. Another important outcome of the model is that the E/I ratio is dependent on the astrocytic glutamate with a limiting coupling parameter value. © 2020 ieee.

关键词： Dynamics

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