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GEFA: Early Fusion Approach in Drug-Target Affinity Prediction

ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第2期19卷 718-728页

作者： Tri Minh Nguyen Thin Nguyen Thao Minh Le Truyen Tran Deakin Univ Appl Artificial Intelligence Inst Geelong Vic 3217 Australia

Predicting the interaction between a compound and a target is crucial for rapid drug repurposing. Deep learning has been successfully applied in drug-target affinity (DTA)problem. However, previous deep learning-based methods ignore modeling the direct interactions between drug and protein residues. This would lead to inaccurate learning of target representation which may change due to the drug binding effects. In addition, previous DTA methods learn protein representation solely based on a small number of protein sequences in DTA datasets while neglecting the use of proteins outside of the DTA datasets. We propose GEFA (Graph Early Fusion Affinity), a novel graph-in-graph neural network with attention mechanism to address the changes in target representation because of the binding effects. Specifically, a drug is modeled as a graph of atoms, which then serves as a node in a larger graph of residues-drug complex. The resulting model is an expressive deep nested graph neural network. We also use pre-trained protein representation powered by the recent effort of learning contextualized protein representation. The experiments are conducted under different settings to evaluate scenarios such as novel drugs or targets. The results demonstrate the effectiveness of the pre-trained protein embedding and the advantages our GEFA in modeling the nested graph for drug-target interaction.

关键词： Proteins Drugs Three-dimensional displays Protein sequence Deep learning Machine learning Feature extraction Drug-target binding affinity graph neural network early fusion representation change

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Diversity and Evolution of RNA Recognition Motifs: A Case Study on the Utility of RdRp in the phylum Duplornaviricota

Diversity and Evolution of RNA Recognition Motifs: A Case St...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

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

ISBN: (纸本)9798350337488

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. © 2023 ieee.

关键词： classification marker gene RdRp RNA recognition motif taxonomy virus

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GenSeg and MR-GenSeg: A Novel Segmentation Algorithm and its Parallel MapReduce Based Approach for Identifying Genomic Regions With Copy Number Variations

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第1期19卷 443-454页

作者： Sinha, Rituparna Pal, Rajat K. De, Rajat K. Heritage Inst Technol Dept Informat Technol Kolkata 700107 India Univ Calcutta Dept Comp Sci & Engn Kolkata 700073 India Indian Stat Inst Machine Intelligence Unit Kolkata 700108 India

Identifying intragenic as well as intergenic sequences of the DNA, having structural alterations, is a significantly important research area, since this may be the root cause of many neurological and autoimmune diseases, including cancer. Working with whole genome NGS data has provided a new insight in this regard, but has lead to huge explosion of data that is growing exponentially. Hence, the challenges lie in efficient means of storage and processing this big data. In this study, we have developed a novel segmentation algorithm, called GenSeg, and its parallel MapReduce based algorithm, called MR-GenSeg, for detecting copy number variations. In order to annotate CNVs (variants), segments formed by GenSeg/MR-GenSeg have been represented in a novel way using a binary tree, where each node is a CNV event. GenSeg considers each position specific data of whole genome DNA sequence, so that precise identification of breakpoints is possible. GenSeg/MR-GenSeg has been compared with twelve popular CNV detection algorithms, where it has outperformed the others in terms of sensitivity, and has achieved a good F-score value. MR-GenSeg has excelled in terms of SpeedUp, when compared with these algorithms. The effect of CNVs on immunoglobulin (IG) genes has also been analysed in this study.

关键词： CNV NGS WGS hadoop MapReduce immunoglobulin genes

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A domain adaptive pre-training language model for sentence classification of Chinese electronic medical record

A domain adaptive pre-training language model for sentence c...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Zou, Yilin Zhang, Peng Ling, Yunchao Lv, Daqing Li, Ziming Lu, Shun Zhang, Guoqing University of Chinese Academy of Sciences Key Laboratory of Systems Health Science of Zhejiang Province School of Life Science Hangzhou Institute for Advanced Study Hangzhou China Shanghai Institute of Nutrition and Health University of Chinese Academy of Sciences Chinese Academy of Sciences Cas Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai China Shanghai Southgene Technology Co. Ltd Shanghai China Shanghai Chest Hospital Shanghai China Shanghai Institute of Nutrition and Health University of Chinese Academy of Sciences Chinese Academy of Sciences Shanghai Sixth People's Hospital Cas Key Laboratory of Computational Biology Bio-Med Big Data Center Shanghai China

ISBN: (纸本)9798350337488

Accurately extracting and classifying Chinese electronic medical record (EMR), which contain huge amounts of valuable medical information, have promising practical application and medical value in the health care of China. While the pivotal issue has gathered escalating attention, the bulk of current research is directed towards operations conducted at the document or entity level within medical records. Only a restricted body of work addresses these concerns at the sentence level, a critical aspect for downstream tasks like medical information retrieval, diagnosis normalization, and question answering. In this paper, we present a domain adaptive pre-training language model named CEMR-LM for sentence classification of Chinese EMRs. CEMR-LM acquires Chinese medical domain knowledge through the utilization of copious unlabeled clinical corpus for pre-training the language model. This is fortified by combining fine-tuning strategy and a dual-channel mechanism, which collectively contribute to the model's heightened performance. Experiments on the benchmark dataset and real world hospital dataset both demonstrate that CEMR-LM is superior to the state-of-the-art methods. Furthermore, CEMR-LM possesses the capability to elucidate indicative elements within medical records by visualizing of the attention weights embedded within the model. The implemented code and experimental datasets are available online at https://***/BioMedBigDataCenter/CEMR-LM. © 2023 ieee.

关键词： Chinese electronic medical record Domain adaptive pre-training Language model Sentence classification

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Prediction of FMN Binding Sites in Electron Transport Chains Based on 2-D CNN and PSSM Profiles

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第6期18卷 2189-2197页

作者： Nguyen-Quoc-Khanh Le Nguyen, Binh P. Taipei Med Univ Profess Master Program Artificial Intelligence Me Taipei 106 Taiwan Victoria Univ Wellington Sch Math & Stat Wellington 6012 New Zealand

Flavin mono-nucleotides (FMNs) are cofactors that hold responsibility for carrying and transferring electrons in the electron transport chain stage of cellular respiration. Without being facilitated by FMNs, energy production is stagnant due to the interruption in most of the cellular processes. Investigation on FMN's functions, therefore, can gain holistic understanding about human diseases and molecular information on drug targets. We proposed a deep learning model using a two-dimensional convolutional neural network and position specific scoring matrices that could identify FMN interacting residues with the sensitivity of 83.7 percent, specificity of 99.2 percent, accuracy of 98.2 percent, and Matthews correlation coefficients of 0.85 for an independent dataset containing 141 FMN binding sites and 1,920 non-FMN binding sites. The proposed method outperformed other previous studies using similar evaluation metrics. Our positive outcome can also promote the utilization of deep learning in dealing with various problems in bioinformatics and computational biology.

关键词： Proteins Deep learning Neural networks bioinformatics Microsoft Windows Computer architecture Feature extraction FMN binding site electron transport chains position specific scoring matrix convolutional neural networks deep learning

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A real-time HFO detection framework for localization of SOZ in iEEG recording of patients with epilepsy 46

A real-time HFO detection framework for localization of SOZ ...

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46th Annual International conference of the ieee Engineering in Medicine and biology Society, EMBC 2024

作者： Besheli, Behrang Fazli Prakash Swamy, Chandra Ayyoubi, Amir Hossein Branco, Luciano R. F. Worrell, Gregory A. Sha, Zhiyi Gavvala, Jay R. Miller, Kai J. Ince, Nuri F. Mayo Clinic Department of Neurology RochesterMN United States University of Minnesota Department of Neurology MinneapolisMN United States Ut Health Department of Neurology HoustonTX United States Mayo Clinic Department of Neurologic Surgery RochesterMN United States University of Minnesota Bioinformatics and Computational Biology Department MinneapolisMN United States University of Houston Department of Biomedical Engineering HoustonTX United States

ISBN: (纸本)9798350371499

Interictal High-frequency Oscillation (HFO) between 80-600 Hz in intracranial EEG (iEEG) is a promising biomarker of the epileptogenic zone in individuals with epilepsy. Numerous studies revealed that the resection of channels with a high rate of HFOs correlates with favorable surgical outcomes. Early feedback to clinicians regarding the distribution of HFOs during the iEEG recording, especially after the implantation of electrodes, would be helpful for clinical decisions. However, iEEG recording can easily get corrupted by various factors mimicking real HFOs. This study presents a real-time HFO detection framework within MATLAB/Simulink that exhibits robustness against such pseudo-HFOs. This detector is responsible for identifying the initial pool of candidate HFOs and transmitting them via user datagram protocol (UDP) to an external application. The external application contains a machine learning tool that is utilized for post-processing and isolating the real-HFOs. It is implemented in a graphical user interface (GUI) that provides visual feedback regarding the distribution and waveforms of HFOs. The entire processing pipeline was validated by randomly selecting 10-minute segments of interictal iEEG recordings from 10 subjects. It successfully identifies the seizure onset zone (SOZ) in these subjects, achieving an average accuracy of 65% using the detected Ripples and 74% using the detected events with both Ripples and Fast Ripples. Importantly, the spatio-temporal distribution of detected HFOs in real-time showed more than 98% spatio-temporal similarity index compared to offline analysis. Our framework proves to be an effective tool for the automatic identification of HFOs in real-time with the ability to promptly stream the HFO analysis results and provide early feedback regarding the probable SOZ regions to clinicians for surgical decision-making. © 2024 ieee.

关键词： Graphical user interfaces

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2nd International Symposium on Biomedical and computational biology, BECB 2022

2nd International Symposium on Biomedical and Computational ...

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2nd International Symposium on Biomedical and computational biology, BECB 2022

ISBN: (纸本)9783031251900

The proceedings contain 58 papers. The special focus in this conference is on Biomedical and computational biology. The topics include: Microanatomy of Left Bundle Branch in Chinese Adult Hearts: Aiming to the Research on Morphological Information;Screening of Key Genes in Retinoblastoma and Construction of ceRNA Regulatory Network;protective Effects of Resveratrol on Brain Edema and Microstructural Changes in Human Brain After Acute Alcohol Intake: Assessment by Diffusion Weighted Kurtosis Imaging;effects of Music Style on Mental Fatigue Induced by Continuous Cognitive Tasks;construction of Constitutive Saccharomyces Cerevisiae Engineered Strain of β-glucosidase;high-Yielding Laccase Strain Breeding and Optimization of Fermentation Conditions;Chinese Medical Text Classification with RoBERTa;operation Status and Optimization Strategy of Hierarchical Medical System in Wuhan;analysis and Forecast of Meteorological Factors on Henoch-SchÖnlein Purpura in Jining;A Self-supervised 3D/2D Registration Method for Incomplete DSA Vessels;human Machine Interaction Using Zero Force Sensing Switches Incorporating Self-adaptation;driving, Personality and Decision-Making: A Study for Understanding Human Behavior;the Brain Activity of the Bilingual Code-Switching Communication;A Symbolic Regression Approach to Hepatocellular Carcinoma Diagnosis Using Hypermethylated CpG Islands in Circulating Cell-Free DNA;multi-agent Neural-Like Models for the Integration of Multimodal Medical Examination Data;the Importance of Prognostic Variables to Monitoring Heart Failure Using Health Management Systems;real-Time Contactless Breathing Monitoring System Using Radar with Web Server;Neuroscience, Neuroaesthetics, Semiotics and Effective Communication of COVID-19 Warning Information;the Effect of CoViD-19 Pandemic on the Hospitalization of a Department of Oncology of an Italian Hospital;Modeling LOS After Percutaneous Valvuloplasty: A Bicentric Study.

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Tissue Specificity Based Isoform Function Prediction

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第5期19卷 3048-3059页

作者： Yu, Guoxian Huang, Qiuyue Zhang, Xiangliang Guo, Maozu Wang, Jun Shandong Univ Sch Software Jinan 250101 Shandong Peoples R China Shandong Univ Joint SDU NTU Ctr Artificial Intelligence Res Jinan 250101 Shandong Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China King Abdullah Univ Sci & Technol Comp Elect & Math Sci & Engn Div Thuwal 23955 Saudi Arabia Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing 100044 Peoples R China Beijing Key Lab Intelligent Proc Bldg Big Data Beijing 100044 Peoples R China

Alternative splicing enables a gene spliced into different isoforms and hence protein variants. Identifying individual functions of these isoforms help deciphering the functional diversity of proteins. Although much efforts have been made for automatic gene function prediction, few efforts have been moved toward computational isoform function prediction, mainly due to the unavailable (or scanty) functional annotations of isoforms. Existing efforts directly combine multiple RNA-seq datasets without account of the important tissue specificity of alternative splicing. To bridge this gap, we introduce a novel approach called TS-Isofun to predict the functions of isoforms by integrating multiple functional association networks with respect to tissue specificity. TS-Isofun first constructs tissue-specific isoform functional association networks using multiple RNA-seq datasets from tissue-wise. Next, TS-Isofun assigns weights to these networks and models the tissue specificity by selectively integrating them with adaptive weights. It then introduces a joint matrix factorization-based data fusion model to leverage the integrated network, gene-level data and functional annotations of genes to infer the functions of isoforms. To achieve coherent weight assignment and isoform function prediction, TS-Isofun jointly optimizes the weights of individual networks and the isoform function prediction in a unified objective function. Experimental results show that TS-Isofun significantly outperforms state-of-the-art methods and the account of tissue specificity contributes to more accurate isoform function prediction.

关键词： Annotations Proteins Splicing RNA Optimization Support vector machines Heterogeneous networks Isoform function prediction tissue specificity Joint matrix factorization data fusion alternative splicing

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DeepIDA: Predicting Isoform-Disease Associations by Data Fusion and Deep Neural Networks

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2022年第4期19卷 2166-2176页

作者： Yu, Guoxian Yang, Yeqian Yan, Yangyang Guo, Maozu Zhang, Xiangliang Wang, Jun Shandong Univ Sch Software Jinan 250101 Peoples R China Shandong Univ Joint SDU NTU Ctr Artificial Intelligence Res Jinan 250101 Peoples R China Southwest Univ Coll Comp & Informat Sci Chongqing 400715 Peoples R China Beijing Univ Civil Engn & Architecture Sch Elect & Informat Engn Beijing 100044 Peoples R China Beijing Univ Civil Engn & Architecture Beijing Key Lab Intelligent Proc Bldg Big Data Beijing 100044 Peoples R China King Abdullah Univ Sci & Technol Thuwal 23955 Saudi Arabia

Alternative splicing produces different isoforms from the same gene locus, it is an important mechanism for regulating gene expression and proteome diversity. Although the prediction of gene(ncRNA)-disease associations has been extensively studied, few (or no) computational solutions have been proposed for the prediction of isoform-disease association (IDA) at a large scale, mainly due to the lack of disease annotations of isoforms. However, increasing evidences confirm the associations between diseases and isoforms, which can more precisely uncover the pathology of complex diseases. Therefore, it is highly desirable to predict IDAs. To bridge this gap, we propose a deep neural network based solution (DeepIDA) to fuse multi-type genomics and transcriptomics data to predict IDAs. Particularly, DeepIDA uses gene-isoform relations to dispatch gene-disease associations to isoforms. In addition, it utilizes two DNN sub-networks with different structures to capture nucleotide and expression features of isoforms, Gene Ontology data and miRNA target data, respectively. After that, these two sub-networks are merged in a dense layer to predict IDAs. The experimental results on public datasets show that DeepIDA can effectively predict IDAs with AUPRC (area under the precision-recall curve) of 0.9141, macro F-measure of 0.9155, G-mean of 0.9278 and balanced accuracy of 0.9303 across 732 diseases, which are much higher than those of competitive methods. Further study on sixteen isoform-disease association cases again corroborates the superiority of DeepIDA. The code of DeepIDA is available at http://***/***?name=DeepIDA.

关键词： Diseases Splicing Annotations Proteins Pathology Feature extraction Data models Isoform-disease associations alternative splicing deep neural networks data fusion class imbalance

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MSCAP: DNA Methylation Age Predictor based on Multiscale Convolutional Neural Network

MSCAP: DNA Methylation Age Predictor based on Multiscale Con...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Wang, Han Cai, Ruirui Zong, Xizeng He, Zhiquan Zhang, Li Northeast Normal University School of Information Science and Technology Institute of Computational Biology Changchun China South China University of Technology Clinical Research Centre Guangzhou First People's Hospital School of Medicine Guangzhou China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen China Changchun University of Technology School of Computer Science and Engineering Changchun China

ISBN: (纸本)9798350337488

DNA methylation can reflect age-related issues in individuals, and age prediction tools developed based on DNA methylation are called Epigenetic clocks. Epigenetic clocks also serve as potent tools for enhancing researchers' understanding of the aging process and aim to contribute to the advancement of aging research. The current prevailing approach involves creating clocks using Elastic Net regression modeling, wherein high-age-correlation CpG sites were selected as features in advance. However, pre-selecting CpG sites in advance may inadvertently overlook interactions between distant CpG sites. Deep learning has significant advantages in dealing with vast feature sets due to the tremendous opportunities that the development of deep learning presents for research. Deep learning architectures have demonstrated greater efficiency in acquiring and representing large numbers of intricate features compared to traditional machine learning models. In this study, we proposed a deep learning model based on a multiscale convolutional neural network (MSCNN) for pan-tissue age prediction, called MSCAP. First, for the model to efficiently predict datasets from different sequencing platforms, we chose a common intersection of sites from the Illumina 27K and Illumina 450K platforms (25,789 CpG sites), then trained and tested the model on 93 datasets. Different tissues of the human body are used in the datasets for training and testing. And independently tested on 7 independent datasets against an Epigenetic clock developed by Horvath in 2013 with high prediction accuracy. The results showed that MSCAP could effectively extract key features from a vast number of potential features without advanced feature selection. In addition, MSCAP outperformed the Horvath clock on all seven independent test datasets. This research helped boost the development of epigenetic clock methods and reinforced the value of deep learning in computational biology. © 2023 ieee.

关键词： age prediction DNA methylation epigenetic clock multiscale convolutional neural network (MSCNN)

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