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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Shen, Claire Bai, Yongsheng Jordan High School Fulshear United States Next-Gen Intelligent Science Training Ann Arbor United States

ISBN: (纸本)9798350337488

Mutated oncogenes and tumor suppressor genes can significantly alter cell functions during the development of cancer. A thorough analysis of their mutation patterns may offer some insights into cancer emergence as well as some suggestions for potential drug targeting. Kidney renal clear cell carcinoma (KIRC) is the most common subtype of kidney cancer. KIRC has a poor prognosis and a high mortality rate. To date, immunotherapy based on immune checkpoints is the most promising treatment. MicroRNAs (miRNAs) down-regulate target genes by binding to the 3' UTR of the respective targets. MiRNAs target genes at the post-transcriptional level after mRNA has been transcribed. The complexity of the miRNA-gene targeting interaction, as a result, has made this research more challenging. There are two classes of genes that this study focuses on which are oncogenes and tumor suppressor genes. Oncogenes function by deregulating cell proliferation and suppressing apoptosis. Tumor suppressor genes function by inhibiting cell proliferation and tumor development. There have been many studies that focus on the targeting relationship between genes and miRNAs, but few studies investigate the big picture of the relationship between oncogenes and tumor suppressor genes and how it can be regulated at the genome-wide level in the context of the miRNA targeting mechanism. We downloaded the significant miRNA-gene targeting cluster files for KIRC from a published study. We then separated and annotated the tumor suppressors and oncogenes based on their survival analysis. We grouped the data into four different categories based on the miRNA-targeted gene relationship (TS-TS, Onco-Onco, TS-Onco, Onco-TS). Based on the survival significance (p-value © 2023 ieee.

关键词： computational biology Kidney Renal Clear Cell Carcinoma The Cancer Genome Atlas

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An Analytical Approach to Predict the Cardio Vascular Disorder 17

An Analytical Approach to Predict the Cardio Vascular Disord...

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17th International conference on Ubiquitous Information Management and Communication, IMCOM 2023

作者： Chauhan, Ritu Gola, Nidhi Yafi, Eiad Amity University Center for Computational Biology and Bioinformatics UP India University of Technology Sydney Faculty of Engineering and Information Technology Sydney Australia

ISBN: (纸本)9781665453486

In recent times, heart disease has been recognized as the world's leading cause of death. However, it is also regarded as the disease that is most easily controlled and prevented. Recently, World Health Organization (WHO) claims that heart disease's progression and associated treatment expenses can both be significantly halted with the help of an early and prompt diagnosis. Therefore, researchers have employed various data mining approaches to diagnose heart disease in consideration of the rising number of deaths caused by the disease. This research study applied data mining classification modeling techniques, specifically discriminant analysis on the heart disease dataset for the prediction of chances of heart disease based on various attributes and assess the contribution of each attribute towards the heart disease. Lastly, the range and the accuracy of the classification are assessed. This dataset has an accuracy of 85.3% in predicting that whether individual has heart disease or not and the specificity of individual possess heart disease is 84.8% while normal individuals acquire specificity of 85.9%. © 2023 ieee.

关键词： Data mining

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Contrastive self-supervised graph convolutional network for detecting the relationship among lncRNAs, miRNAs, and diseases

Contrastive self-supervised graph convolutional network for ...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Sheng, Nan Huang, Lan Wang, Yan Gao, Ling Sun, Huiyan Xie, Xuping Jilin University College of Computer Science and Technology Changchun China Jilin University School of Artificial Intelligence Changchun China

ISBN: (纸本)9798350337488

Inferring potential relationships among long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and diseases play a crucial role in investigation of disease aetiology and pathogenesis. Due to the high cost of laboratory experiments, there is a practical requirement to develop appropriate computational methods that promise to accelerate the experimental screening process for potential lncRNA-disease associations (LDAs), miRNA-disease associations (MDAs), and lncRNA-miRNA interactions (LMIs). However, most existing methods are applied to predict LDAs, MDAs, and LMIs in specific domains, neglecting the important benefits of integrating multiple sources data and limiting the ability of transferring models to other tasks. Furthermore, with the high sparsity of LDA, MDA, and LMI data, it is difficult for many computational models to exploit enough knowledge to learn the comprehensive patterns of node embedding. In this study, inspired by the recent success of graph contrastive learning, we develop a Contrastive Self-supervised Graph convolutional network to identify potential LDAs, MDAs, and LMIs (called CSGLMD). CSGLMD combines supervised learning and self-supervised learning to fully capture node features. Specifically, CSGLMD primarily leverages the rich association and similarity relationships among lncRNA, miRNA, and disease to construct a lncRNA-miRNA-disease heterogeneous graph (LMDHG) that contains three types of biological entities. It can effectively embed multi-source biological data and assist the model extension to other prediction tasks. In addition, we consider applying a label instantiation mechanism to make the LMDHG better adapt graph neural network structures and control the strength of similarity relationships between the same biological entities. Secondly, CSGLMD implements graph convolutional network (GCN) as encoder to extract node embedding features from the LMDHG, and utilizes a multi-relational modelling decoder to predict LDAs, MDAs, or LMIs. Finall

关键词： Contrastive self-supervised Graph convolutional network LncRNA-disease association LncRNA-miRNA interaction MiRNA-disease association

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Effective Prediction of Genus and Species Using a Novel Deep Learning Technique

Effective Prediction of Genus and Species Using a Novel Deep...

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International conference on computational intelligence and Communication Networks (CICN)

作者： Yana Gaur Abhay Chaudhary Rohit Kumar Kaliyar School of Computer Science Engineering and Technology Bennett University Greater Noida India

ISBN: (数字)9798331505264

ISBN: (纸本)9798331505271

Scientists use Palaeoanthropology as a way of clas-sifying hominoids and attempting to untangle the complicated origins of human beings. Comparison with numerous ANN configurations, LSTM, and CNN models confirms that the proposed ANN is absolutely gave best results. our proposed model is able to generalize well in the presence of a simple model and compatible data without necessitating any form of complex regularization or weight initialization techniques. A dataset containing 12,000 examples having quite varied physical characteristics of early hominids has been used for determining different genera or species of hominoids. Using LSTM and CNN models, our concern about over-fitting have restricted their practical value necessitating more validation. Based on preliminary findings in a wide-ranging study, deep learning opens up new possibilities for the paleo-anthropological community-providing them with an opportunity to explore complex issues surrounding human evolution from a different angle. Furthermore, enlightening hominoid taxonomy in order to comprehend the classification system is more advanced by this research and offers a starting point for future computational biology studies.

关键词： Deep learning Evolution (biology) Taxonomy Data models Communication networks Long short term memory computational intelligence computational biology

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ADNPPro: Enhanced Promoter Prediction in Nannochloropsis Using Attention-Based Densenet

ADNPPro: Enhanced Promoter Prediction in Nannochloropsis Usi...

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ieee International conference on bioinformatics and computational biology (ICBCB)

作者： Gan-lin Liu Hai-tao Li Pi-jing Wei Chun-hou Zheng School of Computer Science and Technology Anhui University Hefei China Information Materials and Intelligent Sensing Laboratory of Anhui Province Anhui University Hefei China

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

Nannochloropsis is a significant genus of marine microalgae, renowned for its strain diversity and high productivity. However, it faces challenges in achieving optimal yields using wild-type strains and traditional cultivation methods. Promoters play a crucial role in regulating gene expression, but current algorithms for recognizing Nannochloropsis promoters have limitations in accuracy and interpretability. To address this problem, we introduce ADNPPro (Attention-based DenseNet Predict Promoter), merging an attention mechanism with a densely connected convolutional neural network. By interpreting the deep learning model, we process an integrated analysis of sequence importance scores, enabling a deeper understanding of key promoter features across various Nannochloropsis strains. Experimental results demonstrate the robust generalization capability of ADNPPro across six Nannochloropsis strains. Motif analysis provides insights into their diverse performance capabilities and enhances the understanding of Nannochloropsis promoter characteristics. The introduction of this framework is set to advance synthetic biology, offering insights into the biological significance of sequence features.

关键词： Productivity Deep learning Merging Prediction algorithms Synthetic biology Gene expression Convolutional neural networks Faces Strain computational biology

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Editorial: computational Genomics and Molecular Medicine for Emerging COVID-19

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2021年第4期18卷 1227-1229页

作者： Wei, Dong-Qing Kaushik, Aman Chandra Selvaraj, Gurudeeban Pan, Yi Shanghai Jiao Tong Univ State Key Lab Microbial Metab Shanghai 200240 Peoples R China Shanghai Jiao Tong Univ Sch Life Sci & Biotechnol Shanghai 200240 Peoples R China Peng Cheng Lab Shenzhen 518055 Guangdong Peoples R China Jiangnan Univ Wuxi Sch Med Wuxi 214122 Jiangsu Peoples R China Concordia Univ Ctr Res Mol Modeling CERMM Montreal PQ H4B 1R6 Canada Georgia State Univ Dept Comp Sci Atlanta GA 30302 USA

The papers in this special section focus on computational genomics and molecular medicine for emerging COVID-19. In 2020, World Health Organization announced Coronavirus disease (COVID)-19 is a pandemic disease, which is devastated the socio-economic life around the world. The disease caused by the zoonotic single-strand RNA virus known as “SARS-CoV-2”. To overcome the pandemic, the diagnosis and therapeutics products needs to be developed in short term. Developing therapeutics for infectious diseases, especially viral diseases always a challenging task for the scientific community. However, the utility of high-performance computational resources, artificial intelligence, and machine-learning algorithms can make the process in an affordable way through the usage of genomics, proteomics, pharmacogenomics, and chemical data. Thus, the special section received potential research articles related to computational genomics, molecular medicine, and COVID-19 from reputed scientist around the world. Different articles were employed machine learning, molecular dynamics, computer aided drug design techniques, and emphasizing viral genomics, mutation, drug target, drug candidates, and patient data, were included in this special section.

关键词： Special Issues And Sections COVID 19 Pandemics Genomics Drugs bioinformatics Machine Learning RNA Proteomics Molecular biology Coronaviruses

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A Bio-Inspired computational Astrocyte Model for Spiking Neural Networks

A Bio-Inspired Computational Astrocyte Model for Spiking Neu...

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International Joint conference on Neural Networks (IJCNN)

作者： Kiggins, Jacob Schaffer, J. David Merkel, Cory Rochester Inst Technol Comp Engn Dept Rochester NY 14623 USA Binghamton Univ Coll Community & Publ Affairs Binghamton NY USA

ISBN: (纸本)9781665488679

The mammalian brain is the most capable and complex computing entity known today, and for years there has been research focused on reproducing the brain's capabilities. An early example of this endeavor was the perceptron which has become a core building block of neural network models in the deep learning era. Despite many successes achieved through deep learning, these networks behave much differently than their biological counterparts. In a search for improvements to things like training time and dataset size, power consumption, noise, and adversarial input tolerance, research is looking towards the brain, and bio-inspired computing models. Spiking neural networks (SNNs) take a step closer to biology in their operation and are the focus of much research, geared towards reproducing some of these novel features of the brain. As of yet, SNNs have not reached their full potential. This work explores the advancement of SNNs though introduction of a novel astrocyte model. Astrocytes, initially thought to be passive support cells in the brain are now known to actively participate in neural processing. The proposed astrocyte model is geared towards synaptic plasticity and is shown to generalize and extend spike-timing dependent plasticity (STDP). In addition, the model supports plasticity-focused multi-synapse integration. Logical AND was used as a test function for multi-synapse astrocyte plasticity, and convergence for a single leaky integrate and fire (LIF) neuron with 2, 3, and 4 synapses was demonstrated. The plasticity model is general and many other functions could presumably be used in-place of AND, taking advantage of a multi-synapse view. This leaves a solid path forward for future work.

关键词： Neural networks

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A Comparative Analysis of XAI Techniques for Medical Imaging: Challenges and Opportunities

A Comparative Analysis of XAI Techniques for Medical Imaging...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Paola Barra Attilio Della Greca Ilaria Amaro Augusto Tortora Mariacarla Staffa Department of Science and Technology University of Naples Parthenope Naples Italy Department of Computer Science University of Salerno Salerno Italy Department of Physics "E. Pancini" University of Naples Federico II Naples Italy

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

The application of artificial intelligence (AI) in medical imaging has significantly improved diagnostic accuracy. However, the reliance on black-box models remains a barrier to its widespread adoption in clinical settings. This article compares the main techniques of explainable AI (XAI), such as Grad-CAM, LIME, and SHAP, evaluating their effectiveness in interpreting deep learning models used in medical imaging. Two case studies are analyzed, comparing the three methods and highlighting their strengths and weaknesses. The results of this analysis show that Grad-CAM provides intuitive visualizations; LIME offers excellent flexibility in application; and SHAP delivers complete and accurate explanations, despite its high computational load.

关键词： Heating systems Visualization Accuracy Explainable AI computational modeling Stochastic processes Fasteners Real-time systems Medical diagnostic imaging Load modeling

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DiffDomain Enables Identification of Structurally Reorganized Topologically Associating Domains 26th

DiffDomain Enables Identification of Structurally Reorganiz...

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

作者： Hua, Dunming Gu, Ming Du, Yanyi Qi, Li Du, Xiangjun Bai, Zhidong Zhu, Xiaopeng Tian, Dechao Sun Yat-sen University Guangdong Shenzhen510275 China Chongqing Municipal Center for Disease Control and Prevention Chongqing400042 China KLASMOE and School of Mathematics and Statistics Northeast Normal University Jilin Changchun130024 China Computational Biology Department School of Computer Science Carnegie Mellon University PittsburghPA15213 United States

ISBN: (纸本)9783031047480

The recent development of mapping technologies, such as Hi-C, that probes the 3D genome organization reveals that a chromosome is divided into topologically associating domains (TADs). TADs are genomic regions where chromatin loci are more frequently interacting with chromatin loci from the same TADs rather than from other TADs. TADs are functional units for transcriptional regulation, such as constraining interactions between enhancers and promoters. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Topology

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Predicting COVID-19 Severity Using a Cut-and-Solve Feature Selection Approach

Predicting COVID-19 Severity Using a Cut-and-Solve Feature S...

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2023 ieee International conference on bioinformatics and Biomedicine, BIBM 2023

作者： Smith, Kenneth Chan, Michael Brandenburg, John Jones, Katarina A Campagna, Shawn R. Garvin, Michael Templeton, Alan R. Jacobson, Daniel Cruchaga, Carlos Climer, Sharlee University of Missouri - St. Louis Department of Computer Science Saint Louis United States Optimization GotSport Jacksonville Beach United States Stord United States University of Tennessee Knoxville Department of Chemistry Knoxville United States The University of Tennessee Department of Chemistry Knoxville United States Oak Ridge National Laboratory Computational Predictive Biology Oak Ridge United States Washington University Department of Biology Saint Louis United States Oak Ridge National Laboratory Biosciences Division Oak Ridge United States Washington University School of Medicine Department of Psychiatry Saint Louis United States

ISBN: (纸本)9798350337488

Individuals with coronavirus disease 2019 (COVID-19) infection present in a variety of ways, ranging from asymptomatic or mild cough, to organ failure or death. One of the major challenges for the medical community is the quick and accurate determination of how COVID-19 will progress in an individual. Herein, we introduce a new Cut-and-Solve based feature selection program for identifying predictive feature sets in heterogeneous data. We analyze proteomics data from Washington University to identify models ranging in size from a single feature up to five. Validation of logistic regression models using area under the curve (AUC) were applied for both a holdout data set and an independent data set from Massachusetts General Hospital. A variety of known and novel biomarkers for COVID-19 severity were identified. The best model for predicting severe (ventilation or death) vs. non-severe infection is achieved for CALCOCO2 and STC1, with an average AUC=0.81. Based on the known severity markers, several different proteomic pathways are identified. Enrichment analysis indicates activity associated with inflammatory response, as well as myelination and cardiac function. © 2023 ieee.

关键词： COVID-19 Feature Selection Mixed Integer Programming

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