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4th International Conference on Cybernetics and Intelligent System, ICORIS 2022

作者： Bimantio, Mohammad Prasanto Putra, Dian Pratama Suparyanto, Teddy Ferhat, Amallia Nugraha, Nanda Satya Hidayat, Alam Ahmad Pardamean, Bens Institute of Agriculture STIPER Department of Food and Agricultural Technology Yogyakarta Indonesia Institute of Agriculture STIPER Department of Agrotechnology Yogyakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta11480 Indonesia Institute of Agriculture STIPER Department of Agricultural Socio-Economics Yogyakarta Indonesia Institute of Agriculture STIPER Department of Forestry Yogyakarta Indonesia Bina Nusantara University BINUS Graduate Program - Master of Computer Science Computer Science Department Jakarta11480 Indonesia

ISBN: (纸本)9781665453950

In this era of digital world information, farmers face challenges about information management of huge data and the complexity of processes in precision farming. The data collection carried out so far can cause several difficulties, especially if the data is needed by the government or academics to support decision-making. Agricultural data collection applications only cover 12% of the market share of Android-based agriculture-based applications. In this study, Tani Milenial application is present as an alternative to support agricultural data collection activities in Indonesia. This application encourages farmers to be active and contribute independently to their role as subjects of agricultural development and is accustomed to digitally recording their agricultural activities. In this developed application system, the community, especially farmers and farmer groups, will find it easier to get information about agricultural potential data around where they carry out agricultural activities and can access this information online. Tani Milenial works as a guide to help farmers and transform agriculture but still have some gaps which should be checked and removed. The data collected can be used by various parties for research and decision-making. The app can be further improved to develop new features related to education, market information, and goods. © 2022 IEEE.

关键词： Decision making

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Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph

arXiv

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arXiv 2024年

作者： Wan, Guancheng Liu, Zewen Lau, Max S.Y. Prakash, B. Aditya Jin, Wei Department of Computer Science Emory University United States College of Computing Georgia Institute of Technology United States Department of Biostatistics and Bioinformatics Emory University United States

Effective epidemic forecasting is critical for public health strategies and efficient medical resource allocation, especially in the face of rapidly spreading infectious diseases. However, existing deep-learning methods often overlook the dynamic nature of epidemics and fail to account for the specific mechanisms of disease transmission. In response to these challenges, we introduce an innovative end-to-end framework called Epidemiology-Aware Neural ODE with Continuous Disease Transmission Graph (EARTH) in this paper. To learn continuous and regional disease transmission patterns, we first propose EANO, which seamlessly integrates the neural ODE approach with the epidemic mechanism, considering the complex spatial spread process during epidemic evolution. Additionally, we introduce GLTG to model global infection trends and leverage these signals to guide local transmission dynamically. To accommodate both the global coherence of epidemic trends and the local nuances of epidemic transmission patterns, we build a cross-attention approach to fuse the most meaningful information for forecasting. Through the smooth synergy of both components, EARTH offers a more robust and flexible approach to understanding and predicting the spread of infectious diseases. Extensive experiments show EARTH superior performance in forecasting real-world epidemics compared to state-of-the-art methods. The code will be available at https://***/Emory-Melody/EpiLearn. Copyright © 2024, The Authors. All rights reserved.

关键词： Epidemiology

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A Review of Graph Neural Networks in Epidemic Modeling

arXiv

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arXiv 2024年

作者： Liu, Zewen Wan, Guancheng Prakash, B. Aditya Lau, Max S.Y. Jin, Wei Department of Computer Science Emory University United States College of Computing Georgia Institute of Technology United States Department of Biostatistics and Bioinformatics Emory University United States

Since the onset of the COVID-19 pandemic, there has been a growing interest in studying epidemiological models. Traditional mechanistic models mathematically describe the transmission mechanisms of infectious diseases. However, they often suffer from limitations of oversimplified or fixed assumptions, which could cause sub-optimal predictive power and inefficiency in capturing complex relation information. Consequently, Graph Neural Networks (GNNs) have emerged as a progressively popular tool in epidemic research. In this paper, we endeavor to furnish a comprehensive review of GNNs in epidemic tasks and highlight potential future directions. To accomplish this objective, we introduce hierarchical taxonomies for both epidemic tasks and methodologies, offering a trajectory of development within this domain. For epidemic tasks, we establish a taxonomy akin to those typically employed within the epidemic domain. For methodology, we categorize existing work into Neural Models and Hybrid Models. Following this, we perform an exhaustive and systematic examination of the methodologies, encompassing both the tasks and their technical details. Furthermore, we discuss the limitations of existing methods from diverse perspectives and systematically propose future research directions. This survey aims to bridge literature gaps and promote the progression of this promising field, with a list of relevant papers at https://***/Emory-Melody/awesome-epidemic-modeling-papers. We hope that it will facilitate synergies between the communities of GNNs and epidemiology, and contribute to their collective progress. Copyright © 2024, The Authors. All rights reserved.

关键词： Graph neural networks

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Making Canonical Workflow Building Blocks Interoperable across Workflow Languages

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Data Intelligence 2022年第2期4卷 342-357页

作者： Stian Soiland-Reyes Genis Bayarri Pau Andrio Robin Long Douglas Lowe Ania Niewielska Adam Hospital Paul Groth Department of Computer Science The University of ManchesterManchester M139PLUK Informatics Institute University of AmsterdamAmsterdam 1098 XHThe Netherlands Institute for Research in Biomedicine(IRB Barcelona) The Barcelona Institute of Science and Technology(BIST)Barcelona O8028Spain The Spanish National Bioinformatics Institute(INB) Barcelona Supercomputing Center(BSC)Barcelona 08034Spain Data Science Institute Lancaster UniversityLancasterLancashire LA14YWUK Research IT IT ServicesThe University of ManchesterManchester M139PLUK European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeshire CB101SDUK

We introduce the concept of Canonical Workflow Building Blocks(CWBB),a methodology of describing and wrapping computational tools,in order for them to be utilised in a reproducible manner from multiple workflow languages and execution *** concept is implemented and demonstrated with the BioExcel Building Blocks library(BioBB),a collection of tool wrappers in the field of computational biomolecular *** across different workflow languages is showcased through a protein Molecular Dynamics setup transversal workflow,built using this library and run with 5 different Workflow Manager Systems(WfMS).We argue such practice is a necessary requirement for FAIR Computational Workflows and an element of Canonical Workflow Frameworks for Research(CWFR)in order to improve widespread adoption and reuse of computational methods across workflow language barriers.

关键词： Scientific workflows Interoperable FAIR Computational tools Containers Software packaging FAIR digital object(FDO),BioExcel Building Blocks library(BioBB),Canonical Workflow Frameworks for Research(CWFR)

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OPTIMAL TRANSPORT DISTANCES FOR DIRECTED, WEIGHTED GRAPHS: A CASE STUDY WITH CELL-CELL COMMUNICATION NETWORKS

arXiv

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arXiv 2023年

作者： Nagai, James S. Costa, Ivan G. Schaub, Michael T. Institute for Computational Genomics RWTH Aachen Medical Faculty Germany Department of Computer Science RWTH Aachen University Germany

Comparing graphs by means of optimal transport has recently gained significant attention, as the distances induced by optimal transport provide both a principled metric between graphs as well as an interpretable description of the associated changes between graphs in terms of a transport plan. As the lack of symmetry introduces challenges in the typically considered formulations, optimal transport distances for graphs have mostly been developed for undirected graphs. Here, we propose two distance measures to compare directed graphs based on variants of optimal transport(OT): (i) an earth movers distance (Wasserstein) and (ii) a Gromov-Wasserstein (GW) distance. We evaluate these two distances and discuss their relative performance for both simulated graph data and real-world directed cell-cell communication graphs, inferred from single-cell RNA-seq data. © 2023, CC BY.

关键词： Directed graphs

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DeepLBS: A deep Convolutional Neural Network-Based Ligand-Binding Site Prediction Tool 6

DeepLBS: A deep Convolutional Neural Network-Based Ligand-Bi...

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6th International Conference on Information Systems and computer Networks, ISCON 2023

作者： Semwal, Rahul Aier, Imlimaong Tyagi, Pankaj Raj, Utkarsh Varadwaj, Pritish Kumar IIIT Department of Computer Science and Engineering Maharashtra Nagpur441108 India IIIT-Allahabad Department of Applied Sciences Uttar Pradesh Prayagraj211015 India IIIT-Allahabad Department of Information Technology Uttar Pradesh Prayagraj211015 India Dr. D. Y. Patil Biotechnology and Bioinformatics Institute Dr. D. Y. Patil Vidyapeeth Bioinformatics Centre Maharashtra Pune411033 India

ISBN: (纸本)9798350346961

In the recent past, with the improvement of high throughput technology, the availability of protein structural data has increased exponentially. All these structural data have to be correctly mapped to their functional attributes to decode their biological role. However, to perform the functional annotation of these structural entities, the essential move is to locate the ligand-binding site (LBS) information. Although many approaches have been proposed to locate the LBS, most have low performance in terms of predictive quality. In this proposed work, we are presenting a deep neural network-based approach, DeepLBS, which uses geometrical as well as pharmacophoric properties to quantify the ligand-binding site (LBS) with high accuracy. To determine the efficiency of our work, DeepLBS was compared with the most recently developed deep learning tools. The result demonstrated that DeepLBS outperformed the existing state of art tools in terms of predictive quality.. © 2023 IEEE.

关键词： Deep neural networks

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Intelligent Blood Flow Velocity Calculation using Deep Belief Network with Harmony Search Algorithm 2

Intelligent Blood Flow Velocity Calculation using Deep Belie...

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2nd International Conference on Electronics and Renewable Systems, ICEARS 2023

作者： Mahendran, Radha Ala Walid, Md. Abul Vijaya Pratap, G. Pradeep, Aneesh Soumya, M. Udhayaraja, P. Institute of Science and Technology and Advanced Studies. Pv Department of Bioinformatics Vels Tamilnadu Chennai India Khulna9203 Bangladesh Sga Gov. Degree College Andhra Pradesh India New Uzbekistan University Software Engineering Tashkent Uzbekistan Sr University Department of Computer Science & Artificial Intelligence Telangana Warangal India Excel College for Commerce and Science Department of Microbiology Tamilnadu Namakkal India

ISBN: (纸本)9798350346640

Generally, the procedure of blood flow velocity computation contains gathered data on the movement of red blood cells or other indicators of blood flow and utilizes that data to compute the velocity of blood flows with the blood vessels. The procedure of blood flows velocity computation contains evaluating the speed at which blood moves through a blood vessel. It is done utilizing several approaches like magnetic resonance imaging (MRI), Doppler flowmetry, ultrasound, particle image velocimetry (PIV), or computed tomography (CT) scans. This manuscript involves the design of Intelligent Blood Flow Velocity Calculation using Deep Belief Network with Harmony Search Algorithm (BFV-DBNHSA) technique. The proposed BFV-DBNHSA technique computes the velocity of the blood flow accurately and timely. In the presented BFV-DBNHSA technique, the major aim is to determine the interior blood flow velocity. To accomplish this, the BFV-DBNHSA technique employs DBN model to produce the features of the blood flow velocity. Moreover, the BFV-DBNHSA technique uses HSA algorithm for optimal hyperparameter selection of the DBN model. The experimental outcome investigation of the BFV-DBNHSA system is well studied under different measures. The comprehensive comparison analysis revealed the improvement of the BFV-DBNHSA technique over recent algorithms. © 2023 IEEE.

关键词： Decision making

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Graph-based variant discovery reveals novel dynamics in the human microbiome

arXiv

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arXiv 2024年

作者： Muralidharan, Harihara Subrahmaniam Michaelis, Jacquelyn S. Ghurye, Jay Treangen, Todd Koren, Sergey Fedarko, Marcus Pop, Mihai Department of Computer Science University of Maryland College ParkMD United States Center for Bioinformatics and Computational Biology University of Maryland College ParkMD United States Department of Computer Science Rice University HoustonTX United States Genome Informatics Section Computational and Statistical Genomics Branch National Human Genome Research Institute National Institutes of Health BethesdaMD United States Department of Computer Science University of California San DiegoCA United States

Sequence differences between the strains of bacteria comprising host-associated and environmental microbiota may play a role in community assembly and influence the resilience of microbial communities to disturbances. Tools for characterizing strain-level variation within microbial communities, however, are limited in scope, focusing on just single nucleotide polymorphisms, or relying on reference-based analyses that miss complex functional and structural variants. Here, we demonstrate the power of assembly graph analysis to detect and characterize structural variants in almost 1,000 metagenomes generated as part of the Human Microbiome Project. We identify over nine million variants comprising insertion/deletion events, repeat copy-number changes, and mobile elements such as plasmids. We highlight some of the potential functional roles of these genomic changes. Our analysis revealed striking differences in the rate of variation across body sites, highlighting niche-specific mechanisms of bacterial adaptation. The structural variants we detect also include potentially novel prophage integration events, highlighting the potential use of graph-based analyses for phage discovery. © 2024, CC BY.

关键词： Microorganisms

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Improved prediction of anti-angiogenic peptides based on machine learning models and comprehensive features from peptide sequences

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Scientific Reports 2024年第1期14卷 1-10页

作者： Lee, Yun-Chen Yu, Jen-Chieh Ni, Kuan Lin, Yu-Chuan Chen, Ching-Tai Department of Computer Science and Information Engineering Asia University Taichung 41354 Taiwan Department of Bioinformatics and Medical Engineering Asia University Taichung 41354 Taiwan Graduate Institute of Genomics and Bioinformatics National Chung Hsing University Taichung 40227 Taiwan Center for Precision Health Research Asia University Taichung 41354 Taiwan

Angiogenesis is a key process for the proliferation and metastatic spread of cancer cells. Anti-angiogenic peptides (AAPs), with the capability of inhibiting angiogenesis, are promising candidates in cancer treatment. We propose AAPL, a sequence-based predictor to identify AAPs with machine learning models of improved prediction accuracy. Each peptide sequence was transformed to a vector of 4335 numeric values according to 58 different feature types, followed by a heuristic algorithm for feature selection. Next, the hyperparameters of six machine learning models were optimized with respect to the feature subset. We considered two datasets, one with entire peptide sequences and the other with 15 amino acids from peptide N-termini. AAPL achieved Matthew’s correlation coefficients of 0.671 and 0.756 for independent tests based on the two datasets, respectively, outperforming existing predictors by a range of 5.3% to 24.6%. Further analyses show that AAPL yields higher prediction accuracy for peptides with more hydrophobic residues, and fewer hydrophilic and charged residues. The source code of AAPL is available at https://***/yunzheng2002/Anti-angiogenic. © The Author(s) 2024.

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AI-Based Virtual Screening for Identifying Novel Drug Candidates 5

AI-Based Virtual Screening for Identifying Novel Drug Candid...

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5th IEEE International Conference on Recent Trends in computer science and Technology, ICRTCST 2024

作者： Radhamahendran, S. Deshmukh, Sheetal Vishal Thiyagasundaram, T. Sivakumar, K. Tilak Babu, S.B.G. Singh, Surya Pratap Vels Institute of Science Technology and Advanced Studies School of Life Sciences Department of Bioinformatics Pallavaram Chennai India Bharati Vidyapeeth Yashwantrao Mohite Institute of Management Department of Computer Applications Maharashtra Karad India Sri Sairam Siddha Medical College Department of NoiNaadal Chennai India Saveetha University Simats School of Engineering Department of Mathematics Chennai Tamilnadu Thandalam India Aditya Engineering College Department of ECE Surampalem India UPUMS Department of Multi-Disciplinary Research Unit UP Etawah India

ISBN: (纸本)9798350351378

VR screening uses computers to find large databases of chemical compounds and predict their potential as medications based on factors such as binding affinity, pharmacokinetics, and toxicity. This accelerates the process of discovering novel medications by concentrating on compounds with a higher likelihood of efficacy. This results in decreased expenses and increased effectiveness over an extended period. The study demonstrates the utilization of many artificial intelligence techniques in virtual screening, including deep learning networks, machine learning models, and other sophisticated computer technologies. The analysis examines the training of AI models on diverse datasets, incorporating data from existing databases, research publications, and experimental studies. This demonstrates the potential of AI in discovering novel chemicals for the treatment of cancer, infectious diseases, neurological disorders, and uncommon ailments. There are limitations associated with utilizing Molecular structure-based CADD methods. This research reviews current tools, applications, and methods for medication production speed and cost reduction. Structure-Based Virtual Screening (SBVS) is essential to medication development, according to research. © 2024 IEEE.

关键词： Diseases

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