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Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), International Conference on

作者： Kambham Pratap Joshi M Lakshmi Prasad R Natchadalingam Pundru Chandra Shaker Reddy Saptarshi Mukherjee G Charles Babu Department of CSE(DS&CS) MLR Institute of Technology Dundigal Hyderabad India Computer Science and Engineering Institute of Aeronautical Engineering Dundigal Telangana India CSE-Data Science R.L.Jalappa Institute of Technology Bengaluru Karnataka India School of Computer Science and Engineering Lovely Professional University Phagwara Punjab India M. Tech-Bioinformatics Maulana Abul Kalam Azad University of Technology West Bengal India Computing Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad Telangana India

Coronary Heart-disease (CHD) is one the foremost causes of death globally, making the accurate recognition of it vital. Machine-learning (ML) and deep-learning (DL) are two of the newest technologies being put to use in the healthcare industry. This paper uses a risk factor method to examine how CHD can be predicted. K-Nearest-Neighbors, Binary-Logistic Classification, and Naïve-Bayes are only few of the predictive methods that are measured by accuracy, recall, and receiver operating characteristic (ROC) curves for evaluation. Ensemble modeling strategies including bagging, boosting, and stacking are contrasted to these foundational classifiers. The efficacy of using ensemble approaches to enhance coronary heart disease prediction was investigated using a comparative analytical methodology. The ‘Cardiovascular-Disease dataset,’ which includes 80,000 records of patient data for CHD, is used to evaluate the modeling techniques. It is demonstrated that bagged models, on average, are more accurate than their non-bagged counterparts by the margin of 1.99 percentage points. The highest accuracy of all models was 76.5%, but the AUC for the boosted models was 0.78. Accuracy of 75.1% was achieved by the stacked model consisting of KNN, random-forest classifier, and SVM. In addition, data-analytic methods and K-Folds cross-validation were used to verify the tested models' performance.

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Consensus Clustering Strategy for Cell Type Assignments of scRNA-seq data

Consensus Clustering Strategy for Cell Type Assignments of s...

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IEEE Symposium on Computational Intelligence and bioinformatics and Computational Biology (CIBCB)

作者： Simone G. Riva Brynelle Myers Paolo Cazzaniga Francesca M. Buffa Andrea Tangherloni Weatherall Institute of Molecular Medicine Oxford UK Radcliffe Department of Medicine University of Oxford Oxford UK Wellcome Centre for Human Genetics University of Oxford Oxford UK Department of Human and Social Sciences University of Bergamo Bergamo Italy Bicocca Bioinformatics Biostatistics and Bioimaging Centre - B4 Milano Italy Department of Oncology University of Oxford Oxford UK Department of Computing Sciences Bocconi University Bocconi Institute for Data Science and Analytics Milan Italy Department of Computing Sciences Bocconi University Bocconi Institute for Data Science and Analytics Milan Italy

Cell type annotation is a crucial step for analyzing single-cell RNA sequencing data. Among others, single-cell Automatic Labeling of cell POpulations (scALPO) is a computational pipeline developed to automatically assign the cell types to the identified clusters in scRNA-seq data. Different from most of the approaches, scALPO relies only on the information on marker genes from published literature. Specifically, after the definition of the dataset obtained from gene information retrieved from online databases, the Leiden clustering algorithm is executed to partition cells that are finally annotated. Since the Leiden algorithm might struggle to obtain a reliable outcome under certain circumstances, in this work, we include several clustering algorithms in scALPO, and we propose a pseudo-voting consensus approach that combines the outcome of a set of clustering algorithms. The results obtained on three different datasets show that the consensus approach can improve the cell type annotation without selecting a specific clustering algorithm that best suits the data under investigation.

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Machine learning approaches and databases for prediction of drug-target interaction: a survey paper (vol 22, pg 247, 2021)

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BRIEFINGS IN bioinformatics 2021年第1期22卷 606-606页

作者： Bagherian, Maryam Sabeti, Elyas Wang, Kai Sartor, Maureen A. Nikolovska-Coleska, Zaneta Najarian, Kayvan Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI 48109 USA Michigan Institute for Data Science University of Michigan Ann Arbor MI 48109 USA Department of Biostatistics School of Public Health University of Michigan Ann Arbor MI 48109 USA Department of Pathology University of Michigan Ann Arbor MI 48109 USA Department of Emergency Medicine Medical School University of Michigan Ann Arbor MI 48109 USA Department of Electrical Engineering and Computer Science College of Engineering University of Michigan Ann Arbor MI 48109 USA

The task of predicting the interactions between drugs and targets plays a key role in the process of drug discovery. There is a need to develop novel and efficient prediction approaches in order to avoid costly and laborious yet not-always-deterministic experiments to determine drug–target interactions (DTIs) by experiments alone. These approaches should be capable of identifying the potential DTIs in a timely manner. In this article, we describe the data required for the task of DTI prediction followed by a comprehensive catalog consisting of machine learning methods and databases, which have been proposed and utilized to predict DTIs. The advantages and disadvantages of each set of methods are also briefly discussed. Lastly, the challenges one may face in prediction of DTI using machine learning approaches are highlighted and we conclude by shedding some lights on important future research directions.

关键词： Machine learning drug–target interaction prediction DTI software DTI database

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Unique,Persistent,Resolvable:Identifiers as the Foundation of FAIR

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data Intelligence 2020年第1期2卷 30-39,302页

作者： Nick Juty Sarala M.Wimalaratne Stian Soiland-Reyes John Kunze Carole A.Goble Tim Clark Department of Computer Science The University of ManchesterOxford RoadManchester M139PLUK European Bioinformatics Institute(EMBL-EBI) HinxtonCambridgeCB101SDUK California Digital Library OaklandCalifornia 94612-2901USA Data Science Institute University of VirginiaCharlottesvilleVA 22903-1738USA

The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ***,globally unique identifiers,resolvable on the Web,and associated with a set of additional descriptive metadata,are foundational to FAIR *** we describe some basic principles and exemplars for their design,use and orchestration with other system elements to achieve FAIRness for digital research objects.

关键词： Identifiers metadata findability FAIR data data infrastructures

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Efficient Algorithms for Co-folding of Multiple RNAs 13th

Efficient Algorithms for Co-folding of Multiple RNAs

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13th International Joint Conference on Biomedical Engineering Systems and Technologies, BIOSTEC 2020

作者： Lorenz, Ronny Flamm, Christoph Hofacker, Ivo L. Stadler, Peter F. Institute for Theoretical Chemistry University of Vienna Währingerstraße 17 Wien1090 Austria Bioinformatics and Computational Biology Faculty of Computer Science University of Vienna Währingerstraße 29 Wien1090 Austria Bioinformatics Group Department of Computer Science Interdisciplinary Center for Bioinformatics and Competence Center for Scalable Data Services and Solutions Dresden/Leipzig Universität Leipzig Härtelstraße 16-18 Leipzig04107 Germany Max Planck Institute for Mathematics in the Sciences Inselstraße 22 Leipzig04103 Germany Facultad de Ciencias Universidad National de Colombia Sede Bogotá Colombia Santa Fe Institute 1399 Hyde Park Road Santa FeNM87501 United States

ISBN: (纸本)9783030723781

The simplest class of structures formed by N≥ 2 interacting RNAs consists of all crossing-free base pairs formed over linear arrangements of the constituent RNA sequences. For each permutation of the N strands the structure prediction problem is algorithmically very similar – but not identical – to folding of a single, contiguous RNA. The differences arise from two sources: First, "nicks", i.e., the transitions from one to the next piece of RNA, need to be treated with special care. Second, the connectedness of the structures needs to guaranteed. For the forward recursions, i.e., the computation of folding energies or partition functions, these modifications are rather straightforward and retain the cubic time complexity of the well-known folding algorithms. This is not the case for a straightforward implementation of the corresponding outside recursion, which becomes quartic. Cubic running times, however, can be restored by introducing linear-size auxiliary arrays. Asymptotically, the extra effort over the corresponding algorithms for a single RNA sequence of the same length is negligible in both time and space. An implementation within the framework of the ViennaRNA package conforms to the theoretical performance bounds and provides access to several algorithmic variants, include the handling of user-defined hard and soft constraints. © 2021, Springer Nature Switzerland AG.

关键词： RNA

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Dysregulation of microRNA in cholangiocarcinoma identified through a meta-analysis of microRNA profiling

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World Journal of Gastroenterology 2020年第29期26卷 4356-4371页

作者： Somsak Likhitrattanapisal Supeecha Kumkate Pravech Ajawatanawong Kanokpan Wongprasert Rutaiwan Tohtong Tavan Janvilisri National Center for Genetic Engineering and Biotechnology Pathumthani 12120Thailand Department of Biology Faculty of ScienceMahidol UniversityBangkok 10400Thailand Division of Bioinformatics and Data Management for Research Faculty of Medicine Siriraj HospitalMahidol UniversityBangkok 10700Thailand Department of Anatomy Faculty of ScienceMahidol UniversityBangkok 10400Thailand Department of Biochemistry Faculty of ScienceMahidol UniversityBangkok 10400Thailand

BACKGROUND In the past decades,the potential of microRNA(miRNA)in cancer diagnostics and prognostics has gained a lot of *** this study,a meta-analysis was conducted upon the pooled miRNA microarray data of cholangiocarcinoma(CCA).AIM To identify differentially expressed(DE)miRNAs and perform functional analyses in order to gain insights to understanding miRNA-target interactions involved in tumorigenesis pathways of *** Raw data from 8 CCA miRNA microarray datasets,consisting of 443 samples in total,were integrated and statistically analyzed to identify DE miRNAs via comparison of levels of miRNA expression between CCA and normal bile duct samples using t-tests(P<0.001).The 10-fold cross validation was performed in order to increase the robustness of the t-test *** data showed 70 up-regulated and 48 down-regulated miRNAs in CCA. GeneOntology and pathway enrichment analyses revealed that mRNA targets of DEmiRNAs were significantly involved in several biological processes. The mostprominent dysregulated pathways included phosphatidylinositol-3 kinases/Akt,mitogen-activated protein kinase and Ras signaling *** miRNAs found in our meta-analysis revealed dysregulation in major cancerpathways involved in the development of CCA. These results indicated thenecessity of understanding the miRNA-target interactions and the significance ofdysregulated miRNAs in terms of diagnostics and prognostics of cancers.

关键词： Cholangiocarcinoma Microarray MicroRNA Meta-analysis

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Interlayer antisynchronization in degree-biased duplex networks

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Physical Review E 2023年第3期107卷 034313-034313页

作者： Sayantan Nag Chowdhury Sarbendu Rakshit Chittaranjan Hens Dibakar Ghosh Department of Environmental Science and Policy University of California Davis California 95616 USA Technology Innovation Hub (TIH) IDEAS (Institute of Data Engineering Analytics and Science Foundation) Indian Statistical Institute 203 B. T. Road Kolkata 700108 India Department of Mechanical Engineering University of California Riverside California 92521 USA Center for Computational Natural Sciences and Bioinformatics International Institute of Information Technology Gachibowli Hyderabad 500032 India Physics and Applied Mathematics Unit Indian Statistical Institute 203 B. T. Road Kolkata 700108 India

With synchronization being one of nature's most ubiquitous collective behaviors, the field of network synchronization has experienced tremendous growth, leading to significant theoretical developments. However, most previous studies consider uniform connection weights and undirected networks with positive coupling. In the present article, we incorporate the asymmetry in a two-layer multiplex network by assigning the ratio of the adjacent nodes' degrees as the weights to the intralayer edges. Despite the presence of degree-biased weighting mechanism and attractive-repulsive coupling strengths, we are able to find the necessary conditions for intralayer synchronization and interlayer antisynchronization and test whether these two macroscopic states can withstand demultiplexing in a network. During the occurrence of these two states, we analytically calculate the oscillator's amplitude. In addition to deriving the local stability conditions for interlayer antisynchronization via the master stability function approach, we also construct a suitable Lyapunov function to determine a sufficient condition for global stability. We provide numerical evidence to show the necessity of negative interlayer coupling strength for the occurrence of antisynchronization, and such repulsive interlayer coupling coefficients cannot destroy intralayer synchronization.

关键词： Coupled oscillators Dynamics of networks Synchronization

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Thoughts on Non-IID data Impact in Healthcare with Federated Learning Medical Blockchain

Thoughts on Non-IID Data Impact in Healthcare with Federated...

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IEEE International Conference on Cognitive Machine Intelligence (CogMI)

作者： Zon-Yin Shae Kun-Yi Chen Chi-Yu Chang Yuan-Yu Tsai Che-Yi Chou William I. Baskett Chi-Ren Shyu Jeffrey J.P. Tsai Department of Computer Science and Information Engineering Asia University Taichung Taiwan Department of M-Commerce and Multimedia Applications Asia University Taichung Taiwan Division of Nephrology Asia University Hospital Taichung Taiwan Institute for Data Science and Informatics University of Missouri Columbia USA Department of Bioinformatics and Medical Engineering Asia University Taichung Taiwan

We share the common hypothesis/belief that the more aggregated good quality training data, the better the performance that can be attained by the resulting Artificial Intelligence (AI) model. However, this common belief, in general, is not true in the medical area, since healthcare data sets sourced from different hospitals are often not identically distributed (Non-IID). This imposes severe technical challenges for effectively aggregating the individual hospital data sets together. In this vision paper, instead of offering complete solutions, we will discuss some questions and food for thought with the goal of aiding effective data aggregation and improving federated learning (FL) AI model performance: (1) benchmark and measure the Non-IID degree of medical data sets. (2) include the Non-IID degree metrics in the FL data aggregation mechanism. (3) search for the optimal global model creation strategy among a group of many medical data sets. (4) investigate FL performance better than the centralized learning. This paper will discuss these questions by outlining a visionary approach for exploring a medical blockchain FL mechanism to effectively aggregate medical data across multiple healthcare systems to serve large populations with broad demographics.

关键词： Measurement Federated learning Hospitals Sociology Training data data aggregation data models

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KRONECKER PRODUCT OF TENSORS AND HYPERGRAPHS: STRUCTURE AND DYNAMICS∗

arXiv

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arXiv 2023年

作者： Pickard, Joshua Chen, Can Stansbury, Cooper Surana, Amit Bloch, Anthony Rajapakse, Indika Department of Computational Medicine & Bioinformatics University of Michigan Ann ArborMI48109 United States School of Data Science and Society Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States Raytheon Technologies Research Center East HartfordCT06108 United States Department of Mathematics University of Michigan Ann ArborMI48109 United States Department of Computational Medicine & Bioinformatics Medical School Department of Mathematics University of Michigan Ann ArborMI48109 United States

Hypergraphs and tensors extend classic graph and matrix theory to account for multiway relationships, which are ubiquitous in engineering, biological, and social systems. While the Kronecker product is a potent tool for analyzing the coupling of systems in graph or matrix contexts, its utility in studying multiway interactions, such as those represented by tensors and hypergraphs, remains elusive. In this article, we present a comprehensive exploration of algebraic, structural, and spectral properties of the tensor Kronecker product. We express Tucker and tensor train decompositions and various tensor eigenvalues in terms of the tensor Kronecker product. Additionally, we utilize the tensor Kronecker product to form Kronecker hypergraphs, a tensor-based hypergraph product, and investigate the structure and stability of polynomial dynamics on Kronecker hypergraphs. Finally, we provide numerical examples to demonstrate the utility of the tensor Kronecker product in computing Z-eigenvalues, various tensor decompositions, and determining the stability of polynomial systems. © 2023, CC BY.

关键词： Tensors

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A greener AI-Based crowd counting via efficient deep learning

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AIP Conference Proceedings 2023年第1期2594卷

作者： A. Chrysler R. Gunarso T. Puteri T. W. Cenggoro Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics & Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480

Crowd counting is one of the important AI implementations, which is defined as an AI model that can automatically count humans from CCTV footage. Similar to other implementations of Artificial Intelligence (AI), a crowd counting AI development tends to produce larger models over time. It is caused by the tendency of larger models to have better performance. This trend negatively impacts the environment because the development of larger AI models generates more CO2 that causes global warming. This study instead focuses on developing crowd counting AI that has a small size with competitive performance. Our proposed model achieves competitive performance compared to other state-of-the-art crowd counting models. Currently, our proposed model is also the 3rd best model in terms of performance-size tradeoff among all crowd counting AI with known model size.

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