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Machine Learning Based Early Prediction of Parkinson's Disease for Sustainable Healthcare 1st

Machine Learning Based Early Prediction of Parkinson's Disea...

1st International conference on Sustainable Development through Machine Learning, Artificial intelligence, and IoT, ICSD 2023

作者： Chauhan, Ritu Mehta, Khushi Alankar, Bhavya Kaur, Harleen Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University UP Noida India Amity Institute of Information Technology Amity University UP Mumbai India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9783031470547

The healthcare sector and technology are working hand-in-hand, which has led researchers to focus on Parkinson's disease. In a world where medical science and technology intertwined, there was a group of researchers determined to unravel the mysteries of Parkinson's disease. Medical science with machine learning algorithms, aimed to develop a robust prediction model for diagnosing this complex neurological disorder. The researchers explored various algorithms including Xgboost, Naive Bayes, Tree Classifier, KNN model, SVC model, Logistic Model, Random Forest, AdaBoost, and Gradient Boosting, using a diverse dataset encompassing clinical measurements, genetic information, and demographic factors. The researchers utilized various machine learning algorithms to uncover patterns and correlations, shed light on relationships, make accurate predictions, foster insights, differentiate Parkinson's disease, understand odds and probabilities, tackle uncertainty, and improve accuracy. By combining these algorithms, they constructed a prediction model for the accurate diagnosis of Parkinson's disease, bringing us closer to unraveling its mysteries. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Adaptive boosting

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MinimapPool: an improved flexible and efficient parallel algorithm based on minimap2

MinimapPool: an improved flexible and efficient parallel alg...

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ieee International conference on bioinformatics and Biomedicine (BIBM)

作者： Zhenang Wang Yingbo Cui Jiandong Shang Boya Ji Jianan Li Shaoliang Peng School of Computer Science and Artificial Intelligence Zhengzhou University Zhengzhou China School of Computer National University of Defense Technology Changsha China National Supercomputing Center in Zhengzhou Zhengzhou University Zhengzhou China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Third-generation sequencing techniques have achieved major breakthroughs in sequencing long reads and speed. Continuous improvements in sequencing techniques have reduced sequencing costs, and the number of sequencing data files has shown explosive growth. In terms of sequence alignment, to deal with these high numbers and large-scale data, the conventional serial alignment method can no longer effectively meet the research requirements, therefore, it is of great importance to develop a faster, low-load, and compatible parallel alignment program. In this paper, we propose a parallel task pool algorithm based on the minimap2, a sequence alignment tool, and develop the task pool parallel alignment program based on this algorithm. We compare the program’s work with the average segmentation parallel alignment program. The results show that the task pool parallel alignment program has significant improvement in speedup, memory load, segmentation flexibility, and computational efficiency, it also has good scalability and computational stability. MinimapPool is available at https://***/krkrcc/MinimapPool.

关键词： Sequential analysis Costs Scalability Memory management Explosives computational efficiency Continuous improvement Parallel algorithms bioinformatics

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AutoComplete: Deep Learning-Based Phenotype Imputation for Large-Scale Biomedical Data 26th

AutoComplete: Deep Learning-Based Phenotype Imputation for ...

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

作者： An, Ulzee Cai, Na Dahl, Andy Sankararaman, Sriram Department of Computer Science UCLA Los AngelesCA United States Helmholtz Pioneer Campus Helmholtz Zentrum München Neuherberg Germany Section of Genetic Medicine University of Chicago ChicagoIL United States Department of Human Genetics David Geffen School of Medicine UCLA Los AngelesCA United States Department of Computational Medicine David Geffen School of Medicine UCLA Los AngelesCA United States

ISBN: (纸本)9783031047480

Biomedical datasets that aim to collect diverse phenotypic and genomic data across large numbers of individuals are plagued by the large fraction of missing data The ability to accurately impute or "fill-in" missing entries in these datasets is critical to a number of downstream applications. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Deep learning

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Evaluation of Frameworks for Epidemic Variants and Infectivity using an Evolutionary Algorithm

Evaluation of Frameworks for Epidemic Variants and Infectivi...

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ieee Symposium on computational intelligence and bioinformatics and computational biology (CIBCB)

作者： Michael Dubé Sheridan Houghten Dept. of Mathematics and Statistics University of Guelph Guelph ON Canada Dept. of Computer Science Brock University St. Catharines ON Canada

ISBN: (数字)9781665484626

ISBN: (纸本)9781665484633

An evolutionary algorithm is used to evolve personal contact networks representing the individuals in a population and the interactions between them. Such networks can be used to track the progress of an epidemic, as it passes from infected individuals to others. Two fitness functions are used: epidemic duration and epidemic spread. Each of these is evaluated in the context of new variants being introduced during the course of the epidemic. Individuals infected with one variant obtain immunity to that variant and possible partial immunity to future variants. Two frameworks for epidemic variants are presented. In the first, infectivity is coupled directly to how well an individual's immunity covers the variant. In the second, infectivity is decoupled, causing a much higher number of infections but with many of lessened severity due to immunity.

关键词： Epidemics Sociology Evolutionary computation Statistics bioinformatics Immune system computational biology

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Computing Shortest Hyperpaths for Pathway Inference in Cellular Reaction Networks 27th

Computing Shortest Hyperpaths for Pathway Inference in Cel...

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27th International conference on Research in computational Molecular biology, RECOMB 2023

作者： Krieger, Spencer Kececioglu, John Computational Biology Department Carnegie Mellon University PittsburghPA15213 United States Department of Computer Science The University of Arizona TucsonAZ85721 United States

ISBN: (纸本)9783031291180

Signaling and metabolic pathways, which consist of a series of reactions producing target molecules from source compounds, are cornerstones of cellular biology. The cellular reaction networks containing such pathways can be precisely modeled by directed hypergraphs, where each reaction corresponds to a hyperedge, directed from its set of reactants to its set of products. Given such a network represented by a directed hypergraph, inferring the most likely set of reactions that produce a given target from a given set of sources corresponds to finding a shortest hyperpath, which is NP-complete. The best methods currently available for shortest hyperpaths either offer no guarantee of optimality, or exclude hyperpaths containing cycles even though cycles are abundant in real biological pathways. We derive a novel graph-theoretic characterization of hyperpaths, leveraged in a new formulation of the general shortest hyperpath problem as an integer linear program that for the first time handles hyperpaths containing cycles, and present a novel cutting-plane algorithm that can solve this integer program to optimality in practice. This represents a major advance over the best prior exact algorithm, which was limited to acyclic hyperpaths (and hence fails to find a solution for the many biological instances where all hyperpaths are in fact cyclic). In comprehensive experiments over thousands of instances from the standard NCI-PID and Reactome databases, we demonstrate that our cutting-plane algorithm quickly finds an optimal hyperpath, with a median running-time of under ten seconds and a maximum time of around thirty minutes, even on large instances with many thousands of reactions. Source code implementing our cutting-plane algorithm for shortest hyperpaths in a new tool called Mmunin is available free for research use at http://***. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Molecules

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Neuro-Degenerative Disorder Prediction Using Curated Short Mutated Genome Sequences Through Hybrid Deep Neural Networks

Neuro-Degenerative Disorder Prediction Using Curated Short M...

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ieee International conference on bioinformatics and computational biology (ICBCB)

作者： Naman Bhatia L V S K B Kasyap Varanasi Parth Raghuwanshi Avinash Saroj Chandra Mohan Dasari Computer Science and Engineering Indian Institute Of Information Technology Sri City India Computer Science and Engineering Group Indian Institute of Information Technology Sri City India

ISBN: (数字)9798350375749

ISBN: (纸本)9798350375756

Psychological disorders are a range of conditions characterized by significant disruptions in a person's thoughts, emotions, and behavior. The WHO reports that over a billion people worldwide have neurological illnesses, and ineffective diagnostics hinder treatment. Mental health disorders continue to be a pervasive global health concern, necessitating innovative early detection and intervention approaches. The classical methods often rely on expensive clinical diagnoses and computational techniques in genome-wide association studies (GWAS) that can be time-consuming. We curate the short genome sequence data for five disorders of fixed and variable lengths through two novel approaches to address these limitations. To our knowledge, this is the first work that suggests extracting short genetic sequences for psychiatric disorders. We investigate different deeplearning (DL) techniques to predict mental health disorders based on the prepared datasets. Our study aims to enhance the accuracy and timeliness of mental health assessments, potentially revolutionizing mental health care delivery. The classification accuracies for the proposed graph neural network with HMM representation on fixed length genome sequence were as follows: SCZ=80%; BP=71% which is the highest when compared to the other DL models. The outcomes of our research reveal promising results, with the curated short genome sequences significantly contributing to identifying mental health disorders. The proposed data curation approaches lay a solid basis for the adoption of DL based mental health prediction tools in healthcare systems. The model encourages early intervention and ultimately improves patient outcomes.

关键词： Accuracy computational modeling Genomics Hidden Markov models Mental health Medical services Graph neural networks Encoding bioinformatics computational biology

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Guest Editorial for Special Section on the 14th International conference on Intelligent Computing (ICIC)

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2020年第5期17卷 1474-1475页

作者： Huang, De-Shuang Bevilacqua, Vitoantonio Gromiha, Michael Tongji Univ Instute Machine Learning & Syst Biol Shanghai 201804 Peoples R China Polytech Bari Elect & Elect Dept Via Orabona I-70126 Bari BA Italy Indian Inst Technol IIT Dept Biotechnol Madras Tamil Nadu India

The papers in this special section were presented at the Fourteenth International conference on Intelligent Computing (ICIC) held in Wuhan, China, on August 15-18, 2018.

关键词： Special Issues And Sections computational intelligence Machine Learning bioinformatics computational biology

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IPRIns: A tool with the improved precision and recall for insertion detection in the human genome 9

IPRIns: A tool with the improved precision and recall for in...

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9th ieee International conference on bioinformatics and computational biology, ICBCB 2021

作者： Piwluang, Sakkayaphab Wichadakul, Duangdao Software Engineering Program Faculty of Engineering Chulalongkorn University Department of Computer Engineering Bangkok Thailand Faculty of Engineering Chulalongkorn University Department of Computer Engineering Bangkok Thailand

ISBN: (纸本)9780738132020

An insertion is a specific type of the structural variations. The identification of insertions in a human genome is essential for the study of diseases or their functional effects. There are many tools available for identifying the insertion type with different methods and strategies. However, most of them could not deliver both good recall and precision, especially for the real datasets sequenced with the paired-end short reads. In this paper, we propose iPRIns, a new computational method for detecting insertions aiming to improve both precision and recall. The proposed method with discovering and filtering processes outperformed all other three tools for 5 out of 10 real datasets, the variations of NA12878, for both precision and recall. iPRIns is released under the open-source GPLv3 license. The source code and documentation are available at https://***/cucpbioinfo/iPRIns. © 2021 ieee.

关键词： bioinformatics

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CytoFlow: A Novel computational Method to Construct Signal Transduction Networks at Single-Cell Resolution Based on Flow Networks

CytoFlow: A Novel Computational Method to Construct Signal T...

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Medical Artificial intelligence (MedAI), ieee International conference on

作者： Yi Dai Ziheng Duan Siwei Xu Jing Zhang Department of Computer Science University of California Irvine Irvine USA

ISBN: (数字)9798350377613

ISBN: (纸本)9798350377620

The signal transduction network is essential for eukaryotic cellular communication and response to environmental signals, with disruptions leading to various diseases. As our understanding of intracellular signaling expands, the demand for computational tools that can efficiently synthesize this information into comprehensive networks grows. To meet this need, we developed CytoFlow, a novel computational model designed to construct cell-type-specific signal transduction net-works by leveraging protein-protein interaction and single-cell transcriptomics data. Specifically, it models the network from receptors to transcription factors as a penalized flow network, optimized using linear programming techniques. We validated CytoFlow's superior precision against existing methods in reconstructing the yeast mitogen-activated protein kinase pathway, and demonstrated its ability to identify cell-type-specific signaling patterns in the human prefrontal cortex and peripheral blood mononuclear cells. In summary, CytoFlow offers a precise and cost-effective solution for constructing detailed signal transduction networks, advancing our capacity to understand and analyze cellular signaling processes.

关键词： Proteins computational modeling RNA Transcriptomics Cells (biology) Linear programming Data models computational efficiency Signal resolution Diseases

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Disturbance Observer-Based Adaptive Synchronization of Heterogeneous Chaotic Systems With Uncertainties via DNA Strand Displacement

IEEE TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATIC...

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ieee TRANSACTIONS ON computational biology AND bioinformatics 2025年第1期22卷 250-260页

作者： Liu, Xiaoxiao Lv, Hui Dalian Univ Sch Software Engn Minist Educ Key Lab Adv Design & Intelligent Comp Dalian 116622 Peoples R China

The application of DNA strand displacement (DSD) for synchronizing chaotic systems has made remarkable progress in recent years. However, uncertainty may exist in the systems, and interference is unavoidable during the synchronization process. This article endeavors to tackle the issue of performance degradation in synchronization caused by disturbances and system uncertainties. Primarily, in order to introduce perturbations in chaotic systems using DSD, the corresponding chemical reaction networks (CRNs) is derived from the ordinary differential equations of the perturbation signal in this paper, leading to the establishment of a composite function that combines trigonometric and exponential functions. Furthermore, combining the design of nonlinear observers with adaptive control methods not only simplifies the derivation process but also allows for the construction of observers using fewer DSD reactions. Through the collaborative efforts of the adaptive law and the observer, it enables the estimation of perturbations and system uncertainties, thereby achieving the synchronization of heterogeneous chaotic systems. Finally, the proposed method successfully synchronizes the Lorenz system and Yang system, even in the presence of system uncertainties and disturbances, demonstrating its effectiveness.

关键词： Observers Synchronization Uncertainty Perturbation methods computational biology bioinformatics DNA Chaotic communication Costs Estimation error DNA strand displacement disturbance observer chaotic system adaptive control

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