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Analysis of Interacting Factors Contributing to the Onset of Diabetes Type 2 in the Female Pima Population: A Step towards Understanding the Links

Analysis of Interacting Factors Contributing to the Onset of...

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

作者： Vitaly I. Dobromyslin Dalila B. Megherbi Center for Computer Machine/Human Intelligence Networking and Distributed Systems University of Massachusetts Lowell MA USA Instrumentation Laboratory Bedford MA USA

ISBN: (数字)9781728194684

ISBN: (纸本)9781728194691

One of the biggest challenges in building predictive models relates to proper feature selection. As it is well known, good feature selection will result in accurate prediction independently of the classifier. We propose a novel processing pipeline consisting of unsupervised clustering to balance the class prevalence in the training set, which enables the development of stable models with less overfitting. In this paper, we use the proposed pipeline to predict the onset of Type 2 Diabetes (T2D) in the female Pima American Indian population. The Pima population, living near Phoenix, Arizona in the United States, has been known to have the highest T2D prevalence. While it generally remains unclear how various physiological variables and interacting risk factors contribute to the onset of T2D, here for the first time, we quantify the contribution of each of the physiological variables and their interactions to the predicted probability of developing T2D. Our findings in the adult Pima female T2D cohort point to the existence of two distinct population sub-groups, separated primarily by age and the number of pregnancies. For the first time in this dataset, we show several factor interactions: a) subjects with lower BMI (26.0 ± 2.2 vs. 41.5 ± 4.9) experienced a 57-78% reduction in T2D conversion rate at the high plasma glucose (>133.7 mg/dL) and high serum insulin (>168.0 pmol/L) levels respectively, and b) low serum insulin (70.1 ± 16.7 vs. 294.7 ± 136.6 pmol/L) was associated with a 90% lower risk of T2D conversion rate in individuals with high triceps skin thickness (>34 mm), and interestingly c) non-hypotensive (85.6 ± 7.2 vs. 54.3 ± 7.7 mm Hg) women with ≤ 1 pregnancy had 81% lower risk of T2D conversion. With an increasing number of pregnancies, the trend reversed, and non-hypotensive women experienced a 41% higher rate of T2D conversion. Through the evaluation of multiple feature selection methods and classifiers, we show the potential value of the proposed methodo

关键词： Statistics Sociology Predictive models Diabetes Logistics Insulin Training

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GenoPri' 17: International Workshop on Genome Privacy and Security

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2019年第4期16卷 1322-1323页

作者： Ayday, Erman Naveed, Muhammad Tang, Haixu Case Western Reserve Univ Dept Elect Engn & Comp Sci Cleveland OH 44106 USA Univ Southern Calif Dept Comp Sci Los Angeles CA 90089 USA Indiana Univ Sch Informat & Comp Bloomington IN 47405 USA

The four papers in this special section were presented at the 4th International Workshop on Genome Privacy and Security (GenoPri) in 2017. This workshop aimed to bring together a highly interdisciplinary community involved in all aspects of genome privacy and security research. This workshop built on its three predecessors, GenoPri’14, GenoPri’15, and GenoPri’16 which were collocated with the Privacy Enhancing Technologies symposium (PETS), ieee symposium on Security and Privacy, and American Medical Informatics Association Annual Fall symposium (AMIA), respectively. Over the past several decades, genome sequencing technologies have evolved from slow and expensive systems that were limited in access to a select few scientists and forensics investigators to high-throughput, relatively low-cost tools that are available to consumers.

关键词： Special issues and sections Genomics DNA Data privacy bioinformatics Computer security Medical information systems Patient monitoring

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Exploring the Relationship between Neural Mechanism and Detection in Mental Fatigue by Genetic Algorithm and Hierarchical Clustering 7

Exploring the Relationship between Neural Mechanism and Dete...

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ieee 7th International Conference on bioinformatics and computational biology (ICBCB)

作者： Sheng, Yinhe Huang, Kang Zou, Jiemeng Wang, Liping Wei, Pengfei Chinese Acad Sci CAS Ctr Excellence Brain Sci & Intelligence Techn Brain Cognit & Brain Dis Inst Collaborat Innovat Ctr Brain SciShenzhen Inst Ad Shenzhen 518055 Peoples R China Xi An Jiao Tong Univ Sch Software Xian 710049 Peoples R China

ISBN: (纸本)9781728106410

The mental fatigue affects the state of one's daily life easily, therefore, understanding the neural mechanisms of mental fatigue and better detection of it have been the focus of many researchers. Quit a few previous studies have found EEG indicators and high-precision detection methods related to mental fatigue, however, how to combine these EEG indicators with detection methods for better detection remains to be solved. To classify mental fatigue based on EEG features, our previous research, which adopted GA-SVM method, have demonstrated the optimal channels are mainly distributed in the prefrontal, occipital and temporal lobes, and the optimal channel number is 5. Here, we further explored the question by developing a new method combining genetic algorithm and hierarchical clustering to study the mental fatigue caused by visual search. Our results suggest that the optimal EEG features for assessing fatigue state vary from person to person, while the corresponding optimal channel positions are consistent. The channels with the largest changes in EEG features are mainly distributed in the frontal lobe, followed by the temporal lobe and a small area of the occipital lobe, while the corresponding regions of the almost all parietal lobe and part occipital lobe show little changes in EEG features during fatigue. Current study shows that the optimal EEG features of different individuals are different in the mental fatigue detection, and they need to be determined separately, but only a few of the same channels can be used to achieve the better detection.

关键词： component mental fatigue EEG visual search hierarchical clustering

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A Model for Distributed Processing and Analyses of NGS Data under Map-Reduce Paradigm

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2019年第3期16卷 827-840页

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

Massively parallel sequencing technique, introduced by NGS technology, has resulted in an exponential growth of sequencing data, with greatly reduced cost and increased throughput. This huge explosion of data has introduced new challenges in regard to its storage, integration, processing, and analyses. In this paper, we have proposed a novel distributed model under Map-Reduce paradigm to address the NGS big data problem. The architecture of the model involves Map-Reduce based modularized approach involving three different phases that support various analytical pipelines. The first phase will generate detailed base level information of various individual genomes, by granulating the alignment data. The other two phases independently process this base level information in parallel. One of these two phases will provide an integrated DNA profile of multiple individuals, whereas the other phase will generate contigs with similar features in an individual. Each of these three phases will generate a repository of genomic information that will facilitate other analytical pipelines. A simulated and real experimental prototypes has been provided as results to show the effectiveness of the model and its superiority over a few existing popular models and tools. A detailed description of the scope of applications of this model is also included in this article.

关键词： CNV hadoop personalised medicine cancer NGS fault tolerant model bioinformatics analytical workflow

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Conference Report on 2017 ieee International Conference on computational intelligence in bioinformatics and computational biology (ieee CIBCB 2017) [Conference Reports]

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ieee computational intelligence Magazine 2018年第1期13卷 23-24页

作者： Richard Allmendinger University of Manchester United Kingdom

Presents information on the 2017 ieee International Conference on computational intelligence .

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Identification and prediction of key nucleotide sites using machine learning in bioinformatics: A brief overview 17

Identification and prediction of key nucleotide sites using ...

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ieee Int Conf on Parallel and Distributed Processing with Applications, Big Data and Cloud Computing, Sustainable Computing and Communications, Social Computing and Networking (ISPA/BDCloud/SocialCom/SustainCom)

作者： Cai, Jianhua Wei, Leyi Zeng, Kun Xiao, Guobao Minjiang Univ Fujian Prov Key Lab Informat Proc & Intelligent C Fuzhou Peoples R China Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China

ISBN: (纸本)9781728143286

Nowadays, the burgeon of machine learning has promoted its wide application in various fields. In bioinformatics, machine learning computational method has become an indispensable part. Its efficiency and simplicity break the expensive and inefficient barriers of previous experimental methods. This article briefly reviews the application and development of machine learning in identifying and predicting key nucleotide sites (such as 6mA, 5mC, 4mC, etc.) in DNA or RNA. Among them, supervised learning is one of the main methods to this work Its wide application has provided great convenience for the development of identification and prediction of key nucleotides in bioinformatics.

关键词： machine teaming bioinformatics key nucleotide site supervised teaming identification and prediction

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MOSCA: An Automated Pipeline for Integrated Metagenomics and Metatranscriptomics Data Analysis 12th

MOSCA: An Automated Pipeline for Integrated Metagenomics and...

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12th International Conference on Practical Applications of computational biology and bioinformatics (PACBB)

作者： Sequeira, Joao Carlos Rocha, Miguel Alves, Maria Madalena Salvador, Andreia Ferreira Univ Minho Ctr Biol Engn Campus Gualtar Braga Portugal

ISBN: (纸本)9783319987026;9783319987019

Metagenomics (MG) and Metatranscriptomics (MT) approaches open new perspectives on the interpretation of biological systems composed by complexmicrobial communities. Dealing with large sequencing datasets, to extract the desired information and interpret the results are big challenges associated with meta-omics studies. There are several bioinformatics pipelines forMG data analysis and less toMT. Up to date, none performs a complete analysis integrating both MG and MT data, including the assembly of reads into contigs, functional and taxonomic annotation of identified genes, differential gene expression analysis and the comparison of multiple samples. Here, we present Meta-Omics Software for Community Analysis (MOSCA) that was designed with this purpose. It integrates RNA-Seq analysis with Whole Genome Sequencing as reference. Raw sequencing reads are submitted to preprocessing for quality trimming and rRNA removal, and assembled into contigs, which afterwards are annotated by using a reference database. MOSCA performs differential gene expression and provides graphical visualization of the results and comparison of multiple samples. Validation and reproducibility of the pipeline was obtained by using simulated MG and MT datasets.

关键词： Metagenomics Metatranscriptomics bioinformatics pipeline Community analysis RNA-Seq Whole genome sequencing

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We Are Not Pontius Pilate: Acknowledging Ethics and Policy

We Are Not Pontius Pilate: Acknowledging Ethics and Policy

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ieee symposium Series on computational intelligence (SSCI)

作者： James Alexander Hughes William Hannah Peter Kikkert Barry MacKenzie Wendy Ashlock Sheridan Houghten Daniel Ashlock Matthew Stoodley Michael Dubé Rachel Brown Amanda Saunders St. Francis Xavier University Computer Science Antigonish NS Canada St. Francis Xavier University Antigonish NS Canada St. Francis Xavier University Brian Mulroney Institute of Government Antigonish NS Canada St. Francis Xavier University History Antigonish NS Canada Ashlock & McGuinness Cons. Inc Guelph ON Canada Brock University Computer Science St Catharines ON Canada University of Guelph Mathematics and Statistics Guelph ON Canada University of Guelph Bioinformatics Guelph ON Canada

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

A new AI system is being developed to optimize vaccination strategies based on the structure and shape of a community's social contact network. The technology is minimally constrained and not bound by preconceived notions or human biases. With this come novel outside the box strategies; however, the system is only capable of optimizing what it is instructed to optimize, and does not consider any ethical or political concerns. With the growing concern for systematic discrimination as a result of artificial intelligence, we acknowledge a number of relevant issues that may arise as a consequence of our new technology and categorize them into three classes. We also introduce four normative ethical approaches that are used as a framework for decision-making. Despite the focus on vaccination strategies, our goal is to improve the discussions surrounding public concern and trust over artificial intelligence and demonstrate that artificial intelligence practitioners are addressing these concerns.

关键词： Statistics Sociology Vaccines Artificial intelligence Guidelines Ethics COVID-19

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A Network-Guided Reaction-Diffusion Model of AT[N] Biomarkers in Alzheimer’s Disease

A Network-Guided Reaction-Diffusion Model of AT[N] Biomarker...

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ieee symposium on bioinformatics and Bioengineering (BIBE)

作者： Jingwen Zhang Defu Yang Wei He Guorong Wu Minghan Chen Departmment of Computer Science Wake Forest University Winston Salem USA University of North Carolina at Chapel Hill Chapel Hill USA Genetics Bioinformatics and Computational Biology. Virginia Tech Blacksburg USA

ISBN: (数字)9781728195742

ISBN: (纸本)9781728195759

Currently, many studies of Alzheimer's disease (AD) are investigating the neurobiological factors behind the acquisition of beta-amyloid (A), pathologic tau (T), and neurodegeneration ([N]) biomarkers from neuroimages. However, a system-level mechanism of how these neuropathological burdens promote neurodegeneration and why AD exhibits characteristic progression is largely elusive. In this study, we combined the power of systems biology and network neuroscience to understand the dynamic interaction and diffusion process of AT[N] biomarkers from an unprecedented amount of longitudinal Amyloid PET scan, MRI imaging, and DTI data. Specifically, we developed a network-guided biochemical model to jointly (1) model the interaction of AT[N] biomarkers at each brain region and (2) characterize their propagation pattern across the fiber pathways in the structural brain network, where the brain resilience is also considered as a moderator of cognitive decline. Our biochemical model offers a greater mathematical insight to understand the physiopathological mechanism of AD progression by studying the system dynamics and stability. Thus, an in-depth system-level analysis allows us to gain a new understanding of how AT[N] biomarkers spread throughout the brain, capture the early sign of cognitive decline, and predict the AD progression from the preclinical stage.

关键词： Biological system modeling computational modeling Systems biology Biomarkers Brain modeling Mathematical model Resilience

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Biogeographical Ancestry Inference from Genotype: A Comparison of Ancestral Informative SNPs and Genome-wide SNPs

Biogeographical Ancestry Inference from Genotype: A Comparis...

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ieee symposium Series on computational intelligence (SSCI)

作者： Yue Qu Dat Tran Elisa Martinez-Marroquin Faculty of Science and Technology University of Canberra Canberra Australia

ISBN: (数字)9781728125473

ISBN: (纸本)9781728125480

The biogeographical ancestry (BGA) information can provide supporting information in epidemiology and leading intelligence in forensics. Several sets of ancestral informative markers (AIM) have been proposed to facilitate the BGA inference. A small set of markers can improve efficiency though, it has limitations in their ability of balancing different populations and differentiating sub-populations. Genome-wide SNPs provide much more comprehensive information of an individual's ancestral information. In this paper, we study the problem of BGA inference under the abundance of genome-wide high density data. We studied 1043 individuals from 7 continental populations of the Human Genome Diversity Panel at 32212 genome-wide autosomal single nucleotide polymorphism (SNP) loci. We detected the population structure and compared the BGA inference accuracy using three widely used genetic sequence analysis algorithms through AIMs and genome-wide SNPs. Our results show that genome-wide SNPs reveal population structure with clearer clusterness and provide more accurate BGA inference, confirming the rich information carried by genome-wide SNPs. The findings help to give a clearer picture of candidate ancestral population groups of an individual, and potentially help the BGA inference in a fine population scale.

关键词： Support vector machines Sequences Sociology Genomics Hidden Markov models bioinformatics Statistics

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