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2007 4th ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB 2007

作者： Yang, Jack Y. Yang, Mary Qu Niemierko, Andrzej Luo, Zuojie Li, Jianling Department of Radiation Oncology Massachusetts General Hospital and Harvard Medical School Boston MA 02114 United States National Human Genome Reserch Institute National Institutes of Health U.S. Department of Health and Human Services Bethesda MD 20852 United States Division of Biomathematics and Biostatistics and Dept. of Radiation Oncology Massachusetts General Hospital and Harvard Medical School Boston MA 02114 United States Department of Endocrinology First Affiliated Hospital Guangxi Medical University Nanning Guangxi 500021 China Joslin Diabetes Ctr. Harvard Univ. United States

ISBN: (纸本)1424407109

redicting tumor malignancies is an important but difficult task. For many tumors, especially neural and endocrine tumors, traditional pathological and histological analyses often can not effectively distinguish benign from malignant tumors. Developing synergistic bioinformatics and computational intelligence system is effective, because deterministic cancer markers do not always exist in individual patients. We proposed a parallel paradigm of cancer and use a number of ensemble methods including Boosting, Bagging and Consensus Networking, and have designed a novel classification scheme that advantageously combines several computational intelligence algorithms such as the variants of Self-Organizing Feature Map (SOFM) algorithms and the Maximum Contrast Tree (RMCT) algorithms. Boosting and Bagging have been advantageously combined. When all of the above are integrated into one synergistic intelligent medical decision system, the prediction power for the task has been significantly boosted. The system and features are validated by diagnosing new patients and by a number of laboratory molecular biology measurements. The outcomes of the research have improved cancer diagnosis and treatment planning, and may lead to diagnose microscopic diseases and better understanding of human genome mechanisms relating to malignant transformation. © 2007 ieee.

关键词： Tumors

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computational Analysis of Plasmodium falciparum RNA-Seq data reveals PPIs that might be implicated in the Invasion of the RBCs

Computational Analysis of <i>Plasmodium falciparum</i> RNA-S...

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ieee conference on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Soyemi, Jumoke Isewon, Itunuolwa Ogunlana, Olubanke Solomon, Rotimi Oyelade, Jelili Adebiyi, Ezekiel Fed Polytech Dept Comp Sci Ilaro Nigeria Covenant Univ Dept Comp & Inform Sci Ota Nigeria Covenant Univ Dept Biol Sci Ota Nigeria

ISBN: (纸本)9781538613993

In this study, differentially expressed genes for the trophozoite and schizont stages of Plasmodium falciparum's life cycle were extracted from a time series RNA-Seq gene expression experiment. About 28% of the 5,270 genes used in the experiment were found to show significant expression at these stages. Enrichment analysis using Gene Ontology implicated a total of 62 functions as highly enriched from the list of differentially expressed genes (DEGs). Some include;protein targeting to membrane, protein import, establishment of proteins localization to organelle, ribonucleic protein complex, nucleotide-excision repair and processes related to the mitochondria. A protein interaction network (PIN) for the DEGs at the schizont stage was extracted from experimental data of protein-protein interactions and supplemented with data from a protein interaction database. We predicted a number of protein-protein interactions in Plasmodium falciparum that may be implicated in invasion of the human red blood cells (RBCs). Some of these predictions are consistent with those from previous studies while quite a number of them are novel. We also identified 16 protein complexes from the PIN using the Molecular Complex Detection (MCODE) algorithm. The functional enrichment of the identified protein complexes showed functions related to gene expression, translation, RNA transport and metabolic/biological processes which have been identified to be important in the invasion process. The result from this study is meant to provide better insight into disease at hand.

关键词： RNA-Seq gene expression data Plasmodium falciparum Gene Ontology MCODE Algorithm Differentially Expressed genes

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13th Workshop on Parallel and AI-based bioinformatics and Biomedicine (ParBio): Editorial 24

13th Workshop on Parallel and AI-based Bioinformatics and Bi...

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15th conference on bioinformatics computational biology and Health Informatics

作者： Agapito, Giuseppe Cannataro, Mario Lloyd, Wes J. Zucco, Chiara Magna Graecia Univ Catanzaro Dept Law Econ & Sociol Catanzaro Italy Magna Graecia Univ Catanzaro Dept Med & Surg Sci Catanzaro Italy Univ Washington Tacoma Sch Engn & Technol Washington DC USA

ISBN: (纸本)9798400713026

The goal of ParBio is to bring together scientists in high-performance computing, computational biology, and medicine to discuss parallel implementation of bioinformatics and biomedical applications and the challenges and opportunities of moving these applications to the cloud or edge. The workshop will also address Artificial intelligence (AI), Large Language Models (LLMs), machine learning, and big data analytics in healthcare and bioinformatics, focusing on the integrated analysis of molecular and clinical data. This is motivated by the increasing production of experimental and clinical data and the shift towards data storage, integration, and analysis.

关键词： Parallel algorithms bioinformatics Machine Learning

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AI-based computational Methods in Early Drug Discovery and Post Market Drug Assessment: A Survey

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ieee/ACM Transactions on computational biology and bioinformatics 2024年第1期22卷 97-115页

作者： Rajaei, Flora Minoccheri, Cristian Wittrup, Emily Wilson, Richard C. Athey, Brian D. Omenn, Gilbert S. Najarian, Kayvan University of Michigan Department of Computational Medicine and Bioinformatics Ann ArborMI48109 United States University of York Computer Science Department YorkYO10 5GH United Kingdom

Over the past few years, artificial intelligence (AI) has emerged as a transformative force in drug discovery and development (DDD), revolutionizing many aspects of the process. This survey provides a comprehensive review of recent advancements in AI applications within early drug discovery and post-market drug assessment. It addresses the identification and prioritization of new therapeutic targets, prediction of drug-target interaction (DTI), design of novel drug-like molecules, and assessment of the clinical efficacy of new medications. By integrating AI technologies, pharmaceutical companies can accelerate the discovery of new treatments, enhance the precision of drug development, and bring more effective therapies to market. This shift represents a significant move towards more efficient and cost-effective methodologies in the DDD landscape. © 2004-2012 ieee.

关键词： Drug discovery

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Autumn Algorithm-Computation of Hybridization Networks for Realistic Phylogenetic Trees

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第2期15卷 398-410页

作者： Huson, Daniel H. Linz, Simone Univ Tubingen Ctr Bioinformat D-72076 Tubingen Germany Univ Auckland Dept Comp Sci Auckland New Zealand

A minimum hybridization network is a rooted phylogenetic network that displays two given rooted phylogenetic trees using a minimum number of reticulations. Previous mathematical work on their calculation has usually assumed the input trees to be bifurcating, correctly rooted, or that they both contain the same taxa. These assumptions do not hold in biological studies and "realistic" trees have multifurcations, are difficult to root, and rarely contain the same taxa. We present a new algorithm for computing minimum hybridization networks for a given pair of "realistic" rooted phylogenetic trees. We also describe how the algorithm might be used to improve the rooting of the input trees. We introduce the concept of "autumn trees", a nice framework for the formulation of algorithms based on the mathematics of "maximum acyclic agreement forests". While the main computational problem is hard, the run-time depends mainly on how different the given input trees are. In biological studies, where the trees are reasonably similar, our parallel implementation performs well in practice. The algorithm is available in our open source program Dendroscope 3, providing a platform for biologists to explore rooted phylogenetic networks. We demonstrate the utility of the algorithm using several previously studied data sets.

关键词： computational biology phylogenetics networks hybridization algorithms

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computational analysis of HIV-1 evolution and epidemiology

Computational analysis of HIV-1 evolution and epidemiology

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ieee International conference on bioinformatics and Biomedicine Workshops (BIBMW)

作者： Pond, Sergei L. Kosakovsky Univ Calif San Diego Dept Med San Diego CA 92103 USA

I survey several illuminating applications of diverse next generation sequencing, phylogenetic, machine learning, and network analysis approaches to the study of HIV-1 at the level of a single individual, populations ... 详细信息

ISBN: (纸本)9781457716133

关键词： Drugs HIV-1 evolution Human immunodeficiency virus Immune system Resistance Strain Vaccines Viruses (medical) computational analysis diseases epidemiology genetics genomic recombination genomics illuminating applications learning (artificial intelligence)

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Cross-validation and cross-study validation of kidney cancer with machine learning and whole exome sequences from the National Cancer Institute

Cross-validation and cross-study validation of kidney cancer...

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ieee conference on computational intelligence in bioinformatics and computational biology (CIBCB)

作者： Aljouie, Abdulrhman Patel, Nihir Roshan, Usman King Saud bin Abdulaziz Univ Hlth Sci King Abdullah Int Med Res Ctr Dept Biostat & Bioinformat Riyadh 11426 Saudi Arabia Mt Sinai Hosp Icahn Sch Med Hess Ctr Sci & Med Dept Genet & Genom Sci New York NY 10029 USA New Jersey Inst Technol Dept Comp Sci Newark NJ 07102 USA

ISBN: (纸本)9781538613993

Accurate cancer risk prediction from genetic and environment variables is a key problem in medicine. One approach is to use somatic mutations which could potentially be used in early detection and prevention. SNP based studies are the most common ones utilizing this approach, however most studies lack a cross-study validation component across at least two independent studies. Here we explore the cross-validation and cross-study validation of predicting kidney cancer case and controls with SNPs obtained from whole exome sequences at the National Cancer Institute. From the Genomics Data Commons portal we obtained aligned whole exome sequences of two different kidney cancer studies: 110 cases and controls of KIRP for renal papillary cell carcinoma and 34 cases and controls of KICH for kidney chromophobe cell carcinoma. We performed a rigorous quality control procedure to obtain SNPs and rank them with feature selection. On top ranked SNPs we find the support vector machine to obtain a cross-validation accuracy of 71% (with 10 SNPs) and 72% (with 20 SNPs) in KIRP and KICH respectively. We then learn a model on KIRP and with 10 SNPs achieve an accuracy of 66% on the KICH samples. Our work shows that we can predict kidney chromophobe carcinoma from a kidney papillary carcinoma dataset with better than a random classification which would have 50% accuracy. In continuing work we are expanding these sample sizes and extending crossstudy to other kidney cancer datasets in the NCI GDC portal.

关键词： Cancer Kidney Support vector machines bioinformatics Genomics Correlation Machine learning

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Algorithms for computational biology: Eighth Edition

Algorithms for Computational Biology: Eighth Edition

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作者： Martin-Vide, Carlos Vega-Rodriguez, Miguel A. Rovira i Virgili University Research Group on Mathematical Linguistics Tarragona43002 Spain University of Extremadura ARCO Research Group Cáceres10003 Spain

This special section of ieee/ACM Transactions on computational biology and bioinformatics presents extended versions of some of the best papers accepted at the Eighth International conference on Algorithms for computational biology, AlCoB 2021, held online due to the COVID-19 pandemic on November 9-11, 2021. The conference was organized by the Department of Computer Science at the University of Montana and the Institute for Research Development, Training and Advice - IRDTA, Brussels/London. © 2004-2012 ieee.

关键词： bioinformatics

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Graphical approach for motif recognition in DNA sequences

Graphical approach for motif recognition in DNA sequences

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Proceedings of the 2004 ieee Symposium on computational intelligence in bioinformatics and computational biology, CIBCB'04

作者： Yang, Xiao Rajapakse, Jagath C. School of Computer Engineering Bioinformatics Research Center Nanyang Technological University 639798 Singapore School of Computer Engineering Nanyang Technological University 639798 Singapore

ISBN: (纸本)0780387287

Several algorithms have been developed for motif recognition in the past few years, superior in some sense over others, yet not a single one was declared to be the "best". Some of the well recognized algorithms are based on heuristic methods, such as Gibbs sampling and Expectation Maximization, and enumeration methods, such as Oligo-analysis. However, the inability to solve the "Challenge Problem" in motif recognition showed the drawbacks of the existing heuristic and enumeration methods. Two new algorithms were developed to resolve this problem but still, they suffered from time and space expense and the problem of local optima. In this paper, we proposed a new algorithm which can solve the challenge problem with better performance even in very long sequences by applying dynamic programming for path searching in a graph and scanning with the consensus sequence to eliminate faked motif instances. © 2004 ieee.

关键词： DNA sequences

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Guest Editors' Introduction to the Special Section on the 14th International conference on computational Methods in Systems biology (CMSB 2016)

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ieee-ACM TRANSACTIONS ON computational biology AND bioinformatics 2018年第4期15卷 1122-1123页

作者： Bartocci, Ezio Lio, Pietro Paoletti, Nicola Vienna Univ Technol Fac Informat Treitlstr 3 A-1040 Vienna Austria Univ Cambridge Dept Comp Sci & Technol William Gates Bldg15 JJ Thomson Ave Cambridge CB3 0FD England SUNY Stony Brook Dept Comp Sci Stony Brook NY 11794 USA

No abstract available.

关键词： Special issues and sections Meetings computational biology

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