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Identify Non-mutational p53 Functional Deficiency in Human Cancers

Genomics, Proteomics & Bioinformatics 2024年第5期22卷 85-98页

作者： Qianpeng Li Yang Zhang Sicheng Luo Zhang Zhang Ann L.Oberg David E.Kozono Hua Lu Jann N.Sarkaria Lina Ma Liguo Wang National Genomics Data Center China National Center for BioinformationBeijing 100101China Beijing Institute of Genomics Chinese Academy of SciencesBeijing 100101China University of Chinese Academy of Sciences Beijing 100049China Division of Computational Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Radiation Oncology Dana-Farber Cancer Institute and Brigham and Women's HospitalBostonMA 02215USA Department of Biochemistry&Molecular Biology and Tulane Cancer Center Tulane University School of MedicineNew OrleansLA 70112USA Department of Radiation Oncology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Department of Biochemistry and Molecular Biology Mayo Clinic College of Medicine and ScienceRochesterMN 55905USA Bioinformatics and Computational Biology Graduate Program University of Minnesota RochesterRochesterMN 55904USA

An accurate assessment of p53's functional statuses is critical for cancer genomic ***,there is a significant challenge in identifying tumors with non-mutational p53 inactivation which is not detectable through DNA *** undetected cases are often misclassified as p53-normal,leading to inaccurate prognosis and downstream association *** address this issue,we built the support vector machine(SVM)models to systematically reassess p53's functional statuses in TP53 wild-type(TP53^(WT))tumors from multiple The Cancer Genome Atlas(TCGA)***-validation demonstrated the good performance of the SVM models with a mean area under the receiver operating characteristic curve(AUROC)of 0.9822,precision of 0.9747,and recall of *** study revealed that a significant proportion(87%-99%)of TP53^(WT) tumors actually had compromised p53 *** analyses uncovered that these genetically intact but functionally impaired(termed as predictively reduced function of p53 or TP53^(WT)-pRF)tumors exhibited genomic and pathophysiologic features akin to TP53-mutant tumors:heightened genomic instability and elevated levels of ***,patients with TP53^(WT)-pRF tumors experienced significantly shortened overall survival or progression-free survival compared to those with predictively normal function of p53(TP53^(WT)-pN)tumors,and these patients also displayed increased sensitivity to platinum-based chemotherapy and radiation therapy.

关键词： Cancer Composite expression DNA mutation Machine learning p53 deficiency

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Agile parallel bioinformatics workflow management using Pwrake

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BMC Research Notes 2011年第1期4卷 1-8页

作者： Mishima, Hiroyuki Sasaki, Kensaku Tanaka, Masahiro Tatebe, Osamu Yoshiura, Koh-Ichiro Department of Human Genetics Nagasaki University Graduate School of Biomedical Sciences Nagasaki Nagasaki 1-12-4 Sakamoto Japan Nagasaki University Global Center of Excellence Program Nagasaki Nagasaki 1-12-4 Sakamoto Japan Center for Computational Sciences University of Tsukuba Tsukuba Ibaraki 1-1-1 Tennodai Japan Core Research for Evolutional Science and Technology Japan Science and Technology Agency Kawaguchi Saitama 4-1-8 Honcho Japan Departmentent of Computer Science Graduate School of Systems and Information Engineering University of Tsukuba Tsukuba Ibaraki 1-1-1 Tennodai Japan

Background: In bioinformatics projects, scientific workflow systems are widely used to manage computational procedures. Full-featured workflow systems have been proposed to fulfil the demand for workflow management. However, such systems tend to be over-weighted for actual bioinformatics practices. We realize that quick deployment of cutting-edge software implementing advanced algorithms and data formats, and continuous adaptation to changes in computational resources and the environment are often prioritized in scientific workflow management. These features have a greater affinity with the agile software development method through iterative development phases after trial and error. Here, we show the application of a scientific workflow system Pwrake to bioinformatics workflows. Pwrake is a parallel workflow extension of Ruby's standard build tool Rake, the flexibility of which has been demonstrated in the astronomy domain. Therefore, we hypothesize that Pwrake also has advantages in actual bioinformatics workflows. Findings. We implemented the Pwrake workflows to process next generation sequencing data using the Genomic Analysis Toolkit (GATK) and Dindel. GATK and Dindel workflows are typical examples of sequential and parallel workflows, respectively. We found that in practice, actual scientific workflow development iterates over two phases, the workflow definition phase and the parameter adjustment phase. We introduced separate workflow definitions to help focus on each of the two developmental phases, as well as helper methods to simplify the descriptions. This approach increased iterative development efficiency. Moreover, we implemented combined workflows to demonstrate modularity of the GATK and Dindel workflows. Conclusions: Pwrake enables agile management of scientific workflows in the bioinformatics domain. The internal domain specific language design built on Ruby gives the flexibility of rakefiles for writing scientific workflows. Furthermore, readability

关键词： Ruby Iterative Development Agile Software Development Host Language Bioinformatics Domain

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NOMAD: nonlinear manifold decoders for operator learning 22

NOMAD: nonlinear manifold decoders for operator learning

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jacob H. Seidman Georgios Kissas Paris Perdikaris George J. Pappas Graduate Program in Applied Mathematics and Computational Science University of Pennsylvania Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania Department of Electrical and Systems Engineering University of Pennsylvania

ISBN: (纸本)9781713871088

Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid mechanics, and climate modeling. By directly learning maps (operators) between infinite dimensional function spaces, these models are able to learn discretization invariant representations of target functions. A common approach is to represent such target functions as linear combinations of basis elements learned from data. However, there are simple scenarios where, even though the target functions form a low dimensional submanifold, a very large number of basis elements is needed for an accurate linear representation. Here we present NOMAD, a novel operator learning framework with a nonlinear decoder map capable of learning finite dimensional representations of nonlinear submanifolds in function spaces. We show this method is able to accurately learn low dimensional representations of solution manifolds to partial differential equations while outperforming linear models of larger size. Additionally, we compare to state-of-the-art operator learning methods on a complex fluid dynamics benchmark and achieve competitive performance with a significantly smaller model size and training cost.

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Decoding post-transcriptional regulatory networks by RNA-linked CRISPR screening in human cells

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Nature Methods 2025年 1-10页

作者： Patrick J. Nugent Heungwon Park James N. Yelland Katharine Y. Chen Christine Bynum Grace Quarterman Arvind Rasi Subramaniam Cynthia L. Wladyka Andrew C. Hsieh Sayantani Sinha Stanley C. Lee Basic Sciences Division and Computational Biology Section of the Public Health Sciences Division Fred Hutchinson Cancer Center Seattle WA USA Molecular and Cellular Biology Graduate Program University of Washington Seattle WA USA Department of Biology Spelman College Atlanta GA USA Department of Biochemistry and Department of Genome Sciences University of Washington Seattle WA USA Human Biology Division Fred Hutchinson Cancer Center Seattle WA USA Department of Medicine and Department of Genome Sciences University of Washington Seattle WA USA Translational Science and Therapeutics Division Fred Hutchinson Cancer Center Seattle WA USA

RNAs undergo a complex choreography of metabolic processes that are regulated by thousands of RNA-associated proteins. Here we introduce ReLiC, a scalable and high-throughput RNA-linked CRISPR approach to measure the responses of diverse RNA metabolic processes to knockout of 2,092 human genes encoding all known RNA-associated proteins. ReLiC relies on an iterative strategy to integrate genes encoding Cas9, single-guide RNAs (sgRNAs) and barcoded reporter libraries into a defined genomic locus. Combining ReLiC with polysome fractionation reveals key regulators of ribosome occupancy, uncovering links between translation and proteostasis. Isoform-specific ReLiC captures differential regulation of intron retention and exon skipping by SF3B complex subunits. Chemogenomic ReLiC screens decipher translational regulators upstream of messenger RNA (mRNA) decay and identify a role for the ribosome collision sensor GCN1 during treatment with the anti-leukemic drug homoharringtonine. Our work demonstrates ReLiC as a powerful framework for discovering and dissecting post-transcriptional regulatory networks in human cells.

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A Neuro-Inspired Approach to Solve a Simultaneous Location and Mapping Task Using Shared Information in Multiple Robots Systems 15

A Neuro-Inspired Approach to Solve a Simultaneous Location a...

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15th International Conference on Control, Automation, Robotics and Vision, ICARCV 2018

作者： Menezes, Matheus Chaves De Freitas, Edison Pignaton Cheng, Sen De Oliveira, Alexandre Cesar Muniz De Almeida Ribeiro, Paulo Rogerio Graduate Program on Computer Science Federal University of Maranhão Sao Luis Brazil Computational Neuroscience Ruhr University Bochum Institute for Neural Computation Bochum Germany Department of Informatics Federal University of Maranhão Sao Luis Maranhao Brazil Department of Computer Engineering Federal University of Maranhão Sao Luis Maranhao Brazil

ISBN: (纸本)9781538695821

This paper presents a neuro-inspired mapping approach that uses partial information shared by multiple robots to reduce the time to create a map of an entire environment. Robots using a neurobiologically inspired algorithm, namely RatSLAM, map an environment sharing video information among themselves. RatSLAM, which is based on the navigation system present in the hippocampus of rodents' brain, has been widely used on simultaneous localization and mapping (SLAM) problem. This proposal has been able to generate suitable maps mainly when there is redundant information, e.g. a scene is seen more than once, since this fact activates local view cells that inject activity inside the pose cells via an excitatory link. The work here reported extends this approach by merging partial information acquired by multiple robots. The results from the performed experiments show that the final map built by two robots with shared information is similar to one built by two robots performing the same mapping task individually, i.e. without sharing information. However, the time spent to generate the whole map with the proposed shared approach was smaller than the one without the shared information. Thus, the current approach allows creating a complete map of an environment within a reduced time using multiple robots. © 2018 IEEE.

关键词： Mapping

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Simulation Using Fighter Ants Algorithm with an Application on Efficient Team Building

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International Journal of Intelligent Technologies and Applied Statistics 2014年第2期7卷 115-126页

作者： Vladik Kreinovich Tatcha Sudtasan Komsan Suriya Computational Science Program University of Texas at El Paso Texas U.S.A Graduate School of Asia-Pacific Studies Waseda University Tokyo Japan Faculty of Economics Chiang Mai University Chiang Mai Thailand

This paper compares the performance of two teams with the same average skill but different diversities. It constructs the Fighter Ants Algorithm to mimic the fighting between two groups of ants, the black and red ants. The battle matches individual ants from both sides, compares their strengths and makes only the stronger survives. The winning side is determined by the last-man-standing rule. There are five major results. First, the group with more diversity naturally wins. Second, skill upgrades of the loser side turn the side to be the winner even after a slight improvement of the strength. Third, the addition of some experts with the strongest strength will be able to help the side to be the winner. Fourth, skill improvement of the whole team benefits the organization more than hiring experts. The results will guide an organization to choose a suitable strategy to recruit and develop its human resource in order to improve the competitive performance of the organization. [ABSTRACT FROM AUTHOR]

关键词： Fighter ants algorithm Human capital Organization of production Organizational behaviour Simulation modelling

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Decision fusion of circulating markers for breast cancer detection in premenopausal women

Decision fusion of circulating markers for breast cancer det...

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7th IEEE International Conference on Bioinformatics and Bioengineering, BIBE

作者： Jesneck, Jonathan L. Mukherjee, Sayan Nolte, Loren W. Lokshin, Anna E. Marks, Jeffrey R. Lo, Joseph Department of Biomedical Engineering Department of Statistical Science Computational Biology and Bioinformatics Program Duke Advanced Imaging Labs. Institute for Genome Sciences and Policy Department of Computer Science Duke University Department of Electrical and Computer Engineering Division of Hematology/Oncology University of Pittsburgh School of Medicine University of Pittsburgh Cancer Institute Pittsburgh PA 15213 United States Department of Experimental Surgery Duke University Medical Center Medical Physics Graduate Program Duke University Durham NC 27708 United States

ISBN: (纸本)1424415098

Current mammographic screeningfor breast cancer is less effective for younger women. To complement mammography for premenopausal women, we investigated the feasibility screening test using 98 blood serum proteins. Because the data set was very noisy and contained only weak features, we used a classifier designed for noisy data: decision fusion. Decision fusion outperformed both a support vector machine (SVM) and linear regression with forward stepwise feature selection on all three two-class classification tasks: normal tissue vs. cancer, normal tissue vs. benign lesions, and benign lesions vs. cancer. Decision fusion detected cancer moderately well (AUC=0.84 on normal vs. cancer), demonstrating promise as a screening tool. Decision fusion also detected benign lesions similarly well (AUC=0.83 on normal vs. benign lesions) and was the only classifier to achieve any success in separating benign from malignant lesions (AUC=0.64 on benign vs. cancer). The classification results suggest that the assayed proteins are more indicative of a secondary effect, such as immune response, rather than specific for breast cancer. In conclusion, the decision fusion classifier demonstrated some promise in detecting breast lesions and outperformed other classifiers, especially for the very noisy classification problem of distinguishing benign from malignant lesions. ©2007 IEEE.

关键词： Tissue

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An information theoretic framework for eukaryotic gradient sensing 06

An information theoretic framework for eukaryotic gradient s...

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Proceedings of the 20th International Conference on Neural Information Processing Systems

作者： Joseph M. Kimmel Richard M. Salter Peter J. Thomas Computer Science Program Oberlin College Oberlin Ohio and Computational Neuroscience Graduate Program The University of Chicago Computer Science Program Oberlin College Oberlin Ohio and Oberlin Center for Computation and Modeling Departments of Mathematics Biology and Cognitive Science Case Western Reserve University Cleveland Ohio and Oberlin College Research Associate

Chemical reaction networks by which individual cells gather and process information about their chemical environments have been dubbed "signal transduction" networks. Despite this suggestive terminology, there have been few attempts to analyze chemical signaling systems with the quantitative tools of information theory. Gradient sensing in the social amoeba Dictyostelium discoideum is a well characterized signal transduction system in which a cell estimates the direction of a source of diffusing chemoattractant molecules based on the spatiotemporal sequence of ligand-receptor binding events at the cell membrane. Using Monte Carlo techniques (MCell) we construct a simulation in which a collection of individual ligand particles undergoing Brownian diffusion in a three-dimensional volume interact with receptors on the surface of a static amoeboid cell. Adapting a method for estimation of spike train entropies described by Victor (originally due to Kozachenko and Leonenko), we estimate lower bounds on the mutual information between the transmitted signal (direction of ligand source) and the received signal (spatiotemporal pattern of receptor binding/unbinding events). Hence we provide a quantitative framework for addressing the question: how much could the cell know, and when could it know it? We show that the time course of the mutual information between the cell's surface receptors and the (unknown) gradient direction is consistent with experimentally measured cellular response times. We find that the acquisition of directional information depends strongly on the time constant at which the intracellular response is filtered.

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Impact and optimization of lithography-aware regular layout in digital circuit design

Impact and optimization of lithography-aware regular layout ...

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IEEE International Conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Vinícius Dal Bem Paulo Butzen Felipe S. Marranghello André I. Reis Renato P. Ribas Graduate Program in Microelectronics (PGMicro) Federal University of Rio Grande do Sul Porto Alegre Brazil Center of Computational Science Federal University of Rio Grande Rio Grande Brazil

Regular fabrics are expected to mitigate manufacturing process variations, increasing fabrication yield in deep sub-micron CMOS technologies. This paper presents an extensive analysis of aspects involved in the optimization of regular fabric (based) designs. The choice of the most efficient regular fabric design strategy depends on the area overhead and circuit performance degradation, which may vary according the fabric pattern optimization possibilities. Yield improvements have to be traded-off against area and performance losses due to regular design rules. This paper evaluates the losses introduced by using regular fabrics. Several benchmark circuits have been mapped over different regular layout templates through specific cell libraries built for this purpose. Results have demonstrated that the design impact is quite manageable by choosing appropriately the fabric pattern or template.

关键词： Transistors Libraries Logic gates Fabrics Layout Benchmark testing Optimization

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Noise in ML-EM reconstruction of SPECT images with attenuation correction

Noise in ML-EM reconstruction of SPECT images with attenuati...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： S.C. Liew Y.K. Ng B.H. Hasegawa Program in Computational Science National University of Singapore Singapore Department of Physics National University of Singapore Singapore Department of Radiology and Bioengineering Graduate Group University of California San Francisco USA

The authors derive a closed form expression for the pixel variance in the iterative ML-EM reconstruction of a uniform circular source embedded in a uniformly attenuating circular object. The derivation includes both the photon noise in the projection data and the noise in the attenuation map used for attenuation correction. The variance is found to increase with the number of iterations, but converges to a finite value. The noise to signal ratio at convergence is the sum of two terms which correspond to the contributions from the photon noise in the projection data and the attenuation map noise. The noise component due to the photon noise varies inversely with the total number of detected counts in the projection data. The relative variance at infinite iterations is similar in magnitude to that obtained for the filtered backprojection algorithm. The component due to attenuation map noise is proportional to the relative variance of the attenuation coefficient in the attenuation map. These observations are consistent with the results of the authors' previous computer simulation and experimental studies.

关键词： Image reconstruction Attenuation Iterative algorithms Single photon emission computed tomography Signal to noise ratio Computer simulation Uncertainty Embedded computing Physics computing Radiology

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