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IEEE International Workshop on Genomic Signal Processing and Statistics (GENSIPS)

作者： Yanxun Xu Xiaofeng Zheng Yuan Yuan Marcos R Estecio Jean-Pierre Issa Yuan Ji Shoudan Liang Department of Statistics Rice University Houston TX USA Department of Bioinformatics and Computational Biology University of Texas M.D. Anderson Cancer Center Houston TX USA Graduate Program in Structural and Computational Biology and Molecular Biophysics Baylor College of Medicine Houston TX USA Department of Biochemistry and Molecular Biology University of Texas M.D. Anderson Cancer Center Houston TX USA Fels Institute for Cancer Research and Molecular Biology Temple University Philadelphia PA USA CCRI NorthShore University HealthSystem Chicago IL USA

ISBN: (纸本)9781467352345

A single-nucleotide polymorphism (SNP) is a single base change in the DNA sequence and is the most common polymorphism. Since some SNPs have a major influence on disease susceptibility, detecting SNPs plays an important role in biomedical research. To take fully advantage of the next-generation sequencing (NGS) technology and detect SNP more effectively, we propose a Bayesian approach that computes a posterior probability of hidden nucleotide variations at each covered genomic position. The position with higher posterior probability of hidden nucleotide variation has a higher chance to be a SNP. We apply the proposed method to detect SNPs in two cell lines: the prostate cancer cell line PC3 and the embryonic stem cell line H1. A comparison between our results with dbSNP database shows a high ratio of overlap (≥95 %). The positions that are called only under our model but not in dbSNP may serve as candidates for new SNPs.

关键词： comparison candidates generation Single nucleotide polymorphism sequence number protection a posteriori probability DNA Sequence Bayesian inference Disease Susceptibility Analyses, Sequence

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Feature selection using stochastic gates

arXiv

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arXiv 2018年

作者： Yamada, Yutaro Lindenbaum, Ofir Negahban, Sahand Kluger, Yuval Statistics Department Yale University New HavenCT United States Applied Mathematics Program Yale University New HavenCT United States Computational Biology and Bioinformatics Yale University New HavenCT United States Department of Pathology Yale University New HavenCT United States

Feature selection problems have been extensively studied for linear estimation, for instance, Lasso, but less emphasis has been placed on feature selection for non-linear functions. In this study, we propose a method for feature selection in high-dimensional non-linear function estimation problems. The new procedure is based on minimizing the `0 norm of the vector of indicator variables that represent if a feature is selected or not. Our approach relies on the continuous relaxation of Bernoulli distributions, which allows our model to learn the parameters of the approximate Bernoulli distributions via gradient descent. This general framework simultaneously minimizes a loss function while selecting relevant features. Furthermore, we provide an information-theoretic justification of incorporating Bernoulli distribution into our approach and demonstrate the potential of the approach on synthetic and real-life applications. Copyright © 2018, The Authors. All rights reserved.

关键词： Feature extraction

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Author Correction: Microglial mechanisms drive amyloid-β clearance in immunized patients with Alzheimer's disease

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Nature medicine 2025年 2025 Mar 28页

作者： Lynn van Olst Brooke Simonton Alex J Edwards Anne V Forsyth Jake Boles Pouya Jamshidi Thomas Watson Nate Shepard Talia Krainc Benney Mr Argue Ziyang Zhang Joshua Kuruvilla Lily Camp Mengwei Li Hang Xu Jeanette L Norman Joshua Cahan Robert Vassar Jinmiao Chen Rudolph J Castellani James Ar Nicoll Delphine Boche David Gate Abrams Research Center on Neurogenomics Northwestern University Feinberg School of Medicine Chicago IL USA. The Ken & Ruth Davee Department of Neurology Northwestern University Feinberg School of Medicine Chicago IL USA. Department of Pathology Northwestern University Feinberg School of Medicine Chicago IL USA. Bioinformatics Institute Agency for Science Technology and Research Singapore Singapore. Clinical Neurosciences School of Clinical and Experimental Sciences Faculty of Medicine University of Southampton Southampton UK. Mesulam Center for Cognitive Neurology and Alzheimer's Disease Northwestern University Feinberg School of Medicine Chicago IL USA. Centre for Computational Biology and Program in Cancer and Stem Cell Biology Duke-NUS Medical School Singapore Singapore. Immunology Translational Research Program Department of Microbiology and Immunology Yong Loo Lin School of Medicine National University of Singapore Singapore Singapore. Department of Cellular Pathology University Hospital Southampton National Health Service Trust Southampton UK. Abrams Research Center on Neurogenomics Northwestern University Feinberg School of Medicine Chicago IL USA. dgate@northwestern.edu. The Ken & Ruth Davee Department of Neurology Northwestern University Feinberg School of Medicine Chicago IL USA. dgate@northwestern.edu.

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Functional annotation of chemical libraries across diverse biological processes (vol 13, pg 982, 2017)

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NATURE CHEMICAL biology 2017年第12期13卷 1286-1286页

作者： Piotrowski, Jeff S. Li, Sheena C. Deshpande, Raamesh Simpkins, Scott W. Nelson, Justin Yashiroda, Yoko Barber, Jacqueline M. Safizadeh, Hamid Wilson, Erin Okada, Hiroki Gebre, Abraham A. Kubo, Karen Torres, Nikko P. LeBlanc, Marissa A. Andrusiak, Kerry Okamoto, Reika Yoshimura, Mami DeRango-Adem, Eva van Leeuwen, Jolanda Shirahige, Katsuhiko Baryshnikova, Anastasia Brown, Grant W. Hirano, Hiroyuki Costanzo, Michael Andrews, Brenda Ohya, Yoshikazu Osada, Hiroyuki Yoshida, Minoru Myers, Chad L. Boone, Charles RIKEN Center for Sustainable Resource Science Wako Saitama Japan Department of Computer Science and Engineering University of Minnesota-Twin Cities Minneapolis Minnesota USA Bioinformatics and Computational Biology Program University of Minnesota-Twin Cities Minneapolis Minnesota USA Department of Electrical and Computer Engineering University of Minnesota-Twin Cities Minneapolis Minnesota USA Department of Integrated Biosciences Graduate School of Frontier Sciences University of Tokyo Kashiwa Chiba Japan Donnelly Centre University of Toronto Toronto Ontario Canada Institute of Molecular and Cellular Biosciences Center for Epigenetic Disease University of Tokyo Bunkyo-ku Tokyo Japan Lewis-Sigler Institute for Integrative Genomics Princeton University Princeton New Jersey USA Department of Biochemistry University of Toronto Toronto Ontario Canada.

Chemical-genetic approaches offer the potential for unbiased functional annotation of chemical libraries. Mutations can alter the response of cells in the presence of a compound, revealing chemical-genetic interactions that can elucidate a compound’s mode of action. We developed a highly parallel, unbiased yeast chemical-genetic screening system involving three key components. First, in a drug-sensitive genetic background, we constructed an optimized diagnostic mutant collection that is predictive for all major yeast biological processes. Second, we implemented a multiplexed (768-plex) barcode-sequencing protocol, enabling the assembly of thousands of chemical-genetic profiles. Finally, based on comparison of the chemical-genetic profiles with a compendium of genome-wide genetic interaction profiles, we predicted compound functionality. Applying this high-throughput approach, we screened seven different compound libraries and annotated their functional diversity. We further validated biological process predictions, prioritized a diverse set of compounds, and identified compounds that appear to have dual modes of action.

关键词： Chemical genetics High-throughput screening Networks and systems biology Target identification

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Atomistic mechanisms underlying the activation of the G protein-coupled sweet receptor heterodimer by sugar alcohol recognition (vol 9, 10205, 2019)

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SCIENTIFIC REPORTS 2019年第1期9卷 1-11页

作者： Mahalapbutr, Panupong Darai, Nitchakan Panman, Wanwisa Opasmahakul, Aunchan Kungwan, Nawee Hannongbua, Supot Rungrotmongkol, Thanyada Structural and Computational Biology Research Unit Department of Biochemistry Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Program in Biotechnology Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Multidisciplinary Program of Petrochemistry and Polymer Science Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Computational Chemistry Center of Excellent Department of Chemistry Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Department of Chemistry Faculty of Science Chiang Mai University 50200 Chiang Mai Thailand Center of Excellence in Materials Science and Technology Chiang Mai University 50200 Chiang Mai Thailand Ph.D. Program in Bioinformatics and Computational Biology Faculty of Science Chulalongkorn University 10330 Bangkok Thailand Molecular Sensory Science Center Faculty of Science Chulalongkorn University 10330 Bangkok Thailand

The human T1R2-T1R3 sweet taste receptor (STR) plays an important role in recognizing various low-molecular-weight sweet-tasting sugars and proteins, resulting in the release of intracellular heterotrimeric G protein that in turn leads to the sweet taste perception. Xylitol and sorbitol, which are naturally occurring sugar alcohols (polyols) found in many fruits and vegetables, exhibit the potential caries-reducing effect and are widely used for diabetic patients as low-calorie sweeteners. In the present study, computational tools were applied to investigate the structural details of binary complexes formed between these two polyols and the T1R2-T1R3 heterodimeric STR. Principal component analysis revealed that the Venus flytrap domain (VFD) of T1R2 monomer was adapted by the induced-fit mechanism to accommodate the focused polyols, in which α-helical residues 233–268 moved significantly closer to stabilize ligands. This finding likely suggested that these structural transformations might be the important mechanisms underlying polyols-STR recognitions. The calculated free energies also supported the VFD of T1R2 monomer as the preferential binding site for such polyols, rather than T1R3 region, in accord with the lower number of accessible water molecules in the T1R2 pocket. The E302 amino acid residue in T1R2 was found to be the important recognition residue for polyols binding through a strongly formed hydrogen bond. Additionally, the binding affinity of xylitol toward the T1R2 monomer was significantly higher than that of sorbitol, making it a sweeter tasting molecule. [ABSTRACT FROM AUTHOR]

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Large-scale analysis of human heavy chain V(D)J recombination patterns

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Immunome Research 2008年第1期4卷 3-3页

作者： Volpe, Joseph M Kepler, Thomas B Center for Computational Immunology Duke University Durham NC United States Department of Immunology Duke Univeristy Medical Center Durham NC United States Department of Biostatistics and Bioinformatics Duke Univeristy Medical Center Durham NC United States Institute of Statistics and Decision Sciences Duke Univeristy Durham NC United States Computational Biology and Bioinformatics Graduate Program Institute for Genome Sciences and Policy Duke Univeristy Durham NC United States

Background. The processes involved in the somatic assembly of antigen receptor genes are unique to the immune system and are driven largely by random events. Subtle biases, however, may exist and provide clues to the molecular mechanisms involved in their assembly and selection. Large-scale efforts to provide baseline data about the genetic characteristics of immunoglobulin (Ig) genes and the mechanisms involved in their assembly have recently become possible due to the rapid growth of genetic databases. Results. We gathered and analyzed nearly 6,500 productive human Ig heavy chain genes and compared them with 325 non-productive Ig genes that were originally rearranged out of frame and therefore incapable of being biased by selection. We found evidence for differences in n-nucleotide tract length distributions which have interesting interpretations for the mechanisms involved in n-nucleotide polymerization. Additionally, we found striking statistical evidence for pairing preferences among D and J segments. We present a statistical model to support our hypothesis that these pairing biases are due to multiple sequential D-to-J rearrangements. Conclusion. We present here the most precise estimates of gene segment usage frequencies currently available along with analyses regarding n-nucleotide distributions and D-J segment pair preferences. Additionally, we provide the first statistical evidence that sequential D-J recombinations occur at the human heavy chain locus during B-cell ontogeny with an approximate frequency of 20%.

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Void distributions reveal structural link between jammed packings and protein cores

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Physical Review E 2019年第2期99卷 022416-022416页

作者： John D. Treado Zhe Mei Lynne Regan Corey S. O'Hern Department of Mechanical Engineering & Materials Science Yale University New Haven Connecticut 06520 USA Integrated Graduate Program in Physical & Engineering Biology Yale University New Haven Connecticut 06520 USA Department of Chemistry Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics & Biochemistry Yale University New Haven Connecticut 06520 USA Department of Physics Yale University New Haven Connecticut 06520 USA Department of Applied Physics Yale University New Haven Connecticut 06520 USA Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA

Dense packing of hydrophobic residues in the cores of globular proteins determines their stability. Recently, we have shown that protein cores possess packing fraction ϕ≈0.56, which is the same as dense, random packing of amino-acid-shaped particles. In this article, we compare the structural properties of protein cores and jammed packings of amino-acid-shaped particles in much greater depth by measuring their local and connected void regions. We find that the distributions of surface Voronoi cell volumes and local porosities obey similar statistics in both systems. We also measure the probability that accessible, connected void regions percolate as a function of the size of a spherical probe particle and show that both systems possess the same critical probe size. We measure the critical exponent τ that characterizes the size distribution of connected void clusters at the onset of percolation. We find that the cluster size statistics are similar for void percolation in packings of amino-acid-shaped particles and randomly placed spheres, but different from that for void percolation in jammed sphere packings. We propose that the connected void regions are a defining structural feature of proteins and can be used to differentiate experimentally observed proteins from decoy structures that are generated using computational protein design software. This work emphasizes that jammed packings of amino-acid-shaped particles can serve as structural and mechanical analogs of protein cores, and could therefore be useful in modeling the response of protein cores to cavity-expanding and -reducing mutations.

关键词： Jamming Protein structure Packing & jamming problems Proteins Crystal structures Finite-size scaling Molecular dynamics Percolation theory

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Modeling protein interaction network and mechanisms in exocytosis

Modeling protein interaction network and mechanisms in exocy...

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IEEE Symposium on bioinformatics and Bioengineering (BIBE)

作者： Wen Zhou Tian Xia Jiansong Tong Julie Dickerson Bo Su Xun Gu Department of Mathematics Iowa State University Ames IA USA Program of Bioinformatics and computational biology Iowa State University Ames IA USA Department of Biochemistry Iowa State University Ames IA USA Department of Mathematics Iowa State University Ames IA Department of Genetics Iowa State University Ames IA USA

Exocytosis is an essential process in all eukaryotic cells that allows communication in cells through vesicles which contain a wide range of intracellular molecules such as hormones, matrix proteins and neurotransmitters. Recent studies have shown this process is regulated by molecular interactions among a group of well defined and conserved proteins. To gain insight into the dynamics of these interactions, we use protein interaction network modeling to investigate exocytotic system (particularly in endocrine) computationally and mathematically. The protein interactions are formulated into ordinary differential equations (ODE). We then apply a novel mathematic approach to estimate model parameters from experimental data for SNAREs-only network (with three key proteins), which is a subset of this system. Our approach is able to sense temporal changes in protein concentration and ratios of multiple proteins and precisely reconstruct the dynamical process of exocytosis, including vesicle docking, priming and fusion. Additionally, the model suggests that initial concentration of synaptic proteins plays a crucial role in efficiency of vesicle fusion, based on system stability analysis.

关键词： Proteins Mathematical model Biochemistry Neurotransmitters Endocrine system Computer networks Differential equations Mathematics Parameter estimation Stability analysis

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Assembling genomes on large-scale parallel computers 06

Assembling genomes on large-scale parallel computers

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： A. Kalyanaraman S.J. Emrich P.S. Schnable S. Aluru Department of Electrical and Computer Engineering Iowa State University Ames IA USA Bioinformatics and Computational Biology Program Iowa State University Ames IA USA Iowa State University Ames IA US Dept. of Electr. & Comput. Eng. Iowa State Univ. Ames IA USA

ISBN: (纸本)9781424400546

Assembly of large genomes from tens of millions of short genomic fragments is computationally demanding requiring hundreds of gigabytes of memory and tens of thousands of CPU hours. New gene-enrichment sequencing strategies are expected to further exacerbate this situation. In this paper, we present a massively parallel genome assembly framework. The unique features of our approach include space-efficient and on-demand algorithms that consume only linear space, and heuristic strategies that reduce the number of expensive pairwise sequence alignments while maintaining assembly quality. As part of the ongoing efforts in maize genome sequencing, we applied our assembly framework to the largest available collection of maize genomic data. We report the partitioning of more than 1.6 million fragments of over 1.25 billion nucleotides total size into genomic islands in 2 hours on 1,024 processors of an IBM BlueGene/L supercomputer.

关键词： Assembly Genomics bioinformatics Large-scale systems Concurrent computing Sequences DNA Organisms biology computing Biological cells

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Correlations between associated SNPs cause upward bias in whole-genome based heritability estimation

Correlations between associated SNPs cause upward bias in wh...

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The Global Conference of Chinese Geneticists(2012全球华人遗传学大会)

作者： Cong Li Can Yang John Ferguson Judy Cho Hongyu Zhao IBD Center Division of GastroenterologyDepartment of MedicineYale UniversityNew HavenCT06520 Program in Computational Biology and Bioinformatics Yale UniversityNew HavenCT06520 Department of BiostatisticsYale School of Public HealthNew HavenCT06520 Department of GeneticsYale UniversityNew HavenCT06520

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