5,202 research outputs found
Low-cost solid state nanopore biosensing technology towards early disease detection
Solid-state nanopore based biosensors are cost effective, high-throughput engines for single molecule detection of biomolecules, which is useful for detecting epigenetic modifications on DNA; one of these being the potentially cancerous hypo, or hypermethylation of CpG islands. Despite its immense potential in the realm of disease diagnostics, nanopore detection as it stands faces various limitations that inhibit it from widespread commercial use. These include the complex method of solid-state nanopore fabrication, fast DNA translocations through the pore causing poor resolution, and poor signal to noise ratio. The following work aims to improve the efficacy of the solid-state nanopore biosensing platform as a disease diagnostic tool by improving ease of fabrication with automated MATLAB instrument control and controlled dielectric breakdown fabrication technique and increase signal resolution by using lithium chloride salt concentration gradients. In addition, methylated DNA labeled with certain methyl-binding proteins were tested in an attempt to localize areas of methylation on the DNA strand. These experiments yielded transport events that showed multilevel electrical signals that, in some instances, were able to distinguish between regions of bound protein and unbound DNA on the same strand. Increasing the accuracy of these multilevel event readings will aid in pinpointing localized regions of methylation on DNA and thereby increase the efficacy the solid-state nanopore platform for biosensing
Dagstuhl Reports : Volume 1, Issue 2, February 2011
Online Privacy: Towards Informational Self-Determination on the Internet (Dagstuhl Perspectives Workshop 11061) : Simone Fischer-Hübner, Chris Hoofnagle, Kai Rannenberg, Michael Waidner, Ioannis Krontiris and Michael Marhöfer Self-Repairing Programs (Dagstuhl Seminar 11062) : Mauro Pezzé, Martin C. Rinard, Westley Weimer and Andreas Zeller Theory and Applications of Graph Searching Problems (Dagstuhl Seminar 11071) : Fedor V. Fomin, Pierre Fraigniaud, Stephan Kreutzer and Dimitrios M. Thilikos Combinatorial and Algorithmic Aspects of Sequence Processing (Dagstuhl Seminar 11081) : Maxime Crochemore, Lila Kari, Mehryar Mohri and Dirk Nowotka Packing and Scheduling Algorithms for Information and Communication Services (Dagstuhl Seminar 11091) Klaus Jansen, Claire Mathieu, Hadas Shachnai and Neal E. Youn
Learning Character Strings via Mastermind Queries, with a Case Study Involving mtDNA
We study the degree to which a character string, , leaks details about
itself any time it engages in comparison protocols with a strings provided by a
querier, Bob, even if those protocols are cryptographically guaranteed to
produce no additional information other than the scores that assess the degree
to which matches strings offered by Bob. We show that such scenarios allow
Bob to play variants of the game of Mastermind with so as to learn the
complete identity of . We show that there are a number of efficient
implementations for Bob to employ in these Mastermind attacks, depending on
knowledge he has about the structure of , which show how quickly he can
determine . Indeed, we show that Bob can discover using a number of
rounds of test comparisons that is much smaller than the length of , under
reasonable assumptions regarding the types of scores that are returned by the
cryptographic protocols and whether he can use knowledge about the distribution
that comes from. We also provide the results of a case study we performed
on a database of mitochondrial DNA, showing the vulnerability of existing
real-world DNA data to the Mastermind attack.Comment: Full version of related paper appearing in IEEE Symposium on Security
and Privacy 2009, "The Mastermind Attack on Genomic Data." This version
corrects the proofs of what are now Theorems 2 and 4
Social Fingerprinting: detection of spambot groups through DNA-inspired behavioral modeling
Spambot detection in online social networks is a long-lasting challenge
involving the study and design of detection techniques capable of efficiently
identifying ever-evolving spammers. Recently, a new wave of social spambots has
emerged, with advanced human-like characteristics that allow them to go
undetected even by current state-of-the-art algorithms. In this paper, we show
that efficient spambots detection can be achieved via an in-depth analysis of
their collective behaviors exploiting the digital DNA technique for modeling
the behaviors of social network users. Inspired by its biological counterpart,
in the digital DNA representation the behavioral lifetime of a digital account
is encoded in a sequence of characters. Then, we define a similarity measure
for such digital DNA sequences. We build upon digital DNA and the similarity
between groups of users to characterize both genuine accounts and spambots.
Leveraging such characterization, we design the Social Fingerprinting
technique, which is able to discriminate among spambots and genuine accounts in
both a supervised and an unsupervised fashion. We finally evaluate the
effectiveness of Social Fingerprinting and we compare it with three
state-of-the-art detection algorithms. Among the peculiarities of our approach
is the possibility to apply off-the-shelf DNA analysis techniques to study
online users behaviors and to efficiently rely on a limited number of
lightweight account characteristics
QuanAnts Machine: A Quantum Algorithm for Biomarker Discovery
The discovery of biomarker sets for a targeted pathway is a challenging
problem in biomedical medicine, which is computationally prohibited on
classical algorithms due to the massive search space. Here, I present a quantum
algorithm named QuantAnts Machine to address the task. The proposed algorithm
is a quantum analog of the classical Ant Colony Optimization (ACO). We create
the mixture of multi-domain from genetic networks by representation theory,
enabling the search of biomarkers from the multi-modality of the human genome.
Although the proposed model can be generalized, we investigate the
RAS-mutational activation in this work. To the end, QuantAnts Machine discovers
rarely-known biomarkers in clinical-associated domain for RAS-activation
pathway, including COL5A1, COL5A2, CCT5, MTSS1 and NCAPD2. Besides, the model
also suggests several therapeutic-targets such as JUP, CD9, CD34 and CD74
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