4,084 research outputs found
Self-Assembly of DNA Graphs and Postman Tours
DNA graph structures can self-assemble from branched junction molecules to yield solutions to computational problems. Self-assembly of graphs have previously been shown to give polynomial time solutions to hard computational problems such as 3-SAT and k-colorability problems. Jonoska et al. have proposed studying self-assembly of graphs topologically, considering the boundary components of their thickened graphs, which allows for reading the solutions to computational problems through reporter strands. We discuss weighting algorithms and consider applications of self-assembly of graphs and the boundary components of their thickened graphs to problems involving minimal weight Eulerian walks such as the Chinese Postman Problem and the Windy Postman Problem
A DNA approach to the Road-Coloring Problem
The Road-Coloring Problem in graph theory can be stated as follows: Is any irreducible aperiodic directed graph with constant outdegree 2 road-colorable? In other words, does such a graph have a synchronizing instruction? That is to say: can we label (or color) the two outgoing edges at each vertex, one with “b” or blue color and the other with “r” or red color, in such a manner that there will be an instruction in the form of a finite sequence in “b”s and “r”s (example: rrbrbbbr) such that this instruction will lead each vertex to the same “target” vertex? This thesis is concerned with writing a DNA algorithm which can be followed in the laboratory to produce an explicit solution of a given Road-Coloring problem. This kind of DNA approach was first introduced by Adleman to find an effective method of finding the solution of a given Hamiltonian Path Problem. The Road-Coloring Problem, though introduced over 30 years ago in 1977 by Adler, Goodwyn, and Weiss was only recently solved by Trahtman. But his solution does not give explicitly the synchronizing instruction
Modelling DNA Origami Self-Assembly at the Domain Level
We present a modelling framework, and basic model parameterization, for the
study of DNA origami folding at the level of DNA domains. Our approach is
explicitly kinetic and does not assume a specific folding pathway. The binding
of each staple is associated with a free-energy change that depends on staple
sequence, the possibility of coaxial stacking with neighbouring domains, and
the entropic cost of constraining the scaffold by inserting staple crossovers.
A rigorous thermodynamic model is difficult to implement as a result of the
complex, multiply connected geometry of the scaffold: we present a solution to
this problem for planar origami. Coaxial stacking and entropic terms,
particularly when loop closure exponents are taken to be larger than those for
ideal chains, introduce interactions between staples. These cooperative
interactions lead to the prediction of sharp assembly transitions with notable
hysteresis that are consistent with experimental observations. We show that the
model reproduces the experimentally observed consequences of reducing staple
concentration, accelerated cooling and absent staples. We also present a
simpler methodology that gives consistent results and can be used to study a
wider range of systems including non-planar origami
Link Prediction by De-anonymization: How We Won the Kaggle Social Network Challenge
This paper describes the winning entry to the IJCNN 2011 Social Network
Challenge run by Kaggle.com. The goal of the contest was to promote research on
real-world link prediction, and the dataset was a graph obtained by crawling
the popular Flickr social photo sharing website, with user identities scrubbed.
By de-anonymizing much of the competition test set using our own Flickr crawl,
we were able to effectively game the competition. Our attack represents a new
application of de-anonymization to gaming machine learning contests, suggesting
changes in how future competitions should be run.
We introduce a new simulated annealing-based weighted graph matching
algorithm for the seeding step of de-anonymization. We also show how to combine
de-anonymization with link prediction---the latter is required to achieve good
performance on the portion of the test set not de-anonymized---for example by
training the predictor on the de-anonymized portion of the test set, and
combining probabilistic predictions from de-anonymization and link prediction.Comment: 11 pages, 13 figures; submitted to IJCNN'201
Machine Intelligence for Advanced Medical Data Analysis: Manifold Learning Approach
In the current work, linear and non-linear manifold learning techniques, specifically Principle Component Analysis (PCA) and Laplacian Eigenmaps, are studied in detail. Their applications in medical image and shape analysis are investigated.
In the first contribution, a manifold learning-based multi-modal image registration technique is developed, which results in a unified intensity system through intensity transformation between the reference and sensed images. The transformation eliminates intensity variations in multi-modal medical scans and hence facilitates employing well-studied mono-modal registration techniques. The method can be used for registering multi-modal images with full and partial data.
Next, a manifold learning-based scale invariant global shape descriptor is introduced. The proposed descriptor benefits from the capability of Laplacian Eigenmap in dealing with high dimensional data by introducing an exponential weighting scheme. It eliminates the limitations tied to the well-known cotangent weighting scheme, namely dependency on triangular mesh representation and high intra-class quality of 3D models.
In the end, a novel descriptive model for diagnostic classification of pulmonary nodules is presented. The descriptive model benefits from structural differences between benign and malignant nodules for automatic and accurate prediction of a candidate nodule. It extracts concise and discriminative features automatically from the 3D surface structure of a nodule using spectral features studied in the previous work combined with a point cloud-based deep learning network.
Extensive experiments have been conducted and have shown that the proposed algorithms based on manifold learning outperform several state-of-the-art methods. Advanced computational techniques with a combination of manifold learning and deep networks can play a vital role in effective healthcare delivery by providing a framework for several fundamental tasks in image and shape processing, namely, registration, classification, and detection of features of interest
Macrostate Data Clustering
We develop an effective nonhierarchical data clustering method using an
analogy to the dynamic coarse graining of a stochastic system. Analyzing the
eigensystem of an interitem transition matrix identifies fuzzy clusters
corresponding to the metastable macroscopic states (macrostates) of a diffusive
system. A "minimum uncertainty criterion" determines the linear transformation
from eigenvectors to cluster-defining window functions. Eigenspectrum gap and
cluster certainty conditions identify the proper number of clusters. The
physically motivated fuzzy representation and associated uncertainty analysis
distinguishes macrostate clustering from spectral partitioning methods.
Macrostate data clustering solves a variety of test cases that challenge other
methods.Comment: keywords: cluster analysis, clustering, pattern recognition, spectral
graph theory, dynamic eigenvectors, machine learning, macrostates,
classificatio
Graph Contrastive Learning for Multi-omics Data
Advancements in technologies related to working with omics data require novel
computation methods to fully leverage information and help develop a better
understanding of human diseases. This paper studies the effects of introducing
graph contrastive learning to help leverage graph structure and information to
produce better representations for downstream classification tasks for
multi-omics datasets. We present a learnining framework named Multi-Omics Graph
Contrastive Learner(MOGCL) which outperforms several aproaches for integrating
multi-omics data for supervised learning tasks. We show that pre-training graph
models with a contrastive methodology along with fine-tuning it in a supervised
manner is an efficient strategy for multi-omics data classification
Accelerating DNA Computing via PLP-qPCR Answer Read out to Solve Traveling Salesman Problems
An asymmetric, fully-connected 8-city traveling salesman problem (TSP) was solved by DNA computing using the ordered node pair abundance (ONPA) approach through the use of pair ligation probe quantitative real time polymerase chain reaction (PLP-qPCR). The validity of using ONPA to derive the optimal answer was confirmed by in silico computing using a reverse-engineering method to reconstruct the complete tours in the feasible answer set from the measured ONPA. The high specificity of the sequence-tagged hybridization, and ligation that results from the use of PLPs significantly increased the accuracy of answer determination in DNA computing. When combined with the high throughput efficiency of qPCR, the time required to identify the optimal answer to the TSP was reduced from days to 25 min
- …