4,533 research outputs found
Computational methods in cancer gene networking
In the past few years, many high-throughput techniques have been developed and applied to biological studies. These techniques such as “next generation” genome sequencing, chip-on-chip, microarray and so on can be used to measure gene expression and gene regulatory elements in a genome-wide scale. Moreover, as these technologies become more affordable and accessible, they have become a driving force in modern biology. As a result, huge amount biological data have been produced, with the expectation of increasing number of such datasets to be generated in the future. High-throughput data are more comprehensive and unbiased, but ‘real signals’ or biological insights, molecular mechanisms and biological principles are buried in the flood of data. In current biological studies, the bottleneck is no longer a lack of data, but the lack of ingenuity and computational means to extract biological insights and principles by integrating knowledge and high-throughput data. 

Here I am reviewing the concepts and principles of network biology and the computational methods which can be applied to cancer research. Furthermore, I am providing a practical guide for computational analysis of cancer gene networks
Dynamical Properties of Interaction Data
Network dynamics are typically presented as a time series of network
properties captured at each period. The current approach examines the dynamical
properties of transmission via novel measures on an integrated, temporally
extended network representation of interaction data across time. Because it
encodes time and interactions as network connections, static network measures
can be applied to this "temporal web" to reveal features of the dynamics
themselves. Here we provide the technical details and apply it to agent-based
implementations of the well-known SEIR and SEIS epidemiological models.Comment: 29 pages, 15 figure
Graphlet-based Characterization of Directed Networks
We are flooded with large-scale, dynamic, directed, networked data. Analyses requiring exact comparisons between networks are computationally intractable, so new methodologies are sought. To analyse directed networks, we extend graphlets (small induced sub-graphs) and their degrees to directed data. Using these directed graphlets, we generalise state-of-the-art network distance measures (RGF, GDDA and GCD) to directed networks and show their superiority for comparing directed networks. Also, we extend the canonical correlation analysis framework that enables uncovering the relationships between the wiring
patterns around nodes in a directed network and their expert annotations. On directed World Trade Networks (WTNs), our methodology allows uncovering the core-broker-periphery structure of the WTN, predicting the economic attributes of a country, such as its gross domestic product, from its wiring patterns in the WTN for up-to ten years in the future. It does so by enabling us to track the dynamics of a country’s positioning in the WTN over years. On directed metabolic networks, our framework
yields insights into preservation of enzyme function from the network wiring patterns rather than from sequence data. Overall, our methodology enables advanced analyses of directed networked data from any area of science, allowing domain-specific interpretation of a directed network’s topology
Machine learning discovery of new phases in programmable quantum simulator snapshots
Machine learning has recently emerged as a promising approach for studying
complex phenomena characterized by rich datasets. In particular, data-centric
approaches lend to the possibility of automatically discovering structures in
experimental datasets that manual inspection may miss. Here, we introduce an
interpretable unsupervised-supervised hybrid machine learning approach, the
hybrid-correlation convolutional neural network (Hybrid-CCNN), and apply it to
experimental data generated using a programmable quantum simulator based on
Rydberg atom arrays. Specifically, we apply Hybrid-CCNN to analyze new quantum
phases on square lattices with programmable interactions. The initial
unsupervised dimensionality reduction and clustering stage first reveals five
distinct quantum phase regions. In a second supervised stage, we refine these
phase boundaries and characterize each phase by training fully interpretable
CCNNs and extracting the relevant correlations for each phase. The
characteristic spatial weightings and snippets of correlations specifically
recognized in each phase capture quantum fluctuations in the striated phase and
identify two previously undetected phases, the rhombic and boundary-ordered
phases. These observations demonstrate that a combination of programmable
quantum simulators with machine learning can be used as a powerful tool for
detailed exploration of correlated quantum states of matter.Comment: 9 pages, 5 figures + 12 pages, 10 figures appendi
- …