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BEGIN:VEVENT
DTSTAMP:20260427T211136Z
DTSTART;VALUE=DATE-TIME:20190214T140000
DTEND;VALUE=DATE-TIME:20190214T160000
SUMMARY:CRiSM Seminar
TZID:Europe/London
UID:20190214-8a17841b65af76200165d766bb08018d@warwick.ac.uk
CREATED:20190205T103042Z
DESCRIPTION:Philipp Hermann\, Institute of Applied Statistics\, Johannes 
 Kepler University Linz\, Austria Time: 14:00-15:00 LDJump: Estimating Va
 riable Recombination Rates from Population Genetic Data Recombination is
  a process during meiosis which starts with the formation of DNA double-
 strand breaks and results in an exchange of genetic material between hom
 ologous chromosomes. In many species\, recombination is concentrated in 
 narrow regions known as hotspots\, flanked by large zones with low recom
 bination. As recombination plays an important role in evolution\, its es
 timation and the identification of hotspot positions is of considerable 
 interest. In this talk we introduce LDJump\, our method to estimate loca
 l population recombination rates with relevant summary statistics as exp
 lanatory variables in a regression model. More precisely\, we divide the
  DNA sequence into small segments and estimate the recombination rate pe
 r segment via the regression model. In order to obtain change-points in 
 recombination we apply a frequentist segmentation method. This approach 
 controls a type I error and provides confidence bands for the estimator.
  Overall LDJump identifies hotspots at high accuracy under different lev
 els of genetic diversity as well as demography and is computationally fa
 st even for genomic regions spanning many megabases. We will present a p
 ractical application of LDJump on a region of the human chromosome 21 an
 d compare our estimated population recombination rates with experimental
 ly measured recombination events. (joint work with Andreas Futschik\, Ir
 ene Tiemann-Boege\, and Angelika Heissl) Professor Dr. Ingo Scholtes\, D
 ata Analytics Group\, University of Zürich Time: 15:00-16:00 Optimal Hig
 her-Order Network Analytics for Time Series Data Network-based data anal
 ysis techniques such as graph mining\, social network analysis\, link pr
 ediction and clustering are an important foundation for data science app
 lications in computer science\, computational social science\, economics
  and bioinformatics. They help us to detect patterns in large corpora of
  data that capture relations between genes\, brain regions\, species\, h
 umans\, documents\, or financial institutions. While this potential of t
 he network perspective is undisputed\, advances in data sensing and coll
 ection increasingly provide us with high-dimensional\, temporal\, and no
 isy data on real systems. The complex characteristics of such data sourc
 es pose fundamental challenges for network analytics. They question the 
 validity of network abstractions of complex systems and pose a threat fo
 r interdisciplinary applications of data analytics and machine learning.
  To address these challenges\, I introduce a graphical modelling framewo
 rk that accounts for the complex characteristics of real-world data on c
 omplex systems. I demonstrate this approach in time series data on techn
 ical\, biological\, and social systems. Current methods to analyze the t
 opology of such systems discard information on the timing and ordering o
 f interactions\, which however determines which elements of a system can
  influence each other via paths. To solve this issue\, I introduce a mod
 elling framework that (i) generalises standard network representations t
 owards multi-order graphical models for causal paths\, and (ii) uses sta
 tistical learning to achieve an optimal balance between explanatory powe
 r and model complexity. The framework advances the theoretical foundatio
 n of data science and sheds light on the important question when network
  representations of time series data are justified. It is the basis for 
 a new generation of data analytics and machine learning techniques that 
 account both for temporal and topological characteristics in real-world 
 data.
LOCATION:MSB2.23
CATEGORIES:CRiSM Seminars,Seminars
LAST-MODIFIED:20190205T103042Z
ORGANIZER;CN=Paula Matthews:
END:VEVENT
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