pwc-archive/papers-with-abstracts huggingface dataset:
An Algorithmic Pipeline for Analyzing Multi-parametric Flow Cytometry Data
Date: 2015-01-14 00:00:00

Flow cytometry (FC) is a single-cell profiling platform for measuring the
phenotypes of individual cells from millions of cells in biological samples. FC
employs high-throughput technologies and generates high-dimensional data, and
hence algorithms for analyzing the data represent a bottleneck. This
dissertation addresses several computational challenges arising in modern
cytometry while mining information from high-dimensional and high-content
biological data. A collection of combinatorial and statistical algorithms for
locating, matching, prototyping, and classifying cellular populations from
multi-parametric FC data is developed.
  The algorithmic pipeline, flowMatch, developed in this dissertation consists
of five well-defined algorithmic modules to (1) transform data to stabilize
within-population variance, (2) identify cell populations by robust clustering
algorithms, (3) register cell populations across samples, (4) encapsulate a
class of samples with templates, and (5) classify samples based on their
similarity with the templates. Components of flowMatch can work independently
or collaborate with each other to perform the complete data analysis. flowMatch
is made available as an open-source R package in Bioconductor.
  We have employed flowMatch for classifying leukemia samples, evaluating the
phosphorylation effects on T cells, classifying healthy immune profiles, and
classifying the vaccination status of HIV patients. In these analyses, the
pipeline is able to reach biologically meaningful conclusions quickly and
efficiently with the automated algorithms. The algorithms included in flowMatch
can also be applied to problems outside of flow cytometry such as in microarray
data analysis and image recognition. Therefore, this dissertation contributes
to the solution of fundamental problems in computational cytometry and related
domains.
Whole-brain calcium imaging with cellular resolution in freely behaving C. elegans
Date: 2015-01-14 00:00:00
Tasks: Position

The ability to acquire large-scale recordings of neuronal activity in awake
and unrestrained animals poses a major challenge for studying neural coding of
animal behavior. We present a new instrument capable of recording intracellular
calcium transients from every neuron in the head of a freely behaving C.
elegans with cellular resolution while simultaneously recording the animal's
position, posture and locomotion. We employ spinning-disk confocal microscopy
to capture 3D volumetric fluorescent images of neurons expressing the calcium
indicator GCaMP6s at 5 head-volumes per second. Two cameras simultaneously
monitor the animal's position and orientation. Custom software tracks the 3D
position of the animal's head in real-time and adjusts a motorized stage to
keep it within the field of view as the animal roams freely. We observe calcium
transients from 78 neurons and correlate this activity with the animal's
behavior. Across worms, multiple neurons show significant correlations with
modes of behavior corresponding to forward, backward, and turning locomotion.
By comparing the 3D positions of these neurons with a known atlas, our results
are consistent with previous single-neuron studies and demonstrate the
existence of new candidate neurons for behavioral circuits.
On the Bayesness, minimaxity, and admissibility of point estimators of allelic frequencies
Date: 2015-08-19 00:00:00

In this paper, decision theory was used to derive Bayes and minimax decision
rules to estimate allelic frequencies and to explore their admissibility.
Decision rules with uniformly smallest risk usually do not exist and one
approach to solve this problem is to use the Bayes principle and the minimax
principle to find decision rules satisfying some general optimality criterion
based on their risk functions. Two cases were considered, the simpler case of
biallelic loci and the more complex case of multiallelic loci. For each locus,
the sampling model was a multinomial distribution and the prior was a Beta
(biallelic case) or a Dirichlet (multiallelic case) distribution. Three loss
functions were considered: squared error loss (SEL), Kulback-Leibler loss (KLL)
and quadratic error loss (QEL). Bayes estimators were derived under these three
loss functions and were subsequently used to find minimax estimators using
results from decision theory. The Bayes estimators obtained from SEL and KLL
turned out to be the same. Under certain conditions, the Bayes estimator
derived from QEL led to an admissible minimax estimator (which was also equal
to the maximum likelihood estimator). The SEL also allowed finding admissible
minimax estimators. Some estimators had uniformly smaller variance than the MLE
and under suitable conditions the remaining estimators also satisfied this
property. In addition to their statistical properties, the estimators derived
here allow variation in allelic frequencies, which is closer to the reality of
finite populations exposed to evolutionary forces.
Consistence beats causality in recommender systems
Authors: Zhu Xuzhen, Tian Hui, Hu Zheng, Zhang Ping, Zhou Tao
Date: 2015-01-15 00:00:00
Tasks: Recommendation Systems

The explosive growth of information challenges people's capability in finding
out items fitting to their own interests. Recommender systems provide an
efficient solution by automatically push possibly relevant items to users
according to their past preferences. Recommendation algorithms usually embody
the causality from what having been collected to what should be recommended. In
this article, we argue that in many cases, a user's interests are stable, and
thus the previous and future preferences are highly consistent. The temporal
order of collections then does not necessarily imply a causality relationship.
We further propose a consistence-based algorithm that outperforms the
state-of-the-art recommendation algorithms in disparate real data sets,
including \textit{Netflix}, \textit{MovieLens}, \textit{Amazon} and
\textit{Rate Your Music}.
Estimating phylogenetic trees from genome-scale data
Date: 2015-01-15 00:00:00

As researchers collect increasingly large molecular data sets to reconstruct
the Tree of Life, the heterogeneity of signals in the genomes of diverse
organisms poses challenges for traditional phylogenetic analysis. A class of
phylogenetic methods known as "species tree methods" have been proposed to
directly address one important source of gene tree heterogeneity, namely the
incomplete lineage sorting or deep coalescence that occurs when evolving
lineages radiate rapidly, resulting in a diversity of gene trees from a single
underlying species tree. Although such methods are gaining in popularity, they
are being adopted with caution in some quarters, in part because of an
increasing number of examples of strong phylogenetic conflict between
concatenation or supermatrix methods and species tree methods. Here we review
theory and empirical examples that help clarify these conflicts. Thinking of
concatenation as a special case of the more general model provided by the
multispecies coalescent can help explain a number of differences in the
behavior of the two methods on phylogenomic data sets. Recent work suggests
that species tree methods are more robust than concatenation approaches to some
of the classic challenges of phylogenetic analysis, including rapidly evolving
sites in DNA sequences, base compositional heterogeneity and long branch
attraction. We show that approaches such as binning, designed to augment the
signal in species tree analyses, can distort the distribution of gene trees and
are inconsistent. Computationally efficient species tree methods that
incorporate biological realism are a key to phylogenetic analysis of whole
genome data.
Fixation probability of rare nonmutator and evolution of mutation rates
Date: 2015-12-11 00:00:00

Although mutations drive the evolutionary process, the rates at which the
mutations occur are themselves subject to evolutionary forces. Our purpose here
is to understand the role of selection and random genetic drift in the
evolution of mutation rates, and we address this question in asexual
populations at mutation-selection equilibrium neglecting selective sweeps.
Using a multitype branching process, we calculate the fixation probability of a
rare nonmutator in a large asexual population of mutators, and find that a
nonmutator is more likely to fix when the deleterious mutation rate of the
mutator population is high. Compensatory mutations in the mutator population
are found to decrease the fixation probability of a nonmutator when the
selection coefficient is large. But, surprisingly, the fixation probability
changes nonmonotonically with increasing compensatory mutation rate when the
selection is mild. Using these results for the fixation probability and a
drift-barrier argument, we find a novel relationship between the mutation rates
and the population size. We also discuss the time to fix the nonmutator in an
adapted population of asexual mutators, and compare our results with
experiments.
A New Approach to Model Free Option Pricing
Date: 2015-01-15 00:00:00
Tasks: model

In this paper we introduce a new approach to model-free path-dependent option
pricing. We first introduce a general duality result for linear optimisation
problems over signed measures introduced in [3] and show how the the problem of
model-free option pricing can be formulated in the new framework. We then
introduce a model to solve the problem numerically when the only information
provided is the market data of vanilla call or put option prices. Compared to
the common approaches in the literature, e.g. [4], the model does not require
the marginal distributions of the stock price for different maturities. Though
the experiments are carried out for simple path-dependent options on a single
stock, the model is easy to generalise for multi-asset framework.
Optimal Trading with Alpha Predictors
Date: 2015-01-16 00:00:00
Tasks: Stochastic Optimization

We study the problem of optimal trading using general alpha predictors with
linear costs and temporary impact. We do this within the framework of
stochastic optimization with finite horizon using both limit and market orders.
Consistently with other studies, we find that the presence of linear costs
induces a no-trading zone when using market orders, and a corresponding
market-making zone when using limit orders. We show that, when combining both
market and limit orders, the problem is further divided into zones in which we
trade more aggressively using market orders. Even though we do not solve
analytically the full optimization problem, we present explicit and simple
analytical recipes which approximate the full solution and are easy to
implement in practice. We test the algorithms using Monte Carlo simulations and
show how they improve our Profit and Losses.
On the martingale-fair index of return for investment funds
Date: 2015-01-15 00:00:00

A concept of martingale-fair index of return, consistent with Arbitrage Free
Pricing Theory, is introduced. An explicit formula for the average rate of
return of a group of investment/pension funds in a discrete time stochastic
model is derived and several properties of this index are shown. In particular,
it is proven to be martingale-fair, i.e. be a martingale provided the prices of
assets on the financial market form a vector martingale. The problem of merger
of the funds is treated in detail.
Optimizing information flow in small genetic networks. IV. Spatial coupling
Date: 2015-01-16 00:00:00

We typically think of cells as responding to external signals independently
by regulating their gene expression levels, yet they often locally exchange
information and coordinate. Can such spatial coupling be of benefit for
conveying signals subject to gene regulatory noise? Here we extend our
information-theoretic framework for gene regulation to spatially extended
systems. As an example, we consider a lattice of nuclei responding to a
concentration field of a transcriptional regulator (the "input") by expressing
a single diffusible target gene. When input concentrations are low, diffusive
coupling markedly improves information transmission; optimal gene activation
functions also systematically change. A qualitatively new regulatory strategy
emerges where individual cells respond to the input in a nearly step-like
fashion that is subsequently averaged out by strong diffusion. While motivated
by early patterning events in the Drosophila embryo, our framework is
generically applicable to spatially coupled stochastic gene expression models.
Mean Time-to-Fire for the Noisy LIF Neuron - A Detailed Derivation of the Siegert Formula
Date: 2015-01-16 00:00:00

When stimulated by a very large number of Poisson-like presynaptic current
input spikes, the temporal dynamics of the soma membrane potential $V(t)$ of a
leaky integrate-and-fire (LIF) neuron is typically modeled in the diffusion
limit and treated as a Ornstein-Uhlenbeck process (OUP). When the potential
reaches a threshold value $\theta$, $V(t) = \theta$, the LIF neuron fires and
the membrane potential is reset to a resting value, $V_0 < \theta$, and clamped
to this value for a specified (non-stochastic) absolute refractory period $T_r
\ge 0$, after which the cycle is repeated. The time between firings is given by
the random variable $T_f = T_r+ T$ where $T$ is the random time which elapses
between the "unpinning" of the membrane potential clamp and the next,
subsequent firing of the neuron. The mean time-to-fire, $\widehat{T}_f =
\text{E}(T_f) = T_r + \text{E}(T) = T_r + \widehat{T}$, provides a measure
$\rho$ of the average firing rate of the neuron, \[ \rho = \widehat{T}_f^{-1} =
\frac{1}{T_r + \widehat{T}} . \] This note briefly discusses some aspects of
the OUP model and derives the Siegert formula giving the firing rate, $\rho =
\rho(I_0)$ as a function of an injected current, $I_0$. This is a well-known
classical result and no claim to originality is made. The derivation of the
firing rate given in this report, which closely follows the derivation outlined
in the textbook by Gardiner, minimizes the required mathematical background and
is done in some pedagogic detail to facilitate study by graduate students and
others who are new to the subject. Knowledge of the material presented in the
first five chapters of Gardiner should provide an adequate background for
following the derivation given in this note.
Data manipulation detection via permutation information theory quantifiers
Date: 2015-01-16 00:00:00
Tasks: Time Series, Time Series Analysis

Recent news cast doubts on London Interbank Offered Rate (LIBOR) integrity.
Given its economic importance and the delay with which authorities realize
about this situation, we aim to find an objective method in order to detect
departures in the LIBOR rate that from the expected behavior. We analyze
several interest rates time series and we detect an anomalous behavior in
LIBOR, specially during the period of the financial crisis of 2008. Our
findings could be consistent with data manipulation.
Canonical, Stable, General Mapping using Context Schemes
Date: 2015-06-11 00:00:00

Motivation: Sequence mapping is the cornerstone of modern genomics. However,
most existing sequence mapping algorithms are insufficiently general.
  Results: We introduce context schemes: a method that allows the unambiguous
recognition of a reference base in a query sequence by testing the query for
substrings from an algorithmically defined set. Context schemes only map when
there is a unique best mapping, and define this criterion uniformly for all
reference bases. Mappings under context schemes can also be made stable, so
that extension of the query string (e.g. by increasing read length) will not
alter the mapping of previously mapped positions. Context schemes are general
in several senses. They natively support the detection of arbitrary complex,
novel rearrangements relative to the reference. They can scale over orders of
magnitude in query sequence length. Finally, they are trivially extensible to
more complex reference structures, such as graphs, that incorporate additional
variation. We demonstrate empirically the existence of high performance context
schemes, and present efficient context scheme mapping algorithms.
  Availability and Implementation: The software test framework created for this
work is available from
https://registry.hub.docker.com/u/adamnovak/sequence-graphs/.
  Contact: benedict@soe.ucsc.edu
  Supplementary Information: Six supplementary figures and one supplementary
section are available with the online version of this article.
Numerical calculation of protein-ligand binding rates through solution of the Smoluchowski equation using smooth particle hydrodynamics
Date: 2015-01-17 00:00:00

Background. The calculation of diffusion-controlled ligand binding rates is
important for understanding enzyme mechanisms as well as designing enzyme
inhibitors. We demonstrate the accuracy and effectiveness of a Lagrangian
particle-based method, smoothed particle hydrodynamics (SPH), to study
diffusion in biomolecular systems by numerically solving the time-dependent
Smoluchowski equation for continuum diffusion.
  Results. The numerical method is first verified in simple systems and then
applied to the calculation of ligand binding to an acetylcholinesterase
monomer. Unlike previous studies, a reactive Robin boundary condition (BC),
rather than the absolute absorbing (Dirichlet) boundary condition, is
considered on the reactive boundaries. This new boundary condition treatment
allows for the analysis of enzymes with "imperfect" reaction rates. Rates for
inhibitor binding to mAChE are calculated at various ionic strengths and
compared with experiment and other numerical methods. We find that imposition
of the Robin BC improves agreement between calculated and experimental reaction
rates.
  Conclusions. Although this initial application focuses on a single monomer
system, our new method provides a framework to explore broader applications of
SPH in larger-scale biomolecular complexes by taking advantage of its
Lagrangian particle-based nature.
Retrotransposon mobilization in cancer genomes
Date: 2015-01-18 00:00:00

The Cancer Genome Atlas project was initiated by the National Cancer
Institute in order to characterize the genomes of hundreds of tumors of various
cancer types. While much effort has been put into detecting somatic genomic
variation in these data, somatic structural variation induced by the activity
of transposable element insertions has not been reported. Transposable elements
(TEs) are particularly relevant in cancer in part because of several known
cases in which a TE insertion is directly linked to cancer formation and
studies linking the epigenetic status of retrotransposons to carcinogenesis and
patient outcome. Additionally, evidence for somatic retrotransposition in
eukaryotic genomes suggests that some tissues and therefore some cancer types
may be disposed to increased retrotransposition. We built upon previous work to
develop a highly efficient computational pipeline for the detection of
non-reference mobile ele- ment insertions from high-throughput paired-end whole
genome sequencing data that is capable of detecting breakpoints through a local
assembly strategy. Using this, we analyzed 33 whole genome tumor datasets with
paired normal samples from TCGA across 3 different cancer types: glioblastoma
multiforme (GBM), ovarian serous cystoadenocarcinoma (OV) and colorectal ade-
nocarcinoma (COAD). We detected 72 insertions in colon samples, almost all of
them LINE-1 elements, and none in GBM or OV. The amount of somatic
retrotransposition varies widely between samples with 61 insertions present in
one case. The lack of somatic retrotransposon insertions in GBM and OV samples
suggests that TE activity in cancer is restricted to certain cancer types.
A Hybrid Approach to Web Service Recommendation Based on QoS-Aware Rating and Ranking
Authors: Chen Mingming, Ma Yutao
Date: 2015-01-18 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

As the number of Web services with the same or similar functions increases
steadily on the Internet, nowadays more and more service consumers pay great
attention to the non-functional properties of Web services, also known as
quality of service (QoS), when finding and selecting appropriate Web services.
For most of the QoS-aware Web service recommendation systems, the list of
recommended Web services is generally obtained based on a rating-oriented
prediction approach, aiming at predicting the potential ratings that an active
user may assign to the unrated services as accurately as possible. However, in
some application scenarios, high accuracy of rating prediction may not
necessarily lead to a satisfactory recommendation result. In this paper, we
propose a ranking-oriented hybrid approach by combining the item-based
collaborative filtering and latent factor models to address the problem of Web
services ranking. In particular, the similarity between two Web services is
measured in terms of the correlation coefficient between their rankings instead
of between the traditional QoS ratings. Besides, we also improve the measure
NDCG (Normalized Discounted Cumulative Gain) for evaluating the accuracy of the
top K recommendations returned in ranked order. Comprehensive experiments on
the QoS data set composed of real-world Web services are conducted to test our
approach, and the experimental results demonstrate that our approach
outperforms other competing approaches.
Cryoelectron Microscopy as a Functional Instrument for Systems Biology, Structural Analysis & Experimental Manipulations with Living Cells. A comprehensive review of the current works
Date: 2015-01-18 00:00:00

The aim of this paper is to give an introductory review of the cryoelectron
microscopy as a complex data source for the most of the system biology
branches, including the most perspective non-local approaches known as
"localomics" and "dynamomics". A brief summary of various cryoelectron
mi-croscopy methods and corresponding system biological ap-proaches is given in
the text. The above classification can be considered as a useful framework for
the primary comprehen-sions about cryoelectron microscopy aims and instrumental
tools. We do not discuss any of these concepts in details, but merely point out
that their methodological complexity follows only from the structure-functional
complexity of biological systems which are investigated in this manner. We also
postu-late that one can employ some of the cryoelectron microscopic techniques
not only for observation, but also for modification and structural
refunctionalization of some biological and similar soft matter objects and
microscopic samples. In other worlds, we start with the cryoelectron microscopy
as a tool for the sys-tem biology and progress to its applying as an instrument
for system biology and functional biomimetics; i.e. "system cryobi-ology" goes
over into "synthetic cryobiology" or "cryogenic biomimetics". All these
conclusions can be deduced from the most recent works of the latest years,
including just submitted foreign papers. This article provides an up-to-date
description of the conceptual basis for the novel view on the computational
cryoelectron microscopy (in silico) approaches and the data mining principles
which lie at the very foundation of modern structural analysis and
reconstruction.