pwc-archive/papers-with-abstracts huggingface dataset:
The Electronic Behavior of Zinc-Finger Protein Binding Sites in the Context of the DNA Extended Ladder Model
Date: 2015-08-12 00:00:00
Tasks: Specificity

The eukaryotic Cys2His2 zinc finger proteins bind to DNA ubiquitously at
highly conserved domains, responsible for gene regulation and the spatial
organization of DNA. To study and understand the zinc finger DNA-protein
interaction, we use the extended ladder in the DNA model proposed by Zhu,
Rasmussen, Balatsky \& Bishop (2007) \cite{Zhu-2007}. Considering one single
spinless electron in each nucleotide $\pi$-orbital along a double DNA chain
(dDNA), we find a typical pattern for the {\color{black} bottom of the occupied
molecular orbital (BOMO)}, highest occupied molecular orbital (HOMO) and lowest
unoccupied orbital (LUMO) along the binding sites. We specifically looked at
two members of zinc finger protein family: specificity protein 1 (SP1) and
early grown response 1 transcription factors (EGR1). When the valence band is
filled, we find electrons in the purines along the nucleotide sequence,
compatible with the electric charges of the binding amino acids in SP1 and EGR1
zinc finger.
Identification of Insurance Models with Multidimensional Screening
Date: 2016-01-15 00:00:00

This paper addresses the identification of insurance models with
multidimensional screening where insurees have private information about their
risk and risk aversion. The model includes a random damage and the possibility
of several claims. Screening of insurees relies on their certainty equivalence.
The paper then investigates how data availability on the number of offered
coverages and reported claims affects the identification of the model
primitives under four different scenarios. We show that the model structure is
identified despite bunching due to multidimensional screening and/or a finite
number of offered coverages. The observed number of claims plays a key role in
the identification of the joint distribution of risk and risk aversion. In
addition, the paper derives all the restrictions imposed by the model on
observables. Our results are constructive with explicit equations for
estimation and model testing.
Gene expression dynamics with stochastic bursts: exact results for a coarse-grained model
Date: 2015-08-12 00:00:00

We present a theoretical framework to analyze the dynamics of gene expression
with stochastic bursts. Beginning with an individual-based model which fully
accounts for the messenger RNA (mRNA) and protein populations, we propose a
novel expansion of the master equation for the joint process. The resulting
coarse-grained model reduces the dimensionality of the system, describing only
the protein population while fully accounting for the effects of discrete and
fluctuating mRNA population. Closed form expressions for the stationary
distribution of the protein population and mean first-passage times of the
coarse-grained model are derived and large-scale Monte Carlo simulations show
that the analysis accurately describes the individual-based process accounting
for mRNA population, in contrast to the failure of commonly proposed
diffusion-type models.
Comparing local search paths with global search paths on protein residue networks: allosteric communication
Date: 2016-07-29 00:00:00
Tasks: Clustering

Although proteins have been recognized as small-world networks and their
small-world network properties of clustering and short paths have been
exploited computationally to produce biologically relevant information, they
have not been truly explored as such, i.e. as navigable small-world networks in
the original spirit of Milgram's work. This research seeks to fill this gap by
exploring local search on a network representation of proteins and to probe the
source of navigability in proteins. Previously, we confirmed that proteins are
navigable small-world networks and observed that local search paths exhibit
different characteristics from global search paths. In this paper, we
investigate the biological relevance of the differences in path characteristics
on a type III receptor tyrosine kinase (KIT). A chief difference that works in
favour of local search paths as intra-protein communication pathways is their
weaker proclivity, compared to global search paths, for traversing long-range
edges. Long-range edges tend to be less stable and their inclusion tends to
decrease the communication propensity of a path. The source of protein
navigability is traced to clustering provided by short-range edges. The
majority of a protein's short-range edges reside within structures deemed
important for long-range energy transport and modulation of allosteric
communication in proteins. Therefore, the disruption of intra-protein
communication as a result of the destruction of these structures via random
rewiring is expected. A local search perspective leads us to this expected
conclusion while a global search perspective does not. These findings initiate
the compilation of a list of path properties that are characteristic of
intra-protein pathways and could suggest fresh avenues for evolving and
regulating navigable (small-world) networks.
Molecular Network Control Through Boolean Canalization
Date: 2015-10-25 00:00:00

Boolean networks are an important class of computational models for molecular
interaction networks. Boolean canalization, a type of hierarchical clustering
of the inputs of a Boolean function, has been extensively studied in the
context of network modeling where each layer of canalization adds a degree of
stability in the dynamics of the network. Recently, dynamic network control
approaches have been used for the design of new therapeutic interventions and
for other applications such as stem cell reprogramming. This work studies the
role of canalization in the control of Boolean molecular networks. It provides
a method for identifying the potential edges to control in the wiring diagram
of a network for avoiding undesirable state transitions. The method is based on
identifying appropriate input-output combinations on undesirable transitions
that can be modified using the edges in the wiring diagram of the network.
Moreover, a method for estimating the number of changed transitions in the
state space of the system as a result of an edge deletion in the wiring diagram
is presented. The control methods of this paper were applied to a mutated
cell-cycle model and to a p53-mdm2 model to identify potential control targets.
Genomic study of the Ket: a Paleo-Eskimo-related ethnic group with significant ancient North Eurasian ancestry
Date: 2015-08-13 00:00:00

The Kets, an ethnic group in the Yenisei River basin, Russia, are considered
the last nomadic hunter-gatherers of Siberia, and Ket language has no
transparent affiliation with any language family. We investigated connections
between the Kets and Siberian and North American populations, with emphasis on
the Mal'ta and Paleo-Eskimo ancient genomes using original data from 46
unrelated samples of Kets and 42 samples of their neighboring ethnic groups
(Uralic-speaking Nganasans, Enets, and Selkups). We genotyped over 130,000
autosomal SNPs, determined mitochondrial and Y-chromosomal haplogroups, and
performed high-coverage genome sequencing of two Ket individuals. We
established that the Kets belong to the cluster of Siberian populations related
to Paleo-Eskimos. Unlike other members of this cluster (Nganasans, Ulchi,
Yukaghirs, and Evens), Kets and closely related Selkups have a high degree of
Mal'ta ancestry. Implications of these findings for the linguistic hypothesis
uniting Ket and Na-Dene languages into a language macrofamily are discussed.
Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark
Authors: Winlaw Manda, Hynes Michael B., Caterini Anthony, De Sterck Hans
Date: 2016-01-10 00:00:00
Tasks: Collaborative Filtering, Recommendation Systems

Collaborative filtering algorithms are important building blocks in many
practical recommendation systems. For example, many large-scale data processing
environments include collaborative filtering models for which the Alternating
Least Squares (ALS) algorithm is used to compute latent factor matrix
decompositions. In this paper, we propose an approach to accelerate the
convergence of parallel ALS-based optimization methods for collaborative
filtering using a nonlinear conjugate gradient (NCG) wrapper around the ALS
iterations. We also provide a parallel implementation of the accelerated
ALS-NCG algorithm in the Apache Spark distributed data processing environment,
and an efficient line search technique as part of the ALS-NCG implementation
that requires only one pass over the data on distributed datasets. In serial
numerical experiments on a linux workstation and parallel numerical experiments
on a 16 node cluster with 256 computing cores, we demonstrate that the combined
ALS-NCG method requires many fewer iterations and less time than standalone ALS
to reach movie rankings with high accuracy on the MovieLens 20M dataset. In
parallel, ALS-NCG can achieve an acceleration factor of 4 or greater in clock
time when an accurate solution is desired; furthermore, the acceleration factor
increases as greater numerical precision is required in the solution. In
addition, the NCG acceleration mechanism is efficient in parallel and scales
linearly with problem size on synthetic datasets with up to nearly 1 billion
ratings. The acceleration mechanism is general and may also be applicable to
other optimization methods for collaborative filtering.
On joint subtree distributions under two evolutionary models
Date: 2015-08-13 00:00:00
Tasks: Vocal Bursts Valence Prediction

In population and evolutionary biology, hypotheses about micro-evolutionary
and macro-evolutionary processes are commonly tested by comparing the shape
indices of empirical evolutionary trees with those predicted by neutral models.
A key ingredient in this approach is the ability to compute and quantify
distributions of various tree shape indices under random models of interest. As
a step to meet this challenge, in this paper we investigate the joint
distribution of cherries and pitchforks (that is, subtrees with two and three
leaves) under two widely used null models: the Yule-Harding-Kingman (YHK) model
and the proportional to distinguishable arrangements (PDA) model. Based on two
novel recursive formulae, we propose a dynamic approach to numerically compute
the exact joint distribution (and hence the marginal distributions) for trees
of any size. We also obtained insights into the statistical properties of trees
generated under these two models, including a constant correlation between the
cherry and the pitchfork distributions under the YHK model, the log-concavity
and unimodality of cherry distributions under both models. In particular, we
show the existence of a unique change point for cherry distribution between the
two models, that is, there exists a critical value $\tau_n$ for each $n\geq 4$
such that the probability that a random tree with $n$ leaves generated under
the YHK model contains $k$ cherries is lower than that under the PDA model if
$1<k< \tau_n$, and higher if $\tau_n<k\le n/2$.
Enhanced Diffusion of Enzymes that Catalyze Exothermic Reactions
Date: 2015-08-13 00:00:00

Enzymes have been recently found to exhibit enhanced diffusion due to their
catalytic activities. A recent experiment [C. Riedel et al., Nature 517, 227
(2015)] has found evidence that suggests this phenomenon might be controlled by
the degree of exothermicity of the catalytic reaction involved. Four mechanisms
that can lead to this effect, namely, self-thermophoresis, boost in kinetic
energy, stochastic swimming, and collective heating, are critically discussed,
and it is shown that only the last two could be strong enough to account for
the observations. The resulting quantitative description is used to examine the
biological significance of the effect.
Enabling Complex Wikipedia Queries - Technical Report
Authors: Katz Gilad, Shapira Bracha
Date: 2015-08-13 00:00:00
Tasks: Information Retrieval, Recommendation Systems, Retrieval, Sentiment Analysis

In this technical report we present a database schema used to store Wikipedia
so it can be easily used in query-intensive applications. In addition to
storing the information in a way that makes it highly accessible, our schema
enables users to easily formulate complex queries using information such as the
anchor-text of links and their location in the page, the titles and number of
redirect pages for each page and the paragraph structure of entity pages. We
have successfully used the schema in domains such as recommender systems,
information retrieval and sentiment analysis. In order to assist other
researchers, we now make the schema and its content available online.
Uncertainty analysis and composite hypothesis under the likelihood paradigm
Date: 2015-08-13 00:00:00
Tasks: parameter estimation

The correct use and interpretation of models depends on several steps, two of
which being the calibration by parameter estimation and the analysis of
uncertainty. In the biological literature, these steps are seldom discussed
together, but they can be seen as fitting pieces of the same puzzle. In
particular, analytical procedures for uncertainty estimation may be masking a
high degree of uncertainty coming from a model with a stable structure, but
insufficient data.
  Under a likelihoodist approach, the problem of uncertainty estimation is
closely related to the problem of composite hypothesis. In this paper, we
present a brief historical background on the statistical school of
Likelihoodism, and examine the complex relations between the law of likelihood
and the problem of composite hypothesis, together with the existing proposals
for coping with it. Then, we propose a new integrative methodology for the
uncertainty estimation of models using the information in the collected data.
We argue that this methodology is intuitively appealing under a likelihood
paradigm.
Bayesian hierarchical modelling for inferring genetic interactions in yeast
Date: 2015-08-14 00:00:00
Tasks: Experimental Design

Quantitative Fitness Analysis (QFA) is a high-throughput experimental and
computational methodology for measuring the growth of microbial populations.
QFA screens can be used to compare the health of cell populations with and
without a mutation in a query gene in order to infer genetic interaction
strengths genome-wide, examining thousands of separate genotypes. We introduce
Bayesian, hierarchical models of population growth rates and genetic
interactions that better reflect QFA experimental design than current
approaches. Our new approach models population dynamics and genetic interaction
simultaneously, thereby avoiding passing information between models via a
univariate fitness summary. Matching experimental structure more closely,
Bayesian hierarchical approaches use data more efficiently and find new
evidence for genes which interact with yeast telomeres within a published
dataset.
Evolutionary game on networks with high clustering coefficient
Date: 2015-08-14 00:00:00
Tasks: Clustering, Vocal Bursts Intensity Prediction

This study investigates the influence of lattice structure in evolutionary
games. The snowdrift games is considered in networks with high clustering
coefficients, that use four different strategy-updating. Analytical conjectures
using pair approximation were compared with the numerical results. Results
indicate that general statements asserting that the lattice structure enhances
cooperation are misleading.
CABeRNET: a Cytoscape app for Augmented Boolean models of gene Regulatory NETworks
Date: 2015-07-26 00:00:00

Background. Dynamical models of gene regulatory networks (GRNs) are highly
effective in describing complex biological phenomena and processes, such as
cell differentiation and cancer development. Yet, the topological and
functional characterization of real GRNs is often still partial and an
exhaustive picture of their functioning is missing.
  Motivation. We here introduce CABeRNET, a Cytoscape app for the generation,
simulation and analysis of Boolean models of GRNs, specifically focused on
their augmentation when a only partial topological and functional
characterization of the network is available. By generating large ensembles of
networks in which user-defined entities and relations are added to the original
core, CABeRNET allows to formulate hypotheses on the missing portions of real
networks, as well to investigate their generic properties, in the spirit of
complexity science.
  Results. CABeRNET offers a series of innovative simulation and modeling
functions and tools, including (but not being limited to) the dynamical
characterization of the gene activation patterns ruling cell types and
differentiation fates, and sophisticated robustness assessments, as in the case
of gene knockouts. The integration within the widely used Cytoscape framework
for the visualization and analysis of biological networks, makes CABeRNET a new
essential instrument for both the bioinformatician and the computational
biologist, as well as a computational support for the experimentalist. An
example application concerning the analysis of an augmented T-helper cell GRN
is provided.
Do Brain Networks Evolve by Maximizing their Information Flow Capacity?
Date: 2015-07-28 00:00:00

We propose a working hypothesis supported by numerical simulations that brain
networks evolve based on the principle of the maximization of their internal
information flow capacity. We find that synchronous behavior and capacity of
information flow of the evolved networks reproduce well the same behaviors
observed in the brain dynamical networks of Caenorhabditis elegans and humans,
networks of Hindmarsh-Rose neurons with graphs given by these brain networks.
We make a strong case to verify our hypothesis by showing that the neural
networks with the closest graph distance to the brain networks of
Caenorhabditis elegans and humans are the Hindmarsh-Rose neural networks
evolved with coupling strengths that maximize information flow capacity.
Surprisingly, we find that global neural synchronization levels decrease during
brain evolution, reflecting on an underlying global no Hebbian-like evolution
process, which is driven by no Hebbian-like learning behaviors for some of the
clusters during evolution, and Hebbian-like learning rules for clusters where
neurons increase their synchronization.
Combinatorial Limits of Transcription Factors and Gene Regulatory Networks in Development and Evolution
Date: 2015-07-31 00:00:00

Gene Regulatory Networks (GRNs) consisting of combinations of transcription
factors (TFs) and their cis promoters are assumed to be sufficient to direct
the development of organisms. Mutations in GRNs are assumed to be the primary
drivers for the evolution of multicellular life. Here it is proven that neither
of these assumptions is correct. They are inconsistent with fundamental
principles of combinatorics of bounded encoded networks. It is shown there are
inherent complexity and control capacity limits for any gene regulatory network
that is based solely on protein coding genes such as transcription factors.
This result has significant practical consequences for understanding
development, evolution, the Cambrian Explosion, as well as multi-cellular
diseases such as cancer. If the arguments are sound, then genes cannot explain
the development of complex multicellular organisms and genes cannot explain the
evolution of complex multicellular life.
A conjecture about the efficiency of first price mechanisms
Date: 2015-08-31 00:00:00

We present different versions of a conjecture which would express that first
price mechanisms never work very badly in a very general class of problems. The
definitions include most of the problems where there is a principal (seller)
who has the right to exclude others from the game. The exact definitions are
motivated by the "first price mechanism" in E Cs: "Efficient Teamwork", but the
conjecture is relevant for most auction problems, e.g. for combinatorial
auctions.
Biases in differential expression analysis of RNA-seq data: A matter of replicate type
Date: 2015-08-15 00:00:00

In differential expression (DE) analysis of RNA-seq count data, it is known
that genes with a larger read number are more likely to be differentially
expressed. This bias has a profound effect on the subsequent Gene Ontology (GO)
analysis by perturbing the ranks of gene-sets. Another known bias is that the
commonly used parametric DE analysis methods (e.g., edgeR, DESeq and baySeq)
tend to yield more DE genes as the sequencing depth is increased. We
nevertheless show that these biases are in fact confined to data of the
technical replicate type. We also show the GO or gene-set enrichment analysis
methods applied to technical replicate data result in considerable number of
false positives. In conclusion, the current DE and enrichment analysis methods
can be confidently used for biological replicate count data, while caution
should be exercised when analysing technical replicate data.
Were there two forms of Stegosaurus?
Date: 2015-08-15 00:00:00
Tasks: Vocal Bursts Valence Prediction

We recognise that Stegosaurus exhibited exterior chirality and could,
therefore, have assumed either of two distinct, mirror-image forms. Our
preliminary investigations suggest that both existed. Stegosaurus's exterior
chirality raises new questions such as the validity of well-known exhibits
whilst offering new insights into long-standing questions such as the function
of the plates. We inform our discussions throughout with examples of modern-day
animals that exhibit exterior chirality.
Ribosome Flow Model on a Ring
Date: 2015-08-16 00:00:00
Tasks: model, Translation

The asymmetric simple exclusion process (ASEP) is an important model from
statistical physics describing particles that hop randomly from one site to the
next along an ordered lattice of sites, but only if the next site is empty.
ASEP has been used to model and analyze numerous multiagent systems with local
interactions including the flow of ribosomes along the mRNA strand.
  In ASEP with periodic boundary conditions a particle that hops from the last
site returns to the first one. The mean field approximation of this model is
referred to as the ribosome flow model on a ring (RFMR). The RFMR may be used
to model both synthetic and endogenous gene expression regimes.
  We analyze the RFMR using the theory of monotone dynamical systems. We show
that it admits a continuum of equilibrium points and that every trajectory
converges to an equilibrium point. Furthermore, we show that it entrains to
periodic transition rates between the sites. We describe the implications of
the analysis results to understanding and engineering cyclic mRNA translation
in-vitro and in-vivo.
Asymptotic connectivity for the network of RNA secondary structures
Date: 2015-08-16 00:00:00

Given an RNA sequence a, consider the network G = (V;E), where the set V of
nodes consists of all secondary structures of a, and whose edge set E consists
of all edges connecting two secondary structures whose base pair distance is 1.
Define the network connectivity, or expected network degree, as the average
number of edges incident to vertices of G. Using algebraic combinatorial
methods, we prove that the asymptotic connectivity of length n homopolymer
sequences is 0:473418 ? n. This raises the question of what other network
properties are characteristic of the network of RNA secondary structures.
Programs in Python, C and Mathematica are available at the web site
http://bioinformatics.bc.edu/clotelab/ RNAexpNumNbors.
New Analytical Solutions of a Modified Black-Scholes Equation with the European Put Option
Date: 2015-08-16 00:00:00

Using Maple, we compute some analytical solutions of a modified Black-Scholes
equation, recently proposed, in the case of the European put option. We show
that the modified Black-Scholes equation with the European put option is
exactly solvable in terms of associated Laguerre polynomials. We make some
numerical experiments with the analytical solutions and we compare our results
with the results derived from numerical experiments using the standard
Black-Scholes equation.