8,940 research outputs found
Application of new probabilistic graphical models in the genetic regulatory networks studies
This paper introduces two new probabilistic graphical models for
reconstruction of genetic regulatory networks using DNA microarray data. One is
an Independence Graph (IG) model with either a forward or a backward search
algorithm and the other one is a Gaussian Network (GN) model with a novel
greedy search method. The performances of both models were evaluated on four
MAPK pathways in yeast and three simulated data sets. Generally, an IG model
provides a sparse graph but a GN model produces a dense graph where more
information about gene-gene interactions is preserved. Additionally, we found
two key limitations in the prediction of genetic regulatory networks using DNA
microarray data, the first is the sufficiency of sample size and the second is
the complexity of network structures may not be captured without additional
data at the protein level. Those limitations are present in all prediction
methods which used only DNA microarray data.Comment: 38 pages, 3 figure
Exchangeable Variable Models
A sequence of random variables is exchangeable if its joint distribution is
invariant under variable permutations. We introduce exchangeable variable
models (EVMs) as a novel class of probabilistic models whose basic building
blocks are partially exchangeable sequences, a generalization of exchangeable
sequences. We prove that a family of tractable EVMs is optimal under zero-one
loss for a large class of functions, including parity and threshold functions,
and strictly subsumes existing tractable independence-based model families.
Extensive experiments show that EVMs outperform state of the art classifiers
such as SVMs and probabilistic models which are solely based on independence
assumptions.Comment: ICML 201
A Bayesian Approach to Discovering Truth from Conflicting Sources for Data Integration
In practical data integration systems, it is common for the data sources
being integrated to provide conflicting information about the same entity.
Consequently, a major challenge for data integration is to derive the most
complete and accurate integrated records from diverse and sometimes conflicting
sources. We term this challenge the truth finding problem. We observe that some
sources are generally more reliable than others, and therefore a good model of
source quality is the key to solving the truth finding problem. In this work,
we propose a probabilistic graphical model that can automatically infer true
records and source quality without any supervision. In contrast to previous
methods, our principled approach leverages a generative process of two types of
errors (false positive and false negative) by modeling two different aspects of
source quality. In so doing, ours is also the first approach designed to merge
multi-valued attribute types. Our method is scalable, due to an efficient
sampling-based inference algorithm that needs very few iterations in practice
and enjoys linear time complexity, with an even faster incremental variant.
Experiments on two real world datasets show that our new method outperforms
existing state-of-the-art approaches to the truth finding problem.Comment: VLDB201
Learning and Designing Stochastic Processes from Logical Constraints
Stochastic processes offer a flexible mathematical formalism to model and
reason about systems. Most analysis tools, however, start from the premises
that models are fully specified, so that any parameters controlling the
system's dynamics must be known exactly. As this is seldom the case, many
methods have been devised over the last decade to infer (learn) such parameters
from observations of the state of the system. In this paper, we depart from
this approach by assuming that our observations are {\it qualitative}
properties encoded as satisfaction of linear temporal logic formulae, as
opposed to quantitative observations of the state of the system. An important
feature of this approach is that it unifies naturally the system identification
and the system design problems, where the properties, instead of observations,
represent requirements to be satisfied. We develop a principled statistical
estimation procedure based on maximising the likelihood of the system's
parameters, using recent ideas from statistical machine learning. We
demonstrate the efficacy and broad applicability of our method on a range of
simple but non-trivial examples, including rumour spreading in social networks
and hybrid models of gene regulation
Microarray Data Mining and Gene Regulatory Network Analysis
The novel molecular biological technology, microarray, makes it feasible to obtain quantitative measurements of expression of thousands of genes present in a biological sample simultaneously. Genome-wide expression data generated from this technology are promising to uncover the implicit, previously unknown biological knowledge. In this study, several problems about microarray data mining techniques were investigated, including feature(gene) selection, classifier genes identification, generation of reference genetic interaction network for non-model organisms and gene regulatory network reconstruction using time-series gene expression data. The limitations of most of the existing computational models employed to infer gene regulatory network lie in that they either suffer from low accuracy or computational complexity. To overcome such limitations, the following strategies were proposed to integrate bioinformatics data mining techniques with existing GRN inference algorithms, which enables the discovery of novel biological knowledge. An integrated statistical and machine learning (ISML) pipeline was developed for feature selection and classifier genes identification to solve the challenges of the curse of dimensionality problem as well as the huge search space. Using the selected classifier genes as seeds, a scale-up technique is applied to search through major databases of genetic interaction networks, metabolic pathways, etc.
By curating relevant genes and blasting genomic sequences of non-model organisms against well-studied genetic model organisms, a reference gene regulatory network for less-studied organisms was built and used both as prior knowledge and model validation for GRN reconstructions. Networks of gene interactions were inferred using a Dynamic Bayesian Network (DBN) approach and were analyzed for elucidating the dynamics caused by perturbations. Our proposed pipelines were applied to investigate molecular mechanisms for chemical-induced reversible neurotoxicity
Genet-CNV: Boolean Implication Networks for Modeling Genome-Wide Co-occurrence of DNA Copy Number Variations
Lung cancer is the leading cause of cancer-related death in the world. Lung cancer can be categorized as non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). NSCLC makes up about 80% to 85% of lung cancer cases diagnosed, whereas SCLC is responsible for 10% to 15% of the cases. It remains a challenge for physicians to identify patients who shall benefit from chemotherapy. In such a scenario, identifying genes that can facilitate therapeutic target discoveries and better understanding disease mechanisms and their regulation in different stages of lung cancer, remains an important topic of research.
In this thesis, we develop a computational framework for modelling molecular gene interaction networks, called Genet-CNV, to analyse gene interactions based on DNA Copy Number Variations (CNV). DNA copy number variation is a phenomenon in which sections of the genome are repeated and the number of repeats in the genome varies between individuals in the human population. These variations can be used to study the activity of genes in cancerous cells, compared with that of the normal population. Genet-CNV uses Boolean implication networks to investigate genome-wide DNA CNV to identify relationships called rules, that could potentially lead to the identification of genes of significant biological interest. Boolean implication networks are probabilistic graphical models that express the relationship between two variables terms of six implication rules that can describe if the genes are co-amplified, co-deleted or differentially amplified and deleted. Genet-CNV is run on three publicly available NSCLC genomic datasets. We further evaluate the results obtained with Genet-CNV by comparing them with the benchmark dataset, The Molecular Signatures Database (MSigDB). We identified several genes of interest that are present in survival, apoptosis, proliferation and immunologic pathways. The relationships obtained from this analysis can be tested for biological validations, or to confirm experimental results, thus facilitating the identification of genes playing a significant role in the causation and progress of NSCLC
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