439 research outputs found
Evolutionary Games with Affine Fitness Functions: Applications to Cancer
We analyze the dynamics of evolutionary games in which fitness is defined as
an affine function of the expected payoff and a constant contribution. The
resulting inhomogeneous replicator equation has an homogeneous equivalent with
modified payoffs. The affine terms also influence the stochastic dynamics of a
two-strategy Moran model of a finite population. We then apply the affine
fitness function in a model for tumor-normal cell interactions to determine
which are the most successful tumor strategies. In order to analyze the
dynamics of concurrent strategies within a tumor population, we extend the
model to a three-strategy game involving distinct tumor cell types as well as
normal cells. In this model, interaction with normal cells, in combination with
an increased constant fitness, is the most effective way of establishing a
population of tumor cells in normal tissue.Comment: The final publication is available at http://www.springerlink.com,
http://dx.doi.org/10.1007/s13235-011-0029-
Quantifying cancer progression with conjunctive Bayesian networks
Motivation: Cancer is an evolutionary process characterized by accumulating mutations. However, the precise timing and the order of genetic alterations that drive tumor progression remain enigmatic. Results: We present a specific probabilistic graphical model for the accumulation of mutations and their interdependencies. The Bayesian network models cancer progression by an explicit unobservable accumulation process in time that is separated from the observable but error-prone detection of mutations. Model parameters are estimated by an Expectation-Maximization algorithm and the underlying interaction graph is obtained by a simulated annealing procedure. Applying this method to cytogenetic data for different cancer types, we find multiple complex oncogenetic pathways deviating substantially from simplified models, such as linear pathways or trees. We further demonstrate how the inferred progression dynamics can be used to improve genetics-based survival predictions which could support diagnostics and prognosis. Availability: The software package ct-cbn is available under a GPL license on the web site cbg.ethz.ch/software/ct-cbn Contact: [email protected]
On sound source localization of speech signals using deep neural networks
In recent years artificial neural networks are successfully applied especially in the context of automatic speech recognition. As information processing systems, neural networks are trained by, e.g., backpropagation or restricted Boltzmann machines to classify patterns at the input of the system. The current work presents the implementation of a deep neural network (DNN) architecture for acoustic source localization.EC/FP7/318381/EU/Experimenting Acoustics in Real environments using Innovative Test-beds/EAR-ITEC/FP7/284628/EU/Sounds for Energy Control of Buildings/S4ECoBEC/FP7/609180/EU/Energy efficient & Cost competitive retrofitting solutions for Shopping buildings/ECOSHOPPIN
Waiting time models of cancer progression
Cancer progression is an evolutionary process that is driven by mutation and
selection in a population of tumor cells. We discuss mathematical models of
cancer progression, starting from traditional multistage theory. Each stage is
associated with the occurrence of genetic alterations and their fixation in the
population. We describe the accumulation of mutations using conjunctive
Bayesian networks, an exponential family of waiting time models in which the
occurrence of mutations is constrained to a partial temporal order. Two
opposing limit cases arise if mutations either follow a linear order or occur
independently. We derive exact analytical expressions for the waiting time
until a specific number of mutations have accumulated in these limit cases as
well as for the general conjunctive Bayesian network. Finally, we analyze a
stochastic population genetics model that explicitly accounts for mutation and
selection. In this model, waves of clonal expansions sweep through the
population at equidistant intervals. We present an approximate analytical
expression for the waiting time in this model and compare it to the results
obtained for the conjunctive Bayesian networks
An Investigation of Monotonic Transducers for Large-Scale Automatic Speech Recognition
The two most popular loss functions for streaming end-to-end automatic speech
recognition (ASR) are the RNN-Transducer (RNN-T) and the connectionist temporal
classification (CTC) objectives. Both perform an alignment-free training by
marginalizing over all possible alignments, but use different transition rules.
Between these two loss types we can classify the monotonic RNN-T (MonoRNN-T)
and the recently proposed CTC-like Transducer (CTC-T), which both can be
realized using the graph temporal classification-transducer (GTC-T) loss
function. Monotonic transducers have a few advantages. First, RNN-T can suffer
from runaway hallucination, where a model keeps emitting non-blank symbols
without advancing in time, often in an infinite loop. Secondly, monotonic
transducers consume exactly one model score per time step and are therefore
more compatible and unifiable with traditional FST-based hybrid ASR decoders.
However, the MonoRNN-T so far has been found to have worse accuracy than RNN-T.
It does not have to be that way, though: By regularizing the training - via
joint LAS training or parameter initialization from RNN-T - both MonoRNN-T and
CTC-T perform as well - or better - than RNN-T. This is demonstrated for
LibriSpeech and for a large-scale in-house data set.Comment: Submitted to Interspeech 202
Streaming Audio-Visual Speech Recognition with Alignment Regularization
Recognizing a word shortly after it is spoken is an important requirement for
automatic speech recognition (ASR) systems in real-world scenarios. As a
result, a large body of work on streaming audio-only ASR models has been
presented in the literature. However, streaming audio-visual automatic speech
recognition (AV-ASR) has received little attention in earlier works. In this
work, we propose a streaming AV-ASR system based on a hybrid connectionist
temporal classification (CTC)/attention neural network architecture. The audio
and the visual encoder neural networks are both based on the conformer
architecture, which is made streamable using chunk-wise self-attention (CSA)
and causal convolution. Streaming recognition with a decoder neural network is
realized by using the triggered attention technique, which performs
time-synchronous decoding with joint CTC/attention scoring. For frame-level ASR
criteria, such as CTC, a synchronized response from the audio and visual
encoders is critical for a joint AV decision making process. In this work, we
propose a novel alignment regularization technique that promotes
synchronization of the audio and visual encoder, which in turn results in
better word error rates (WERs) at all SNR levels for streaming and offline
AV-ASR models. The proposed AV-ASR model achieves WERs of 2.0% and 2.6% on the
Lip Reading Sentences 3 (LRS3) dataset in an offline and online setup,
respectively, which both present state-of-the-art results when no external
training data are used.Comment: Submitted to ICASSP202
The Temporal Order of Genetic and Pathway Alterations in Tumorigenesis
Cancer evolves through the accumulation of mutations, but the order in which mutations occur is poorly understood. Inference of a temporal ordering on the level of genes is challenging because clinically and histologically identical tumors often have few mutated genes in common. This heterogeneity may at least in part be due to mutations in different genes having similar phenotypic effects by acting in the same functional pathway. We estimate the constraints on the order in which alterations accumulate during cancer progression from cross-sectional mutation data using a probabilistic graphical model termed Hidden Conjunctive Bayesian Network (H-CBN). The possible orders are analyzed on the level of genes and, after mapping genes to functional pathways, also on the pathway level. We find stronger evidence for pathway order constraints than for gene order constraints, indicating that temporal ordering results from selective pressure acting at the pathway level. The accumulation of changes in core pathways differs among cancer types, yet a common feature is that progression appears to begin with mutations in genes that regulate apoptosis pathways and to conclude with mutations in genes involved in invasion pathways. H-CBN models provide a quantitative and intuitive model of tumorigenesis showing that the genetic events can be linked to the phenotypic progression on the level of pathways
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