11 papers · ranked by Valyu relevance
Shuji Shinohara, Nobuhito Manome, Kouta Suzuki, Ung-il Chung + 5 more
Bayesian inference is a process of narrowing down hypotheses (causes) to one that best explains observational data (effects). To accurately estimate a cause, a considerable amount of data is required to be observed for as long as possible. However, the object of inference is not always constant. In this case, a method…
Hy Vuong, Thao Truong, Thang Tran, Son Pham
RSEM has been mainly known for its accuracy in transcript abundance quantification. However, its quantification time is extremely high compared to that of recent quantification tools. In this paper, we revised the RSEM’s EM algorithm. In particular, we derived accurate M-step updates to eliminate incorrect heuristic…
So Nakashima, Yuki Sughiyama, Tetsuya J. Kobayashi
Phenotypic variability in a population of cells can work as the bet-hedging of the cells under an unpredictably changing environment, the typical example of which is the bacterial persistence. To understand the strategy to control such phenomena, it is indispensable to identify the phenotype of each cell and its…
Joan Saurina-i-Ricos, Daniel Mas Montserrat, Alexander G. Ioannidis
Estimating genetic clusters from sequencing data is a fundamental task in population and medical genetics, enabling demographic inference and adjustment for population structure in association studies. ADMIXTURE, a widely used model-based clustering method, employs an accelerated Expectation–Maximization (EM) algorithm…
Ketan Mehta, Rebecca F. Goldin, David Marchette, Joshua T. Vogelstein + 2 more
This work presents a novel strategy for classifying neurons, represented by nodes of a directed graph, based on their circuitry (edge connectivity). We assume a stochastic block model (SBM) where neurons belong together if they connect to neurons of other groups according to the same probability distributions.…
Tien-Wen Lee
The General Linear Model (GLM) has been widely used in research, where error term has been treated as noise. However, compelling evidence suggests that in biological systems, the target variables may possess their innate variances. A modified GLM was proposed to explicitly model biological variance and non-biological…
Siva Rajesh Kasa, Vaibhav Rajan
We study two practically important cases of model based clustering using Gaussian Mixture Models: (1) when there is misspecification and (2) on high dimensional data, in the light of recent advances in Gradient Descent (GD) based optimization using Automatic Differentiation (AD). Our simulation studies show that EM has…
Hanjin Liu, Tomohiro Shima
The hidden Markov model (HMM) is widely used to analyze biophysical chronological data with discrete states, such as binding/detachment of biomolecules, protein/nucleotide conformational changes and step-like movement of single proteins. Despite its usefulness, classical HMM fitting has practical drawbacks that it…
Ludger Starke, Dirk Ostwald
Variational Bayes (VB), variational maximum likelihood (VML), restricted maximum likelihood (ReML), and maximum likelihood (ML) are cornerstone parametric statistical estimation techniques in the analysis of functional neuroimaging data. However, the theoretical underpinnings of these model parameter estimation…
Soohyun Kim, Young-geun Kim, Yuanjia Wang
One of the goals of precision psychiatry is to characterize mental disorders in an individualized manner, taking into account the underlying dynamic processes. Recent advances in mobile technologies have enabled the collection of Ecological Momentary Assessments (EMAs) that capture multiple responses in real-time at…
Fortunato Bianconi, Lorenzo Tomassoni, Chiara Antonini, Paolo Valigi
Computational modeling is a common tool to quantitatively describe biological processes. However, most model parameters are usually unknown because they cannot be directly measured. Therefore, a key issue in Systems Biology is model calibration, i.e. estimate parameters from experimental data. Existing methodologies…