FIBS: A Generic Framework for Classifying Interval-based Temporal Sequences

We study the problem of classifying interval-based temporal sequences (IBTSs). Since common classification algorithms cannot be directly applied to IBTSs, the main challenge is to define a set of features that effectively represents the data such that classifiers can be applied. Most prior work utilizes frequent pattern mining to define a feature set based on discovered patterns. However, frequent pattern mining is computationally expensive and often discovers many irrelevant patterns. To address this shortcoming, we propose the FIBS framework for classifying IBTSs. FIBS extracts features relevant to classification from IBTSs based on relative frequency and temporal relations. To avoid selecting irrelevant features, a filter-based selection strategy is incorporated into FIBS. Our empirical evaluation on eight real-world datasets demonstrates the effectiveness of our methods in practice. The results provide evidence that FIBS effectively represents IBTSs for classification algorithms, which contributes to similar or significantly better accuracy compared to state-of-the-art competitors. It also suggests that the feature selection strategy is beneficial to FIBS's performance.Comment: In: Big Data Analytics and Knowledge Discovery. DaWaK 2020. Springer, Cha

Widespread cell stress and mitochondrial dysfunction in early Alzheimer’s Disease

Cell stress and impaired oxidative phosphorylation are central to mechanisms of synaptic loss and neurodegeneration in the cellular pathology of Alzheimer’s disease (AD). We quantified the in vivo density of the endoplasmic reticulum stress marker, the sigma 1 receptor (S1R) using [11C]SA4503 PET, as well as that of mitochondrial complex I (MC1) with [18F]BCPP-EF and the pre-synaptic vesicular protein SV2A with [11C]UCB-J in 12 patients with early AD and in 16 cognitively normal controls. We integrated these molecular measures with assessments of regional brain volumes and brain perfusion (CBF) measured with MRI arterial spin labelling. 8 AD patients were followed longitudinally to estimate rates of change with disease progression over 12-18 months. The AD patients showed widespread increases in S1R (≤ 27%) and regional decreases in MC1 (≥ -28%), SV2A (≥ -25%), brain volume (≥ -23%), and CBF (≥ -26%). [18F]BCPP-EF PET MC1 density (≥ -12%) and brain volumes (≥ -5%) were further reduced at follow up in brain regions consistent with the differences between AD patients and controls at baseline. Exploratory analyses showing associations of MC1, SV2A and S1R density with cognitive changes at baseline and longitudinally with AD, but not in controls, suggested a loss of metabolic functional reserve with disease. Our study thus provides novel in vivo evidence for widespread cellular stress and bioenergetic abnormalities in early AD and that they may be clinically meaningful

Parameter estimation for robust HMM analysis of ChIP-chip data

Tiling arrays are an important tool for the study of transcriptional activity, protein-DNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis. Results: Here we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip tiling array data, using t emission distributions to increase robustness towards outliers. Maximum likelihood estimates are used for all model parameters. Two different approaches to parameter estimation are investigated and combined into an efficient procedure. Conclusion: We illustrate an efficient parameter estimation procedure that can be used for HMM based methods in general and leads to a clear increase in performance when compared to the use of ad hoc estimates. The resulting hidden Markov model outperforms established methods like TileMap in the context of histone modification studies.13 page(s

The Functional DRD3 Ser9Gly Polymorphism (rs6280) Is Pleiotropic, Affecting Reward as Well as Movement

Abnormalities of motivation and behavior in the context of reward are a fundamental component of addiction and mood disorders. Here we test the effect of a functional missense mutation in the dopamine 3 receptor (DRD3) gene (ser9gly, rs6280) on reward-associated dopamine (DA) release in the striatum. Twenty-six healthy controls (HCs) and 10 unmedicated subjects with major depressive disorder (MDD) completed two positron emission tomography (PET) scans with [11C]raclopride using the bolus plus constant infusion method. On one occasion subjects completed a sensorimotor task (control condition) and on another occasion subjects completed a gambling task (reward condition). A linear regression analysis controlling for age, sex, diagnosis, and self-reported anhedonia indicated that during receipt of unpredictable monetary reward the glycine allele was associated with a greater reduction in D2/3 receptor binding (i.e., increased reward-related DA release) in the middle (anterior) caudate (p<0.01) and the ventral striatum (p<0.05). The possible functional effect of the ser9gly polymorphism on DA release is consistent with previous work demonstrating that the glycine allele yields D3 autoreceptors that have a higher affinity for DA and display more robust intracellular signaling. Preclinical evidence indicates that chronic stress and aversive stimulation induce activation of the DA system, raising the possibility that the glycine allele, by virtue of its facilitatory effect on striatal DA release, increases susceptibility to hyperdopaminergic responses that have previously been associated with stress, addiction, and psychosis

Dual Use of Veterans Health Administration and Indian Health Service: Healthcare Provider and Patient Perspectives

Many American Indian and Alaska Native veterans are eligible for healthcare from Veterans Health Administration (VHA) and from Indian Health Service (IHS). These organizations executed a Memorandum of Understanding in 2003 to share resources, but little was known about how they collaborated to deliver healthcare. To describe dual use from the stakeholders’ perspectives, including incentives that encourage cross-use, which organization’s primary care is “primary,” and the potential problems and opportunities for care coordination across VHA and IHS. VHA healthcare staff, IHS healthcare staff and American Indian and Alaska Native veterans. Focus groups were conducted using a semi-structured guide. A software-assisted text analysis was performed using grounded theory to develop analytic categories. Dual use was driven by variation in institutional resources, leading patients to actively manage health-seeking behaviors and IHS providers to make ad hoc recommendations for veterans to seek care at VHA. IHS was the “primary” primary care for dual users. There was little coordination between VHA and IHS resulting in delays and treatment conflicts, but all stakeholder groups welcomed future collaboration. Fostering closer alignment between VHA and IHS would reduce care fragmentation and improve accountability for patient care

Graphical models for inferring single molecule dynamics

<p>Abstract</p> <p>Background</p> <p>The recent explosion of experimental techniques in single molecule biophysics has generated a variety of novel time series data requiring equally novel computational tools for analysis and inference. This article describes in general terms how graphical modeling may be used to learn from biophysical time series data using the variational Bayesian expectation maximization algorithm (VBEM). The discussion is illustrated by the example of single-molecule fluorescence resonance energy transfer (smFRET)<it> versus</it> time data, where the smFRET time series is modeled as a hidden Markov model (HMM) with Gaussian observables. A detailed description of smFRET is provided as well.</p> <p>Results</p> <p>The VBEM algorithm returns the model’s evidence and an approximating posterior parameter distribution given the data. The former provides a metric for model selection via maximum evidence (ME), and the latter a description of the model’s parameters learned from the data. ME/VBEM provide several advantages over the more commonly used approach of maximum likelihood (ML) optimized by the expectation maximization (EM) algorithm, the most important being a natural form of model selection and a well-posed (non-divergent) optimization problem.</p> <p>Conclusions</p> <p>The results demonstrate the utility of graphical modeling for inference of dynamic processes in single molecule biophysics.</p

Computational Analysis of Whole-Genome Differential Allelic Expression Data in Human

Allelic imbalance (AI) is a phenomenon where the two alleles of a given gene are expressed at different levels in a given cell, either because of epigenetic inactivation of one of the two alleles, or because of genetic variation in regulatory regions. Recently, Bing et al. have described the use of genotyping arrays to assay AI at a high resolution (∼750,000 SNPs across the autosomes). In this paper, we investigate computational approaches to analyze this data and identify genomic regions with AI in an unbiased and robust statistical manner. We propose two families of approaches: (i) a statistical approach based on z-score computations, and (ii) a family of machine learning approaches based on Hidden Markov Models. Each method is evaluated using previously published experimental data sets as well as with permutation testing. When applied to whole genome data from 53 HapMap samples, our approaches reveal that allelic imbalance is widespread (most expressed genes show evidence of AI in at least one of our 53 samples) and that most AI regions in a given individual are also found in at least a few other individuals. While many AI regions identified in the genome correspond to known protein-coding transcripts, others overlap with recently discovered long non-coding RNAs. We also observe that genomic regions with AI not only include complete transcripts with consistent differential expression levels, but also more complex patterns of allelic expression such as alternative promoters and alternative 3′ end. The approaches developed not only shed light on the incidence and mechanisms of allelic expression, but will also help towards mapping the genetic causes of allelic expression and identify cases where this variation may be linked to diseases

Explicit-Duration Hidden Markov Model Inference of UP-DOWN States from Continuous Signals

Neocortical neurons show UP-DOWN state (UDS) oscillations under a variety of conditions. These UDS have been extensively studied because of the insight they can yield into the functioning of cortical networks, and their proposed role in putative memory formation. A key element in these studies is determining the precise duration and timing of the UDS. These states are typically determined from the membrane potential of one or a small number of cells, which is often not sufficient to reliably estimate the state of an ensemble of neocortical neurons. The local field potential (LFP) provides an attractive method for determining the state of a patch of cortex with high spatio-temporal resolution; however current methods for inferring UDS from LFP signals lack the robustness and flexibility to be applicable when UDS properties may vary substantially within and across experiments. Here we present an explicit-duration hidden Markov model (EDHMM) framework that is sufficiently general to allow statistically principled inference of UDS from different types of signals (membrane potential, LFP, EEG), combinations of signals (e.g., multichannel LFP recordings) and signal features over long recordings where substantial non-stationarities are present. Using cortical LFPs recorded from urethane-anesthetized mice, we demonstrate that the proposed method allows robust inference of UDS. To illustrate the flexibility of the algorithm we show that it performs well on EEG recordings as well. We then validate these results using simultaneous recordings of the LFP and membrane potential (MP) of nearby cortical neurons, showing that our method offers significant improvements over standard methods. These results could be useful for determining functional connectivity of different brain regions, as well as understanding network dynamics

Probabilistic machine learning and artificial intelligence.

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract