Sonia M. Leach

Bounded-parameter Markov decision process

In this paper, we introduce the notion of a {\em bounded parameter Markov decision process\/} as a generalization of the traditional {\em exact\/} MDP. A bounded parameter MDP is a set of exact MDPs specified by giving upper and lower bounds on transition probabilities and rewards (all the MDPs in the set share the same state and action space). Bounded parameter MDPs can be used to represent variation or uncertainty concerning the parameters of sequential decision problems. Bounded parameter MDPs can also be used in aggregation schemes to represent the variation in the transition probabilities for different base states aggregated together in the same aggregate state. We introduce {\em interval value functions\/} as a natural extension of traditional value functions. An interval value function assigns a closed real interval to each state, representing the assertion that the value of that state falls within that interval. An interval value function can be used to bound the performance of a policy over the set of exact MDPs associated with a given bounded parameter MDP. We describe an iterative dynamic programming algorithm called {\em interval policy evaluation\/} which computes an interval value function for a given bounded parameter MDP and specified policy. Interval policy evaluation on a policy

Expression of cilium-associated genes defines novel molecular subtypes of idiopathic pulmonary fibrosis

policy

Biomedical Discovery Acceleration, with Applications to Craniofacial Development

computes the most restrictive interval value function that is sound, i.e. that bounds the value function for

The peripheral blood transcriptome identifies the presence and extent of disease in idiopathic pulmonary fibrosis.

policy

Spatial and Temporal Analysis of Gene Expression during Growth and Fusion of the Mouse Facial Prominences

in every exact MDP in the set defined by the bounded parameter MDP. A simple modification of interval policy evaluation results in a variant of value iteration [Bellman57] that we call {\em interval value iteration\/} which computes a policy for an bounded parameter MDP that is optimal in a well-defined sense.

Improving protein function prediction methods with integrated literature data

Background Idiopathic pulmonary fibrosis (IPF) is an untreatable lung disease with a median survival of only 3–5 years that is diagnosed using a combination of clinical, radiographic and pathologic criteria. Histologically, IPF is characterised by usual interstitial pneumonia (UIP), a fibrosing interstitial pneumonia with a pattern of heterogeneous, subpleural regions of fibrotic and remodelled lung. We hypothesised that gene expression profiles of lung tissue may identify molecular subtypes of disease that could classify subtypes of IPF/UIP that have clinical implications. Methods and findings We collected transcriptional profiles on lung tissue from 119 patients with IPF/UIP and 50 non-diseased controls. Differential expression of individual transcripts was identified using an analysis of covariance (ANCOVA) model incorporating the clinical diagnosis of each patient as well as age, gender and smoking status. Validation was performed in an independent cohort of 111 IPF/UIP and 39 non-diseased controls. Our analysis identified two subtypes of IPF/UIP based on a strong molecular signature associated with expression of genes previously associated with fibrosis (matrix metalloproteinases, osteopontin, keratins), cilium genes and genes with unknown function. We demonstrate that elevated expression of cilium genes is associated with more extensive microscopic honeycombing and higher expression of both the airway mucin gene MUC5B and the metalloproteinase MMP7, a gene recently implicated in attenuating ciliated cell differentiation during wound repair. Conclusions Expression of cilium genes appears to identify two unique molecular phenotypes of IPF/UIP. The different molecular profiles may be relevant to therapeutic responsiveness in patients with IPF/UIP.

Three Unique Interstitial Macrophages in the Murine Lung at Steady State

The profusion of high-throughput instruments and the explosion of new results in the scientific literature, particularly in molecular biomedicine, is both a blessing and a curse to the bench researcher. Even knowledgeable and experienced scientists can benefit from computational tools that help navigate this vast and rapidly evolving terrain. In this paper, we describe a novel computational approach to this challenge, a knowledge-based system that combines reading, reasoning, and reporting methods to facilitate analysis of experimental data. Reading methods extract information from external resources, either by parsing structured data or using biomedical language processing to extract information from unstructured data, and track knowledge provenance. Reasoning methods enrich the knowledge that results from reading by, for example, noting two genes that are annotated to the same ontology term or database entry. Reasoning is also used to combine all sources into a knowledge network that represents the integration of all sorts of relationships between a pair of genes, and to calculate a combined reliability score. Reporting methods combine the knowledge network with a congruent network constructed from experimental data and visualize the combined network in a tool that facilitates the knowledge-based analysis of that data. An implementation of this approach, called the Hanalyzer, is demonstrated on a large-scale gene expression array dataset relevant to craniofacial development. The use of the tool was critical in the creation of hypotheses regarding the roles of four genes never previously characterized as involved in craniofacial development; each of these hypotheses was validated by further experimental work.

Query processing in annotated logic programming: Theory and implementation

Rationale Peripheral blood biomarkers are needed to identify and determine the extent of idiopathic pulmonary fibrosis (IPF). Current physiologic and radiographic prognostic indicators diagnose IPF too late in the course of disease. We hypothesize that peripheral blood biomarkers will identify disease in its early stages, and facilitate monitoring for disease progression. Methods Gene expression profiles of peripheral blood RNA from 130 IPF patients were collected on Agilent microarrays. Significance analysis of microarrays (SAM) with a false discovery rate (FDR) of 1% was utilized to identify genes that were differentially-expressed in samples categorized based on percent predicted DLCO and FVC. Main Measurements and Results At 1% FDR, 1428 genes were differentially-expressed in mild IPF (DLCO >65%) compared to controls and 2790 transcripts were differentially- expressed in severe IPF (DLCO >35%) compared to controls. When categorized by percent predicted DLCO, SAM demonstrated 13 differentially-expressed transcripts between mild and severe IPF (< 5% FDR). These include CAMP, CEACAM6, CTSG, DEFA3 and A4, OLFM4, HLTF, PACSIN1, GABBR1, IGHM, and 3 unknown genes. Principal component analysis (PCA) was performed to determine outliers based on severity of disease, and demonstrated 1 mild case to be clinically misclassified as a severe case of IPF. No differentially-expressed transcripts were identified between mild and severe IPF when categorized by percent predicted FVC. Conclusions These results demonstrate that the peripheral blood transcriptome has the potential to distinguish normal individuals from patients with IPF, as well as extent of disease when samples were classified by percent predicted DLCO, but not FVC.

Bounded Parameter Markov Decision Processes

Orofacial malformations resulting from genetic and/or environmental causes are frequent human birth defects yet their etiology is often unclear because of insufficient information concerning the molecular, cellular and morphogenetic processes responsible for normal facial development. We have, therefore, derived a comprehensive expression dataset for mouse orofacial development, interrogating three distinct regions – the mandibular, maxillary and frontonasal prominences. To capture the dynamic changes in the transcriptome during face formation, we sampled five time points between E10.5–E12.5, spanning the developmental period from establishment of the prominences to their fusion to form the mature facial platform. Seven independent biological replicates were used for each sample ensuring robustness and quality of the dataset. Here, we provide a general overview of the dataset, characterizing aspects of gene expression changes at both the spatial and temporal level. Considerable coordinate regulation occurs across the three prominences during this period of facial growth and morphogenesis, with a switch from expression of genes involved in cell proliferation to those associated with differentiation. An accompanying shift in the expression of polycomb and trithorax genes presumably maintains appropriate patterns of gene expression in precursor or differentiated cells, respectively. Superimposed on the many coordinated changes are prominence-specific differences in the expression of genes encoding transcription factors, extracellular matrix components, and signaling molecules. Thus, the elaboration of each prominence will be driven by particular combinations of transcription factors coupled with specific cell:cell and cell:matrix interactions. The dataset also reveals several prominence-specific genes not previously associated with orofacial development, a subset of which we externally validate. Several of these latter genes are components of bidirectional transcription units that likely share cis-acting sequences with well-characterized genes. Overall, our studies provide a valuable resource for probing orofacial development and a robust dataset for bioinformatic analysis of spatial and temporal gene expression changes during embryogenesis.

Cell Origin Dictates Programming of Resident versus Recruited Macrophages during Acute Lung Injury

BackgroundDetermining the function of uncharacterized proteins is a major challenge in the post-genomic era due to the problems complexity and scale. Identifying a proteins function contributes to an understanding of its role in the involved pathways, its suitability as a drug target, and its potential for protein modifications. Several graph-theoretic approaches predict unidentified functions of proteins by using the functional annotations of better-characterized proteins in protein-protein interaction networks. We systematically consider the use of literature co-occurrence data, introduce a new method for quantifying the reliability of co-occurrence and test how performance differs across species. We also quantify changes in performance as the prediction algorithms annotate with increased specificity.ResultsWe find that including information on the co-occurrence of proteins within an abstract greatly boosts performance in the Functional Flow graph-theoretic function prediction algorithm in yeast, fly and worm. This increase in performance is not simply due to the presence of additional edges since supplementing protein-protein interactions with co-occurrence data outperforms supplementing with a comparably-sized genetic interaction dataset. Through the combination of protein-protein interactions and co-occurrence data, the neighborhood around unknown proteins is quickly connected to well-characterized nodes which global prediction algorithms can exploit. Our method for quantifying co-occurrence reliability shows superior performance to the other methods, particularly at threshold values around 10% which yield the best trade off between coverage and accuracy. In contrast, the traditional way of asserting co-occurrence when at least one abstract mentions both proteins proves to be the worst method for generating co-occurrence data, introducing too many false positives. Annotating the functions with greater specificity is harder, but co-occurrence data still proves beneficial.ConclusionCo-occurrence data is a valuable supplemental source for graph-theoretic function prediction algorithms. A rapidly growing literature corpus ensures that co-occurrence data is a readily-available resource for nearly every studied organism, particularly those with small protein interaction databases. Though arguably biased toward known genes, co-occurrence data provides critical additional links to well-studied regions in the interaction network that graph-theoretic function prediction algorithms can exploit.