Biomedical subjects

Daphne Koller

Publications and source records attributed to Daphne Koller.

7 recordsLinked to original sources

PubMed2005

From signatures to models: understanding cancer using microarrays.

Genomics has the potential to revolutionize the diagnosis and management of cancer by offering an unprecedented comprehensive view of the molecular underpinnings of pathology. Computational analysis is essential to transform the masses of generated data into a mechanistic understanding of disease. Here we review current research aimed at uncovering the modular organization and function of transcriptional networks and responses in cancer. We first describe how methods that analyze biological processes in terms of higher-level modules can identify robust signatures of disease mechanisms. We then discuss methods that aim to identify the regulatory mechanisms underlying these modules and processes. Finally, we show how comparative analysis, combining human data with model organisms, can lead to more robust findings. We conclude by discussing the challenges of generalizing these methods from cells to tissues and the opportunities they offer to improve cancer diagnosis and management.

Algorithms↗

PubMed2005

Probabilistic discovery of overlapping cellular processes and their regulation.

In this paper, we explore modeling overlapping biological processes. We discuss a probabilistic model of overlapping biological processes, gene membership in those processes, and an addition to that model that identifies regulatory mechanisms controlling process activation. A key feature of our approach is that we allow genes to participate in multiple processes, thus providing a more biologically plausible model for the process of gene regulation. We present algorithms to learn each model automatically from data, using only genomewide measurements of gene expression as input. We compare our results to those obtained by other approaches and show that significant benefits can be gained by modeling both the organization of genes into overlapping cellular processes and the regulatory programs of these processes. Moreover, our method successfully grouped genes known to function together, recovered many regulatory relationships that are known in the literature, and suggested novel hypotheses regarding the regulatory role of previously uncharacterized proteins.

Computational Biology↗

PubMed2004

A module map showing conditional activity of expression modules in cancer.

DNA microarrays are widely used to study changes in gene expression in tumors, but such studies are typically system-specific and do not address the commonalities and variations between different types of tumor. Here we present an integrated analysis of 1,975 published microarrays spanning 22 tumor types. We describe expression profiles in different tumors in terms of the behavior of modules, sets of genes that act in concert to carry out a specific function. Using a simple unified analysis, we extract modules and characterize gene-expression profiles in tumors as a combination of activated and deactivated modules. Activation of some modules is specific to particular types of tumor; for example, a growth-inhibitory module is specifically repressed in acute lymphoblastic leukemias and may underlie the deregulated proliferation in these cancers. Other modules are shared across a diverse set of clinical conditions, suggestive of common tumor progression mechanisms. For example, the bone osteoblastic module spans a variety of tumor types and includes both secreted growth factors and their receptors. Our findings suggest that there is a single mechanism for both primary tumor proliferation and metastasis to bone. Our analysis presents multiple research directions for diagnostic, prognostic and therapeutic studies.

Databases, Genetic↗

PubMed2004

Sfp1 is a stress- and nutrient-sensitive regulator of ribosomal protein gene expression.

Yeast cells modulate their protein synthesis capacity in response to physiological needs through the transcriptional control of ribosomal protein (RP) genes. Here we demonstrate that the transcription factor Sfp1, previously shown to play a role in the control of cell size, regulates RP gene expression in response to nutrients and stress. Under optimal growth conditions, Sfp1 is localized to the nucleus, bound to the promoters of RP genes, and helps promote RP gene expression. In response to inhibition of target of rapamycin (TOR) signaling, stress, or changes in nutrient availability, Sfp1 is released from RP gene promoters and leaves the nucleus, and RP gene transcription is down-regulated. Additionally, cells lacking Sfp1 fail to appropriately modulate RP gene expression in response to environmental cues. We conclude that Sfp1 integrates information from nutrient- and stress-responsive signaling pathways to help control RP gene expression.

Cyclic AMP-Dependent Protein Kinases↗

PubMed2004

Understanding tuberculosis epidemiology using structured statistical models.

Molecular epidemiological studies can provide novel insights into the transmission of infectious diseases such as tuberculosis. Typically, risk factors for transmission are identified using traditional hypothesis-driven statistical methods such as logistic regression. However, limitations become apparent in these approaches as the scope of these studies expand to include additional epidemiological and bacterial genomic data. Here we examine the use of Bayesian models to analyze tuberculosis epidemiology. We begin by exploring the use of Bayesian networks (BNs) to identify the distribution of tuberculosis patient attributes (including demographic and clinical attributes). Using existing algorithms for constructing BNs from observational data, we learned a BN from data about tuberculosis patients collected in San Francisco from 1991 to 1999. We verified that the resulting probabilistic models did in fact capture known statistical relationships. Next, we examine the use of newly introduced methods for representing and automatically constructing probabilistic models in structured domains. We use statistical relational models (SRMs) to model distributions over relational domains. SRMs are ideally suited to richly structured epidemiological data. We use a data-driven method to construct a statistical relational model directly from data stored in a relational database. The resulting model reveals the relationships between variables in the data and describes their distribution. We applied this procedure to the data on tuberculosis patients in San Francisco from 1991 to 1999, their Mycobacterium tuberculosis strains, and data on contact investigations. The resulting statistical relational model corroborated previously reported findings and revealed several novel associations. These models illustrate the potential for this approach to reveal relationships within richly structured data that may not be apparent using conventional statistical approaches. We show that Bayesian methods, in particular statistical relational models, are an important tool for understanding infectious disease epidemiology.

Adult↗

PubMed2003

A gene-coexpression network for global discovery of conserved genetic modules.

To elucidate gene function on a global scale, we identified pairs of genes that are coexpressed over 3182 DNA microarrays from humans, flies, worms, and yeast. We found 22,163 such coexpression relationships, each of which has been conserved across evolution. This conservation implies that the coexpression of these gene pairs confers a selective advantage and therefore that these genes are functionally related. Many of these relationships provide strong evidence for the involvement of new genes in core biological functions such as the cell cycle, secretion, and protein expression. We experimentally confirmed the predictions implied by some of these links and identified cell proliferation functions for several genes. By assembling these links into a gene-coexpression network, we found several components that were animal-specific as well as interrelationships between newly evolved and ancient modules.

Animals↗

PubMed2003

Module networks: identifying regulatory modules and their condition-specific regulators from gene expression data.

Much of a cell's activity is organized as a network of interacting modules: sets of genes coregulated to respond to different conditions. We present a probabilistic method for identifying regulatory modules from gene expression data. Our procedure identifies modules of coregulated genes, their regulators and the conditions under which regulation occurs, generating testable hypotheses in the form 'regulator X regulates module Y under conditions W'. We applied the method to a Saccharomyces cerevisiae expression data set, showing its ability to identify functionally coherent modules and their correct regulators. We present microarray experiments supporting three novel predictions, suggesting regulatory roles for previously uncharacterized proteins.

Algorithms↗