Virtual Trials: Causally-validated treatment effects efficiently learned from an observational cancer registry.

Randomized controlled trials (RCTs) offer a clear causal interpretation of treatment effects, but are inefficient in terms of information gain per patient. Moreover, because they are intended to test cohort-level effects, RCTs rarely provide information to support precision medicine, which strives to choose the best treatment for an individual patient. If causal information could be efficiently extracted from widely available real-world data, the rapidity of treatment validation could be increased, and its costs reduced.

Is Cancer Solvable? Towards Efficient and Ethical Biomedical Science.

Global Cumulative Treatment Analysis (GCTA) is a novel clinical research model combining expert knowledge, and treatment coordination based upon global information-gain, to treat every patient optimally while efficiently searching the vast space that is the realm of cancer research.

Prototyping a precision oncology 3.0 rapid learning platform.

Background: We describe a prototype implementation of a platform that could underlie a Precision Oncology Rapid Learning system.

Results: We describe the prototype platform, and examine some important issues and details. In the Appendix we provide a complete walk-through of the prototype platform.

Rapid learning for precision oncology.

The emerging paradigm of Precision Oncology 3.0 uses panomics and sophisticated methods of statistical reverse engineering to hypothesize the putative networks that drive a given patient's tumour, and to attack these drivers with combinations of targeted therapies. Here, we review a paradigm termed Rapid Learning Precision Oncology wherein every treatment event is considered as a probe that simultaneously treats the patient and provides an opportunity to validate and refine the models on which the treatment decisions are based.

Groups: knowledge spreadsheets for symbolic biocomputing.

Knowledge spreadsheets (KSs) are a visual tool for interactive data analysis and exploration. They differ from traditional spreadsheets in that rather than being oriented toward numeric data, they work with symbolic knowledge representation structures and provide operations that take into account the semantics of the application domain. 'Groups' is an implementation of KSs within the Pathway Tools system.

A novel classification of lung cancer into molecular subtypes.

The remarkably heterogeneous nature of lung cancer has become more apparent over the last decade. In general, advanced lung cancer is an aggressive malignancy with a poor prognosis. The discovery of multiple molecular mechanisms underlying the development, progression, and prognosis of lung cancer, however, has created new opportunities for targeted therapy and improved outcome.

A melanoma molecular disease model.

While advanced melanoma remains one of the most challenging cancers, recent developments in our understanding of the molecular drivers of this disease have uncovered exciting opportunities to guide personalized therapeutic decisions. Genetic analyses of melanoma have uncovered several key molecular pathways that are involved in disease onset and progression, as well as prognosis. These advances now make it possible to create a "Molecular Disease Model" (MDM) for melanoma that classifies individual tumors into molecular subtypes (in contrast to traditional histological subtypes), with proposed treatment guidelines for each subtype including specific assays, drugs, and clinical trials.

Targeted Therapy Database (TTD): a model to match patient's molecular profile with current knowledge on cancer biology.

Background: The efficacy of current anticancer treatments is far from satisfactory and many patients still die of their disease. A general agreement exists on the urgency of developing molecularly targeted therapies, although their implementation in the clinical setting is in its infancy. In fact, despite the wealth of preclinical studies addressing these issues, the difficulty of testing each targeted therapy hypothesis in the clinical arena represents an intrinsic obstacle.

The promise and perils of pre-publication review: a multi-agent simulation of biomedical discovery under varying levels of review stringency.

The Internet has enabled profound changes in the way science is performed, especially in scientific communications. Among the most important of these changes is the possibility of new models for pre-publication review, ranging from the current, relatively strict peer-review model, to entirely unreviewed, instant self-publication. Different models may affect scientific progress by altering both the quality and quantity of papers available to the research community.

Soccer science and the Bayes community: exploring the cognitive implications of modern scientific communication.

Science is a form of distributed analysis involving both individual work that produces new knowledge and collaborative work to exchange information with the larger community. There are many particular ways in which individual and community can interact in science, and it is difficult to assess how efficient these are, and what the best way might be to support them. This paper reports on a series of experiments in this area and a prototype implementation using a research platform called CACHE.

BioBIKE: a Web-based, programmable, integrated biological knowledge base.

BioBIKE (biobike.csbc.vcu.

Extraction of Conditional Probabilities of the Relationships Between Drugs, Diseases, and Genes from PubMed Guided by Relationships in PharmGKB.

Guided by curated associations between genes, treatments (i.e., drugs), and diseases in pharmGKB, we constructed n-way Bayesian networks based on conditional probability tables (cpt's) extracted from co-occurrence statistics over the entire Pubmed corpus, producing a broad-coverage analysis of the relationships between these biological entities.

UNDERSTANDING NITROGEN LIMITATION IN AUREOCOCCUS ANOPHAGEFFERENS (PELAGOPHYCEAE) THROUGH cDNA AND qRT-PCR ANALYSIS(1).

Brown tides of the marine pelagophyte Aureococcus anophagefferens Hargraves et Sieburth have been investigated extensively for the past two decades. Its growth is fueled by a variety of nitrogen (N) compounds, with dissolved organic nitrogen (DON) being particularly important during blooms. Characterization of a cDNA library suggests that A.

Alternative photosynthetic electron flow to oxygen in marine Synechococcus.

Cyanobacteria dominate the world's oceans where iron is often barely detectable. One manifestation of low iron adaptation in the oligotrophic marine environment is a decrease in levels of iron-rich photosynthetic components, including the reaction center of photosystem I and the cytochrome b6f complex [R.F.

Deductive biocomputing.

Background: As biologists increasingly rely upon computational tools, it is imperative that they be able to appropriately apply these tools and clearly understand the methods the tools employ. Such tools must have access to all the relevant data and knowledge and, in some sense, "understand" biology so that they can serve biologists' goals appropriately and "explain" in biological terms how results are computed.

Methodology/principal Findings: We describe a deduction-based approach to biocomputation that semiautomatically combines knowledge, software, and data to satisfy goals expressed in a high-level biological language.

EST assembly supported by a draft genome sequence: an analysis of the Chlamydomonas reinhardtii transcriptome.

Clustering and assembly of expressed sequence tags (ESTs) constitute the basis for most genomewide descriptions of a transcriptome. This approach is limited by the decline in sequence quality toward the end of each EST, impacting both sequence clustering and assembly. Here, we exploit the available draft genome sequence of the unicellular green alga Chlamydomonas reinhardtii to guide clustering and to correct errors in the ESTs.

Constructing explanatory process models from biological data and knowledge.

Objective: We address the task of inducing explanatory models from observations and knowledge about candidate biological processes, using the illustrative problem of modeling photosynthesis regulation.

Methods: We cast both models and background knowledge in terms of processes that interact to account for behavior. We also describe IPM, an algorithm for inducing quantitative process models from such input.

Generation of an oligonucleotide array for analysis of gene expression in Chlamydomonas reinhardtii.

The availability of genome sequences makes it possible to develop microarrays that can be used for profiling gene expression over developmental time, as organisms respond to environmental challenges, and for comparison between wild-type and mutant strains under various conditions. The desired characteristics of microarrays (intense signals, hybridization specificity and extensive coverage of the transcriptome) were not fully met by the previous Chlamydomonas reinhardtii microarray: probes derived from cDNA sequences (approximately 300 bp) were prone to some nonspecific cross-hybridization and coverage of the transcriptome was only approximately 20%. The near completion of the C.

Insights into the survival of Chlamydomonas reinhardtii during sulfur starvation based on microarray analysis of gene expression.

Responses of photosynthetic organisms to sulfur starvation include (i) increasing the capacity of the cell for transporting and/or assimilating exogenous sulfate, (ii) restructuring cellular features to conserve sulfur resources, and (iii) modulating metabolic processes and rates of cell growth and division. We used microarray analyses to obtain a genome-level view of changes in mRNA abundances in the green alga Chlamydomonas reinhardtii during sulfur starvation. The work confirms and extends upon previous findings showing that sulfur deprivation elicits changes in levels of transcripts for proteins that help scavenge sulfate and economize on the use of sulfur resources.

BioLingua: a programmable knowledge environment for biologists.

Unlabelled: BioLingua is an interactive, web-based programming environment that enables biologists to analyze biological systems by combining knowledge and data through direct end-user programming. BioLingua embeds a mature symbolic programming language in a frame-based knowledge environment, integrating genomic and pathway knowledge about a class of similar organisms. The BioLingua language provides interfaces to numerous state-of-the-art bioinformatic tools, making these available as an integrated package through the novel use of web-based programmability and an integrated Wiki-based community code and data store.

Chlamydomonas reinhardtii genome project. A guide to the generation and use of the cDNA information.

The National Science Foundation-funded Chlamydomonas reinhardtii genome project involves (a) construction and sequencing of cDNAs isolated from cells exposed to various environmental conditions, (b) construction of a high-density cDNA microarray, (c) generation of genomic contigs that are nucleated around specific physical and genetic markers, (d) generation of a complete chloroplast genome sequence and analyses of chloroplast gene expression, and (e) the creation of a Web-based resource that allows for easy access of the information in a format that can be readily queried. Phases of the project performed by the groups at the Carnegie Institution and Duke University involve the generation of normalized cDNA libraries, sequencing of cDNAs, analysis and assembly of these sequences to generate contigs and a set of predicted unique genes, and the use of this information to construct a high-density DNA microarray. In this paper, we discuss techniques involved in obtaining cDNA end-sequence information and the ways in which this information is assembled and analyzed.

Guiding revision of regulatory models with expression data.

BioLingua is a computational system designed to support biologists' efforts to construct models, make predictions, and interpret data. In this paper, we focus on the specific task of revising an initial model of gene regulation based on expression levels from gene microarrays. We describe BioLingua's formalism for representing process models, its method for predicting qualitative correlations from such models, and its use of data to constrain search through the space of revised models.