Toni Kazic

Image Analysis for Mapping Immeasurable Phenotypes in Maize [Life Sciences]

This work will allow bio-informaticians to analyze the ever-increasing gene sequence data, discover valuable knowledge in maize biology and related plant; development, and understand subtle variations among different phenotypes. Furthermore, successful measuring of visual phenotypes will advance plant research by finding the genes and/or environmental factors that cause a given visual phenotype. In what follows, the field of plant genetics is introduced (particularly quantitative trait loci and disease scoring) to the signal processing community, discuss the challenges involved, and present an image analysis system for precisely quantifying and mapping immeasurable phenotypes in maize

HIV-associated dementia, Alzheimer's disease, multiple sclerosis, and schizophrenia: gene expression review.

RNA and protein gene expression technologies are revolutionizing our view and understanding of human diseases and enable us to analyze the concurrent expression patterns of large numbers of genes. These new technologies allow simultaneous study of thousands of genes and their changes in regulation and modulation patterns in relation to disease state, time, and tissue specificity. This review summarizes the application of this modern technology to four common neurological and psychiatric disorders: HIV-1-associated dementia, Alzheimers disease, multiple sclerosis, and schizophrenia and is a first comparison of these diseases using this approach.

Semiotes: a semantics for sharing

MOTIVATION Reliable, automated communication of biological information requires methods to declare the informations semantics. In this paper I describe an approach to semantic declaration intended to permit independent, distributed databases, algorithms, and servers to exchange and process requests for information and computations without requiring coordination or agreement among them on universe of discourse, data model, schema, or implementation. RESULTS This approach uses Glossa, a formal language defining the semantics of biological ideas, information, and algorithms, to executably define the semantics of complex ideas and computations by constructs of semiotes, terms which axiomatically define very simple notions. A database or algorithm wishing to exchange information or computations maintains a set of mappings between its particular notions and semiotes, and a parser to translate between its indigenous ideas and implementation and the semiotes. Requests from other databases or algorithms are issued as semiotic messages, locally interpreted and processed, and the results returned as semiotes to the requesting entity. Thus, semiotes serve as a shared, abstract layer of definitions which can be computably combined by each database or algorithm according to its own needs and ideas. By combining the explicit declaration of semantics with the computation of the semantics of complex ideas, Glossa and its semiotes permit independent computational entities to lightly federate their capabilities as desired while maintaining their unique perspectives on both scientific and technical questions.

Putting semantics into the semantic web: how well can it capture biology?

Could the Semantic Web work for computations of biological interest in the way its intended to work for movie reviews and commercial transactions? It would be wonderful if it could, so its worth looking to see if its infrastructure is adequate to the job. The technologies of the Semantic Web make several crucial assumptions. I examine those assumptions; argue that they create significant problems; and suggest some alternative ways of achieving the Semantic Webs goals for biology.

An opinion on imaging challenges in phenotyping field crops

Almost all the world’s food is grown in open fields, where plant phenotypes can be very different from those observed in greenhouses. Geneticists and agronomists studying food crops routinely detect, measure, and classify a wide variety of phenotypes in fields that contain many visually distinct types of a single crop. Augmenting humans in these tasks by automatically interpreting images raises some important and nontrivial challenges for research in computer vision. Nonetheless, the rewards for overcoming these obstacles could be exceptionally high for today’s 7 billion people, let alone the 9.6 billion projected by 2050 (United Nations Department of Economic and Social Affairs, Population Division, World Population Prospects: The 2012 Revision). To stimulate dialog between researchers in computer vision and those in genetics and agronomy, we offer our views on three computational challenges that are central to many phenotyping tasks. These are disambiguating one plant from another; assigning an individual plant’s organs to it; and identifying field phenotypes from those shown in archival images. We illustrate these challenges with annotated photographs of maize highlighting the regions of interest. We also describe some of the experimental, logistical, and photographic constraints on image collection and processing. While collecting the data sets needed for algorithmic experiments requires sustained collaboration and funding, the images we show and have posted should allow one to consider the problems, think of possible approaches, and decide on the next steps.

Ten Simple Rules for Experiments' Provenance.

Everyone needs experimental data to understand biology. Exactly how and from what the data were obtained determines an experiment’s results, specifies how it can be reproduced, and conditions our analyses and interpretations. These details of materials, methods, and analyses are the experiment’s provenance.

Bioinformatics models in drug abuse and Neuro-AIDS: Using and developing databases

The magnitude of the problems of drug abuse and Neuro-AIDS warrants the development of novel approaches for testing hypotheses in diagnosis and treatment ranging from cell culture models to developing databases. In this study, cultured neurons were treated with/without HIV-TAT, ENV, or cocaine in a 2x2x2 expression study design. RNA was purified, labeled, and expression data were produced and analyzed using ANOVA. Thus, we identified 35 genes that were significantly expressed across treatment conditions. A diagram is presented showing examples of molecular relationships involving a significantly expressed gene in the current study (SOX2). Also, we use this information to discuss examples of gene expression interactions as a means to portray significance and complexity of gene expression studies in Drug Abuse and Neuro-AIDS. Furthermore, we discuss here that critical interactions remain undetected, which may be unravelled by developing robust database systems containing large datasets and gleaned information from collaborating scientists . Hence, we are developing a public domain database we named The Agora database , that will served as a shared infrastructure to query, deposit, and review information related to drug abuse and dementias including Neuro-AIDS. A workflow of this database is also outlined in this paper.

Gene Expression in HIV-Associated Dementia

In past decades, the prime methods used for the study of genes and their respective products were Northern blots, Western blots, in situ hybridization, and immunocytochemistry. The applicability of these methods was restricted to the study of single genes or a few simultaneously (1,2).

Factors Influencing the Adoption of the Semantic Web in the Life Sciences

The Semantic Web today is a vision of transparent search, request, manipulation, and delivery of information to the user by an interconnected set of services. This vision would change the way scientists interact with data, computations, and even each other. Realizing it begins with understanding the needs of biologists and the dynamic continuum of factors that will determine whether, in what form, and at what rate the Semantic Web is likely to be adopted as a scientific tool by this community. In this chapter I look at this continuum and hazard some predictions.

Whither biological database research

We consider how the landscape of biological databases may evolve in the future, and what research is needed to realize this evolution. We suggest todays dispersal of diverse resources will only increase as the number and size of those resources, driving the need for semantic interoperability even more strongly. Because the complexity of the questions biologists want answered automatically continues to rapidly escalate, we will need to draw upon high-performance computing resources such as the GRID to process complex queries. Finally, we still need data, and our ways of acquiring and curating data must improve by orders of magnitude.