Another Word For It Patrick Durusau on Topic Maps and Semantic Diversity

Humanizing Bioinformatics by Saaien Tist.

From the post:

I was invited last week to give a talk at this year’s meeting of the Graduate School Structure and Function of Biological Macromolecules, Bioinformatics and Modeling (SFMBBM). It ended up being a day with great talks, by some bright PhD students and postdocs. There were 2 keynotes (one by Prof Bert Poolman from Groningen (NL) and one by myself), and a panel discussion on what the future holds for people nearing the end of their PhDs.

My talk was titled “Humanizing Bioinformatics” and received quite well (at least some people still laughed at my jokes (if you can call them that); even at the end). I put the slides up on slideshare, but I thought I’d explain things here as well, because those slides will probably not convey the complete story.

Let’s ruin the plot by mentioning it here: we need data visualization to counteract the alienation that’s happening between bioinformaticians and bright data miners on the one hand, and the user/clinician/biologist on the other. We need to make bioinformatics human again. (emphasis in original)

I just wish there had been a video recording of this presentation!

Questions:

1. Do you agree with the issues that Saalen raises? Are there more that you would raise? 2-3 pages (no citations)
2. Have “semantics” become what can be evaluated by a computer? Pick yes, no, undecided and cite web examples for your position. 2-3 pages
3. How much do you trust the answers to your searches? (Classroom discussion question.)

Computational Omics and Systems Biology Group

From the webpage:

>Introduction

The Computational Omics and Systems Biology Group, headed by Prof. Dr. Lennart Martens, is part of the Department of Biochemistry of the Faculty of Medicine and Health Sciences of Ghent University, and the Department of Medical Protein Research of VIB, both in Ghent, Belgium.

The group has its roots in Ghent, but has active members all over Europe, and specializes in the management, analysis and integration of high-throughput data (as obtained from various Omics approaches) with an aim towards establishing solid data stores, processing methods and tools to enable downstream systems biology research.

A major source of open source software, standards and other work.

Sage Bionetworks Synapse Project – Webinar – Weds. 7 Sept. 2011

Call-in Details:

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To join the online meeting (Now from mobile devices!)
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1. Go to https://stanford.webex.com/stanford/j.php?ED=107799137&UID=0&PW=NNjE3OWYzODk3&RT=MiM0
2. If requested, enter your name and email address.
3. If a password is required, enter the meeting password: ncbo
4. Click “Join”.

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To join the audio conference only
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To receive a call back, provide your phone number when you join the meeting, or call the number below and enter the access code.
Call-in toll number (US/Canada): 1-650-429-3300
Global call-in numbers: https://stanford.webex.com/stanford/globalcallin.php?serviceType=MC&ED=107799137&tollFree=0

Access code:926 719 478

Abstract:

The recent exponential growth of biological “omics” data has occurred concurrently with a decline in the number of New Molecular Entities approved by the FDA, proving that biological research productivity does not scale with biological data generation and the analysis and interpretation of genomic data is a bottleneck in the development of new treatments. Sage Bionetworks’ mission is to catalyze a cultural transition from the traditional single lab, single-company, and single-therapy R&D paradigm to a model with broad precompetitive collaboration on the analysis of large scale data in medical sciences. Part of Sage’s solution is Synapse, a platform for open, reproducible data-driven science, which will support the reusability of information facilitated by ontology-based services and applications directed at scientific researchers and data curators. Sage Bionetworks is actively pursuing the acquisition, curation, statistical quality control, and hosting of datasets that integrate both clinical phenotype and genomic data along with an intermediate molecular layer such as gene expression or proteomic data. We expect hosting these sorts of unique, integrative, high value datasets in the public domain on Synapse will seed a variety of analytical approaches to drive new treatments based on better understanding of disease states and the biological effects of existing drugs. In this webinar, Dr. Michael Kellen, Director of Technology at Sage Bionetworks will provide a demonstration of an alpha version of the Synapse platform, and discuss its application to clinical science.

Interesting claim about the decline in the number of New Molecular Entities (NMEs) approved by the FDA, see: NMEs approved by CDER. Approvals are on average about the same. But then applications for NMEs have to be filed in order to be approved.

Just for background reading, you might want to look at: New Chemical Entity over at Wikipedia.

Or, The Scope of New Chemical Entity Exclusivity and FDA’s “Umbrella” Exclusivity Policy

I don’t disagree that better data analysis tools are needed but remain puzzled what the FDA approval rate for NMEs has to do with the problem.

Bio-recipes (Bioinformatics recipes) in Darwin

If you are working on topic maps and bioinformatics, you are likely to find this a useful resource.

From the webpage:

Bio-recipes are a collection of Darwin example programs. They show how to solve standard problems in Bioinformatics. Each bio-recipe consists of an introduction, explanations, graphs, figures, and most importantly, Darwin commands (the input commands and the output that they produce) that solve the given problem.

Darwin is an interactive language of the same lineage as Maple designed to solve problems in Bioinformatics. It relies on a simple language for the interactive user, plus the infrastructure necessary for writing object oriented libraries, plus very efficient primitive operations. The primitive operations of Darwin are the most common and time consuming operations typical of bioinformatics, including linear algebra operations.

The reasons behind this particular format are the following.

1. It is much easier to understand an algorithm or a procedure or even a theorem, when it is illustrated with a running example.
2. The procedures, as written, may be run on different data and hence serve a useful purpose.
3. It is an order of magnitude easier to modify a correct, existing program, than to write a new one from scratch. This is particularly true for non-computer scientists.
4. The full examples show some features of the language and of the system that may not known to the casual user of the Darwin, hence they serve a tutorial purpose.

BTW, see also:

DARWIN – A Genetic Algorithm Programming Language

The Darwin Manual

Virtual Cell Software Repository

From the webpage:

Developing large volume multi-scale systems dynamics interpretation technology is very important source for making virtual cell application systems. Also, this technology is focused on the core research topics in a post-genome era in order to maintain the national competitive power. It is new analysis technology which can analyze multi-scale from nano level to physiological level in system level. Therefore, if using excellent information technology and super computing power in our nation, we can hold a dominant position in the large volume multi-scale systems dynamics interpretation technology. In order to take independent technology, we need to research a field of study which have been not well known in the bio system informatics technology like the large volume multi-scale systems dynamics interpretation technology.

The purpose of virtual cell application systems is developing the analysis technology and service which can model bio application circuits based on super computing technology. For success of virtual cell application systems based on super computing power, we have researched large volume multi-scale systems dynamics technology as a core sub technology.

• Developing analysis and modeling technology of multi-scale convergence information from nano level to physiological level
• Developing protein structure modeling algorithm using multi-scale bio information
• Developing quality and quantity character analysis technology of multi-scale networks
• Developing protein modification search algorithm
• Developing large volume multi-scale systems dynamics interpretation technology interpreting possible circumstances in complex parameter spaces

Amazing set of resources available here:

PSExplorer: Parameter Space Explorer

Mathematical models of biological systems often have a large number of parameters whose combinational variations can yield distinct qualitative behaviors. Since it is intractable to examine all possible combinations of parameters for nontrivial biological pathways, it is required to have a systematic way to explore the parameter space in a computational way so that distinct dynamic behaviors of a given pathway are estimated.

We present PSExplorer, an efficient computational tool to explore high dimensional parameter space of computational models for identifying qualitative behaviors and key parameters. The software supports input models in SBML format. It provides a friendly graphical user interface allowing users to vary model parameters and perform time-course simulations at ease. Various graphical plotting features helps users analyze the model dynamics conveniently. Its output is a tree structure that encapsulates the parameter space partitioning results in a form that is easy to visualize and provide users with additional information about important parameters and sub-regions with robust behaviors.

MONET: MOdularized NETwork learning

Although gene expression data has been continuously accumulated and meta-analysis approaches have been developed to integrate independent expression profiles into larger datasets, the amount of information is still insufficient to infer large scale genetic networks. In addition, global optimization such as Bayesian network inference, one of the most representative techniques for genetic network inference, requires tremendous computational load far beyond the capacity of moderate workstations.

MONET is a Cytoscape plugin to infer genome-scale networks from gene expression profiles. It alleviates the shortage of information by incorporating pre-existing annotations. The current version of MONET utilizes thousands of parallel computational cores in the supercomputing center in KISTI, Korea, to cope with the computational requirement for large scale genetic network inference.

RBSDesigner

RBS Designer was developed to computationally design synthetic ribosome binding sites (RBS) to control gene expression levels. Generally transcription processes are the major target for gene expression control, however, without considering translation processes the control could lead to unexpected expression results since translation efficiency is highly affected by nucleotide sequences nearby RBS such as coding sequences leading to distortion of RBS secondary structure. Such problems obscure the intuitive design of RBS nucleotides with a desired level of protein expression. We developed RBSDesigner based on a mathematical model on translation initiation to design synthetic ribosome binding sites that yield a desired level of expression of user-specified coding sequences.

SBN simulator: Switching Boolean Networks Simulator

Switching Boolean Networks Simulator(SBNsimulator) was developed to simulate large-scale signaling network. Boolean Networks is widely used in modeling signaling networks because of its straightforwardness, robustness, and compatibility with qualitative data. Signaling networks are not completely known yet in Biology. Because of this, there are gaps between biological reality and modeling such as inhibitor-only or activator-only in signaling networks. Synchronous update algorithm in threshold Boolean network has limitation which cannot sample differences in the speed of signal propagation. To overcome these limitation which are modeling anomaly and Limitation of synchronous update algorithm, we developed SBNsimulator. It can simulate how each node effect to target node. Therefore, It can say which node is important for signaling network.

MKEM: Multi-level Knowledge Emergence Model

Since Swanson proposed the Undiscovered Public Knowledge (UPK) model, there have been many approaches to uncover UPK by mining the biomedical literature. These earlier works, however, required substantial manual intervention to reduce the number of possible connections and are mainly applied to disease-effect relation. With the advancement in biomedical science, it has become imperative to extract and combine information from multiple disjoint researches, studies and articles to infer new hypotheses and expand knowledge. We propose MKEM, a Multi-level Knowledge Emergence Model, to discover implicit relationships using Natural Language Processing techniques such as Link Grammar and Ontologies such as Unified Medical Language System (UMLS) MetaMap. The contribution of MKEM is as follows: First, we propose a flexible knowledge emergence model to extract implicit relationships across different levels such as molecular level for gene and protein and Phenomic level for disease and treatment. Second, we employ MetaMap for tagging biological concepts. Third, we provide an empirical and systematic approach to discover novel relationships.

The system constitutes of two parts, tagger and the extractor (may require compilation)

A sentence of interest is given to the tagger which then proceeds to the creation of rule sets. The tagger stores this in a folder by the name of “ruleList”. These rule sets are then given by copying this folder to the extractor directory.

I blogged about an article on this project at: MKEM: a Multi-level Knowledge Emergence Model for mining undiscovered public knowledge.

NCBI Handbook

From the website:

Bioinformatics consists of a computational approach to biomedical information management and analysis. It is being used increasingly as a component of research within both academic and industrial settings and is becoming integrated into both undergraduate and postgraduate curricula. The new generation of biology graduates is emerging with experience in using bioinformatics resources and, in some cases, programming skills.

The National Center for Biotechnology Information (NCBI) is one of the world’s premier Web sites for biomedical and bioinformatics research. Based within the National Library of Medicine at the National Institutes of Health, USA, the NCBI hosts many databases used by biomedical and research professionals. The services include PubMed, the bibliographic database; GenBank, the nucleotide sequence database; and the BLAST algorithm for sequence comparison, among many others.

Although each NCBI resource has online help documentation associated with it, there is no cohesive approach to describing the databases and search engines, nor any significant information on how the databases work or how they can be leveraged, for bioinformatics research on a larger scale. The NCBI Handbook is designed to address this information gap.

An extraordinary resource for learning about bioinformatics information sources.