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ISMB/ECCB 2011
International Conference on Intelligent Systems for Molecular Biology
The talk specific feeds will be created each day shortly before the start of the first presentation. Find talk specific blogs by searching here for the title of the talk or the talk identifier as given in the program (like HL03 for the 3rd Highlight paper). The feeds can also be accessed on the conference pages in the according sections: SIGs, Keynotes, LBR, Proceedings, Tech Track and Highlights.
Happy blogging !
The talk specific feeds will be created each day shortly before the start of the first presentation. Find talk specific blogs by searching here for the title of the talk or the talk identifier as given in the program (like HL03 for the 3rd Highlight paper). The feeds can also be accessed on the conference pages in the according sections: SIGs, Keynotes, LBR, Proceedings, Tech Track and Highlights.
Happy blogging !
TT30: Mickael Goujon - Using the EMBL-EBI services through the JDispatcher framework
The Slim Donkey liked this
http://www.ebi.ac.uk/Tools...
- Hamish McWilliam
http://www.ebi.ac.uk/Tools...
- Hamish McWilliam
http://www.ebi.ac.uk/Tools...
- Hamish McWilliam
http://www.ebi.ac.uk/Tools...
- Hamish McWilliam
TT41: Desmond Higgins - Clustal Omega for Protein Multiple Sequence Alignment
Surehy Breter liked this
Keynote: Michael Ashburner - From sequences to ontologies - adventures in informatics
The Slim Donkey, Anne Tuukkanen, Venkata P. Satagopam and Roland Krause liked this
"The father of ontologies in biology" speaks without slides.
- Roland Krause
One well turned phrase is worth a thousand power points....
- Shannon McWeeney
Almost 50 years after he started his undergrad in Cambridge in geology to venture into paleontology. His developing interest in zoology was not matched by the respective department. Was asked to be the chairman of the department.
- Roland Krause
PhD on Drosophila in Cambridge, PostDoc at Caltech.
- Roland Krause
Stresse the importance of God-father for young scientists as role model etc.
- Roland Krause
No problems in funding in the 60s and early 70s.
- Roland Krause
Six month in Bruce Alberts lab. # not so easy to keep up with all the great people that Michael Ashburner worked with
- Roland Krause
No knowledge of repetitive DNA, formulation of the C-value paradox.
- Roland Krause
Work on Drosophila alcohol dehydrogenase spanning 20 years.
- Roland Krause
Sequencing of ADH of 4 species using radionucleotides led to a PhD and a Nature paper. Almost no software available, major hardware incompatibility. Only one ARPAnet node in Europe at the University London.
- Roland Krause
Initially 120 baud bandwidth.
- Roland Krause
1983, first version of the EMBL database, came on magnetic tape 60kB. First thing: Print and read annotations.
- Roland Krause
at that time size is 60kb
- Venkata P. Satagopam
No relational integrity, lots of integrity. Moaning about it led to a position on the advisory board.
- Roland Krause
Promoter of the establishment of the EBI at the Cambridge site. No genomics at the EMBL in the 80s. Raised 30 M GPB to convince the EMBL council.
- Roland Krause
Gopher!
- Roland Krause
Flybase establishment. Built in Sybase, output in files, distribution via Gopher. Later, contact with Amos Bairoch and Expasy led to use of a webserver.
- Roland Krause
AceDB memories.
- Roland Krause
Hierarchical, structured language use in FlyBase, extension to other model organisms.
- Roland Krause
Presented in 1997 at the ISMB in Greece.
- Roland Krause
Whitepaper for the 1998 in Montreal. Lot's of resistance from various groups.
- Roland Krause
Gene Ontology sealed in 1999, support from the yeast and mouse databases.
- Roland Krause
Incyte had a patent on controlled vocabulary. (Utter not reproducable here)
- Roland Krause
Drosophila genome project, finalized in 6 month, genome annotation jamboree.
- Roland Krause
PT46: Julia Sivriver - An Integrative Clustering and Modeling Algorithm for Dynamical Gene Expression Data
Surehy Breter liked this
Transcriptional response with wide dynamical range. Is it enough to only know the TFs? No, need to know the answer times etc. Cannot ask the genes, yield only expression at one point.
- Roland Krause
How to come to the dynamical response? Fitting to a model. Early models by Chechik et al. JCB, 2009 had 7 parameters (peak height, onset and offset rates etc), can be learned by conjugate gradient descent.
- Roland Krause
Too few time points lead to overfitting.
- Roland Krause
We can cluster time course data as not every gene has its own dynamical prototype. Can work on the clusters for parameter learning. Traditional distance measures don't work well.
- Roland Krause
Use the clusters to extract dynamical prototypes to get model parameters, then use the model for cluster. Start with naive k-means, iterate until satisfaction.
- Roland Krause
Fitting to priors (prototypes), generate model and paramters.
- Roland Krause
Validation by synthetic data, add noise to expression data. Compare to k-means using euclidian and correlation distance. Use mutual information for assessment, works comparable.
- Roland Krause
Model avoids overfitting, median fit error compared to Chechik et al.
- Roland Krause
Case study: response to LPS and PolyIC in mice, Amit et al , Science 2009, 24h study. Chose 1000 genes repressed or induced in the study, used both stimuli.
- Roland Krause
Several clusters, corresponding to early response, stimuli, motifs (every clusters show at least one motif overrepresented)
- Roland Krause
Sets are characterized by parameters.
- Roland Krause
A general approach for analyzing time course data. # nice method and talk
- Roland Krause
Q: Do you think number of clusters change? A: No, is fixed from beginning.Can use iterative approach if clusters are very similar.
- Roland Krause
Q/A: Simulated data, number of clusters was fixed.
- Roland Krause
Q/A: Dependence on starting conditions, use different starting positions.
- Roland Krause
HL37: Saurabh Sinha - Quantitative analysis of the Drosophila segmentation regulatory network using pattern generating potentials
The Slim Donkey liked this
Initial overview on key bioinformatics challnege - predicting TF binding loci in genome
- Shannon McWeeney
HL35: Magnus Rattray - Model-based method for transcription factor target identification with limited data
Surehy Breter liked this
Question of which F combinations are most likel given evidence from temporal data? Use of fitting regulation models to address this
- Shannon McWeeney
Fitting a single TF linear activation model (concentraton based) - goal is to fit genome wide
- Shannon McWeeney
Challenges - model parameters unknown ; few time points; noisy data; "open" system because of f(t) - concentration of tf. Their solution: can model f(t) as Gaussian process
- Shannon McWeeney
examples of approach from Honkela et al PNAS 2010
- Shannon McWeeney
interesting discussion on how to evaluate using chIP-seq data
- Shannon McWeeney
Discussion of new extensions - multiple TF
- Shannon McWeeney
examination of enrichment of pairs (TF-target link and TF-TF)
- Shannon McWeeney
noted that TF activated by signalling can be modelled as latent
- Shannon McWeeney
tigre bioconductor package
- Shannon McWeeney
could incorporate chIP-seq data for priors (with-held here for evaluation purposes)
- Shannon McWeeney
Q: could you adapt model to focus on TF that spike - influence behavior of other TF quickly? A: could adapt via binary switches
- Shannon McWeeney
PT44: Paweł P. Łabaj - Characterization and improvement of RNA-Seq precision in quantitative transcript expression profiling
Low reproducability with next generation sequencing of transcript expression.
- Roland Krause
More complex algorithms correct for expected read counts and many other known biases.
- Roland Krause
Use of TopHat and CuffLink (gene models and transcription levels) improve much over simple models relying on unique hits.
- Roland Krause
Summary due to WLAN issues: Comparison of combinations of popular tools
- Roland Krause
Q: For microarray you need genes... A: Yes, but we can use the RNA-Seq. Q: How much do you gain from MA to RNA-Seq? Q: Large numbers unresolved but not possible to say at this stage.
- Roland Krause
Q: Isn't RNA-Seq a curse for bioinformatics. You need 100 M reads to be comparable to an Affy platform. A: Yes, at least in higher eukaryotes. Combination of technologies will yield improvements.
- Roland Krause
Q: Comparison RPKM and expression? A: No, wanted to check the tools that are popular.
- Roland Krause
SS05 Part D: C. Forbes Dewer, Jr - Bioinformatics for synthetic biology: managing the 'BRICKS' of synthetic biology
From MIT.
- Barb Bryant
Standardizing: each component consists of a circular vector of dsDNA with the component regulatory sequence, flanked by EcoRI and XbaI sites.
- Barb Bryant
Combine to get the logic you want.
- Barb Bryant
Reference: Basu et al, Nature 434, 2005.
- Barb Bryant
iGEM program brings together students and labs to tackle synthetic biology problems, creating community. Last year >1000 people. This year, 3 regional conferences with a final iGEM conference at MIT. All of the constructs the teams develop go into an open repository.
- Barb Bryant
Learning from the past - situations where there were multiple labs developing individual solutions, and then wanting to combine solutions.
- Barb Bryant
Example: DICOM (Digital Image COmmunication in Medicine). Started in mid-1980s. Unsuccessful. Attempt to break the monopoly of Siemens, GE, other company on equipment. It wasn't until 1993 that there was an international standard adopted. By 1998-9, that standard covered all of the individual areas of medical informatics.
- Barb Bryant
A problem that came up: this consensus on the standard had to be hammered out in committees and agreed to. The vocabulary was totally controlled. That takes a lot of time and can become politicized.
- Barb Bryant
As an example: Take microscope image, make object definition as had been done with medical imaging.
- Barb Bryant
For example, in a microscope, you have an objective, you have filters - and all this information must accompany the image.
- Barb Bryant
Unfortunately, took this definition to a lab at Harvard Medical School. But they had different definitions. To make further progress, had to reconcile the semantics.
- Barb Bryant
It is possible to avoid this problem or get by it, by using communication pipes between the different datasets. Don't get trapped in semantic fights; agree to use lists of synonyms. Re-use the work of others. Use machine-parsable information. Create ontologies. Reference existing curated collections.
- Barb Bryant
SBOL = Synthetic Biology Open Language
- Barb Bryant
Clotho - idea of setting up the equivalent of cloud computing. Site on the web - don't have to know the details of how the functionality works for these synth bio modules.
- Barb Bryant
BioPAX ontology. Demir et al Nature Biotech 28:935
- Barb Bryant
PT37: Michael B. Mayhew - A generalized model for multi-marker analysis of cell-cycle progression in synchrony experiments
Collaboration with Bipolar Genome Study (BiGS) Consortium
- Shannon McWeeney
Q: could you use initial results from GWAS to weight secondary analysis? A: issue of bias if intiail results are false positives - could be used in 2 stage design with independent data if wanted to apply ouside of intended secondary analysis use case
- Shannon McWeeney
Q: issue of filtering for MAF for pairs? A: QA/QC was done for individual SNPs; More of issue for 3 or more SNPs - could be added as additional filter
- Shannon McWeeney
Q: Curious about results for use of hubs in narrowing search space? A: Could be that not as critical for BP. More likely that search space is larger given how highly connected they are and algorithm can get "stuck"
- Shannon McWeeney
SS05 Part C. Chris Voigt (presented by Brit (?)): Expanding the genetic language to program cells.
Lab is moving from UCSF to MIT.
- Barb Bryant
She'll talk about 3 projects in the lab.
- Barb Bryant
First, introduction -- cells can be programmed to carry out a variety of tasks.
- Barb Bryant
DNA is synthesized and transformed into host cell.
- Barb Bryant
Have engineered E coli to sense light, carry out computing. Engineered bacteria to carry out complex image processing task - edge detection. Made remote-controlled bacteria to control bacterial chemotaxis. In absence of Ara, swim toward some gradient; in presence, swim toward something else.
- Barb Bryant
Three components: sensors, circuits, actuators. Communication between components via changes in gene expression.
- Barb Bryant
The program can get large: tens of kilobases of DNA.
- Barb Bryant
Developing a genetic compiler. Given a desired truth table, select genetic parts and assemble them into the final circuit topology, and then DNA sequence.
- Barb Bryant
Currently don't have very many robust parts for the circuit library, and that's the topic of the rest of the talk.
- Barb Bryant
She's been working on identifying additional parts. She'll also talk about making the parts more reliable. She'll finish by talking about the largest part they've built.
- Barb Bryant
She screened a library of 73 repressors, each a homolog of Tet repressor. Output was a reporter containing the LmrA operator. So they use this to identify repressor-operator pairs.
- Barb Bryant
Can connect circuits to each other. Can connect a gate to a sensor, etc. Need the intrinsic transfer function of the gate to not change as a function of genetic context.
- Barb Bryant
Someone else in the lab looked into this.
- Barb Bryant
Optimized (removed genetic-context-specific variability) using spacer sequences.
- Barb Bryant
AND gates, with readout being secretion from cell. Built using parts from various organisms.
- Barb Bryant
Achieve independence or orthogonality of different gates -- transcription factor only binds this one promoter and so on. Undesirable interactions removed by directed evolution.
- Barb Bryant
Promoter engineering to enhance dynamic range.
- Barb Bryant
Shows 4-input AND gate comprised of 11 orthogonal regulators. 4 are the sensors, and a single RFP output. It works.
- Barb Bryant
Total size is 21kb; on 3 distinct plasmids.
- Barb Bryant
Hope to identify additional orthogonal regulators. Have identified about 25 previously unidentified orghogonal regulators. Screening hundreds obtained by synthesis.
- Barb Bryant
SS05 Part B: Ron Weiss - Special session on synthetic biology: from parts to modules in therapeutic systems
Ron Weiss is from MIT. 15 years ago as a computer scientist got interested in programming biology the way we program computers.
- Barb Bryant
How could we use directed evolution?
- Barb Bryant
Applications: biomedical, bioenergy, environmental
- Barb Bryant
Library of parts & devices includes regulation, gene activity, RNAi, protein-protein interactions. Cell-cell communication. Reporters (e.g., color fluorescence). Interface wtih cell and environment.
- Barb Bryant
The toolbox of parts is expanding rapidly.
- Barb Bryant
Modules implemented in Weiss lab: ultrasensitive switch (transcriptional cascade; output is NOT-NOT of input). Going for digital logic. The output becomes more step-like.
- Barb Bryant
Noisy biological components can be used to create robust digital systems.
- Barb Bryant
Another module from Weiss lab: pulse generator. Cells engineered to send AHL to antoher cell; resulting in GFP pulse.
- Barb Bryant
Ring-like spatial patterns is another example, across multiple cells. Can express fluor protein if have medium level of signaling protein.
- Barb Bryant
Working on things like differentiating embryonic stem cells on demand.
- Barb Bryant
Working on automating the process, where there is a high level description from a human, and then the formal description can be um implemented automatically. Compiler brings it down to genetic networks, sends assembly instructions to a robot and puts into cells.
- Barb Bryant
Then iterate over design.
- Barb Bryant
Example of high level spec: (yellow (not (cyan (AHL)))). [yay - LISP!] This means if AHL is high, produce Cyan else produce Yellow.
- Barb Bryant
He shows how that gets compiled down into available parts, and then into instructions for the robot for assembly.
- Barb Bryant
3 months from now expect to have full system working.
- Barb Bryant
The BioCompiler can take any combinatorial logic function and implement it.
- Barb Bryant
They can also implement state and spatial patterns. Described not at level of individual cells but rather at the level of cell community.
- Barb Bryant
The circuit compiled from a spatial description is identical to what was designed in the lab by hand.
- Barb Bryant
Now working on the notion of transient behavior.
- Barb Bryant
Example: cancer therapy -- decipher the transcriptome.
- Barb Bryant
Problem: most existing cancer therapies not specific enough; result in significant collateral damage.
- Barb Bryant
Looking at a single cell surface receptor is not sufficient to identify cancer cells. They want to develop a therapy that evaluates internal cell state using combinatorial logic.
- Barb Bryant
They use an RNAi based logic circuit. A smart virus infects a cell, computes whether the transcriptome is indicative of cancer and then destroys the cell.
- Barb Bryant
Example: HeLa cell classifier (with Benenson lab)
- Barb Bryant
Program: HeLa = miR-21 and (miR=17 + miR-30a) and not miR-141 and not miR-142 and not miR-146.
- Barb Bryant
Gotta say, this stuff is real scary.
- Barb Bryant
he shows a HeLa-Low sensor - a part that is on when the miRNA is low.
- Barb Bryant
AND gate of multiple sensors: just put the recognition sequences along the gene.
- Barb Bryant
A "high" sensor is a "not not" of the input. So you have an activator and a repressor; the miR inhibits both... I'll just believe this.
- Barb Bryant
Two combine two sensors, you combine their output. They have the same transcription factors but different regions that sense miRs. He shows full circuit for the original logical expression.
- Barb Bryant
They tested this circuit with the 32 different input combinations. Shows that output (RFP) is high only when the have the particular input they want.
- Barb Bryant
OMG - tested on 7 different cell lines, and see strong response only for HeLa and not the other cancer cell lines. Even though didn't specifically engineer it to not detect these other cell types.
- Barb Bryant
Since then, optimized the circuit further, and got a 25-fold difference between HeLa and next best match.
- Barb Bryant
Tested for killing efficiency.
- Barb Bryant
New example - artificial tissue homeostasis for beta cells, diabetes application.
- Barb Bryant
In diabetes type I, the auto-immune response slowly kills insulin-producing pancreatic beta cells.
- Barb Bryant
Want to maintain the population level of beta cells using auto-regulated differentiation of ES cells that counter-balances the auto-immune attacks.
- Barb Bryant
This is a huge system with 22 components; not yet done. Trying to understand issues with such a complex system. Biggest system published to date has 6 parts.
- Barb Bryant
Some results -- have created a module to differentiate ES cells to cells with beta-like properties. Can produce insulin.
- Barb Bryant
Also have cell-cell communication, and a toggle switch. If decide need to become beta cells, need to remember that decision.
- Barb Bryant
Shows program for the whole thing.
- Barb Bryant
Shows system architecture created by hand. Includes stem cell and beta cell population control units. Then AND gate going into differentiation decision.
- Barb Bryant
In simulation, see some cases with oscillation (not ideal). Version 2 was attempt to remove the oscillations. Noticed long feedback delay ... added fast toggle switch so that the commitment decision is fed back even before differentiation happens.
- Barb Bryant
... integrated noise ... Large fluctuations result...
- Barb Bryant
Shows simulation; having trouble maintaining steady levels in the context of noise. Next revision includes using noise to make a more robust system.
- Barb Bryant
Oscillators rather quickly go out of phase; they use these bad oscillators to generate useful noise to break symmetry in the system.
- Barb Bryant
Simulations show that this works to obtain steady levels.
- Barb Bryant
Often in system design you optimize the modules first. Um, I think he is saying that you simulate the system to see how module characteristics affect system performance?
- Barb Bryant
He shows the full homeostasis gene network and talks about how individual modules have been successfully implemented.
- Barb Bryant
Now the challenge is systems integration.
- Barb Bryant
In summary, soon we will have end-to-end, high-level-design to automated DNA assembly
- Barb Bryant
In future want better understanding of circuit interaction with cellular context, and predictive design and construction of large circuits.
- Barb Bryant
My summary: Wow. just wow. And, still, scary.
- Barb Bryant
Q: cells are so complicated and the circuits are so complex. How can these designed circuits possibly work in real cell systems? A: we can already predict what a population of cells will do, with small modules. Optimistic about tackling larger circuits, using systems built out of orthogonal parts with minimal interaction with cell systems.
- Barb Bryant
TT40: Matthew Bellgard - YABI: A sophisticated Internet-based cross–omics analytic environment
Code also seems to be here: http://code.google.com/p...
- Scott Edmunds
SS05: Richard I. Kitney - Special session on synthetic biology: System Design Challenges
The aim of synthetic biology is to build applications from BioParts. The parts encode biological functions and are made from synthetically designed DNA.
- Barb Bryant
Devices are made from a collection of parts, and encode human-defined functions like logic gates.
- Barb Bryant
Systems are made from devices and carry out tasks like counting or control.
- Barb Bryant
Design cycle: specifications --> design --> modeling --> implementation --> testing/validation --> specification
- Barb Bryant
They do small-scale assembly of parts and devices in-house. Large scale assembly of parts and devices happens at gene synthesis companies, like Gene Arts in Germany.
- Barb Bryant
Applications in various industries: healthcare, pharma, biofuels, agroscience.
- Barb Bryant
They have an integrated BioCAD and modeling suite: SynBIS
- Barb Bryant
There are four levels in the modeling: interface, communication (XML), application, and database.
- Barb Bryant
Application example: biosensor design. Detection of medically problematic pathogenic biofilms.
- Barb Bryant
Solution is to detect a protein with a three-stage device: detector, amplifier and indicator.
- Barb Bryant
Can convert the description of device behavior into standard systems diagrams and equations; can simulate device behavior.
- Barb Bryant
Second example: logic AND gate. Can be used to measure two carbon sources, presence of two organisms, etc.
- Barb Bryant
TT37: Daniel Blankenberg - NGS Best Practices through Galaxy: Cloud-based variant discovery with visual analytics
Keynote: Luis Serrano - M pneumoniae (Towards a full quantitive understanding of a free-living system)
Syed Asad Rahman and Ruchira S. Datta liked this
Fully understanding of Micoplasma pneumoniae -
- Venkata P. Satagopam
Can we successfully analyze and integrate large number of data types in order to predict changes in system in face of any perturbation? Use case: M pneumoniae
- Shannon McWeeney
It contains 689 ORFs + 44 RNSs , 10TFs, kinases and one phosphatase
- Venkata P. Satagopam
10 TF ( classical) 2 kinases, 1 phosphatase, full chemical signaling repertoire
- Shannon McWeeney
EM tomography used to get the cytoskeleton
- Venkata P. Satagopam
Related papers from this work: Yus et al 2009; Güell et al 2009
- Shannon McWeeney
Metabolome - it was difficult to draw the metabolic network, mined last 20years of literature and final network contains 129 enzymes, 140 genes
- Venkata P. Satagopam
Strong argument that structural analysis is key
- Shannon McWeeney
Modeled using flux balance analysis (FBA)
- Venkata P. Satagopam
Motabolomics analysis done using MS and NMR ... developed complete validated map, but missing parameters for reactions, regulatory loops, effect of post translational modifications, enzymes for reactions,
- Venkata P. Satagopam
Transcriptome, initially used microarrays, then time-dependent tilling array, then did single strand sequencing
- Venkata P. Satagopam
Expression profiling: ncRNA everywhere. expression appears to decay along the operon - "staircase behavior"
- Barb Bryant
take home points non-coding RNA abundant; staircase behaviour of operons (steps coincide with end of gene in operon) ? of how this is regulated.
- Shannon McWeeney
more than 1 promoter for one operon seems common
- Shannon McWeeney
There is a whole huge world of translational regulation, and we "don't have a clue"
- Barb Bryant
suggestion to replace operon model
- Shannon McWeeney
Cool - there are different RNA polymerase complexes that recognize different operon-start locations; each one also recognizes particular stop signals, and so each may transcribe a different operon.
- Barb Bryant
how to explain complexity with so few tf?
- Shannon McWeeney
other proteins can act as TF
- Shannon McWeeney
possible role of noncoding
- Shannon McWeeney
found new putative RNA/DNA binding proteins
- Venkata P. Satagopam
Tiny RNAs accurately mark the transcription start sites of genes, about 40 bases long. Have also been found in eukaryotes. Don't know function.
- Barb Bryant
Summary - transcriptional complexity in M. pneumoniae could be explained by new TFs, ncRNA, tiny RNAs, new RNA/DNA binding proteins, etc.
- Barb Bryant
Missing - who regulates the TFs and DNA binding proteins
- Venkata P. Satagopam
Analysis of protein complexes - Sebestian Kuhner et al Science 326, 1235 (2009)
- Venkata P. Satagopam
Electron tomography: can visualize large complexes. Allows you to count the number of complexes per cell.
- Barb Bryant
Full quantification of proteins and transcripts to get copies per cell, with half-life modeling, and taking into consideration point in growth curve. From this, create full computer model.
- Barb Bryant
Statistically more abundant proteins are more essential like Ecoli
- Venkata P. Satagopam
Some mRNAs present at (way) less than 1 copy per cell.
- Barb Bryant
Poor correlation between mRNA and proteins (~0.5)
- Barb Bryant
low copy number of noise - low translation efficiency and long protein half-life eliminates noise
- Venkata P. Satagopam
150 ribosomes, 400 promoters, 140 RNA polymerase
- Venkata P. Satagopam
There are probably different ribosomes, with different subunit compositions.
- Barb Bryant
protein/volume ratio is a magic number - a universal constant: 200 g/l. This is true for 3 bacteria -- is it true for human?
- Barb Bryant
They have observed post-translational modifications, but we don't yet know how they are placed, or their function.
- Barb Bryant
It's tough to convince students and post-docs to dive into the badly needed research on one or a few proteins when omics experiments are so much faster and often publishable in better journals.
- Barb Bryant
HL25: Raamesh Deshpande - A scalable approach for discovering conserved active subnetworks across species
HL26: Lixia Yao - Benchmarking Ontologies: Bigger or Better?
LBR17: Egor Dolzhenko - STAGR: Software To Annotate Genome Rearrangement
PT33: Jasmin Fisher - The role of proteosome-mediated proteolysis in modulating the activity of potentially harmful transcription factor activity in Saccharomyces cerevisiae
PT34: Limin Li - ccSVM: Correcting Support Vector Machines for confounding factors in biological data classification
TT36: Benoit Bely - Overview of The Universal Protein Resource (UniProt)
TT38: Nadezhda Doncheva - New Interactive Approach to the Visual Analysis of Protein Structure and Function
HL27: Yves Lussier - Network Modeling Identifies Molecular Functions Targeted by miR-204 to Suppress Head and Neck Tumor Metastasis and Mechanisms of Therapeutic Resistance
HL28: Wankyu Kim - miRGator v2.0 and the construction of miRNA-disease network
LBR18: Maureen Stolzer - Methods for Phylogenetic Inference of Multidomain Evolution
PT35: Nicholas Furlotte - Mixed Model Coexpression (MMC): calculating gene coexpression while accounting for expression heterogeneity
PT36: Filipe Santana da Silva - Ontology Patterns for Tabular Representations of Knowledge on Neglected Tropical Diseases
TT39: Jinbo Xu - RaptorX: an integrated web server for protein sequence-structure alignment and protein structure prediction
HL29: Christopher Baker - Mutation Impact Mining using SADI Semantic Web Services
HL30: Holger Fröhlich - Fast and Efficient Dynamic Nested Effects Models
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