Fairly recently I did enjoy the webinar by Biosymposia. Dr. Leonard Guarente made a presentation first, and then there was a Q&A section.

After taking WIPO’s distant learning course in IP, I started paying more attention to distant learning techniques. In the long run, I assume that more and more structured knowledge (whole courses and learning programs) will be made available to the public for free.

WIPO’s IP course was as a matter of fact a collection of textual and audio materials presented in a specific order, and interspersed with self-assessment questions to control learning progress, with a final exam at the end. Acknowledging the difference between the distant learning program and a webinar, Longevity webinar felt more like a tele-auditorium, with live-seeming (though actually pre-recorded) streaming presentation and video (side-by-side) by Dr. Leonard Guarente. It was just like attending a lecture :). The Q&A felt more like a TV show, in that listeners could write their questions during and a bit after the presentation, for the webinar hostess to ask Dr. Guarente those questions.

Longevity is a question which bothers most of the people at some point in their lives. Amrita, the elixir of life, is one of the dominant topics of the Medieval Ages (alongside the any_metal-2-gold transformation idea).
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The title of this post is my current - “forthcome”, as in “done” - field of interest.

First article on topic: Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data.

Another one, on combining different high-throughput data sources to get higher-quality results: Uncovering signal transduction networks from high-throughput data by integer linear programming.

I’m especially interested in time-series network reconstruction algorithms. If you have a good advice to share with a newcomer to the networks field - don’t hesitate

The title of this post is my current - “forthcome”, as in “done” - field of interest.

First article on topic: Fast network component analysis (FastNCA) for gene regulatory network reconstruction from microarray data.

Another one, on combining different high-throughput data sources to get higher-quality results: Uncovering signal transduction networks from high-throughput data by integer linear programming.

I’m especially interested in time-series network reconstruction algorithms. If you have a good advice to share with a newcomer to the networks field - don’t hesitate

With this post, I’m finally announcing the opening of the (mostly) functional COTRASIF web-tool, created for the genome-wide identification of promoter regulatory sequences (transcription factor binding sits, TFBS). You can learn more from the About and Help pages. For an example of use, see the Supplement page (article is currently being prepared; as soon as it’s ready, I’ll make it available).

If you are interested - have a look at the News page, where there is information on joining COTRASIF Google group. For non-public enquiries, please use my contact page.

Note: the problem of identifying eukaryotic transcription factor binding sites stays acute for many years in a row - see e.g. the most recent Eukaryotic transcription factor binding sites - modelling and integrative search methods.

With this post, I’m finally announcing the opening of the (mostly) functional COTRASIF web-tool, created for the genome-wide identification of promoter regulatory sequences (transcription factor binding sits, TFBS). You can learn more from the About and Help pages. For an example of use, see the Supplement page (article is currently being prepared; as soon as it’s ready, I’ll make it available).

If you are interested - have a look at the News page, where there is information on joining COTRASIF Google group. For non-public enquiries, please use my contact page.

Note: the problem of identifying eukaryotic transcription factor binding sites stays acute for many years in a row - see e.g. the most recent Eukaryotic transcription factor binding sites - modelling and integrative search methods.

Quite an entertaining story:
SCIgen at wikipedia
SCIgen tool
SCIgen blog

Finally, just a useful resource if you need a shiny name for your brand-new 2.0-beta project: anagram server.

Cytoscape 2.5.1 supports proxies, including HTTP proxies, but there is no support for HTTP proxies requiring authentication/authorization. It’s easy to use Cytoscape in the authentication-requiring proxy scenario; below is one possible method.

Note, that exactly the same method can be used to allow any software, which supports proxies but not proxies with authentication, to be able to access the internet.
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Cytoscape 2.5.1 supports proxies, including HTTP proxies, but there is no support for HTTP proxies requiring authentication/authorization. It’s easy to use Cytoscape in the authentication-requiring proxy scenario; below is one possible method.

Note, that exactly the same method can be used to allow any software, which supports proxies but not proxies with authentication, to be able to access the internet.
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Googling for “practical artificial intelligence” gives only two (somewhat) relevant links:

• Open Content Free Web Books (on practical AI programming)
• Artificial Intelligence Lab (with some practical applications)

Looks like it isn’t widely acknowledged, that AI is, in fact, quite widely used. Though primarily in OCR, TTS, STT :), and NLP (including machine translation).

Full Featured Multiple Sequence Alignment Editor, Analyser and Shading Utility for Windows.

Small and convenient. Can do sequence alignments (I recommend to limit the length to 2kb for alignments).

Latest version I found: updated July, 2001, GeneDoc version # 2.6.02.

Drawback: windows only (but has GNU-licence sources).

Full Featured Multiple Sequence Alignment Editor, Analyser and Shading Utility for Windows.

Small and convenient. Can do sequence alignments (I recommend to limit the length to 2kb for alignments).

Latest version I found: updated July, 2001, GeneDoc version # 2.6.02.

Drawback: windows only (but has GNU-licence sources).

I added a new video to my Conspiracy theory in gallery.

I am an advocate of the official position: that is, HIV causes AIDS. In the movie description, I put my comments which dis-approve the point set and mentioned in that movie. I would strongly recommend that you stick to the official position in everything that is related to your health!

And now, with all the warnings, you may want to see it (326 MiB):
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I’m planning to reconstruct (based on literature and some original research) a specific cellular regulatory network. For this I decided to use some specialized biological modelling software. The requirements I had were pretty simple:

• must have SBML support. SBML appears de-facto standard for biological model notation;
• must be fairly frequently updated;
• should be feature-packed and easy to use. However, this requirement can only be checked after some use, and I was pre-selecting, not reviewing.

Software put into the title of the post was found to be the most mature and interesting from the usage perspective. However, there are more than those mentioned software tools reviewed. Reviews are based primarily on the information from official websites and documentation; some tools (like VirtualCell) are reviewed somewhat more thoroughly.
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I’m planning to reconstruct (based on literature and some original research) a specific cellular regulatory network. For this I decided to use some specialized biological modelling software. The requirements I had were pretty simple:

• must have SBML support. SBML appears de-facto standard for biological model notation;
• must be fairly frequently updated;
• should be feature-packed and easy to use. However, this requirement can only be checked after some use, and I was pre-selecting, not reviewing.

Software put into the title of the post was found to be the most mature and interesting from the usage perspective. However, there are more than those mentioned software tools reviewed. Reviews are based primarily on the information from official websites and documentation; some tools (like VirtualCell) are reviewed somewhat more thoroughly.
Read the rest of this entry »

Nonanticipatory (system or predictor) is a (system or predictor) where the output y(t) at some specific instant t₀ only depends on the input x(t) for values of t less than or equal to t₀. Therefore these kinds of (systems or predictors) have outputs and internal states that depend only on the current and previous input values.

In simpler words, nonanticipatory systems can “take into account” only past and present, and cannot base their behaviour/decisions on future expectations.

Nonanticipatory systems are also known as causal systems.

Reference and further reading: article at answers.com.

“… consider a universal predictor based on pattern matching: Given a sequence X_i,… ,X_n drawn from a stationary mixing source, it predicts the next symbol X_n+i based on selecting a context of X_n+i. The predictor, called the Sampled Pattern Matching (SPM), is a modification of the Ehrenfeucht-Mycielski pseudo random generator algorithm. It predicts the value of the most frequent symbol appearing at the so called sampled positions. These positions follow the occurrences of a fraction of the longest suffix of the original sequence that has another copy inside X_iX₂ … X_n. In other words, in SPM the context selection consists of taking certain fraction of the longest match. The study of the longest match for lossless data compression was initiated by [Aaron D.] Wyner and Ziv in their 1989 seminal paper.”
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“… consider a universal predictor based on pattern matching: Given a sequence X_i,… ,X_n drawn from a stationary mixing source, it predicts the next symbol X_n+i based on selecting a context of X_n+i. The predictor, called the Sampled Pattern Matching (SPM), is a modification of the Ehrenfeucht-Mycielski pseudo random generator algorithm. It predicts the value of the most frequent symbol appearing at the so called sampled positions. These positions follow the occurrences of a fraction of the longest suffix of the original sequence that has another copy inside X_iX₂ … X_n. In other words, in SPM the context selection consists of taking certain fraction of the longest match. The study of the longest match for lossless data compression was initiated by [Aaron D.] Wyner and Ziv in their 1989 seminal paper.”
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… regularly updated since 2003: systems biology upcoming conferences

Here’s the list as of the moment of writing:
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… regularly updated since 2003: systems biology upcoming conferences

Here’s the list as of the moment of writing:
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(This series started with Pattern matching and prediction, part 1)

For part 2, I wanted to start (and probably also end) with Cybula’s AURA (universal pattern matcher, white-paper dated 2004). AURA is said to be built around Correlation Matrix Memory (CMM). CMMs were developed (or picked up for development?) by Prof. Austin, the founder of Cybula, in 1986.

The white paper tells us that

The now ubiquitous neural network methods such as Kohonen Networks, Radial Basis Function networks and Kohnen networks all allow users develop good pattern matching systems for small problems, where they excel. However, when the problems grow to large datasets, and where very high performance is needed, they become limited. … The well known k-Nearest Neighbour methods (k-NN) is a relatively good pattern matching method that has been constantly shown to operate well on many problems, however, it suffers from slow operation on large data problems.

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