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Searching phased siRNAs from mapping result over genomes in linear (O(n)) time complexity

by Forrest Sheng Bao http://fsbao.net

In bioinformatics, we prefer constant or linear algorithms. The reason is obvious. But, sometimes, something strange just happened in some papers.

In 2006, Ho-Ming Chen, Yi-Hang Li and Shu-Hsing Wu published a paper on PNAS, "Bioinformatic prediction and experimental validation of a microRNA-directed tandem trans-acting siRNA cascade in Arabidopsis", http://www.pnas.org/content/104/9/3318.abstract.

32-, 64- and 128-channel EEG samling system

by Forrest Sheng Bao http://fsbao.net

The 32-channel system (a.k.a. 10-20 system):

The 64-channel system:

The 128-channel system:

Reviewing STFT, DTFT, DFT and FFT, again

by Forrest Sheng Bao http://fsbao.net

Actually, I have written such an article before. http://narnia.cs.ttu.edu/drupal/node/46 Just because recently I am fighting back on biomedical signal processing, I think I'd better rewrite it again.

Fourier transform, ok, I don't need to repeat it again. (Don't bother me on MathML or LaTeX!)

F(w) = \int_{-\inf}^{+\inf} f(t)*exp(j*w*t) dt (1)

where f(t) is the signal in time domain and F(w) is signal in frequency domain.

Truncating siRNAs by 5' and 3' adapters and categorizing them

by Forrest Sheng Bao http://fsbao.net

Some genes in DNA sequences are very short, for example 15 bases. They are too short to sequence. So, we can add some adapters at 5' and 3' to extend it to 34 bases. Besides, we have a huge pool of sequences from different sources and we mixed them up for sequencing. So we can also use the adapter as an indicator to mark the source of the gene.

PyWavelets and pywfdb library for biomedical time series analysis

by Forrest Sheng Bao http://fsbao.net

I am so excited today that I have found a very cool Python library for doing wavelet decomposition, the PyWavelets. It is developed by Filip Wasilewski

Classification and parameters of epileptic EEG

by Forrest Sheng Bao http://fsbao.net

data for healthy people

You can classify them by their brain activities: rest, sleep or cognitive activities.

And you can also classify them by the state of eyes: with eyes open or with eyes close.

Thinking in AI [0]: Why I don't need to know how to read EEG plots?

by Forrest Sheng Bao http://fsbao.net

Recently I wanna write a computer program that can diagnose a neural disease automatically from EEG data. Since I have no idea on diagnosis of this disease, those medical guys couldn't understand how I can "teach" a computer to some stuff without knowing how to it by myself.

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