Machine learning is not a new method, but Doctor Chen Yu Zong's （http://bidd.nus.edu.sg/group/bidd.htm） lecture really impressed me. I have some thoughts, mostly about science research. Although some of them have been in my mind for a long time, they have never been considered in such a practical way.
Fisrt, Being Scrupulous. Details may be very important, not in the experiments but also in the data analysis. For instance, he said, choosing suitable training samples can be the most important thing in machine learning. Methods, such as SVM, are tumblers. You can always find a solution from a group of samples no matter how you do the sampling. Sometimes the results may seem perfect, but indeed, there are several flaws. What I want to say is: some methods seem perfect, some results seem perfect. However, we should not be satisfied by them, while they could be mistakes.
Secondly, to be an expert in the field before action. Doctor Zong did a thorough statistic about previous studies on cancer gene biomarker. In this way, his research is based on a good investigation. He knows what is the problem in this kind of study. In contrast, many researchers just read few papers before their proposal. Then what can they do with so little relevant knowledge? A couple of days ago, Prof. Shen said: ”We are in the best institute of China, then don’t you feel awful if you are doing things which can be finished by a regular researcher? ” We must do better, upon the knowledge of previous scientists, that’s how a researcher looks like!
This blog hasn’t been updated for a long time. I’ll try my best to keep this site active. Thank you for your visit.
Thursday, October 25, 2007
Sunday, September 16, 2007
Wavelet Coherence and Its Application in Analyzing Auditory and Motor Task Event-Related Potentials
WU Jie，ZHANG Ning，YANG Zhuo，ZHANG Tao, Wavelet coherence and its application in analyzing auditory and motor task event-related potentials.
This paper has been accepted by ACTA BIOPYSICA SINICA，here is its abstract.
Related links:Tips about Wavelet , Wavelet Coherence Method,A Vivid Example to Show How Wavelet Coherence Works, Some Concepts about ERP signals ,Wavelet and EEG Signals. These links can help to gain further information about this paper.
Abstract:
Wavelet coherence method is applied in analyzing single trial of ERP (event-related potential). There are three groups of experiments: auditory single task, motor single task 1 and motor single task 2. Data from 12 participants is analyzed around 40 Hz by wavelet coherence method and the coherence values between prefrontal area and other areas in the brain are calculated. It is found that the coherence values in motor tasks are larger than those in auditory task and there are significantly differences. Furthermore, in different tasks, the distributions of the coherence values are obviously different, and the values are changing in particularly ways according the varying of the time. This analysis indicates that wavelet coherence method has its advantages in investigating short time EEG signals.
Brief descriptions:
The coherence values, around 40Hz between prefrontal area and other areas in the cerebral cortex, were measured. It was found that the coherence values in the MST (Motor Single Task) are larger than that in the AST (Auditory Single Task) with significant differences.Brain dealing with complicated tasks can have more information to process, and there should be more information communication between different areas of the brain. This can be denoted by coherence values.
Wavelet Coherence Values along the time axis. The colors in the images indicate the coherence values between prefrontal area and other areas in the cerebral cortex around 40Hz(the relationship between the color and the value is shown in the color-bar).large coherence values exactly locate in Auditory Cortex at temporal lobe in AST conditions, whilst in MST conditions, the big values are in motor cortex which is in parietal area.
The data show that the wavelet methods calculations of non-stationary signals, compared to the Fourier methods, can characterize the time-frequency features of neural mechanisms underlying cognitive control. Furthermore, wavelet approach can provide higher resolution in both temporal and spatial scales and can be applied in analyzing other physiological signals.
Main reference:
Lachaux, J.-P., et al., Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence. Neurophysiol Clin., 2002. 32: p. 157-174.
This paper has been accepted by ACTA BIOPYSICA SINICA，here is its abstract.
Related links:Tips about Wavelet , Wavelet Coherence Method,A Vivid Example to Show How Wavelet Coherence Works, Some Concepts about ERP signals ,Wavelet and EEG Signals. These links can help to gain further information about this paper.
Abstract:
Wavelet coherence method is applied in analyzing single trial of ERP (event-related potential). There are three groups of experiments: auditory single task, motor single task 1 and motor single task 2. Data from 12 participants is analyzed around 40 Hz by wavelet coherence method and the coherence values between prefrontal area and other areas in the brain are calculated. It is found that the coherence values in motor tasks are larger than those in auditory task and there are significantly differences. Furthermore, in different tasks, the distributions of the coherence values are obviously different, and the values are changing in particularly ways according the varying of the time. This analysis indicates that wavelet coherence method has its advantages in investigating short time EEG signals.
Brief descriptions:
The coherence values, around 40Hz between prefrontal area and other areas in the cerebral cortex, were measured. It was found that the coherence values in the MST (Motor Single Task) are larger than that in the AST (Auditory Single Task) with significant differences.Brain dealing with complicated tasks can have more information to process, and there should be more information communication between different areas of the brain. This can be denoted by coherence values.
Wavelet Coherence Values along the time axis. The colors in the images indicate the coherence values between prefrontal area and other areas in the cerebral cortex around 40Hz(the relationship between the color and the value is shown in the color-bar).large coherence values exactly locate in Auditory Cortex at temporal lobe in AST conditions, whilst in MST conditions, the big values are in motor cortex which is in parietal area.
The data show that the wavelet methods calculations of non-stationary signals, compared to the Fourier methods, can characterize the time-frequency features of neural mechanisms underlying cognitive control. Furthermore, wavelet approach can provide higher resolution in both temporal and spatial scales and can be applied in analyzing other physiological signals.
Main reference:
Lachaux, J.-P., et al., Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence. Neurophysiol Clin., 2002. 32: p. 157-174.
A Vivid Example to Show How Wavelet Coherence Works
Some formulas have been given in previous article, but they are not friendly to understand. Now there is a vivid example to show what Wavelet Coherence analysis looks like.
Please pay attention to the differences of the two pictures below. The description following the pictures will tell you the details.
References of this article:
Lachaux J-P, Lutz A, Rudrauf D, Cosmelli D, Quyen MLV, Martinerie J,Varela F. Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence. Neurophysiol Clin., 2002, 32: 157-174.
Please pay attention to the differences of the two pictures below. The description following the pictures will tell you the details.
a
b
Two signals in experiments were randomly chosen and a synchronization in 2~3 s and 10~40Hz was created between them. Then the Wavelet Coherence values were calculated. (a) result of the signals without artificial synchronization, (b) result of the signals with artificial synchronization. The color-bar in the right shows the relationship between coherence values and the colors. High coherence values can be seen in area with designed synchronization (2~3 s and 10~40Hz).References of this article:
Lachaux J-P, Lutz A, Rudrauf D, Cosmelli D, Quyen MLV, Martinerie J,Varela F. Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence. Neurophysiol Clin., 2002, 32: 157-174.
Wavelet Analysis of ERP Recordings for Dual Tasks in Man
WU J, YANG Z, ZHANG T, Wavelet analysis of ERP recordings for dual tasks in man.
This paper has been accepted by ICCN'07 & SICPB'07 which will be held in Shanghai, China during November 17-21, 2007. The abstract is shown as below.
Click Wavelet Packet Transform, Relative Wavelet Energy, ERP for more relevant information.
Abstract:
The study was to examine the application of wavelet packet method to electrophysiological responses recorded during single and dual task performance. Relative energies of both EEG alpha and beta frequency bands were significantly higher in the single task conditions compared with that of the dual task condition (P is less than 0.05). The data demonstrated that relative energy measurements based on wavelet transform could be a useful alternative approach to analyzing short duration EEG signals on a time scale of seconds.
It is impossible and unnecessary to show the details of this paper in a single short article, so only the abstract is shown here.
The most important point is that, compared to the Fourier methods, wavelet method can be an alternative and much better method to deal with short time EEG signals. Accordingly, wavelet method can be used in analyzing other data in our lab, those data include EEG, ERP and RSNA signals. They are all complicated signals and represent nonlinear dynamic systems with high dimensionality.
There will be other artcles in this blog to illustrate the advantages of Wavelet method compared to regular frequency analysis methods.
This paper has been accepted by ICCN'07 & SICPB'07 which will be held in Shanghai, China during November 17-21, 2007. The abstract is shown as below.
Click Wavelet Packet Transform, Relative Wavelet Energy, ERP for more relevant information.
Abstract:
The study was to examine the application of wavelet packet method to electrophysiological responses recorded during single and dual task performance. Relative energies of both EEG alpha and beta frequency bands were significantly higher in the single task conditions compared with that of the dual task condition (P is less than 0.05). The data demonstrated that relative energy measurements based on wavelet transform could be a useful alternative approach to analyzing short duration EEG signals on a time scale of seconds.
It is impossible and unnecessary to show the details of this paper in a single short article, so only the abstract is shown here.
The most important point is that, compared to the Fourier methods, wavelet method can be an alternative and much better method to deal with short time EEG signals. Accordingly, wavelet method can be used in analyzing other data in our lab, those data include EEG, ERP and RSNA signals. They are all complicated signals and represent nonlinear dynamic systems with high dimensionality.
There will be other artcles in this blog to illustrate the advantages of Wavelet method compared to regular frequency analysis methods.
Relative Wavelet Energy
It is only a short introduction about Relative Wavelet Energy. Click here for some basic information about wavelet.
The energy of a discrete signal s(k) is defined as
and the relative energy is defined as
where is the total energy, which is calculated by adding here(Rosso, et al., 2006).
Rosso, O.A., Martin, M.T., Figliola, A., Keller, K. and Plastino, A. (2006) EEG analysis using wavelet-based information tools, Journal of Neuroscience Methods, 153, 163-182.
The energy of a discrete signal s(k) is defined as
and the relative energy is defined as
where is the total energy, which is calculated by adding here(Rosso, et al., 2006).
Rosso, O.A., Martin, M.T., Figliola, A., Keller, K. and Plastino, A. (2006) EEG analysis using wavelet-based information tools, Journal of Neuroscience Methods, 153, 163-182.
Wavelet Packet Transform
The wavelet packet transform (WPT) represents a generalization of the wavelet methods and it has recently been applied to various science and engineering fields with great success. Here are some tips about WPT from Matlab tutorial.
From WT to WPT:
In wavelet analysis, a signal is split into an approximation and a detail. The approximation is then itself split into a second-level approximation and detail, and the process is repeated. For an n-level decomposition, there are n+1 possible ways to decompose or encode the signal. More basic information about wavelet can be got here.
In wavelet packet analysis, the details as well as the approximations can be split.
The wavelet decomposition tree is a part of this complete binary tree. For instance, wavelet packet analysis allows the signal S to be represented as A1 + AAD3 + DAD3 + DD2.
Here is a sample transform of an EEG signal in our lab. The original waveform was decomposed into five waveforms in WPT method, db5 mother wavelet was employed and 7 levels decomposition was taken. We can see DELTA, THETA, ALPHA, BETA, GAMMA waves in this figure.
From WT to WPT:
In wavelet analysis, a signal is split into an approximation and a detail. The approximation is then itself split into a second-level approximation and detail, and the process is repeated. For an n-level decomposition, there are n+1 possible ways to decompose or encode the signal. More basic information about wavelet can be got here.
In wavelet packet analysis, the details as well as the approximations can be split.
The wavelet decomposition tree is a part of this complete binary tree. For instance, wavelet packet analysis allows the signal S to be represented as A1 + AAD3 + DAD3 + DD2.
Here is a sample transform of an EEG signal in our lab. The original waveform was decomposed into five waveforms in WPT method, db5 mother wavelet was employed and 7 levels decomposition was taken. We can see DELTA, THETA, ALPHA, BETA, GAMMA waves in this figure.
Wavelet and EEG Signals
EEG signals are wildly studied to determine how patterns contained within them might reflect control by the nervous system. Generally, they are investigated with frequency analysis based on FFT. However, EEG data sets (including signals in ERP experiments) are non-stationary in both time and space. And FFT is not good at analyzing waveforms with this feature，see this article.
The main advantage of wavelet transform (WT) in the analysis of EEG signals is that it allows accurate decomposition of a neuro-electrical record into a set of component waveforms (detail functions). These detail functions can isolate all scales of waveform structure, from the largest to smallest pattern of variation in time and space that is available in the signal. Consequently WT provides flexible control over the resolution with which different activities and events contained in the neuroelctrical signal can be localized in time, space and scale. This leads to several important applications like(Samar, et al., 1999): (a) noise filtering and signal separation; (b) preprocessing neuroelectric data; (c) neuroelectric signal compressions; (d) spike and transients detections; (e) component and event detection; (f) time-scale and space-scale analysis of neuroelectric waveforms; among others(Rosso, et al., 2006).
Many details have been reviewed by Samar(Samar, et al., 1999).
Rosso, O.A., Martin, M.T., Figliola, A., Keller, K. and Plastino, A. (2006) EEG analysis using wavelet-based information tools, Journal of Neuroscience Methods, 153, 163-182.
Samar, V.J., Bopardikar, A., Rao, R. and Swartz, K. (1999) Wavelet analysis of neuroelectric waveforms: a conceptual tutorial, Brain Lang, 66, 7-60.
The main advantage of wavelet transform (WT) in the analysis of EEG signals is that it allows accurate decomposition of a neuro-electrical record into a set of component waveforms (detail functions). These detail functions can isolate all scales of waveform structure, from the largest to smallest pattern of variation in time and space that is available in the signal. Consequently WT provides flexible control over the resolution with which different activities and events contained in the neuroelctrical signal can be localized in time, space and scale. This leads to several important applications like(Samar, et al., 1999): (a) noise filtering and signal separation; (b) preprocessing neuroelectric data; (c) neuroelectric signal compressions; (d) spike and transients detections; (e) component and event detection; (f) time-scale and space-scale analysis of neuroelectric waveforms; among others(Rosso, et al., 2006).
Many details have been reviewed by Samar(Samar, et al., 1999).
Rosso, O.A., Martin, M.T., Figliola, A., Keller, K. and Plastino, A. (2006) EEG analysis using wavelet-based information tools, Journal of Neuroscience Methods, 153, 163-182.
Samar, V.J., Bopardikar, A., Rao, R. and Swartz, K. (1999) Wavelet analysis of neuroelectric waveforms: a conceptual tutorial, Brain Lang, 66, 7-60.
Sunday, September 9, 2007
Some Concepts about ERP signals
Enterprise Resource Planning is usually recognized as ERP for common people, but the "ERP" here means Event related Potential. For brain scientists, it is a common way to investigate the brain. Event-related potential is the measurement of the brain's electrical activity in response to different types of events, such as attention, words, thinking or sounds. By measuring the brain's response to such events we can learn how different types of information are processed.
In an Event related Potential experiment, signals together with the events are recorded. The events (for example, auditory stimulus, vibrative stimulus, human actions) are designed according to the purpose. Thus, ERP experiments provide a possibility to learn how the events are processed in the brain.
Normally, dozens of individual raw ERP experiment signals are averaged together to gain the ERP, this method allow us to cancels out the noise and make the responses clearly. There are three measurable aspects of the ERP waveform, i.e., amplitude, latency, and scalp distribution. Component amplitude provides index of neural activation extent (how the component responds functionally to experimental variables); component latency (the point in time at which the peak occurs) reveals the timing of this activation; and a component’s scalp distribution (the pattern of voltage gradient over the scalp at any point in time) provides information on the overall pattern of activated brain areas.
Note: some contents above comes from Wikipedia
A sample from http://www.city.ac.uk/psychology/research/CNRU/eegintro.html,This is an averaged ERP signal, in which you can see the responses (positive and negative components).Basic research are based on the measurements of this type of components.
In an Event related Potential experiment, signals together with the events are recorded. The events (for example, auditory stimulus, vibrative stimulus, human actions) are designed according to the purpose. Thus, ERP experiments provide a possibility to learn how the events are processed in the brain.
Normally, dozens of individual raw ERP experiment signals are averaged together to gain the ERP, this method allow us to cancels out the noise and make the responses clearly. There are three measurable aspects of the ERP waveform, i.e., amplitude, latency, and scalp distribution. Component amplitude provides index of neural activation extent (how the component responds functionally to experimental variables); component latency (the point in time at which the peak occurs) reveals the timing of this activation; and a component’s scalp distribution (the pattern of voltage gradient over the scalp at any point in time) provides information on the overall pattern of activated brain areas.
Note: some contents above comes from Wikipedia
A sample from http://www.city.ac.uk/psychology/research/CNRU/eegintro.html,This is an averaged ERP signal, in which you can see the responses (positive and negative components).Basic research are based on the measurements of this type of components.
Friday, September 7, 2007
Wavelet Coherence Method
The coherence function is a direct measure of thecorrelation between the spectra of two random processes. Fourier method can provide accurate estimates of stationary signals. However, Most signals ( including our signals from brains) are non-stationary waveforms.
Wavelet coherence method was first used in physics to estimate interactions among non-stationary signals, now it has been applied in investigating neuroelectric waveforms such as EEG signals.
First of all, signals are decomposed along the Morlet wavelet family, the advantage of which is that it is simple and well suited for spectral estimations. It is defined for frequency f and time τ by:
The wavelet transform of a signal x(u) is a function of time (τ) and frequency ( f ) given by the convolution of x with this wavelet family:
From the wavelet transforms of two signals x and y, we can define the wavelet cross-spectrum between x and y around time t and frequency f
Whereδis an important parameter which depends on the frequency
Finally, analogous to the Fourier-based coherence, the wavelet coherence WCo(t, f) is defined at time t and frequency f by:
WCo(t, f) takes its values between 0 and 1, the bigger this value, the more dependence between x and y around time t and frequency f.
NOTE: It is a brief introduction of Wavelet Coherence Method. For further information about this method, please refer to relevant papers.
References of this article:
Lachaux J-P, Lutz A, Rudrauf D, Cosmelli D, Quyen MLV, Martinerie J,Varela F. Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence. Neurophysiol Clin., 2002, 32: 157-174.
Li X, Yao X, G J, Jefferys,Fox J. Interaction Dynamics of Neuronal Oscillations Analysed Using Wavelet Transforms. J. Neurosci. Methods, 2007, 160(1): 178-185.
Wavelet coherence method was first used in physics to estimate interactions among non-stationary signals, now it has been applied in investigating neuroelectric waveforms such as EEG signals.
First of all, signals are decomposed along the Morlet wavelet family, the advantage of which is that it is simple and well suited for spectral estimations. It is defined for frequency f and time τ by:
The wavelet transform of a signal x(u) is a function of time (τ) and frequency ( f ) given by the convolution of x with this wavelet family:
From the wavelet transforms of two signals x and y, we can define the wavelet cross-spectrum between x and y around time t and frequency f
Whereδis an important parameter which depends on the frequency
Finally, analogous to the Fourier-based coherence, the wavelet coherence WCo(t, f) is defined at time t and frequency f by:
WCo(t, f) takes its values between 0 and 1, the bigger this value, the more dependence between x and y around time t and frequency f.
NOTE: It is a brief introduction of Wavelet Coherence Method. For further information about this method, please refer to relevant papers.
References of this article:
Lachaux J-P, Lutz A, Rudrauf D, Cosmelli D, Quyen MLV, Martinerie J,Varela F. Estimating the time-course of coherence between single-trial brain signals: an introduction to wavelet coherence. Neurophysiol Clin., 2002, 32: 157-174.
Li X, Yao X, G J, Jefferys,Fox J. Interaction Dynamics of Neuronal Oscillations Analysed Using Wavelet Transforms. J. Neurosci. Methods, 2007, 160(1): 178-185.
Thursday, September 6, 2007
In the 20th International CODATA Conference
The 20th International CODATA Conference: Scientific Data and Knowledge within the Information Society, was held during October 23-25th 2006 in Peking. We had two posters there. Some photos and our posters are shown as below.
Welcome Reception
Palace Convention Hall
Wonderful presentations
Our posters and me
Our poster
Welcome Reception
Palace Convention Hall
Wonderful presentations
Our posters and me
Our poster
Our poster
Cocktails
Wednesday, September 5, 2007
Tips about Wavelet
This introduction about Wavelet is summarized from matlab wavelet tutorial, for this tutorial, in my opinion, is concise and easy to understand.
From Fourier to Wavelet
In an effort to correct this deficiency, Dennis Gabor (1946) adapted the Fourier transform to analyze only a small section of the signal at a time -- a technique called windowing the signal. Gabor's adaptation, called the Short-Time Fourier Transform (STFT), maps a signal into a two-dimensional function of time and frequency.
Fig.2 Wavelet Transform
NOTE: Most of the information in this article comes from MATLAB tutorial, many thanks for MATLAB! The knowledge here are only basic tips, so if you want more details, see relevant books or papers please!
From Fourier to Wavelet
Fourier method is the most well-known method for signal analysis. It breaks down a signal into constituent sinusoids of different frequencies, in other words, it transforms the signals from time-based to frequency-based.
Fig.1 Fourier Transform
Although this useful technique has been wildly used by people for a long time, it has a serious drawback: time information is lost during the transform, thus Fourier is not suited to detecting nonstatinary signals, such as EEG.
Fig.1 Fourier Transform
In an effort to correct this deficiency, Dennis Gabor (1946) adapted the Fourier transform to analyze only a small section of the signal at a time -- a technique called windowing the signal. Gabor's adaptation, called the Short-Time Fourier Transform (STFT), maps a signal into a two-dimensional function of time and frequency.
Fig.2 Short Time Fourier Transform
However, in STFT a particular size for time window should be chosen, the drawback is that this window can not provide a flexible analysis for all frequencies.
Wavelet analysis represents the next logical step: a windowing technique with variable-sized regions. Wavelet analysis allows the use of long time intervals where we want more precise low-frequency information, and shorter regions where we want high-frequency information.
Fig.2 Wavelet Transform
NOTE: Most of the information in this article comes from MATLAB tutorial, many thanks for MATLAB! The knowledge here are only basic tips, so if you want more details, see relevant books or papers please!
What is EEG?
Electroencephalography is the neurophysiologic measurement of the electrical activity of the brain by recording from electrodes placed on the scalp or, in special cases, subdurally or in the cerebral cortex. The resulting traces are known as an electroencephalogram (EEG) and represent a summation of post-synaptic potentials from a large number of neurons. These are sometimes called brainwaves, though this use is discouraged, because the brain does not broadcast electrical waves.The EEG is a brain function test, but in clinical use it is a "gross correlate of brain activity". Electrical currents are not measured, but rather voltage differences between different parts of the brain.
The basic knowledge above comes from Wikipedia. It can give you a concept: EEG is a kind of signal from brain.
There are some details about the EEG data in our lab in Nankai University.
Some of the EEG data come from brains of human beings, including epileptic EEG signals from patients in hospitals, EEG from healthy people and ERP (Event Related Potentials) raw data from cognization research centers. Most of these signals have been collected in a database, you can see the articles with the tag database for more introduction.
Some EEG data were obtained from rats, which were healthy or in ill condition (ischemia model or epilepsy model). Signals from animals may be much more easier for us to detect the things happening in the brain. We recorded the data from the hippocampus and also the cortex.
Obviously, our purpose of calculating these data is to investigate the brain and to find out different features in the nerve systems in different physiology conditions. The analysis methods will be discussed in other articles.
The basic knowledge above comes from Wikipedia. It can give you a concept: EEG is a kind of signal from brain.
There are some details about the EEG data in our lab in Nankai University.
Some of the EEG data come from brains of human beings, including epileptic EEG signals from patients in hospitals, EEG from healthy people and ERP (Event Related Potentials) raw data from cognization research centers. Most of these signals have been collected in a database, you can see the articles with the tag database for more introduction.
Some EEG data were obtained from rats, which were healthy or in ill condition (ischemia model or epilepsy model). Signals from animals may be much more easier for us to detect the things happening in the brain. We recorded the data from the hippocampus and also the cortex.
Obviously, our purpose of calculating these data is to investigate the brain and to find out different features in the nerve systems in different physiology conditions. The analysis methods will be discussed in other articles.
Monday, September 3, 2007
ERPDB: a Database of ERP Data for Neuroinformatics Research
Event-related potential (ERP) is the measurement of the brain's electrical activity in response to different types of events. These signals are useful for learning how information is processed in the human brain. Being a neuroinformatics laboratory, we deal with lots of ERP signals, which come from various research programs. Different programs have different experiment designs, different parameters settings, and different data formats. These could be big problems for our study. For this reason, arranging the data and constructing a database to store them is quite necessary.
ERPDB contains the information of the participants and the rearranged signals.The data have been transformed into more organized data, which are more convenient for further computation and analysis. Also, new data can be easily added into the database according to our standard. This database is available on the web.
Publications:
Qinghong Yan, Ning Zhang, Jie Wu. The Constitution of ERP Database Based on SQL Server2000. The 20th International CODATA Conference (CODATA2006 ABSTRACT，China，Beijing):2006, 308
QingHong Yan, Ning Zhang, Jie Wu, Tao Zhang. ERPDB: an integrated database of ERP datafor neuroinformatics research, Data science journal, 2007
ERPDB contains the information of the participants and the rearranged signals.The data have been transformed into more organized data, which are more convenient for further computation and analysis. Also, new data can be easily added into the database according to our standard. This database is available on the web.
Publications:
Qinghong Yan, Ning Zhang, Jie Wu. The Constitution of ERP Database Based on SQL Server2000. The 20th International CODATA Conference (CODATA2006 ABSTRACT，China，Beijing):2006, 308
QingHong Yan, Ning Zhang, Jie Wu, Tao Zhang. ERPDB: an integrated database of ERP datafor neuroinformatics research, Data science journal, 2007
Sunday, September 2, 2007
SheetsPair: a Database of Amino Acids Pairs in Protein Sheet Structures
Sheet is a basic secondary structural element of proteins. To provide a data resource for further analysis on "Sheet", a database which contains a large number of sheet structures was constructed.
The protein dataset utilized to populate the database was obtained from PDB, excluding those that have no sheet structure and those with modified residues, that is, the database is rigorous, precise and faithworthy, with no uncertain residues or modified residues or uncertain structures. The database now contains a total of 756,897 amino acids pairs in sheet structures of 10,704 proteins. There are more details in the publications.
The website interface is shown below:
The protein dataset utilized to populate the database was obtained from PDB, excluding those that have no sheet structure and those with modified residues, that is, the database is rigorous, precise and faithworthy, with no uncertain residues or modified residues or uncertain structures. The database now contains a total of 756,897 amino acids pairs in sheet structures of 10,704 proteins. There are more details in the publications.
The website interface is shown below:
Publication:
Ning Zhang, Jie Wu, Tao Zhang. SheetsPair: a database of amino acids pairs in protein sheetstructures. The 20th International CODATA Conference (CODATA2006 ABSTRACT，China，Beijing): 295
Ning Zhang, Jishou Ruan, Jie Wu, Tao Zhang. SheetsPair: a database of amino acids pairs inprotein sheet structures, Data science journal, 2007
An Epilepsy EEG Database
Research on Epilepsy EEG is a big part in our lab-- Biocomputation and Neuroinformatics Laboratory. By analyzing the EEG signals in rat models, we aim to find some way to predict seizures before they begin, which could be helpful for treatment of epilepsy. Some samples of human epilepsy EEG were also collected from hospitals and research centers. For the data formats are distinctive, there was some problems to analyze them together.
Being a preparation for a further investigation, an EEG database was constituted. EEG signals from healthy and epilepsy humans together with the annotations were collected and standardized in this database.
The website of this database is shown as below.
Publication: Zhang Ning,Wu Jie,Yang Zhuo,et al,Constitution of an EEG database based on SQL server. Chinese Chinese High Technology Letters,2006,16(12): 48-52.
Being a preparation for a further investigation, an EEG database was constituted. EEG signals from healthy and epilepsy humans together with the annotations were collected and standardized in this database.
The website of this database is shown as below.
Publication: Zhang Ning,Wu Jie,Yang Zhuo,et al,Constitution of an EEG database based on SQL server. Chinese Chinese High Technology Letters,2006,16(12): 48-52.
Overview of the Databases
Several databases were constituted, including Sheetspair Database, ERPDB, epileptic EEG Database, and a database for Prediction of Protein Secondary Structures. These databases are created to make the data more effecient and simpler for further investigation. The data had been carefully parsed and standardized before they were stored into the databases, thus accurate and convenient sequences can be easily retrieved for further analysis. Also, the reorganized data can be shared in the web, and fresh data can be added in a simple way.
The databases above were constituted in Nankai University, all of them were based on SQL Server and websites were built for each database, providing friendly interfaces.
The details of these databases will be introduced in other articles of this blog.
The databases above were constituted in Nankai University, all of them were based on SQL Server and websites were built for each database, providing friendly interfaces.
The details of these databases will be introduced in other articles of this blog.
Monday, August 27, 2007
Entering the Realm of Bioinformatics
Early in the middle school, just in my first biology lesson, I was totally captured by this fantastic science---Biology. Compared to other sciences, in my opinion, biology is more practical. With the knowledge in other fields—physics, chemistry, mathematics, even social sciences, biology helps us to know better about ourselves. Being human beings, to understand ourselves can be the most important thing. Indeed, LIFE is the most wonderful and complex thing in the world, it worth a profound study.
Due to my great interests in biology, the two biology teachers in my middle school helped me a lot and both suggested me to take biology as my major in the college. Considering that studying biology with physics theory would be more interesting, I chose Biophysics as my major in Nankai University. In those four years I took many basic courses, not only in biology but also in physics. I got a much deeper understanding of biology in my college. However, I didn't feel my knowledge was sufficient. An advanced study was badly required.
In the year of 2004, I first stepped into the field of bioinformatics.That's in Professor Zhang's Bio-computation and Neuroinformatics Laboratory in Nankai University. There I began to do something about Neuroinformatics and Molecule Informatics. We deal with signals obtained from rats' nerve system. By comparing EEG, RSNA ( Renal sympathetic nerve signals) among disease group and normal group, we aim to find some different features. The analysis methods mainly include non-linear analysis and time-frequency analysis. Besides, we also do something about Molecule informatics, like protein sequence and structure analysis, DNA sequence analysis, and so on. I should say, I learned a lot in that team.
Now I am working in Shanghai Institute of Materia Medica, Chinese Academy of Sciences. I still do some analysis job here, like collecting molecule data from the internet, analyzing the sequences, constituting databases. I hope that I can learn something more advancing and do some profound research with great happiness.
Due to my great interests in biology, the two biology teachers in my middle school helped me a lot and both suggested me to take biology as my major in the college. Considering that studying biology with physics theory would be more interesting, I chose Biophysics as my major in Nankai University. In those four years I took many basic courses, not only in biology but also in physics. I got a much deeper understanding of biology in my college. However, I didn't feel my knowledge was sufficient. An advanced study was badly required.
In the year of 2004, I first stepped into the field of bioinformatics.That's in Professor Zhang's Bio-computation and Neuroinformatics Laboratory in Nankai University. There I began to do something about Neuroinformatics and Molecule Informatics. We deal with signals obtained from rats' nerve system. By comparing EEG, RSNA ( Renal sympathetic nerve signals) among disease group and normal group, we aim to find some different features. The analysis methods mainly include non-linear analysis and time-frequency analysis. Besides, we also do something about Molecule informatics, like protein sequence and structure analysis, DNA sequence analysis, and so on. I should say, I learned a lot in that team.
Now I am working in Shanghai Institute of Materia Medica, Chinese Academy of Sciences. I still do some analysis job here, like collecting molecule data from the internet, analyzing the sequences, constituting databases. I hope that I can learn something more advancing and do some profound research with great happiness.
Friday, August 24, 2007
Overview of this Blog
Writing this blog is to show myself to all the world, I should thank Z. Song for his wonderful idea--to show myself on the website. Actually, this blog will mainly include things about my study, my research, my interest, and so on. Since this blog is for people who may come from any countries except for China, it is writen in English.
This blog will introduce myself from following aspects:
This blog will introduce myself from following aspects:
- What I have done in my study.
- What I am doing in my study (research).
- What I am going to do in the future.
- My thoughts and my interests.
This blog will keep being updated. Thank you for your coming to my blog! !
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