nuclear magnetic resonance spectroscopy ( nmr , or mrs ) has enormous potential for the study of biochemical and physiological changes in cancer tissues , due to its noninvasive nature and the large quantity of specific molecular information it can generate . despite the sensitivity limitations of the technique , the inherent complexity of the spectra , and inevitable presence of overlapping resonances , there have been several successful nmr - metabonomics studies of cell tissue culture and culture extracts .
the focus has been on elucidating the physiopathology of tumors and tumor cells , their drug toxicology and drug resistance , often with a view to identifying diagnostic markers [ 18 ] .
a further significant complication in such studies arises from variability in the metabolite profile from sample to sample .
this reflects many factors   including minor variations in growing conditions , the biochemical heterogeneity of the growing cells , the effect of different batches of sera ( if used ) , and variations in cell and sample preparation .
these additional factors may mask the inherent metabolite distribution , which may be diagnostic of the pathophysiological state of interest .  
experimental complications and difficulties also compromise the extraction of critical information from in vivo mrs experiments . in this case
, the problems arise from the use of different mr - protocols , which affect the quality of the water suppression , differences in echo time and in the baseline , and so forth .
while the causes are different in origin , they have a similar effect on the application . for both forms of magnetic resonance ,
many of these issues can , in principle , be addressed by improved experimental design , however , it is common for additional sources of variance to be identifiable only after extensive experimentation .
in addition to technical issues are the natural physiological variability and the individual treatment history of the subject . as a result
, there is an ongoing requirement for the development of magnetic resonance - based diagnostics using advanced statistical- , or other data- , analysis techniques which can reduce or compensate for additional sources of variability . 
				
h nmr spectra of intact tissues or whole - cell samples are inherently complex due to the large number of contributing species which results in significantly overlapping resonance signals
. cell membranes also produce magnetic field inhomogeneity , further broadening the spectra . in the case of cancer cells , a significant proportion of the lipids reside in a fluid environment and hence appear in the liquid - state h spectra as strong  mobile - lipid  resonances [ 7 , 8 , 11 ] .
although the identification of the major resonances in h nmr spectra can be used to characterise the metabolite profile , the complexity of the data sets usually necessitates the use of data reduction and pattern recognition techniques .
these can provide information on the biochemical and physiological changes in cancer tissues , related to their physiopathology , drug toxicology , and drug resistance [ 12 , 13 ] .
prominent amongst such techniques is principal component analysis ( pca ) , [ 14 , 15 ] which involves diagonalisation of the spectral correlation or covariance matrix to identify independent sources of variance ( principal components ) across the set of spectra , and ranking of the components by their contribution to the overall variance .
thus , pca is an unsupervised approach to data reprojection that can reveal the presence of classes , it has been applied to a variety of problems in biological science [ 16 , 17 ] . artificial neural networks ( anns ) belong to the so - called artificial intelligence group of methods , which were inspired by neurobiology and by the architecture of the human brain . in recent times
for example , they have been used in chemotaxonomy to classify limpets   from hplc mass spectrometric data and in the identification of insect species from morphological measurements .
anns can be used to model data where the relations , or functions , are not known .  
there have been some reports of the use of artificial intelligence and network methods in medical diagnostics which have involved analysis of magnetic resonance spectroscopic data .
el - deredy et al .   used anns to achieve reasonable prediction of the measured in vitro chemotherapeutic response from h nmr of glioma biopsy extracts .
more recently , suna et al .   demonstrated the diagnostic potential of unsupervised approaches to classification by successfully analysing simulated h nmr spectra using self - organising maps .
this approach allowed the identification of stages along a metabolic pathway ranging from  normolipidaemic  to  metabolic syndrome  .
tate and coworkers   reported the trial of an automated decision support system for classification of brain tumors from in vivo mrs , which showed a small but significant improvement in diagnostic accuracy over spectroscopy used and interpreted on its own . in recent work , we reported pca of  h nmr spectra recorded for a group of human lung carcinoma cell lines in culture and h nmr analysis of extracts from the same samples .
the samples studied were cells of lung tumor origin with differing chemotherapy drug resistance patterns .
for whole - cell samples , it was found that the statistically significant causes of spectral variation were an increase in the choline and a decrease in the methylene and mobile lipid h resonance intensities , which were correlated with our knowledge of the level of resistance displayed by the different cell lines . in this paper
, we investigate the use of artificial neural network ( ann ) , a supervised method , to classify lung carcinoma .
these were recorded for two groups of human lung carcinoma cell lines , these were grown in culture and characterised over two different periods by two different groups of researchers ( each consisting of a biologist and a spectroscopist ) , who both adhered to the same experimental protocol and used the same spectrometer .
the cell lines studied include ( i ) the parent cell line dlkp , a human squamous nonsmall cell lung carcinoma ; ( ii ) dlkp - a ; ( iii ) dlkp - a5f , two resistant daughter lines ; ( iv ) a549 , a human lung adenocarcinoma cell line .
the study also examines the capability of supervised techniques to compensate for experimental sources of variance , which may include operator bias and the cell culture growth process and in particular provide a test case for the application of ann architectures in the identification and monitoring of resistance states in cancer tissue by mrs .
the cell lines dlkp [ 25 , 26 ] , dlkp - a , dlkp - a5f , and a549 were grown in culture to approximately 7080% confluency in 175  cm tissue culture flasks .
culture conditions were as follows : dlkp , dlkp - a , and dlkp - a5f and were cultured in minimal essential medium / hams f12 ( 1  :  1 , v / v ) supplemented with 5% fetal calf serum and 2  mm l - glutamine .
a549 was cultured in dulbecco 's modified eagle 's medium / hams f12 ( 1  :  1 , v / v ) supplemented with 5% fetal calf serum .
cells were cultured as monolayers in tissue culture flasks and incubated at 37c . a cell count was performed and c. 5  10 cells were separated and pelleted .
these were then resuspended in deuterated pbs buffer and were kept in a container at 37c before the start of the nmr measurements .
dlkp cells express a small amount of the multidrug resistance protein-1 ( mrp-1 ) mdr drug efflux pump [ 25 , 26 ] .
dlkp - a   is a highly resistant clone of dlkp , which overexpresses the p - gp drug efflux pump .
dlkp - a5f   was derived from dlkp by a different drug exposure profile , it is also highly drug resistant .
a549 is an unrelated human lung adenocarcinoma cell line which was obtained from the american type culture collection .
the first group of 13 samples , g1_13_21 , were grown by a biologist during a six - month period , they were analysed by a first nmr spectroscopist .
g1_13_21 contained 21 spectra and so was relatively sparse , it comprised dlkp [ 4 samples , 6 spectra ] , dlkp - a [ 4 , 6 ] , dlkp - a5f [ 3 , 5 ] , and a549 [ 2 , 4 ] .
the second group of 17 samples , g2_17_33 , was grown independently , by a second biologist during a later six - month period and was analysed by a second spectroscopist .
g2_17_33 contained 33 spectra , it comprised dlkp [ 3 , 6 ] , dlkp - a [ 5 , 10 ] , dlkp - a5f [ 5 , 9 ] , and a549 [ 4 , 8 ] .
thus for the integrated study presented here , a total of 30 samples were prepared and 54 h spectra was recorded .
the same protocols and methods were used by all the researchers for cell growth and nmr spectroscopy .
the biologist and spectroscopist who produced g1_13_21 will be collectively referred to as r1 , and the biologist and spectroscopist who produced g2_17_33 will be referred to as r2 . due to the significant work involved in producing the large number of cells required for each spectrum , the number of samples in the study is inevitably somewhat limited . however , the total data set is larger than those usually reported in the analysis of nmr data by pattern recognition methods [ 16 , 17 , 29 ] .
nmr spectra of the intact cell samples were recorded in deuterated pbs buffer on a bruker dpx 400 spectrometer operating at 400.13  mhz for h. before all nmr experiments , the sample temperature was calibrated and controlled at 36.4  0.2c using an internal ethylene glycol thermometer ( 80% solution of ethane-1,2-diol in dimethyl sulfoxide - d ) .
h nmr spectra were acquired , without spinning , using wet   solvent suppression , with two carr - purcell - meiboom - gill ( cpmg ) echoes appended , using an echo delay of 1  ms .
chemical shifts were referenced to an external 0.1% solution of sodium trimethylsilyl-[2,2,3,3-d4]-propionate ( tsp ) in d2o .
all experiments were performed with a spectral width of 5200  hz , an acquisition time of 3.15  s , and relaxation delay of 2  s. three acquisition schemes were used to record the one - dimensional h nmr spectra , all amounting to 128 scans . the first scheme ( i ) employed cycles of 16 dummy scans followed by four acquisition scans , ( 16,4)32 , giving an acquisition time of 3/4 hour . in the second scheme ( ii ) , 16 dummy scans were applied once prior to acquisition 16((0,16)8 ) , giving an acquisition time of 13 minutes . in the third scheme ( iii ) ,
16 dummy scans and 128 acquisition scans were collected into 32  k data points , giving an acquisition time of 15 minutes .
the time taken from resuspension to the start of data acquisition was typically less than 3/4 hour , and never more than 1 hour .
all the data presented were recorded within 1 hour . for the first group of 13 samples ( g1_13_21 ) in the study ,
the acquisition schemes ( i ) and ( ii ) were used for each sample . for the second group of 17 samples ( g2_17_33 ) ,
the inclusion of multiple spectra in the analysis from the same sample tests the stability of the samples over the time of the analysis .
the insensitivity of the spectra to the sampling scheme used demonstrates that the samples do not change , for example , due to sedimentation , over the timescale that a single spectrum is acquired . in the spectral region from 1.08 to 1.20  ppm , ethanol was observed , which was probably the result of endogenous processes . however , its intensity was highly variable , even within the same cell line , so this region was excluded from the analysis .
the region containing the residual water resonance signal ( 3.566.05  ppm ) was also excluded .
the region above 6.05  ppm contained no features of sufficient intensity for reliable quantification , given the linewidth .
for this study , we chose , as descriptors , the integrals over chemical shift regions ( bins ) of size 0.04  ppm   which was found to produce the clearest separation of the cell types in the scores plots and the least noise in the corresponding loadings plots .
thus , the nmr spectra were reduced to 71 descriptors , with bin centres in the range 0.601.04 , 1.243.56  ppm .
we adopted the conventional approach   of normalisation relative to the total sum of the bin intensities in the region of interest .
all the measures were implemented through writing an matlab ( version 6.5.1 , the mathworks inc . ) code making use of the built in eigensolver .    
anns are a sophisticated computational modelling tool , which can be used to solve a wide variety of complex problems .
the attractiveness of anns comes from their capability to  learn  and/or model very complex systems and from the possibility of using them in classification .
an ann is a computational model formed from a certain number of single units , artificial neurons , or nodes , connected with coefficients ( weights ) , wij , which constitute the neural structure .
the network is conventionally constructed with three or more layers , that is , input , output , and hidden layers , figure 1 .
the input layer receives the information about the system ( the nodes of this layer are simple distributive nodes , which do not alter the input value at all ) .
the hidden layer processes the information initiated at the input , while the output layer is the observable response or behaviour .
the inputs , inputi , multiplied by connection weights wij are first summed and then passed through a transfer function to produce the output , outi .
the determination of the appropriate number of hidden layers and number of hidden nodes in each layer is one of the most critical tasks in ann design . unlike the input and output layers ,
the use of ann consists of two steps :  training  and  prediction  .
the  training  consists first of selecting input and output data for the network .
this data is referred to as the training set . in the training phase , where actual data must be used , the optimum structure , weight coefficients and biases of the network are identified .
training is considered complete when the neural networks achieve the desired statistical accuracy , that is , when they produce the required outputs for a given sequence of inputs . a good criterion to find the correct network structure and therefore to stop
the learning process is to minimise the root mean square ( rms ) error as follows : 
								 ( 1)rms=i=1nj1m(yijoutij)2nm , 
							 where yij is the element of the matrix ( n  m ) for the training set or test set , and outij is the element of the output matrix ( n  m ) of the neural network , where n is the number of variables in the pattern , and m is the number of samples .
after a supervised network performs well on training data , it is important to check its performance with data that has not been used in training .
this testing is critical to insure that the network has not simply memorised the training set but has learned the general patterns involved within an application . at this stage ,
other input data are submitted to the network in order to evaluate if it can predict the outputs . in this case
, the outputs are already known , but they are not shown to the network .
the predicted value is compared to the experimental one to see how well the network is performing .
if the system does not give reasonable outputs for this test set , the training period is not over or the network is able to model the data but can not predict them .   in this work , ann was used as a supervised method where a training data set was created from the library of nmr spectra , and the lung carcinoma classification of this training data set was known .
firstly , the optimal ann architecture was searched for and when the correct classification in the training phase was obtained , the usefulness of the created database and the prediction power of the networks were validated using an independent verification set . for the ann analysis , we used 72 inputs ; the 71 binned nmr intensities and the identity of the pairs of researchers ( r1 and r2 ) as numbers 1 and 2 . for output 4 ,
nominal values were used , these identify the four cell lines , dlkp , dlkpa , dlkp - a5f , and a549 , for which there were 12 , 16 , 14 , and 12 spectra , respectively .
all calculations were performed using the software trajan neural network simulator , release 3.0  d. ( trajan software ltd 19961998 , uk ) , on a standard pc computer running microsoft windows professional xp 2000 .
the appearance of the spectra and the assignment suggested below are broadly similar for all the cell samples analysed . a tentative assignment which is consistent with the literature [ 2 , 4 , 32 , 33 ] is included in the figure .
direct quantitative analysis of the whole - cell spectra is hampered by the potential multiple contributions from different metabolites to any given resonance line by the nonlorentzian lineshapes and by the broadness of the resonance lines .
the resonances in the downfield region arise from species that are at low concentration , so quantification is precluded by the sensitivity limitations of the nmr measurement .
separation of the four cell types , within each of the two data sets , is apparent using the first two pcs , demonstrating that resistance type can be classified by pca .
it also demonstrates that the samples were stable over the course of the experiment and that the spectra are insensitive to the nmr sampling scheme .
loadings analysis shows that , for each data set , the spectral regions that contribute significantly to the first two principal components are from 1.24 to 1.50  ppm , corresponding to overlapped resonances from lipid methylenes and lactate methyls , and from 2.90 to 3.40  ppm , corresponding to overlapped resonances from n - methyl signals in the choline moieties of phosphatidylcholine , phosphocholine , and glycerophosphocholine . the contribution from other spectral regions to these two principal components is marginal .   despite
the fact that the same spectral regions allow separation within each data set , separation using pca fails when the two sets of spectra are combined into one ; see supplementary material available at doi:10.1155/2011/158094 .
it is apparent that , in addition to the metabolite differences of biological interest , there are subtle differences between g1_13_21 and g2_17_33 in the distribution of metabolites , which prevent classification of the entire ( 54 spectra ) data set .
the loadings analysis indicates contributions from across the spectral range , which may suggest variations in more than one metabolite .
these spectral differences arise despite stringent efforts of the second group of researchers to adhere to the original experimental protocols and are reflected in the fact that there is not a simple correspondence between the orientation of the first two principal components between the two sets of spectra , figure 3 .
, we adopted the strategy of choosing multiple verification sets of spectra at random from the 54 spectra available . in training , the first aim is to find an optimal ann architecture to enable classification of the training data set .
initially we adopted the simplest 3 layers architecture , in which case the search of the optimal architecture consists of optimising the number of nodes in the single hidden layer , effectively determining the corresponding weights , wij , to minimize the rms ( root mean square error ) value according to ( 1 ) . for our analysis ,
the rms value ceases to decrease significantly above 5 to 6 nodes , figure 4 , we therefore used networks with 6 hidden nodes for verification .
this optimal architecture will be labelled ( 72 , 6 , 4 ) , with it we obtained an rms = 1.38  10 .
figure 4 illustrates the process of searching for the optimal network architecture . in spite of the fact that very low values for the residual mean squares were achieved using the ( 72 , 6 , 4 ) architecture , the appropriateness of the architecture and of the training set
initially , five spectra were randomly chosen and excluded from the training set and used then as the verification set . from 10 combinations and 10 independent networks trained , in only two cases were any of the 5 spectra classified as unknown , table 1 .
these results are encouraging ; two cases represent ~4% of the total , so for ( 72 , 6 , 4 ) the classification was verified as 96% successful .
the failures may have arisen due to an insufficient number of spectra in the training set or because networks with three layers have insufficient complexity for 100% prediction accuracy , in this case .  
from several cases examined , it was found that four - layer ann architectures performed similarly to simpler three layers architectures .
networks of the form ( 72 , 4 , 3 , 4 ) or ( 72 , 5 , 4 , 4 ) were investigated , note that the numbers in brackets refer to the number of inputs , the number of nodes in the first and in the second hidden layers , and the number of outputs .
acceptable rms values , of 1.22  10 and 1.41  10 were obtained for ( 72 , 4 , 3 , 4 ) and ( 72 , 5 , 4 , 4 ) , respectively , which are similar to the values obtained using the optimal three - layer architecture .
networks with the architecture ( 72 , 4 , 3 , 4 ) performed very similarly to ( 72 , 5 , 4 , 4 ) and require fewer unknowns ( or weights , wij ) , 312 as opposed to 396 . as a result , ( 72 , 4 , 3 , 4 ) was found to converge faster and to be less sensitive to the number of spectra excluded from training to form the verification set .
in fact , we found that 5 to 10 samples could be used for verification with 100% correct classification of the spectra , see table 1 .
so in summary , the optimal 3- and 4-layer architectures were found to be ( 72 , 6 , 4 ) and ( 72 , 4 , 3 , 4 ) , respectively , figure 5 .
the h nmr spectra of intact cells for both g1_13_21 and g2_17_33 have similar general appearance with severe signal overlap and line broadening .
reprojection of either data set , using pca , demonstrates that separation by cell types is possible due to systematic differences in the lipid methylene and lactate methyl resonances and the overlapped n - methyl h nuclei of the choline - containing species .
alterations in signal intensity and chemical shift from such cellular metabolites and biochemical intermediates have been described by other researchers in the area [ 6 , 11 ] . however , because of the complex biochemical role played by these substances , we can not ascribe a particular functional role to the findings , what is more the alterations appear to correlate and associate with particular phenotypic changes , for example , drug resistance .  
on the basis of the principal component analysis of either group , one could speculate that metabolite profiling by in vivo mrs has potential applications in monitoring the development of resistance in a given cancerous tissue . however , for the full data set such a possibility is effectively prevented by other influences on the metabolite distribution , which are comparable to , and nonorthogonal with , the  relevant  biochemical variation .
we have shown that this significant obstacle can be eliminated , at least for in vitro studies of cell culture , by using a suitable ann architecture .
after appropriate training , the ( 72 , 4 , 3 , 4 ) architecture enabled 100% successful classification . our approach may , in time , be expanded to the classification of larger data sets of spectra which have been recorded with less stringent control over sources of variance unrelated to the classification of interest .
this result is encouraging and it is , to our knowledge , the first reported application of the use of anns specifically to correctly classify h nmr spectra in a data set when additional  nonrelevant  sources of variance are included .  
other related examples of the combination of supervised and unsupervised methods include a report by griffiths and coworkers , who obtained 85% accurate classification of meningiomas from nonmeningiomas , by initially using pca to reduce the dimensionality of h nmr spectra recorded for tumor biopsy extracts .
the first thirty pcs from this first stage of analysis were then classified using a network .
more recently , the performance of lineshape fitting and quantitative ann analyses were compared by hiltunen et al .   for both in vivo and simulated h spectra .
the good correlation obtained with these two approaches , for simulated data at least , suggested that anns have potential for quantification of in vivo mrs long echo time spectra .
a further advantage of anns in the development of analysis methods for in vivo mrs is that they require less processing time than line fitting or other computational approaches .
thus , our study adds to the growing number of applications of supervised techniques for exploiting the diagnostic potential of h nmr spectra for biomedical purposes .
we have found that nmr data recorded for human lung carcinoma whole - cell culture samples can be used for analysis and classification . when sources of variation not directly related to the biological state of interest ( drug resistance ) are minimised or kept constant , visual separation of the cell type
can be achieved using unsupervised pattern recognition techniques , such as pca . on the other hand ,
when this condition is not met , in our case when different researchers were responsible for cell culture and spectroscopy , successful classification of the cell type could be achieved using artificial neural networks .
the experimental and ann methodology developed are a step towards the goal of robust and reliable diagnostics based on magnetic resonance spectral data .
furthermore , as similar experimental problems may be encountered in metabolomics applications using other spectroscopic techniques , biological classification using anns of data sets that include  nonbiological  sources of variance may be generally possible .
 we report the successful classification , by artificial neural networks ( anns ) , of    1h nmr spectroscopic data recorded on whole - cell culture samples of four different lung carcinoma cell lines , which display different drug resistance patterns . 
 the robustness of the approach was demonstrated by its ability to classify the cell line correctly in 100% of cases , despite the demonstrated presence of operator - induced sources of variation , and irrespective of which spectra are used for training and for validation . 
 the study demonstrates the potential of ann for lung carcinoma classification in realistic situations . 
1. Introduction
2. Experimental
3. Results
4. Discussion
5. Conclusions