Patent Application
20060259246
The present invention relates generally to analysis of biological data. More particularly, it relates to methods for mining broad data sets of biological measurements to identify subsets of measurements that are predictive of clinical endpoints such as clinical classifications (e.g., disease conditions or responses to drug therapy) or continuous clinical response variables (e.g., degree of disease progression).
An important goal of a growing number of biological researchers is to discover and identify novel biological markers. A biological marker, or biomarker, is a characteristic that is measured and evaluated as an indication of normal biological processes, pathogenic processes, or pharmacological responses to therapeutic intervention. New biomarkers are being sought to enable diseases to be diagnosed more accurately or earlier than is currently possible. Responses to drug therapy can also be gauged earlier and more accurately using biomarkers, promising to accelerate the progress and reduce the cost of clinical trials. Biomarker discovery is concentrated primarily on chronic diseases for which many of the complex pathogenic mechanisms are still unknown, such as Alzheimer's disease, rheumatoid arthritis, and diabetes.
Although a variety of approaches are possible for biomarker discovery, one of the most promising is the so-called shotgun approach, in which enormous volumes of biological measurements are acquired from different classes of subjects and then mined to identify biomarkers capable of distinguishing among the subject classes or otherwise predicting clinical endpoints. The philosophy behind this approach is that any type of measurement may be important to a particular disease, and so measurements should not be constrained to those known to be relevant. The shotgun approach has been made possible in recent years through advances in high-throughput measurement technologies such as gene chips, protein chips, and mass spectrometry. These tools are capable of detecting hundreds of thousands of proteins and small organic molecules within tiny volumes of biological materials, resulting in high volumes of measurement data. In fact, the current bottleneck in biomarker discovery is not in obtaining varied biological data, but in managing and analyzing the generated data.
One of the problems is that data mining techniques developed for financial or commercial applications are not directly applicable to the biotechnology domain. Because of the context in which they are acquired, biological measurement data are fundamentally different from other data types. Biomarker discovery is commonly performed on data gathered from clinical studies investigating a particular condition or set of conditions or a particular drug treatment. Studies are described by a well-characterized collection of subjects, a particular sample type (e.g., blood) and conditions for sample acquisition, and specific measurement methods. The table of
One important characteristic of biological data sets such as that of
A number of biomarker discovery methods have been proposed in the prior art. In general, these methods are not scalable to broad data sets with hundreds or thousands of measurements per observation, but apply only to data sets with dimensionality of a few hundred or fewer, and particularly to data sets having more observations that dimensions. For example, PCT publication number WO 01/44269 discloses novel brain protein markers indicative of a neurological disorder. 217 proteins were identified using two-dimensional gel electrophoresis, and a multivariate analysis revealed that eight of the proteins were related to one or more psychiatric diagnoses. In addition, a principle component analysis was performed to identify a panel of 19 proteins capable of distinguishing between normal and depression samples. While these techniques are useful for identifying important factors from a relatively small collection of potential biomarkers, they cannot be applied to a large number of measurements. When principle component analysis is applied to a data set of very high dimensionality, it may identify a small number of new dimensions most relevant for distinguishing classes. However, the new dimensions, which are linear combinations of the original dimensions, are not themselves measurable quantities. A large number of values must still be measured, and it is not practical for such a large number to be used as biomarkers. Thus the disclosed method cannot be used to discover biomarkers in broad data sets.
PCT publication number WO 00/70340 discloses a method for determining diagnostic markers indicative of particular types of cancer. Using two-dimensional gel electrophoresis, a large number of spots were identified from tumor cells and non-cancerous cells. Principle component analysis and partial least squares were applied to the variables to identify 170 markers capable of classifying samples into disease group. The set of markers discovered was only moderately successful, correctly classifying only 11 of 18 samples in the test set. In this method, the optimal number of markers desired is between 100 and 200. While this number is suitable for markers obtained from a single assay such as a two-dimensional gel, it is not very practical for measures obtained from a variety of different sources such as cytometers, mass spectrometers, and case report forms. Additionally, a prediction that is correct for only 61% of cases is not sufficiently accurate for most purposes. Furthermore, a model developed from such a small training set cannot be generalized reliably to unknown samples and therefore has little predictive accuracy. This method is therefore not suitable for discovering biomarkers in broad data sets containing data from a variety of sources.
A system for predicting future health is described in U.S. Pat. No. 6,059,724, issued to Campell et al. A set of biological measures is acquired from a large number of patients, each in one of two classes, and the measures are analyzed to locate biological markers capable of distinguishing between the classes. The number of measures to be considered is gradually reduced, and a discriminant analysis is performed on the remaining measures to identify a set of biological markers. The biomarkers can then be used to predict the risk of a new person of acquiring a disease corresponding to one of the classes. Although the method is stated to apply to any number of measures, the number of measures must be reduced sufficiently to allow the discriminant analysis to be performed; this analysis requires the number of measures to be smaller than the number of samples. In the example given, an initial set of 36 measures is reduced to 18 based on a sample size of over 400 patients. This is a qualitatively different problem from discovering biomarkers in an initial set of 5000, or even 1000, measurements from 100 subjects. Additionally, in this method, an important factor both in choosing the original set of potential biomarkers and in reducing the set is knowledge of the particular disease and of the biological factors already known to be important in the disease. This is almost the opposite of the problem of searching for markers not previously known to have any correlation with the disease of interest. The method produces a single set of biomarkers believed to distinguish the two classes, and the backward stepwise discriminant analysis employed does not allow for backtracking if an incorrect marker was removed from the set.
Similar problems have been addressed in the analysis of data produced by DNA microarrays, which provide expression data for thousands of genes in a single experiment. Most current approaches to the computational analysis of gene expression data attempt to learn functionally significant classifications of genes either in a supervised or unsupervised manner. Common techniques include hierarchical clustering, self-organizing maps, and support vector machines (SVM). In general, these techniques aim not to locate specific features capable of classifying patients, but rather to cluster different genes into functional classes. For example, hierarchical cluster analysis (HCA) has been used to visualize genes' functional relationships [M. B. Eisen et al., “Cluster analysis and display of genome-wide expression patterns,” Proc. Natl. Acad. Sci. 95, 14863-14868, 1998]. Based on the cluster trees obtained, a user can hypothesize new gene functional classes. SVMs have been used to classify genes based on gene expression, using a training set in which the number of genes (corresponding to observations) is larger than the number of dimensions (experiments) [M. P. S. Brown et al., “Knowledge-based analysis of microarray gene expression data by using support vector machines,” Proc. Natl. Acad. Sci. 97, 262-267, 20000]. When SVMs are applied to broad data sets, the resulting models are unreliable, i.e., not generalizable to unknown data beyond the training set. Additionally, SVMs are generally used to build a model from the entire data set, not from subsets of measurements within a data set.
Thus none of the prior art is suitable for discovering biomarkers within broad data sets, and there is still a need for a computationally efficient method of biomarker discovery in large volumes of high-dimensional biological data. There is a particular need for discovering biomarkers for diseases about which very little is known, where domain knowledge cannot be used to assist in the identification of relevant biomarkers.
The present invention provides a method for identifying biological markers in broad data sets containing n biological measurements for each of p observations. The biological markers can be used to predict clinical endpoints, e.g., to classify observations into one of a number of clinical classes or to predict values of a continuous response variable such as disease severity. Preferably, n>10p, and the measurements are obtained from different sources. Each biological marker consists of a group of at most k measurements; k is preferably less than p/5 and can be selected by a user or in dependence on a desired computation time or predictive accuracy. Thus the method is capable of efficiently locating small subsets of relevant biological measurements within large volumes of data. The method has two main steps: (a) reducing the set of n measurements to a set of m candidate measurements, and (b) selecting one or more biological markers (subsets of k or fewer measurements) from the set of m candidate measurements.
In one embodiment of the method, the set of n initial measurements is reduced by performing a correlation analysis, preferably a correlation-based cluster analysis, and most preferably a correlation-based hierarchical cluster analysis. The amount of reduction can depend upon a user-selected similarity threshold or on the reduction necessary to facilitate locating biomarkers with k or fewer members. Alternatively, or in addition to the correlation analysis, a differential significance analysis can be performed, in part in dependence on a user-selected hypothesis testing significance threshold.
Subsets of the measurements that serve as biological markers can be identified by examining all possible subsets of k or fewer measurements, preferably in parallel. Alternatively, the biomarkers can be found by non-exhaustive techniques such as simulated annealing. The identified biomarker subsets can then be ranked based on their accuracy of prediction. Additionally, a market-basket analysis can be performed on the identified biomarkers to locate recurring patterns of associations among measurements that make up the biomarkers.
The invention also provides a program storage device accessible by a processor and tangibly embodying a program of instructions executable by the processor to perform steps for the methods described above.
The present invention provides a method for mining broad biological data sets for biological markers that are predictive of a clinical endpoint. A clinical endpoint is a clinically meaningful measure of how a patient feels, functions, or survives. In general terms, there are two main types of predictive modeling involved, classification and regression. Classification predicts a subject's clinical class such as disease condition, response to therapy, or other categorical clinical endpoints. Any conceivable classification for which biological markers are desired is within the scope of the present invention. Regression predicts the value of a clinically-relevant continuous variable such as disease severity or progression.
In broad data sets, the number of measurements or dimensions n is much larger than the number of observations p (e.g., biological samples or subjects in an experimental study). In a preferred embodiment, n>10 p. Measurements can include any quantitative or qualitative (categorical) biological factors; examples include but are not limited to blood cell populations, cell-surface antigen levels, and soluble factor concentrations obtained from cytometry measurements; levels of specific proteins or small organic molecules in tissue or biological fluids; gene expression data from DNA microarray hybridization experiments; spectral components generated by techniques such as mass spectrometry or chromatography (e.g., mass spectrum peaks); concentrations of molecules obtained from immunoassays; responses to health-related questionnaires; and patient data obtained from case report forms. It is not uncommon for between five and ten thousand measurements to be acquired for each of fewer than one hundred subjects. For the purposes of the present invention, the source and nature of the biological measurements are irrelevant. Preferably, however, measurements are obtained from a variety of different sources and mined together.
Rather than consider each measurement as a potential biological marker, as is commonly done in the prior art, the present invention considers a biological marker to be a set of measurements, i.e., a subset of the total number of measurements. Typical subset sizes are less than ten. In addition, the present invention considers that there are multiple biomarkers for predicting a given clinical endpoint, and that different biomarkers can include different numbers of measures. For example, the two best biomarkers for a particular disease can be a set of six biological measurements and a set of three biological measurements. These different measurement sets may have overlapping members. The maximum number of measures k in a biomarker is preferably less than the number of observations p. In a preferred embodiment, k<p/5, and most preferably, k<p/10. Note that these restrictions are somewhat arbitrary; the reasons for limiting k are to reduce the number of measurement subsets that are potential biomarkers, and to limit the number of measurements that must be obtained once a biomarker has been established. Large numbers of measurements are not practical for inclusion in biomarkers.
In contrast to prior art measurements used as biomarkers, measurements in the biomarkers of the present invention preferably include those at much lower granularity. For example, rather than concentrations of blood cell populations such as CD4+ T cells, measurements of the present invention can include subspecies of CD4+ T cells. One reason for considering lower-grained factors is that modern bioanalytical instruments are capable of making such fine-grained measurements. Clearly, if finer grained measurements are being obtained, then a larger number of total measurements is produced and considered for inclusion in biomarker sets. Additionally, the biomarkers of the present invention can include measurements that do not correspond directly to known biological entities. For example, features of spectral data can include peak locations (i.e., mass-to-charge ratios) and intensities whose responsible molecular species are not yet determined.
In addition, because of the potential interactions between biological entities, many of which are currently unknown, derived measures are commonly considered in addition to base measures. A base measure is one that is acquired directly, while a derived measure is obtained by combining or otherwise transforming base measures. For example, the ratio between T cell and total white blood cell count is known to be a better indicator of asthma that either absolute cell count by itself. Allowing for such combinations of an already large number of potential measurements increases the number of measurements to consider enormously.
Note that there are two types of values associated with each observation, and that the distinction between the two is somewhat arbitrary. One type is measurements, values that are measured using bioanalytical instruments. The other type, referred to as annotations, can include any descriptor not having a value obtained from an analytical instrument. For example, the class to which each subject belongs (disease versus not disease, drug responder versus non-responder) is a descriptor. Subject data such as age, sex, and lifestyle information can be either measurements or annotations. External factors (e.g., pollen count for allergy treatment studies) are also relevant annotations. When used as measurements, these data are treated just as bioanalytical measurements are. However, when used as annotations, the values can serve as additional factors for defining a response variable whose value is predicted, e.g., female drug responders versus female non-responders.
Given this framework, choosing subsets of at most k measurements from an initial set of n measurements, the total number of possible biomarkers is nCk+nCk-1+ . . . +nC1. Using standard notation, nCk represents the number of distinct combinations of k objects from a set of n objects. For a typical data set, figures are as follows:
Clearly, when the number of variables is large, it is not feasible to examine systematically all potential subsets of measurements. The high combinatorics involved in mining broad data sets makes it imperative to reduce the number of variables from which biological markers can be derived.
A flow diagram outlining the general steps of a method 10 of the invention is shown in
The first step 12, dimensionality reduction, assumes that among the initially large pool of dimensions, many are not useful in discriminating between different clinical classes or predicting response variable values and thus can be eliminated from consideration. Preferably, two types of dimensionality reduction steps are included. One type of dimension to eliminate is an irrelevant dimension, i.e., one that cannot by itself predict a clinical endpoint. In step 12a, referred to as differential significance evaluation, each dimension is evaluated separately, using any technique that scores how well it can discriminate between classes or predict the response variable. Dimensions that are not sufficiently effective at predicting, as defined by a user-selected significance threshold, are eliminated from consideration.
In the case of classification, for each measurement, the mean values of the different clinical classes are compared to determine whether they are statistically significantly different. Any statistical method that tests for significant difference between independent sample populations can be used. One suitable method is the non-parametric Kruskal-Wallis test, which makes no assumption about data distribution. Alternatively, for normally distributed data, the ANOVA F-statistic can be used. In any method, dimensions are eliminated based on a threshold p-value, which can be set by the user. The p-value indicates the probability that the mean values could have been identical by chance alone. P-values can be adjusted to correct for multiple tests being performed on a single data set, using, e.g., a Bonferroni or Bayesian correction. A typical threshold p-value is 0.05, but values as low as 0.001 can be used. Dimensions yielding p-values exceeding the threshold can be eliminated from consideration for inclusion in biomarker sets. For regression, each measurement is correlated with the continuous outcome variable. A low correlation eliminates the measurement from further consideration. The user can select a p-value or correlation coefficient threshold to determine whether a measurement will be eliminated.
The second type of variable to eliminate is a redundant variable, one that is strongly similar to another variable and therefore provides no additional information. All variables that are sufficiently similar can be replaced by any one of them. In step 12b, a correlation analysis is performed to determine sets of variables that are sufficiently similar to be considered redundant. Note that unlike step 12a, which is specific to the clinical endpoint considered, similarity between variables is independent of class or response variable. A measure of correlation such as a Pearson (parametric) or Spearman (non-parametric) correlation test is used to evaluate variable similarity. Any pair or group of variables whose similarity exceeds a user-specified similarity or correlation threshold can be replaced by one of the variables in the group, with the rest eliminated from consideration. Preferably, the most relevant variable of the group, as determined by its differential significance, is retained.
In addition to simply reducing the number of relevant variables to consider, the correlation step 12b helps improve the success of the linear predictive models developed in the subsequent step 14. In such models, highly correlated variables generate nearly singular matrices that are problematic for many algorithms to invert. Furthermore, when linear model coefficients are used to assess the importance of associated variables, coefficients of highly correlated variables are divided among variables, resulting in an artificially decreased apparent importance of the variables.
In a preferred embodiment of the method 10, the correlation analysis 12b is a correlation-based hierarchical cluster analysis (HCA). HCA is a well-known technique, but to the knowledge of the present inventor, has never been applied to dimensionality reduction for biological data mining. This technique is illustrated in
For the present invention, the variable reduction can be performed in one of two ways. In one method, a threshold correlation value is selected on the horizontal (correlation) axis. Variables contained within the same cluster to the left of this threshold, shown as a line 20 in
Note that the user-selected thresholds for steps 12a and 12b have a significant effect on the resulting sets of biomarkers. If the data reduction is too aggressive, then information is lost and good biomarkers might not be discovered. This can occur particularly for dimensions that are bad predictors individually but excellent predictors when used in combination with other variables. However, if the data are not reduced sufficiently, then step 14 (described below) will be too computationally intensive to arrive at the biomarkers efficiently.
The user-selected thresholds can be derived based on a desired computation time. For example, the amount of time necessary to perform the subsequent step 14 can be determined empirically for a variety of data set sizes. In general, a formula for computation time cannot be determined, because of unknown processor-dependent factors, but the time can be determined empirically. The user can then select a desired computation time, and the required data reduction can be determined from the empirical results. The necessary data reduction determines the number of clusters m to select, which is an input to step 28 of
After the number of variables is reduced sufficiently, step 14, selection and evaluation of subsets of measurements as biomarkers, is performed. The user can select a value of k, the maximum size of the subsets, as input to step 14. Broadly, there are two types of subset selection, an exhaustive search method and a heuristic method that finds a few good but not necessarily globally optimal biomarkers.
In the first type of method, an exhaustive search is used to find globally optimal biomarkers. Typically, the exhaustive search is best performed when step 12 has yielded sufficient dimensionality reduction. For example, a suitable scenario is as follows:
When the number of potential biomarkers is small enough, it is computationally feasible to enumerate and evaluate each potential biomarker. In this process, all subsets of between one and k variables are enumerated from the measurements remaining after the final dimension reduction step. For each such subset, a test is applied to determine the subset's accuracy at predicting classification or response variable values. For example, a discriminant analysis can be used. In some cases, it may be desirable to begin evaluating subsets of 1 or 2 measurements and then proceed to subsets of increasing size until subsets of k measurements are evaluated. In these situations, the measurement pairs with low predictive accuracy can be eliminated from consideration in larger subsets, particularly when available computation time is limited. For example, consider the case of m=100 and k=5. Subsets of size 1, 2, and 3 can be evaluated relatively quickly. For subsets of size 4, 100C4 is approximately 4×106, which can still be computed in a reasonable amount of time. 100C5, however, is approximately 76×106, which (at current processor speeds) is not feasible to compute in a reasonable amount of time with a reasonable number of processors. By keeping only a small number of the best 4-tuples, however, the number of measurements to consider for inclusion in 5-tuples can be reduced, e.g., to 50. Then 50C5, which is less than 3×106, is more manageable to compute.
Accuracy can be determined by any suitable error measurement. For example, classification accuracy can be assessed as the percentage of correct classifications. In the case of two classes such as disease and not disease, the error rate can be reported as the numbers of false positives, i.e., samples incorrectly classified into the disease group, and false negatives, disease samples classified as not diseased. In general, because false positives and false negatives are related, a higher false positive rate is preferred to minimize the number of false negatives, but the desired ratio depends on the particular data set. To measure predictive accuracy of regression, any suitable fitness criterion, such as the adjusted R2 criterion, can be used. After evaluation, subsets are ranked by accuracy, and the top few subsets selected to be biomarkers. To better estimate predictive accuracy, a technique such as cross validation, leave-one-out, or bootstrapping is preferably used.
Because each potential biomarker can be evaluated independently, the evaluation is preferably parallelized. In a parallel process, different portions of the potential biomarker space are evaluated by different processors to reduce the total time to evaluate all biomarkers. In many cases, the ability for parallel biomarker evaluation enables an exhaustive search that would be prohibitively slow if only a single processor were used.
A suitable scheme for parallel biomarker evaluation is shown in the block diagram of
This implementation can be made very efficient with the proper choice of representation for potential biomarkers. For small values of m, one technique is to use a bitmap representation, in which each potential biomarker subset is represented by a binary number, each position of which corresponds to a particular measurement. A 1 in the position means that the measurement is included in the potential biomarker, and a 0 means it is not. A given biomarker then contains all the measurements whose corresponding positions contain 1's. Each subset is uniquely defined by the integer of its binary representation, and the entire set of biomarkers is enumerated simply by counting from one to the maximum number of potential biomarkers. To represent the three lists described above, it is necessary only to maintain a current count C, the maximum integer value of biomarkers already evaluated or currently being evaluated, and a small list of the biomarkers currently being evaluated. As will be apparent to those of skill in the art, there are numerous efficient biomarker representations for larger values of m.
A hardware system 40 for implementing the parallel exhaustive biomarker search is shown in
In an alternative embodiment of the exhaustive search method, the complete biomarker space is not searched. This may be necessary if there are too many potential biomarkers or if the user desires to impose arbitrary computational resource limitations, such as response time or percentage of the biomarker space searched. In this case, a sorted list is maintained of the biomarkers that have already been evaluated, and the process can be stopped at any time and the current best biomarkers extracted. Preferably, the coordinator process can stop the search and resume where it left off. When the coordinator process receives a signal to stop the search, it stops assigning new tasks to the worker processes and waits to receive current evaluation results from the ongoing worker processes. It then saves the value of C, the highest biomarker that has been evaluated, to allow resumption of the evaluation process. In order to allow the user to stop the process at any point, a computation thread is added to the coordinator process to detect events from the user interface.
In the second type of biomarker selection method, the complete biomarker space is not searched exhaustively. Rather, a heuristic technique is used that finds a few good, but not necessarily globally optimal, solutions. In general, any existing technique for feature subset selection can be used in the context of biomarker discovery according to the present invention. Feature subset selection methods typically find one good subset of the data, but can also be used to find multiple good subsets.
One suitable technique is simulated annealing, a method used in large optimization problems to find solutions that are good but not necessarily globally optimal. For example, simulated annealing has been used extensively for layout problems in circuit design. It has also been used in the field of chemometrics for determining three-dimensional molecular structure information to predict the toxicity of novel compounds produced by combinatorial chemistry. However, it has not previously been applied to biomarker discovery. The method is analogous to heating a crystalline material and then slowly cooling it, causing it to anneal. During the slow cooling, the molecules of the material can move around and settle into lower energy states. In the biomarker discovery context, multiple iterations of random changes are made to an initial biomarker, and the changes are either accepted or rejected. Higher energy states are analogous to less accurate biomarkers; there is always some probability that changes to higher energy states will be accepted. Changes to more accurate states are always accepted. The method begins at one “temperature,” and the temperature is decreased in stages. As the temperature decreases, it is less likely that changes to higher energy states will be accepted. Thus the search is much more likely to backtrack during the initial stages.
The states of the biomarker search space consist of sets of measurements containing k or fewer members. A state change represents either adding a measurement to or removing a measurement from a given state. For example, consider a search for biomarkers containing three or fewer measures from a set of measures A, B, and C.
A flow diagram of a simulated annealing method 50 for searching for biomarkers is shown in
The method next evaluates whether the maximum number of iterations per temperature stage (step 64) has been reached. If not, the method returns to step 56 to make a random change to the current biomarker. If the maximum number has been reached, the temperature is evaluated in step 66 to determine whether the minimum temperature has been reached. If it has, the method ends at step 68 and the current biomarker is reported. Alternatively, the temperature is lowered in step 70 and the method returns to step 56.
A variety of parameters must be set upon beginning the simulated annealing method. Any suitable values for the initial temperature, temperature decrease per stage, and number of iterations per stage can be used; optimal values typically depend upon the data set. Preferably, the number of iterations per stage is chosen so that the most accurate biomarker is found at each temperature. One way to select the initial value of T is to begin with a value of 1 and successively double the value until an acceptance rate of 90% is achieved in 100 possible random changes. T can then be reduced linearly, e.g., Tnew=α Told, with α between 0 and 1. Typically, the optimal parameters can be determined empirically.
Simulated annealing arrives at a single good biomarker made up of k or fewer measurements. However, the method can also be used to obtain multiple biomarkers. Because simulated annealing is a probabilistic method, it does not produce the same result when repeated. Thus the simulated annealing algorithm can be run as many times as the number of desired biomarkers, each time producing a different measurement subset.
Biomarkers identified by the method of the invention are used to predict clinical endpoints of new observations, such as clinical classifications or response variable values. Measurements are taken of the variables in the final biomarker set, and their values used to determine a value of the response variable or in which class the subject falls.
An optional additional step can be included after a number of biomarker subsets have been selected by any of the above-listed or other exhaustive or non-exhaustive search methods. In this additional step, a market-basket analysis is performed to identify patterns of recurring subsets of measurements among identified biomarkers. Each biomarker is treated as a market basket, with measurements analogous to items in the basket. Any existing method for association rule mining can be used. One suitable algorithm is the well-known Apriori algorithm [R. Agrawal et al., “Fast Algorithms for Mining Association Rules,” Proc. 20th Int. Conf. Very Large Data Bases, 487-499, 1994]. The market-basket analysis can be performed on a predetermined number of the highest-ranked biomarkers or on all biomarkers exceeding a user-set accuracy threshold. The resulting frequent itemsets represent combinations of measurements that occur frequently in good biomarkers, allowing the user to gain biological insight into how certain combinations of measures are correlated with clinical outcomes.
The goal of the market-basket analysis is not primarily to determine the most important measurements to predict a particular clinical endpoint, but rather to gain biological insight into a medical condition or drug activity. For example, if a high value of measurement X often occurs with a low value of measurement Y, then these measurements might indicate previously unknown pathogenic mechanisms. The results of the market-basket analysis can then be used to direct further biological research.
Although not limited to any particular hardware configuration, the invention is preferably implemented in one or more computers, each containing a processor, data storage device, memory, and input and output devices. The data set is stored in a database accessed by the computer. Methods of the invention are executed by the processor under the direction of computer program code stored within the computer. Using techniques well known in the computer arts, such code is tangibly embodied within a computer program storage device accessible by the processor, e.g., within system memory or on a computer readable storage medium such as a hard disk or CD-ROM. The methods may be implemented by any means known in the art. For example, any number of computer programming languages, such as Java, C++, or LISP may be used. Furthermore, various programming approaches such as procedural or object oriented may be employed. It is to be understood that the steps described above are highly simplified versions of the actual processing performed by the computers, and that methods containing additional steps or rearrangement of the steps described are within the scope of the present invention.
It should be noted that the foregoing description is only illustrative of the invention. Various alternatives and modifications can be devised by those skilled in the art without departing from the invention. Accordingly, the present invention is intended to embrace all such alternatives, modifications and variances which fall within the scope of the disclosed invention.
This application claims the benefit of U.S. application Ser. No. 09/994,576, “Methods for Efficiently Mining Broad Data Sets for Biological Markers,” filed Nov. 27, 2001, which claims the benefit of U.S. Provisional Application No. 60/253,656, “Methods for Efficiently Mining Broad Data Sets for Biological Markers,” filed Nov. 28, 2000; and U.S. Provisional Application No. 60/271,091, “Data Analysis and Mining in the Life Sciences,” filed Feb. 23, 2001, all of which are herein incorporated by reference.
| Number | Date | Country | |
|---|---|---|---|
| 60253656 | Nov 2000 | US | |
| 60271091 | Feb 2001 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 09994576 | Nov 2001 | US |
| Child | 11327169 | Jan 2006 | US |
