Abstract: Disclosed herein are methods and systems for administering therapy to subjects who have been determined to display or not display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received the therapy. The subject and prior subjects may suffer from a disease, disorder, or condition for which it is desired to predict whether the subject will respond to the therapy. In an aspect, the disease, disorder, or condition may be an autoimmune disorder such as ulcerative colitis. The subject may be administered an anti-TNF therapy or an alternative to anti-TNF therapy based upon predictions provided by methods and systems described herein.

Abstract: Presented herein are systems and methods for developing classifiers useful for predicting response to particular treatments. For example, in some embodiments, the present disclosure provides a method of treating subjects suffering from an autoimmune disorder, the method comprising administering an alternative to anti-TNF therapy to subjects who have been determined to be non-responsive via a classifier established to distinguish between responsive and non-responsive prior subjects in a cohort who have received the anti-TNF therapy.

Abstract: Presented herein are systems and methods for developing classifiers useful for predicting response to particular treatments. For example, in some embodiments, the present disclosure provides a method of treating subjects suffering from an autoimmune disorder, the method comprising administering an alternative to anti-TNF therapy to subjects who have been determined to be non-responsive via a classifier established to distinguish between responsive and non-responsive prior subjects in a cohort who have received the anti-TNF therapy.

Abstract: Presented herein are systems and methods for developing classifiers useful for predicting response to particular treatments. For example, in some embodiments, the present disclosure provides a method of treating subjects suffering from an autoimmune disorder, the method comprising administering an alternative to anti-TNF therapy to subjects who have been determined to be non-responsive via a classifier established to distinguish between responsive and non-responsive prior subjects in a cohort who have received the anti-TNF therapy.

Abstract: Methods and systems for administering anti-TNF therapy to subjects who have been determined to display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy.

Abstract: Methods of treating a subject suffering from an autoimmune disease, disorder, or condition with an alternative to anti-TNF therapy.

Abstract: Presented herein are systems and methods for developing classifiers useful for predicting response to particular treatments. For example, in some embodiments, the present disclosure provides a method of treating subjects suffering from an autoimmune disorder, the method comprising administering an alternative to anti-TNF therapy to subjects who have been determined to be non-responsive via a classifier established to distinguish between responsive and non-responsive prior subjects in a cohort who have received the anti-TNF therapy.

Abstract: Presented herein are systems and methods for developing classifiers useful for predicting response to particular treatments. For example, in some embodiments, the present disclosure provides a method of treating subjects suffering from an autoimmune disorder, the method comprising administering an alternative to anti-TNF therapy to subjects who have been determined to be non-responsive via a classifier established to distinguish between responsive and non-responsive prior subjects in a cohort who have received the anti-TNF therapy.

Abstract: Methods and systems for administering therapy to subjects who have been determined to not display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received anti-TNF therapy.

Abstract: Presented herein are systems and methods for developing classifiers useful for predicting response to particular treatments. For example, in some embodiments, the present disclosure provides a method of treating subjects suffering from an autoimmune disorder, the method comprising administering an alternative to anti-TNF therapy to subjects who have been determined to be non-responsive via a classifier established to distinguish between responsive and non-responsive prior subjects in a cohort who have received the anti-TNF therapy.

Abstract: Presented herein are systems and methods for developing classifiers useful for predicting response to particular treatments. For example, in some embodiments, the present disclosure provides a method of treating subjects suffering from an autoimmune disorder, the method comprising a step of: administering an anti-TNF therapy to subjects who have been determined to be responsive via a classifier established to distinguish between responsive and non-responsive prior subjects in a cohort who have received the anti-TNF therapy.

Abstract: Presented herein are systems and methods for developing classifiers useful for predicting response to particular treatments. For example, in some embodiments, the present disclosure provides a method of treating subjects suffering from an autoimmune disorder, the method comprising a step of: administering an anti-TNF therapy to subjects who have been determined to be responsive via a classifier established to distinguish between responsive and non-responsive prior subjects in a cohort who have received the anti-TNF therapy.

Abstract: Methods and systems for administering anti-TNF therapy to subjects who have been determined to display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy.

Abstract: Methods and systems for administering anti-TNF therapy to subjects who have been determined to display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy.

Abstract: Presented herein are systems and methods for developing classifiers useful for predicting response to particular treatments. For example, in some embodiments, the present disclosure provides a method of treating subjects suffering from an autoimmune disorder, the method comprising a step of: administering an anti-TNF therapy to subjects who have been determined to be responsive via a classifier established to distinguish between responsive and non-responsive prior subjects in a cohort who have received the anti-TNF therapy.

Abstract: Presented herein are systems and methods for developing classifiers useful for predicting response to particular treatments. For example, in some embodiments, the present disclosure provides a method of treating subjects suffering from an autoimmune disorder, the method comprising a step of: administering an anti-TNF therapy to subjects who have been determined to be responsive via a classifier established to distinguish between responsive and non-responsive prior subjects in a cohort who have received the anti-TNF therapy.

Abstract: Methods and systems for administering anti-TNF therapy to subjects who have been determined to display a gene expression response signature established to distinguish between responsive and non-responsive prior subjects who have received the anti-TNF therapy.

Abstract: A nucleotide sequence, specifically a CTG triplet repeat, is shown to be expanded in individuals affected with myotonic dystrophy and can be identified in a sample obtained from an individual. Individuals in whom the CTG triplet repeat is present in normal copy number are likely to be minimally affected and individuals in whom the CTG triplet repeat occurs in abnormally high copy number are likely to be more severely affected.

Abstract: A method by which a nucleotide sequence, specifically a CTG triplet repeat, shown to be expanded in individuals affected with myotonic dystrophy can be identified in a sample obtained from an individual. The present method can be used to identify individuals in whom the CTG triplet repeat is present in normal copy number and individuals in whom the CTG triplet repeat occurs in abnormally high copy number, as well as to further identify individuals likely to be minimally affected and individuals likely to be more severely affected.