added link to dysbiosis to 2023-11-01-statistical-modeling-meta-analy…

…sis.md

projects/_posts/2023-11-01-statistical-modeling-meta-analysis.md

@@ -24,4 +24,4 @@ osf:
 ---
 {% include JB/setup %}
 
-The human gut microbiome (the complex ecosystem of microbes in the human intestines) is recognized as an important factor influencing human health. In the last decade, a plethora of studies have described associations between the gut microbiome and human diseases, manifesting in the gut, such as colorectal cancer, or in other organ systems, such as cardiovascular or neurodegenerative diseases. These studies have sparked interest in the use of the human microbiome for diagnostic and prognostic purposes in the form  of biomarkers identified through statistical modelling and machine learning. However, differences in methodologies and lack of thorough statistical assessments have often generated discrepant results across studies. To address these issues, we develop software frameworks for the identification of associations and machine-learning based microbiome disease signatures and biomarkers. Importantly, we engineer them to handle (observed) confounders and make them applicable in meta-analyses allowing researchers to compare findings across cohorts to obtain more robust findings. We have validated our software pipelines across many microbiome-disease association data sets and implemented easy-to-use interfaces. 
+The human gut microbiome (the complex ecosystem of microbes in the human intestines) is recognized as an important factor influencing human health. In the last decade, a plethora of studies have described associations between the gut microbiome and human diseases, manifesting in the gut, such as colorectal cancer, or in other organ systems, such as cardiovascular or neurodegenerative diseases  (see [dysbiosis](/projects/gut-dysbiosis-disease-association) project). These studies have sparked interest in the use of the human microbiome for diagnostic and prognostic purposes in the form  of biomarkers identified through statistical modelling and machine learning. However, differences in methodologies and lack of thorough statistical assessments have often generated discrepant results across studies. To address these issues, we develop software frameworks for the identification of associations and machine-learning based microbiome disease signatures and biomarkers. Importantly, we engineer them to handle (observed) confounders and make them applicable in meta-analyses allowing researchers to compare findings across cohorts to obtain more robust findings. We have validated our software pipelines across many microbiome-disease association data sets and implemented easy-to-use interfaces.