Introducing DASC-PM: A Data Science Process Model

Data-driven disciplines like data mining and knowledge management already provide process-based frameworks for data analysis projects, such as the well-known cross-industry standard process for data mining (CRISP-DM) or knowledge discovery in databases (KDD). Although the domain of data science addresses a much broader problem space, i.e., also considers economic, social, and ecological impacts of data-driven projects, a corresponding domain-specific process model is still missing. Consequently, based on a total of four identified meta requirements and 17 corresponding requirements that were collected from experts of theory and practice, this contribution proposes the empirically grounded data science process model (DASC-PM)—a framework that maps a data science project as a four-step process model and contextualizes it among scientific procedures, various areas of application, IT infrastructures, and impacts. To illustrate the phase-oriented specification capabilities of the DASCPM, we exemplarily present competence and role profiles for the analysis phase of a data science project

Association of depressive disorders, depression characteristics and antidepressant medication with inflammation

Growing evidence suggests that immune dysregulation may be involved in depressive disorders, but the exact nature of this association is still unknown and may be restricted to specific subgroups. This study examines the association between depressive disorders, depression characteristics and antidepressant medication with inflammation in a large cohort of controls and depressed persons, taking possible sex differences and important confounding factors into account. Persons (18–65 years) with a current (N=1132) or remitted (N=789) depressive disorder according to DSM-IV criteria and healthy controls (N=494) were selected from the Netherlands Study of Depression and Anxiety. Assessments included clinical characteristics (severity, duration and age of onset), use of antidepressant medication and inflammatory markers (C-reactive protein (CRP), interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α)). After adjustment for sociodemographics, currently depressed men, but not women, had higher levels of CRP (1.33 versus 0.92 mg l−1, P<0.001, Cohen's d=0.32) and IL-6 (0.88 versus 0.72 pg ml−1, P=0.01, Cohen's d=0.23) than non-depressed peers. Associations reduced after considering lifestyle and disease indicators — especially body mass index — but remained significant for CRP. After full adjustment, highest inflammation levels were found in depressed men with an older age of depression onset (CRP, TNF-α). Furthermore, inflammation was increased in men using serotonin–norepinephrine reuptake inhibitors (CRP, IL-6) and in men and women using tri- or tetracyclic antidepressants (CRP), but decreased among men using selective serotonin reuptake inhibitors (IL-6). In conclusion, elevated inflammation was confirmed in depressed men, especially those with a late-onset depression. Specific antidepressants may differ in their effects on inflammation

DASC-PM v1.0 : ein Vorgehensmodell für Data-Science-Projekte

Das Thema Data Science hat in den letzten Jahren in vielen Organisationen stark an Aufmerksamkeit gewonnen. Häufig herrscht jedoch weiterhin große Unklarheit darüber, wie diese Disziplin von anderen abzugrenzen ist, welche Besonderheiten der Ablauf eines Data-Science-Projekts besitzt und welche Kompetenzen vorhanden sein müssen, um ein solches Projekt durchzuführen. In der Hoffnung, einen kleinen Beitrag zur Beseitigung dieser Unklarheiten leisten zu können, haben wir von April 2019 bis Februar 2020 in einer offenen und virtuellen Arbeitsgruppe mit Vertretern aus Theorie und Praxis das vorliegende Dokument erarbeitet, in dem ein Vorgehensmodell für Data-Science-Projekte beschrieben wird – das Data Science Process Model (DASC-PM). Ziel war es dabei nicht, neue Herangehensweisen zu entwickeln, sondern viel-mehr, vorhandenes Wissen zusammenzutragen und in geeigneter Form zu strukturieren. Die Ausarbeitung ist als Zusammenführung der Erfahrung sämtlicher Teilnehmerinnen und Teilnehmer dieser Arbeitsgruppe zu verstehen

Innate and adaptive immunity in the development of depression: : An update on current knowledge and technological advances

The inflammation theory of depression, proposed over 20years ago, was influenced by early studies on T cell responses and since then has been a stimulus for numerous research projects aimed at understanding the relationship between immune function and depression. Observational studies have shown that indicators of immunity, especially C reactive protein and proinflammatory cytokines, such as interleukin 6, are associated with an increased risk of depressive disorders, although the evidence from randomized trials remains limited and only few studies have assessed the interplay between innate and adaptive immunity in depression. In this paper, we review current knowledge on the interactions between central and peripheral innate and adaptive immune molecules and the potential role of immune-related activation of microglia, inflammasomes and indoleamine-2,3-dioxygenase in the development of depressive symptoms. We highlight how combining basic immune methods with more advanced 'omics' technologies would help us to make progress in unravelling the complex associations between altered immune function and depressive disorders, in the identification of depression-specific biomarkers and in developing immunotherapeutic treatment strategies that take individual variability into account.Peer reviewe

Is there Progress? An Overview of Select Biomarker Candidates for Major Depressive Disorder

Major Depressive Disorder (MDD) contributes to a significant worldwide disease burden, expected to be second only to heart disease by 2050. However, accurate diagnosis has been a historical weakness in clinical psychiatry. As a result, there is a demand for diagnostic modalities with greater objectivity that could improve on current psychiatric practice that relies mainly on self-reporting of symptoms and clinical interviews. Over the past two decades, literature on a growing number of putative biomarkers for MDD increasingly suggests that MDD patients have significantly different biological profiles compared to healthy controls. However, difficulty in elucidating their exact relationships within depression pathology renders individual markers inconsistent diagnostic tools. Consequently, further biomarker research could potentially improve our understanding of MDD pathophysiology as well as aid in interpreting response to treatment, narrow differential diagnoses, and help refine current MDD criteria. Representative of this, multiplex assays using multiple sources of biomarkers are reported to be more accurate options in comparison to individual markers that exhibit lower specificity and sensitivity, and are more prone to confounding factors. In the future, more sophisticated multiplex assays may hold promise for use in screening and diagnosing depression and determining clinical severity as an advance over relying solely on current subjective diagnostic criteria. A pervasive limitation in existing research is heterogeneity inherent in MDD studies, which impacts the validity of biomarker data. Additionally, small sample sizes of most studies limit statistical power. Yet, as the RDoC project evolves to decrease these limitations, and stronger studies with more generalizable data are developed, significant advances in the next decade are expected to yield important information in the development of MDD biomarkers for use in clinical settings

Enhancing Text Classification to Improve Information Filtering

Text classification, information filtering, semi-supervised learning, quality controlMagdeburg, Univ., Fak. für Informatik, Diss., 2001von Carsten Lanquillo