Machine Learning and Integrative Analysis of Biomedical Big Data.

Recent developments in high-throughput technologies have accelerated the accumulation of massive amounts of omics data from multiple sources: genome, epigenome, transcriptome, proteome, metabolome, etc. Traditionally, data from each source (e.g., genome) is analyzed in isolation using statistical and machine learning (ML) methods. Integrative analysis of multi-omics and clinical data is key to new biomedical discoveries and advancements in precision medicine. However, data integration poses new computational challenges as well as exacerbates the ones associated with single-omics studies. Specialized computational approaches are required to effectively and efficiently perform integrative analysis of biomedical data acquired from diverse modalities. In this review, we discuss state-of-the-art ML-based approaches for tackling five specific computational challenges associated with integrative analysis: curse of dimensionality, data heterogeneity, missing data, class imbalance and scalability issues

Typology of Ideal Personality: Applicant Perspective In Job Selection Setting

Previously studies suggest that job applicant fake response on a personality test based on their stereotype of ideal employee. This study explored typology of personality that relied on applicants’ stereotype. Experimental participant (N=200) completed three factors of BFI-44 (extroversion, conscientiousness, openness) under two different instruction. An honest fashion (honest condition) and under instructions to a response as if applying for a job (applicant condition). In both conditions, latent class analysis yields three classes for each personality factors with different type of personality. Typologies of personalities that rest on a stereotype of an applicant were different from existing typology. Keywords: Faking Response, Type of Personality, Stereotype, Job Applican

The logic of post-communist capitalist collective inaction

The relationship between politics and business is obviously a key feature of policy-making in capitalist democracies, and business associations are an important element of that relationship. A consensus has now emerged that organised business is remarkably weak in East-Central Europe. This article proposes a theoretical synthesis with which to explain that weakness. It shows how the strength of trade unions, varieties of capitalism and interest diversity are specifications of Olson’s logic of collective action, specifications without which the logic itself is too general to explain concrete outcomes. Detailed evidence in favour of the theory is provided from the Polish case. It is argued that the analysis should also apply to other post-communist countries