Identify novel elements of knowledge with word embedding.

As novelty is a core value in science, a reliable approach to measuring the novelty of scientific documents is critical. Previous novelty measures however had a few limitations. First, the majority of previous measures are based on recombinant novelty concept, attempting to identify a novel combination of knowledge elements, but insufficient effort has been made to identify a novel element itself (element novelty).

Role of machine and organizational structure in science.

The progress of science increasingly relies on machine learning (ML) and machines work alongside humans in various domains of science. This study investigates the team structure of ML-related projects and analyzes the contribution of ML to scientific knowledge production under different team structure, drawing on bibliometric analyses of 25,000 scientific publications in various disciplines. Our regression analyses suggest that (1) interdisciplinary collaboration between domain scientists and computer scientists as well as the engagement of interdisciplinary individuals who have expertise in both domain and computer sciences are common in ML-related projects; (2) the engagement of interdisciplinary individuals seem more important in achieving high impact and novel discoveries, especially when a project employs computational and domain approaches interdependently; and (3) the contribution of ML and its implication to team structure depend on the depth of ML.

Measuring novelty in science with word embedding.

Novelty is a core value in science, and a reliable measurement of novelty is crucial. This study proposes a new approach of measuring the novelty of scientific articles based on both citation data and text data. The proposed approach considers an article to be novel if it cites a combination of semantically distant references.

Impact of Ph.D. training: a comprehensive analysis based on a Japanese national doctoral survey.

Ph.D. training in academic labs offers the foundation for the production of knowledge workers, indispensable for the modern knowledge-based society.

Contribution of structural biology to clinically validated target proteins.

We identified six groups of diseases expected to cause serious future health issues on the basis of a WHO report. Approved drugs for these diseases were associated with 409 target proteins; however, the percentage of selected proteins with full-length structural information deposited in the Protein Data Bank (PDB) was only 9.8%.

Effect of mergers and acquisitions on drug discovery: perspective from a case study of a Japanese pharmaceutical company.

The pharmaceutical industry has experienced intermittent waves of mergers and acquisitions (M&As) since the 1980s and recently appeared to be in yet another wave. Previous studies indicated rather negative impacts of consolidation on research and development, suggesting that they do not necessarily lead to long-term reinforcement of research capabilities, although they may enrich the drug pipeline in the short term. However, recent studies have implied a positive side in terms of knowledge-base transfer.

Identification of a C-terminal region that is required for the nuclear translocation of ERK2 by passive diffusion.

Extracellular signal-regulated kinase 2 (ERK2) is located in the cytoplasm of resting cells and translocates into the nucleus upon extracellular stimuli by active transport of a dimer. Passive transport of an ERK2 monomer through the nuclear pore is also reported to coexist. We attempted to characterize the cytoplasmic retention and nuclear translocation of fusion proteins between deletion and site-directed mutants of ERK2 and green fluorescent protein (GFP).