Industry-scale application and evaluation of deep learning for drug target prediction

Artificial intelligence (AI) is undergoing a revolution thanks to the breakthroughs of machine learning algorithms in computer vision, speech recognition, natural language processing and generative modelling. Recent works on publicly available pharmaceutical data showed that AI methods are highly promising for Drug Target prediction. However, the quality of public data might be different than that of industry data due to different labs reporting measurements, different measurement techniques, fewer samples and less diverse and specialized assays. As part of a European funded project (ExCAPE), that brought together expertise from pharmaceutical industry, machine learning, and high-performance computing, we investigated how well machine learning models obtained from public data can be transferred to internal pharmaceutical industry data. Our results show that machine learning models trained on public data can indeed maintain their predictive power to a large degree when applied to industry data. Moreover, we observed that deep learning derived machine learning models outperformed comparable models, which were trained by other machine learning algorithms, when applied to internal pharmaceutical company datasets. To our knowledge, this is the first large-scale study evaluating the potential of machine learning and especially deep learning directly at the level of industry-scale settings and moreover investigating the transferability of publicly learned target prediction models towards industrial bioactivity prediction pipelines.Web of Science121art. no. 2

Prospects for asteroseismology

The observational basis for asteroseismology is being dramatically strengthened, through more than two years of data from the CoRoT satellite, the flood of data coming from the Kepler mission and, in the slightly longer term, from dedicated ground-based facilities. Our ability to utilize these data depends on further development of techniques for basic data analysis, as well as on an improved understanding of the relation between the observed frequencies and the underlying properties of the stars. Also, stellar modelling must be further developed, to match the increasing diagnostic potential of the data. Here we discuss some aspects of data interpretation and modelling, focussing on the important case of stars with solar-like oscillations.Comment: Proc. HELAS Workshop on 'Synergies between solar and stellar modelling', eds M. Marconi, D. Cardini & M. P. Di Mauro, Astrophys. Space Sci., in the press Revision: correcting abscissa labels on Figs 1 and

Nurses' perceptions of aids and obstacles to the provision of optimal end of life care in ICU

Contains fulltext : 172380.pdf (publisher's version ) (Open Access

Inmigracion judía en Chile durante el siglo XIX

After sorne observations conceming Jews and non-Catholic foreigners living in Chile during the nineteenth century the author specifies problems dealing with historical investigation of Jewish inmigration in Chile. He mentions and comments on the sources of information, national and foreign, that investigators may make use of in these matters; likewise, he makes valuable methodological suggestions concerning their use, thus contributing towards the scientific validity of this kind of historical-cultural studies

Inmigracion judía en Chile durante el siglo XIX

After sorne observations conceming Jews and non-Catholic foreigners living in Chile during the nineteenth century the author specifies problems dealing with historical investigation of Jewish inmigration in Chile. He mentions and comments on the sources of information, national and foreign, that investigators may make use of in these matters; likewise, he makes valuable methodological suggestions concerning their use, thus contributing towards the scientific validity of this kind of historical-cultural studies