ChemProt-2.0: visual navigation in a disease chemical biology database

ChemProt-2.

Experimental analysis of EVM and BER for indoor radio-over-fibre networks using polymer optical fibre

This paper presents a theoretical investigation and experimental implementation of indoor radio-over-fibre (RoF) using polymer optical fibre. We characterise the system based on the error vector magnitude (EVM), bit error rate (BER), and the signal-to-noise ratio (SNR). We consider three modulation formats of quadrature phase shift keying (QPSK), 16-quadrature amplitude modulation (QAM) and 64-QAM. The results show the effect of modulation order on the higher acceptable EVM limit that can be linked with the BER estimation process. Furthermore, the analysis of input signal power penalty for the three modulations indicates the advantage of higher order formats. We conclude that even with linear increment of the power penalty, higher orders modulation can offer a higher bandwidth without a significant difference compared to lower orders

Semantic text mining in early drug discovery for type 2 diabetes

BackgroundSurveying the scientific literature is an important part of early drug discovery; and with the ever-increasing amount of biomedical publications it is imperative to focus on the most interesting articles. Here we present a project that highlights new understanding (e.g. recently discovered modes of action) and identifies potential drug targets, via a novel, data-driven text mining approach to score type 2 diabetes (T2D) relevance. We focused on monitoring trends and jumps in T2D relevance to help us be timely informed of important breakthroughs.MethodsWe extracted over 7 million n-grams from PubMed abstracts and then clustered around 240,000 linked to T2D into almost 50,000 T2D relevant 'semantic concepts'. To score papers, we weighted the concepts based on co-mentioning with core T2D proteins. A protein's T2D relevance was determined by combining the scores of the papers mentioning it in the five preceding years. Each week all proteins were ranked according to their T2D relevance. Furthermore, the historical distribution of changes in rank from one week to the next was used to calculate the significance of a change in rank by T2D relevance for each protein.ResultsWe show that T2D relevant papers, even those not mentioning T2D explicitly, were prioritised by relevant semantic concepts. Well known T2D proteins were therefore enriched among the top scoring proteins. Our 'high jumpers' identified important past developments in the apprehension of how certain key proteins relate to T2D, indicating that our method will make us aware of future breakthroughs. In summary, this project facilitated keeping up with current T2D research by repeatedly providing short lists of potential novel targets into our early drug discovery pipeline