SIENA: Semi-automatic semantic enhancement of datasets using concept recognition.

BACKGROUND: The amount of available data, which can facilitate answering scientific research questions, is growing. However, the different formats of published data are expanding as well, creating a serious challenge when multiple datasets need to be integrated for answering a question. RESULTS: This paper presents a semi-automated framework that provides semantic enhancement of biomedical data, specifically gene datasets. The framework involved a concept recognition task using machine learning, in combination with the BioPortal annotator. Compared to using methods which require only the BioPortal annotator for semantic enhancement, the proposed framework achieves the highest results. CONCLUSIONS: Using concept recognition combined with machine learning techniques and annotation with a biomedical ontology, the proposed framework can provide datasets to reach their full potential of providing meaningful information, which can answer scientific research questions.

ADDI: Recommending alternatives for drug-drug interactions with negative health effects.

Investigating the interactions among various drugs is an indispensable issue in the field of computational biology. Scientific literature represents a rich source for the retrieval of knowledge about the interactions between drugs. Predicting drug-drug interaction (DDI) types will help biologists to evade hazardous drug interactions and support them in discovering potential alternatives that increase therapeutic efficacy and reduce toxicity. In this paper, we propose a general-purpose method called ADDI (standing for Alternative Drug-Drug Interaction) that applies deep learning on PubMed abstracts to predict interaction types among drugs. As an application, ADDI recommends alternatives for drug-drug interactions (DDIs) which have Negative Health Effects Types (NHETs). ADDI clearly outperforms state-of-the-art methods, on average by 13%, with respect to accuracy by using only the textual content of the online PubMed papers. Additionally, manual evaluation of ADDI indicates high precision in recommending alternatives for DDIs with NHETs.

An Ecological Momentary Intervention for weight loss and healthy eating via smartphone and Internet: study protocol for a randomised controlled trial.

BACKGROUND: Long-term weight loss maintenance is difficult to achieve. Effectiveness of obesity interventions could be increased by providing extended treatment, and by focusing on person-environment interactions. Ecological Momentary Intervention (EMI) can account for these two factors by allowing an indefinite extension of a treatment protocol in everyday life. EMI relies on observations in daily life to intervene by providing appropriate in-the-moment treatment. The Think Slim intervention is an EMI based on the principles of cognitive behavioural therapy (CBT), and its effectiveness will be investigated in the current study. METHODS: A randomised controlled trial (RCT) will be conducted. At least 134 overweight adults (body mass index (BMI) above 25 kg/m(2)) will be randomly assigned to an 8-week immediate intervention group (Diet + Think Slim intervention, n = 67) or to an 8-week diet-only control group (followed by the Think Slim intervention, n = 67). The Think Slim intervention consists of (1) an app-based EMI that estimates and intervenes when people are likely to overeat, based on Ecological Momentary Assessment data, and (2) ten online computerised CBT sessions which work in conjunction with an EMI module in the app. The primary outcome is BMI. Secondary outcomes include (1) scores on self-report questionnaires for dysfunctional thinking, eating styles, eating disorder pathology, general psychological symptomatology, and self-esteem, and (2) eating patterns, investigated via network analysis. Primary and secondary outcomes will be obtained at pre- and post-intervention measurements, and at 3- and 12-month follow-up measurements. DISCUSSION: This is the first EMI aimed at treating obesity via a cognitive approach, provided via a smartphone app and the Internet, in the context of an RCT. TRIAL REGISTRATION: This trial has been registered at the Netherlands Trial Register, part of the Dutch Cochrane Centre ( NTR5473 ; registration date: 26 October 2015).

ARWAR: A network approach for predicting Adverse Drug Reactions.

Predicting novel drug side-effects, or Adverse Drug Reactions (ADRs), plays an important role in the drug discovery process. Existing methods consider mainly the chemical and biological characteristics of each drug individually, thereby neglecting information hidden in the relationships among drugs. Complementary to the existing individual methods, in this paper, we propose a novel network approach for ADR prediction that is called Augmented Random-WAlk with Restarts (ARWAR). ARWAR, first, applies an existing method to build a network of highly related drugs. Then, it augments the original drug network by adding new nodes and new edges to the network and finally, it applies Random Walks with Restarts to predict novel ADRs. Empirical results show that the ARWAR method presented here outperforms the existing network approach by 20% with respect to average Fmeasure. Furthermore, ARWAR is capable of generating novel hypotheses about drugs with respect to novel and biologically meaningful ADR.

A combined ion source for fast switching between electrospray and matrix-assisted laser desorption/ionization in Fourier transform ion cyclotron resonance mass spectrometry.

A new ion source has been developed for Fourier transform ion cyclotron resonance mass spectrometry (FTICRMS) that enables quick changes between matrix-assisted laser desorption/ionization (MALDI) and electrospray ionization (ESI) modes. When operating as an ESI source, the sample solution is sprayed through an angled nebulizer. The generated ions pass through a glass capillary followed by a skimmer and three sequential hexapole ion guides. Ions can be accumulated in the third hexapole (storage hexapole) before they are injected into the ICR trap. The second hexapole is mounted on a movable platform which also carries the MALDI sample plate. During the switch from ESI to MALDI, this platform moves the second hexapole out of the hexapole series and locates a MALDI sample plate with 384 sample positions into the area directly in front of the storage hexapole. The storage hexapole is in a medium pressure chamber (MPC) which has windows both for the incoming laser beam and for the observation optics, as well as a gas tube for pulsing collision gas into the chamber. During the MALDI operation the focused laser beam enters the MPC, passes between the hexapole rods and irradiates a MALDI sample on the target plate. The sample molecules are desorbed/ionized into the storage hexapole and simultaneously cooled by collisions with the pulsed gas. Ions desorbed from multiple laser shots can be accumulated in this hexapole before they are transferred to the ICR trap. With the combined ion source a computer-controlled switch between MALDI and ESI modes is possible in less than a minute, depending on the position of the MALDI target on the 384-spot plate. Immediate acquisition of mass spectra is possible after mode switching without the need for tuning or re-calibration.