Progresses in treating agitation: a major clinical challenge in Alzheimer's disease.

Expert Opin Pharmacother. 2015;16(17):2581-8

Authors: Panza F, Solfrizzi V, Seripa D, Imbimbo BP, Santamato A, Lozupone M, Prete C, Greco A, Pilotto A, Logroscino G

Abstract
INTRODUCTION: Treatment of neuropsychiatric symptoms (NPS) represents a major clinical challenge in Alzheimer's disease (AD). Agitation and aggression are frequently seen during institutionalization and increase patient morbidity and mortality and caregiver burden. Off-label use of atypical antipsychotics for treating agitation in AD showed only modest clinical benefits, with high side-effect burden and risk of mortality. Non-pharmacological treatment approaches have become the preferred first-line option. When such treatment fails, pharmacological options are often used. Therefore, there is an urgent need to identify effective and safe pharmacological treatments for efficiently treating agitation and aggression in AD and dementia.
AREAS COVERED: Emerging evidence on the neurobiological substrates of agitation in AD has led to several recent clinical trials of repositioned and novel therapeutics for these NPS in dementia as an alternative to antipsychotics. We operated a comprehensive literature search for published articles evaluating pharmacological interventions for agitation in AD, with a review of recent clinical trials on mibampator, dextromethorphan/quinidine, cannabinoids, and citalopram.
EXPERT OPINION: Notwithstanding the renewed interest for the pharmacological treatment of agitation in AD, progresses have been limited. A small number and, sometimes methodologically questionable, randomized controlled trials (RCTs) have produced disappointing results. However, recently completed RCTs on novel or repositioned drugs (mibampator, dextromethorphan/quinidine, cannabinoids, and citalopram) showed some promise in treating agitation in AD, but still with safety concerns. Further evidence will come from ongoing Phase II and III trials on promising novel drugs for treating these distressing symptoms in patients with AD and dementia.

PMID: 26389682 [PubMed - indexed for MEDLINE]

Pharmacogenetic considerations in the treatment of Alzheimer's disease.

Pharmacogenomics. 2016 Jun 13;

Authors: Cacabelos R, Torrellas C, Teijido O, Carril JC

Abstract
The practical pharmacogenetics of Alzheimer's disease (AD) is circumscribed to acetylcholinesterase inhibitors (AChEIs) and memantine. However, pharmacogenetic procedures should be applied to novel strategies in AD therapeutics including: novel AChEIs and neurotransmitter regulators, anti-Aβ treatments, anti-tau treatments, pleiotropic products, epigenetic drugs and combination therapies. Genes involved in the pharmacogenetic network are under the influence of the epigenetic machinery which regulates gene expression transcriptionally and post-transcriptionally, configuring the fundamentals of pharmacoepigenomics. Over 60% of AD patients present concomitant pathologies demanding additional treatments which increase the likelihood of drug-drug interactions. Lipid metabolism dysfunction is a pathogenic mechanism inherent to AD neurodegeneration. The therapeutic response to hypolipidemic compounds is influenced by the APOE and CYP genotypes. The development of novel compounds and the use of combination/multifactorial treatments require the implantation of pharmacogenomic procedures for the avoidance of ADRs and the optimization of therapeutics.

PMID: 27291247 [PubMed - as supplied by publisher]

Will personalized drugs for cardiovascular disease become an option? - Defining 'Evidence-based personalized medicine' for its implementation and future use.

Expert Opin Pharmacother. 2015;16(17):2549-52

Authors: de Denus S, Dubé MP, Tardif JC

Abstract
It is generally accepted that the implementation of pharmacogenomics and, more broadly, personalized medicine will have to be 'evidence-based'. However, there is a lack of consensus on the level of evidence required to justify the use of pharmacogenomic testing in clinical practice. In the cardiovascular field, this lack of agreement has led to somewhat contradicting recommendations by different organizations regarding the clinical utility and use of pharmacogenomic tests or information. Here, we argue that randomized, controlled trials are paramount in order to enable and accelerate the widespread implementation of pharmacogenomics, not only to demonstrate the clinical efficacy and cost-effectiveness of such tests, but because such level of evidence is required to support the considerable changes associated with the implantation of pharmacogenomics in clinical practice.

PMID: 26371722 [PubMed - indexed for MEDLINE]

Expiratory Flow Limitation for Monitoring Cystic Fibrosis. Ready for the Starting Gun?

Ann Am Thorac Soc. 2016 Jun;13(6):770-771

Authors: Bush A

PMID: 27295150 [PubMed - as supplied by publisher]

Cystic Fibrosis Revisited - a Review Study.

Med Chem. 2016 Jun 8;

Authors: Kuca K, Klimova B, Novotny M, Maresova P

Abstract
BACKGROUND: Cystic fibrosis (CF) is an incurable, chronic disease, which causes severe damages to respiratory and digestive tracts. It is the most common genetically inherited disease among caucasians. This disease is caused by defects in CF genes, the so-called mutations in cystic fibrosis transmembrane conductance regulator (CFTR) gene population. At present over 100,000 people suffer from this disease worldwide.
OBJECTIVE: The purpose of this review study is to describe the pathophysiology of CF and provide the latest information on its diagnosis and treatment therapies with respect to the improvement of patient's quality of life and emphasis on targeted specialized care.
METHOD: The methodological approaches include a method of literature review of available sources exploring the issue of cystic fibrosis both from a global and specific perspective point of view. A search was performed in the databases PubMed, MEDLINE, Web of Science, Scopus, Springer and ScienceDirect. Furthermore, other sources cited in the analyzed studies were also examined. On the basis of evaluation of these literature sources, the research issue was explored.
RESULTS: The main benefits (e.g., specialized centres for the treatment of CF exist or a new breakthrough in the gene therapy of CF has been made) and limitations (e.g., comorbidity of CF, lifelong and costly treatment, or adverse impact on patient's and caregiver's quality of life) in the treatment of narcolepsy are highlighted.
CONCLUSION: CF requires an integrated treatment approach in specialized CF centers, involving various factors contributing to a better patient's state of health in the form of relevant and well-balanced non-pharmacological and pharmacological therapies. In addition, further large scale clinical trials are needed in order to develop compounds that are aimed at the most common classes of CFTR.

PMID: 27292156 [PubMed - as supplied by publisher]

[Italian Cystic Fibrosis Register - Report 2010].

Epidemiol Prev. 2016 Mar-Apr;40(2 Suppl 2):1-47

Authors: Amato A, Ferrigno L, Salvatore M, Toccaceli V, Gruppo di lavoro RIFC/ICFR Working Group

Abstract
UNLABELLED: The Italian National CF Registry (INCFR) is based on the official agreement between the clinicians of the Italian National Referral Centers for Cystic Fibrosis and the researchers of the Istituto Superiore di Sanità (National Center for Rare Diseases; National Center for Epidemiology, Surveillance and Health Care Promotion). OBJECTIVES The main aim of INCFR is to contribute to the improvement in CF patients health care and clinical management through: i. the estimates of CF prevalence and incidence in Italy; ii. the analyses of medium and long term clinical and epidemiological trends of the disesase; iii. the identification of the main health care needs at regional and national level to contribute to the Health Care programmes and to the distribution of resources. MATERIALS AND METHODS Analyses and results described in the present Report are referred to patients in charge to the Italian National Referral Centers for Cystic Fibrosis in 2010. Data were sent by Centers by means of a specific software (Camilla, Ibis Informatica). The Italian National Referral Centers for Cystic Fibrosis sent a total of 5,271 individual records; 1,112 records were excluded from the analyses due to restricted inclusion criteria. The total number of patients included in INCFR for analyses is 4,159. RESULTS INCFR database includes all prevalent cases at 1th January 2010 as well as all new diagnoses done in 2010. The present Report has been organized into 9 sections. 1. Demography: estimated 2010 CF prevalence was 7/100,000 residents in Italy; 52% of the patients were male, CF distribution showed higher frequency in patients aged 7 to 35 years. In 2010, 48.9% of the patients were more than 18 years old. 2. Diagnoses: most of the CF patients were diagnosed before two years of age (66.7%); a significant percentage of patients (11.4%) was diagnosed in adult-age. 3. New diagnoses (2010): new diagnoses were 168. Sixty-five percent of them was diagnosed before the second year of age and 17%in adulthood. No differences were observed between male and female. Incidence at birth was estimated 1/4,854 living births. 4.
GENETICS: in 95.9% of patients, 2 (or more) CFTR mutations were identified. [delta]508F mutation was the most frequent (45.1%). 5. Respiratory function: analyses were performed on 2,966 out of 3,341 patients aged 7 years or older. FEV1 (Forced Expiratory Volume in the first second) scores progressively decreased before adult age, in accordance with the natural history of the disease. 6. Nutrition: most critical periods are during the first 6 months of life and during adolescence. Fourteen per cent of the patients within 2-18 years resulted malnourished. From 18 years onwards, optimal BodyMass Index (BMI) values were detected in 36.5%of males and in 28%of females. BMI also improved during age. 7. Transplantation: in 2010, 20 patients (10 males and 10 females) were bi-pulmunary transplanted; age was comprised between 11 and 46 years, median age at transplantation was 27.5 years. Eleven out of the 20 patients resulted still alive on the 31th December 2010. 8. Microbiology: analyses were performed on 3.272 patients (887 did not report these data) and were exclusively referred to tests performed in 2010. A percentage of 34 patients, younger than 18 years of age, was characterized by the presence of Pseudomonas aeruginosa compared to 61.8% of the older patients. Prevalence of Burkholderia Cepacia was 0.8% in patients aged up to 17 years; in patients aged more than 17 years, prevalence was 6.8%. Staphylococcus aureus meticillino sensitive prevalence was not correlated with patients' age. 9.
MORTALITY: 34 patients aged from 0 to 45 years died in 2010 (16 males and 18 females). Respiratory insufficiency was the main cause of death (73.5%). CONCLUSIONS The report aims at being an instrument for CF community, with particular attention to the needs of patients and their families. Information collected within INCFR are an important starting point for further studies from health care perspectives. Finally, INCFR represents an important tool to foster research and innovative treatment for CF, as the rareness of the disease is a constraint to clinical trials and other studies set-up. A significant subset of data are regularly sent to the European Registry of Cystic Fibrosis.

PMID: 27291389 [PubMed - in process]

Design of Probabilistic Boolean Networks Based on Network Structure and Steady-State Probabilities.

IEEE Trans Neural Netw Learn Syst. 2016 Jun 6;

Authors: Kobayashi K, Hiraishi K

Abstract
In this brief, we consider the problem of finding a probabilistic Boolean network (PBN) based on a network structure and desired steady-state properties. In systems biology and synthetic biology, such problems are important as an inverse problem. Using a matrix-based representation of PBNs, a solution method for this problem is proposed. The problem of finding a BN has been studied so far. In the problem of finding a PBN, we must calculate not only the Boolean functions, but also the probabilities of selecting a Boolean function and the number of candidates of the Boolean functions. Hence, the problem of finding a PBN is more difficult than that of finding a BN. The effectiveness of the proposed method is presented by numerical examples.

PMID: 27295690 [PubMed - as supplied by publisher]

How modeling standards, software, and initiatives support reproducibility in systems biology and systems medicine.

IEEE Trans Biomed Eng. 2016 Jun 2;

Authors: Waltemath D, Wolkenhauer O

Abstract
Only reproducible results are of significance to science. A lack of suitable standards and appropriate support of standards in software tools has led to numerous publications with irreproducible results. Our objectives are to identify the key challenges of reproducible research and to highlight existing solutions.
RESULTS: In this paper, we summarise problems concerning reproducibility in systems biology and systems medicine. We focus on initiatives, standards and software tools that aim to improve the reproducibility of simulation studies.
CONCLUSIONS: The long-term success of systems biology and systems medicine depends on trustworthy models and simulations. This requires openness to ensure reusability and transparency to enable reproducibility of results in these fields.

PMID: 27295645 [PubMed - as supplied by publisher]

Metabolomics as a Challenging Approach for Medicinal Chemistry and Personalized Medicine.

J Med Chem. 2016 Jun 13;

Authors: Frederich M, Pirotte B, Fillet M, De Tullio P

Abstract
"Omics" sciences have been developed to provide a holistic point of view of biology and to better understand the complexity of an organism as a whole. These systems biology approaches can be examined at different levels, starting from the most fundamental, i.e., the genome, and finishing with the most functional, i.e., the metabolome. Similar to how genomics is applied to the exploration of DNA, metabolomics is the qualitative and quantitative study of metabolites. This emerging field is clearly linked to genomics, transcriptomics and proteomics. In addition, metabolomics provides a unique and direct vision of the functional outcome of an organism's activities that are required for it to survive, grow and respond to internal and external stimuli or stress, e.g., pathologies and drugs. The links between metabolic changes, patient phenotype, physiological and/or pathological status and treatment are now well established and have opened a new area for the application of metabolomics in the drug discovery process and in personalized medicine.

PMID: 27295417 [PubMed - as supplied by publisher]

Unbiased Rare Event Sampling in Spatial Stochastic Systems Biology Models Using a Weighted Ensemble of Trajectories.

PLoS Comput Biol. 2016 Feb;12(2):e1004611

Authors: Donovan RM, Tapia JJ, Sullivan DP, Faeder JR, Murphy RF, Dittrich M, Zuckerman DM

Abstract
The long-term goal of connecting scales in biological simulation can be facilitated by scale-agnostic methods. We demonstrate that the weighted ensemble (WE) strategy, initially developed for molecular simulations, applies effectively to spatially resolved cell-scale simulations. The WE approach runs an ensemble of parallel trajectories with assigned weights and uses a statistical resampling strategy of replicating and pruning trajectories to focus computational effort on difficult-to-sample regions. The method can also generate unbiased estimates of non-equilibrium and equilibrium observables, sometimes with significantly less aggregate computing time than would be possible using standard parallelization. Here, we use WE to orchestrate particle-based kinetic Monte Carlo simulations, which include spatial geometry (e.g., of organelles, plasma membrane) and biochemical interactions among mobile molecular species. We study a series of models exhibiting spatial, temporal and biochemical complexity and show that although WE has important limitations, it can achieve performance significantly exceeding standard parallel simulation--by orders of magnitude for some observables.

PMID: 26845334 [PubMed - indexed for MEDLINE]

Quantitative Selection Analysis of Bacteriophage φCbK Susceptibility in Caulobacter crescentus.

J Mol Biol. 2016 Jan 29;428(2 Pt B):419-30

Authors: Christen M, Beusch C, Bösch Y, Cerletti D, Flores-Tinoco CE, Del Medico L, Tschan F, Christen B

Abstract
Classical molecular genetics uses stringent selective conditions to identify mutants with distinct phenotypic responses. Mutations giving rise to less pronounced phenotypes are often missed. However, to gain systems-level insights into complex genetic interaction networks requires genome-wide assignment of quantitative phenotypic traits. In this paper, we present a quantitative selection approach coupled with transposon sequencing (QS-TnSeq) to globally identify the cellular components that orchestrate susceptibility of the cell cycle model bacterium Caulobacter crescentus toward bacteriophage φCbK infection. We found that 135 genes representing 3.30% of the Caulobacter genome exhibit significant accumulation of transposon insertions upon φCbK selection. More than 85% thereof consist of new factors not previously associated with phage φCbK susceptibility. Using hierarchical clustering of dose-dependent TnSeq datasets, we grouped these genes into functional modules that correlate with different stages of the φCbK infection process. We assign φCbK susceptibility to eight new genes that represent novel components of the pilus secretion machinery. Further, we demonstrate that, from 86 motility genes, only seven genes encoding structural and regulatory components of the flagellar hook increase phage resistance when disrupted by transposons, suggesting a link between flagellar hook assembly and pili biogenesis. In addition, we observe high recovery of Tn5 insertions within regulatory sequences of the genes encoding the essential NADH:ubiquinone oxidoreductase complex indicating that intact proton motive force is crucial for effective phage propagation. In sum, QS-TnSeq is broadly applicable to perform quantitative and genome-wide systems-genetics analysis of complex phenotypic traits.

PMID: 26593064 [PubMed - indexed for MEDLINE]

PGSB PlantsDB: updates to the database framework for comparative plant genome research.

Nucleic Acids Res. 2016 Jan 4;44(D1):D1141-7

Authors: Spannagl M, Nussbaumer T, Bader KC, Martis MM, Seidel M, Kugler KG, Gundlach H, Mayer KF

Abstract
PGSB (Plant Genome and Systems Biology: formerly MIPS) PlantsDB (http://pgsb.helmholtz-muenchen.de/plant/index.jsp) is a database framework for the comparative analysis and visualization of plant genome data. The resource has been updated with new data sets and types as well as specialized tools and interfaces to address user demands for intuitive access to complex plant genome data. In its latest incarnation, we have re-worked both the layout and navigation structure and implemented new keyword search options and a new BLAST sequence search functionality. Actively involved in corresponding sequencing consortia, PlantsDB has dedicated special efforts to the integration and visualization of complex triticeae genome data, especially for barley, wheat and rye. We enhanced CrowsNest, a tool to visualize syntenic relationships between genomes, with data from the wheat sub-genome progenitor Aegilops tauschii and added functionality to the PGSB RNASeqExpressionBrowser. GenomeZipper results were integrated for the genomes of barley, rye, wheat and perennial ryegrass and interactive access is granted through PlantsDB interfaces. Data exchange and cross-linking between PlantsDB and other plant genome databases is stimulated by the transPLANT project (http://transplantdb.eu/).

PMID: 26527721 [PubMed - indexed for MEDLINE]

High-throughput, big data and complexity in clinical proteomics: an interview with Jasminka Godovac-Zimmermann.

Expert Rev Mol Diagn. 2015;15(10):1241-4

Authors: Godovac-Zimmermann J, Raison C

Abstract
Interview with Professor Jasminka Godovac-Zimmermann, PhD by Claire Raison (Commissioning Editor) Professor Jasminka Godovac-Zimmermann is Head of the Proteomics and Molecular Cell Dynamics Group at University College London, UK. Professor Godovac-Zimmermann trained at the Max Planck Institute of Biochemistry, Germany, and specialized in protein chemistry. Her research focuses on proteomics in cancer and systems biology. Here she talks about the clinical impact of her work and her hopes and predictions for how proteomics and diagnostics could work together in future.

PMID: 26367346 [PubMed - indexed for MEDLINE]

Plant sulfur nutrition: From Sachs to Big Data.

Plant Signal Behav. 2015;10(9):e1055436

Authors: Kopriva S

Abstract
Together with water and carbon dioxide plants require 14 essential mineral nutrients to finish their life cycle. The research in plant nutrition can be traced back to Julius Sachs, who was the first to experimentally prove the essentiality of mineral nutrients for plants. Among those elements Sachs showed to be essential is sulfur. Plant sulfur nutrition has been not as extensively studied as the nutrition of nitrogen and phosphate, probably because sulfur was not limiting for agriculture. However, with the reduction of atmospheric sulfur dioxide emissions sulfur deficiency has become common. The research in sulfur nutrition has changed over the years from using yeast and algae as experimental material to adopting Arabidopsis as the plant model as well as from simple biochemical measurements of individual parameters to system biology. Here the evolution of sulfur research from the times of Sachs to the current Big Data is outlined.

PMID: 26305261 [PubMed - indexed for MEDLINE]

Recent strategies and progress in identifying host factors involved in virus replication.

Curr Opin Microbiol. 2015 Aug;26:79-88

Authors: König R, Stertz S

Abstract
Viruses are completely dependent on their host cells for the successful production of progeny viruses. At each stage of the viral life cycle an intricate interplay between virus and host takes place with the virus aiming to usurp the host cell for its purposes and the host cell trying to block the intruder from propagation. In recent years these interactions have been studied on a global level by systems biology approaches, such as RNA interference screens, transcriptomic or proteomic methodologies, and exciting new insights into the pathogen-host relationship have been revealed. In this review, we summarize the available data, give examples for important findings from such studies and point out current limitations and potential future directions.

PMID: 26112615 [PubMed - indexed for MEDLINE]

Synechocystis sp. PCC6803 metabolic models for the enhanced production of hydrogen.

Crit Rev Biotechnol. 2015 Jun;35(2):184-98

Authors: Montagud A, Gamermann D, Fernández de Córdoba P, Urchueguía JF

Abstract
In the present economy, difficulties to access energy sources are real drawbacks to maintain our current lifestyle. In fact, increasing interests have been gathered around efficient strategies to use energy sources that do not generate high CO2 titers. Thus, science-funding agencies have invested more resources into research on hydrogen among other biofuels as interesting energy vectors. This article reviews present energy challenges and frames it into the present fuel usage landscape. Different strategies for hydrogen production are explained and evaluated. Focus is on biological hydrogen production; fermentation and photon-fuelled hydrogen production are compared. Mathematical models in biology can be used to assess, explore and design production strategies for industrially relevant metabolites, such as biofuels. We assess the diverse construction and uses of genome-scale metabolic models of cyanobacterium Synechocystis sp. PCC6803 to efficiently obtain biofuels. This organism has been studied as a potential photon-fuelled production platform for its ability to grow from carbon dioxide, water and photons, on simple culture media. Finally, we review studies that propose production strategies to weigh this organism's viability as a biofuel production platform. Overall, the work presented in this review unveils the industrial capabilities of cyanobacterium Synechocystis sp. PCC6803 to evolve interesting metabolites as a clean biofuel production platform.

PMID: 24090244 [PubMed - indexed for MEDLINE]

DrugQuest - a text mining workflow for drug association discovery.

BMC Bioinformatics. 2016;17(Suppl 5):182

Authors: Papanikolaou N, Pavlopoulos GA, Theodosiou T, Vizirianakis IS, Iliopoulos I

Abstract
BACKGROUND: Text mining and data integration methods are gaining ground in the field of health sciences due to the exponential growth of bio-medical literature and information stored in biological databases. While such methods mostly try to extract bioentity associations from PubMed, very few of them are dedicated in mining other types of repositories such as chemical databases.
RESULTS: Herein, we apply a text mining approach on the DrugBank database in order to explore drug associations based on the DrugBank "Description", "Indication", "Pharmacodynamics" and "Mechanism of Action" text fields. We apply Name Entity Recognition (NER) techniques on these fields to identify chemicals, proteins, genes, pathways, diseases, and we utilize the TextQuest algorithm to find additional biologically significant words. Using a plethora of similarity and partitional clustering techniques, we group the DrugBank records based on their common terms and investigate possible scenarios why these records are clustered together. Different views such as clustered chemicals based on their textual information, tag clouds consisting of Significant Terms along with the terms that were used for clustering are delivered to the user through a user-friendly web interface.
CONCLUSIONS: DrugQuest is a text mining tool for knowledge discovery: it is designed to cluster DrugBank records based on text attributes in order to find new associations between drugs. The service is freely available at http://bioinformatics.med.uoc.gr/drugquest .

PMID: 27295093 [PubMed - as supplied by publisher]

TEES 2.2: Biomedical Event Extraction for Diverse Corpora.

BMC Bioinformatics. 2015;16 Suppl 16:S4

Authors: Björne J, Salakoski T

Abstract
BACKGROUND: The Turku Event Extraction System (TEES) is a text mining program developed for the extraction of events, complex biomedical relationships, from scientific literature. Based on a graph-generation approach, the system detects events with the use of a rich feature set built via dependency parsing. The TEES system has achieved record performance in several of the shared tasks of its domain, and continues to be used in a variety of biomedical text mining tasks.
RESULTS: The TEES system was quickly adapted to the BioNLP'13 Shared Task in order to provide a public baseline for derived systems. An automated approach was developed for learning the underlying annotation rules of event type, allowing immediate adaptation to the various subtasks, and leading to a first place in four out of eight tasks. The system for the automated learning of annotation rules is further enhanced in this paper to the point of requiring no manual adaptation to any of the BioNLP'13 tasks. Further, the scikit-learn machine learning library is integrated into the system, bringing a wide variety of machine learning methods usable with TEES in addition to the default SVM. A scikit-learn ensemble method is also used to analyze the importances of the features in the TEES feature sets.
CONCLUSIONS: The TEES system was introduced for the BioNLP'09 Shared Task and has since then demonstrated good performance in several other shared tasks. By applying the current TEES 2.2 system to multiple corpora from these past shared tasks an overarching analysis of the most promising methods and possible pitfalls in the evolving field of biomedical event extraction are presented.

PMID: 26551925 [PubMed - indexed for MEDLINE]

Application of the EVEX resource to event extraction and network construction: Shared Task entry and result analysis.

BMC Bioinformatics. 2015;16 Suppl 16:S3

Authors: Hakala K, Van Landeghem S, Salakoski T, Van de Peer Y, Ginter F

Abstract
BACKGROUND: Modern methods for mining biomolecular interactions from literature typically make predictions based solely on the immediate textual context, in effect a single sentence. No prior work has been published on extending this context to the information automatically gathered from the whole biomedical literature. Thus, our motivation for this study is to explore whether mutually supporting evidence, aggregated across several documents can be utilized to improve the performance of the state-of-the-art event extraction systems.
RESULTS: In the GE task, our re-ranking approach led to a modest performance increase and resulted in the first rank of the official Shared Task results with 50.97% F-score. Additionally, in this paper we explore and evaluate the usage of distributed vector representations for this challenge.
CONCLUSIONS: For the GRN task, we were able to produce a gene regulatory network from the EVEX data, warranting the use of such generic large-scale text mining data in network biology settings. A detailed performance and error analysis provides more insight into the relatively low recall rates.

PMID: 26551766 [PubMed - indexed for MEDLINE]

SynLethDB: synthetic lethality database toward discovery of selective and sensitive anticancer drug targets.

Nucleic Acids Res. 2016 Jan 4;44(D1):D1011-7

Authors: Guo J, Liu H, Zheng J

Abstract
Synthetic lethality (SL) is a type of genetic interaction between two genes such that simultaneous perturbations of the two genes result in cell death or a dramatic decrease of cell viability, while a perturbation of either gene alone is not lethal. SL reflects the biologically endogenous difference between cancer cells and normal cells, and thus the inhibition of SL partners of genes with cancer-specific mutations could selectively kill cancer cells but spare normal cells. Therefore, SL is emerging as a promising anticancer strategy that could potentially overcome the drawbacks of traditional chemotherapies by reducing severe side effects. Researchers have developed experimental technologies and computational prediction methods to identify SL gene pairs on human and a few model species. However, there has not been a comprehensive database dedicated to collecting SL pairs and related knowledge. In this paper, we propose a comprehensive database, SynLethDB (http://histone.sce.ntu.edu.sg/SynLethDB/), which contains SL pairs collected from biochemical assays, other related databases, computational predictions and text mining results on human and four model species, i.e. mouse, fruit fly, worm and yeast. For each SL pair, a confidence score was calculated by integrating individual scores derived from different evidence sources. We also developed a statistical analysis module to estimate the druggability and sensitivity of cancer cells upon drug treatments targeting human SL partners, based on large-scale genomic data, gene expression profiles and drug sensitivity profiles on more than 1000 cancer cell lines. To help users access and mine the wealth of the data, we developed other practical functionalities, such as search and filtering, orthology search, gene set enrichment analysis. Furthermore, a user-friendly web interface has been implemented to facilitate data analysis and interpretation. With the integrated data sets and analytics functionalities, SynLethDB would be a useful resource for biomedical research community and pharmaceutical industry.

PMID: 26516187 [PubMed - indexed for MEDLINE]