"Initial investigation into computer scoring of candidate essays for personnel selection": Correction to Campion et al. (2016).

J Appl Psychol. 2016 Jul;101(7):975

Authors:

Abstract
Reports an error in "Initial Investigation Into Computer Scoring of Candidate Essays for Personnel Selection" by Michael C. Campion, Michael A. Campion, Emily D. Campion and Matthew H. Reider (Journal of Applied Psychology, Advanced Online Publication, Apr 14, 2016, np). In the article the affiliations for Emily D. Campion and Matthew H. Reider were originally incorrect. All versions of this article have been corrected. (The following abstract of the original article appeared in record 2016-18130-001.) Emerging advancements including the exponentially growing availability of computer-collected data and increasingly sophisticated statistical software have led to a "Big Data Movement" wherein organizations have begun attempting to use large-scale data analysis to improve their effectiveness. Yet, little is known regarding how organizations can leverage these advancements to develop more effective personnel selection procedures, especially when the data are unstructured (text-based). Drawing on literature on natural language processing, we critically examine the possibility of leveraging advances in text mining and predictive modeling computer software programs as a surrogate for human raters in a selection context. We explain how to "train" a computer program to emulate a human rater when scoring accomplishment records. We then examine the reliability of the computer's scores, provide preliminary evidence of their construct validity, demonstrate that this practice does not produce scores that disadvantage minority groups, illustrate the positive financial impact of adopting this practice in an organization (N ∼ 46,000 candidates), and discuss implementation issues. Finally, we discuss the potential implications of using computer scoring to address the adverse impact-validity dilemma. We suggest that it may provide a cost-effective means of using predictors that have comparable validity but have previously been too expensive for large-scale screening. (PsycINFO Database Record

PMID: 27379396 [PubMed - as supplied by publisher]

Transfer Learning for Class Imbalance Problems with Inadequate Data.

Knowl Inf Syst. 2016 Jul;48(1):201-228

Authors: Al-Stouhi S, Reddy CK

Abstract
A fundamental problem in data mining is to effectively build robust classifiers in the presence of skewed data distributions. Class imbalance classifiers are trained specifically for skewed distribution datasets. Existing methods assume an ample supply of training examples as a fundamental prerequisite for constructing an effective classifier. However, when sufficient data is not readily available, the development of a representative classification algorithm becomes even more difficult due to the unequal distribution between classes. We provide a unified framework that will potentially take advantage of auxiliary data using a transfer learning mechanism and simultaneously build a robust classifier to tackle this imbalance issue in the presence of few training samples in a particular target domain of interest. Transfer learning methods use auxiliary data to augment learning when training examples are not sufficient and in this paper we will develop a method that is optimized to simultaneously augment the training data and induce balance into skewed datasets. We propose a novel boosting based instance-transfer classifier with a label-dependent update mechanism that simultaneously compensates for class imbalance and incorporates samples from an auxiliary domain to improve classification. We provide theoretical and empirical validation of our method and apply to healthcare and text classification applications.

PMID: 27378821 [PubMed - as supplied by publisher]

6 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/07/05

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

9 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

"Cystic Fibrosis"

These pubmed results were generated on 2016/07/05

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Leveraging 3D chemical similarity, target and phenotypic data in the identification of drug-protein and drug-adverse effect associations.

J Cheminform. 2016;8:35

Authors: Vilar S, Hripcsak G

Abstract
BACKGROUND: Drug-target identification is crucial to discover novel applications for existing drugs and provide more insights about mechanisms of biological actions, such as adverse drug effects (ADEs). Computational methods along with the integration of current big data sources provide a useful framework for drug-target and drug-adverse effect discovery.
RESULTS: In this article, we propose a method based on the integration of 3D chemical similarity, target and adverse effect data to generate a drug-target-adverse effect predictor along with a simple leveraging system to improve identification of drug-targets and drug-adverse effects. In the first step, we generated a system for multiple drug-target identification based on the application of 3D drug similarity into a large target dataset extracted from the ChEMBL. Next, we developed a target-adverse effect predictor combining targets from ChEMBL with phenotypic information provided by SIDER data source. Both modules were linked to generate a final predictor that establishes hypothesis about new drug-target-adverse effect candidates. Additionally, we showed that leveraging drug-target candidates with phenotypic data is very useful to improve the identification of drug-targets. The integration of phenotypic data into drug-target candidates yielded up to twofold precision improvement. In the opposite direction, leveraging drug-phenotype candidates with target data also yielded a significant enhancement in the performance.
CONCLUSIONS: The modeling described in the current study is simple and efficient and has applications at large scale in drug repurposing and drug safety through the identification of mechanism of action of biological effects.

PMID: 27375776 [PubMed]

Using systems biology to evaluate targets and mechanism of action of drugs for diabetes comorbidities.

Diabetologia. 2016 Jul 4;

Authors: Mayer B

Abstract
Medications approved for diabetes-associated renal and cardiovascular morbidities and candidate drugs currently in development are subject to substantial variability in drug response. Heterogeneity on a molecular phenotype level is not apparent at clinical presentation, which means that inter-individual differences in drug effect at the molecular level are masked. These findings identify the need for optimising patient phenotyping via use of molecular biomarkers for a personalised therapy approach. Molecular diversity may, on the one hand, result from the effect of genetic polymorphisms on drug transport, metabolism and effective target modulation. Equally relevant, differences may be due to molecular pathologies. The presence of distinct molecular phenotypes is suggested by classifiers aimed at modelling progressive disease. Such functions for prognosis incorporate a complex set of clinical variables or a multitude of molecular markers reflecting a diverse set of molecular disease mechanisms. This information on disease pathology and the mechanism of action of the drug needs to be systematically integrated with data on molecular biomarkers to develop an experimental tool for personalising medicine. The large amount of molecular data available for characterising diabetes-associated morbidities allows for elucidation of molecular process model representations of disease pathologies. Selecting biomarker candidates on such grounds and, in turn identifying their association with progressive disease allows for the identification of molecular processes associated with disease progression. The molecular effect of a drug can also be modelled at a molecular process level, and the integration of disease pathology and drug effect molecular models reveals candidate biomarkers for assessing drug response. Such tools serve as enrichment strategies aimed at adding precision to drug development and use.

PMID: 27376542 [PubMed - as supplied by publisher]

Affinity and dose of TCR engagement yield proportional enhancer and gene activity in CD4+ T cells.

Elife. 2016;5

Authors: Allison KA, Sajti E, Collier JG, Gosselin D, Troutman TD, Stone EL, Hedrick SM, Glass CK

Abstract
Affinity and dose of T cell receptor (TCR) interaction with antigens govern the magnitude of CD4+ T cell responses, but questions remain regarding the quantitative translation of TCR engagement into downstream signals. We find that while the response of mouse CD4+ T cells to antigenic stimulation is bimodal, activated cells exhibit analog responses proportional to signal strength. Gene expression output reflects TCR signal strength, providing a signature of T cell activation. Expression changes rely on a pre-established enhancer landscape and quantitative acetylation at AP-1 binding sites. Finally, we show that graded expression of activation genes depends on ERK pathway activation, suggesting that an ERK-AP-1 axis plays an important role in translating TCR signal strength into proportional activation of enhancers and genes essential for T cell function.

PMID: 27376549 [PubMed - as supplied by publisher]

Using systems biology to evaluate targets and mechanism of action of drugs for diabetes comorbidities.

Diabetologia. 2016 Jul 4;

Authors: Mayer B

Abstract
Medications approved for diabetes-associated renal and cardiovascular morbidities and candidate drugs currently in development are subject to substantial variability in drug response. Heterogeneity on a molecular phenotype level is not apparent at clinical presentation, which means that inter-individual differences in drug effect at the molecular level are masked. These findings identify the need for optimising patient phenotyping via use of molecular biomarkers for a personalised therapy approach. Molecular diversity may, on the one hand, result from the effect of genetic polymorphisms on drug transport, metabolism and effective target modulation. Equally relevant, differences may be due to molecular pathologies. The presence of distinct molecular phenotypes is suggested by classifiers aimed at modelling progressive disease. Such functions for prognosis incorporate a complex set of clinical variables or a multitude of molecular markers reflecting a diverse set of molecular disease mechanisms. This information on disease pathology and the mechanism of action of the drug needs to be systematically integrated with data on molecular biomarkers to develop an experimental tool for personalising medicine. The large amount of molecular data available for characterising diabetes-associated morbidities allows for elucidation of molecular process model representations of disease pathologies. Selecting biomarker candidates on such grounds and, in turn identifying their association with progressive disease allows for the identification of molecular processes associated with disease progression. The molecular effect of a drug can also be modelled at a molecular process level, and the integration of disease pathology and drug effect molecular models reveals candidate biomarkers for assessing drug response. Such tools serve as enrichment strategies aimed at adding precision to drug development and use.

PMID: 27376542 [PubMed - as supplied by publisher]

DNA methyltransferase inhibitors: an updated patent review (2012-2015).

Expert Opin Ther Pat. 2016 Jul 4;

Authors: Xu P, Hu G, Luo C, Liang Z

Abstract
INTRODUCTION: DNA methyltransferases (DNMTs), important enzymes involved in epigenetic regulation of gene expression, represent promising targets in cancer therapy. DNMT inhibitors (DNMTi), which can modulate the aberrant DNA methylation pattern in a reversible way via inhibiting DNMT activity, have attracted significant attention in recent years.
AREAS COVERED: This review outlines the newly patented inhibitors targeting DNMTs, mainly incorporating small molecular inhibitors and oligonucleotide derivatives. The chemical structures, biological activity, and the encouraging clinical research in progress are delineated in detail.
EXPERT OPINION: Two drugs, azacitidine and decitabine, have evidently shown efficacy in hematologic malignancies, yet do not work well on solid tumors, have low specificity, substantial toxicity, and poor bioavailability. With the rapid advancement in systems biology, drug combinations, such as DNMTi, in conjugation with histone deacetylase inhibitors (HDACi) or immunotherapy, probably serve as an efficient way of implementing epigenetic therapy. Meanwhile, the resolved autoinhibitory structures of DNMTs afford a novel strategy for targeting the protein-protein interface involved in the autoinhibitory interactions. The molecular mechanism underlying the conformational transitions would also shed new light on the design of allosteric inhibitors. Both strategies would produce inhibitors with more selectivity compared to nucleotide derivatives.

PMID: 27376512 [PubMed - as supplied by publisher]

Dynamic and modular gene regulatory networks drive the development of gametogenesis.

Brief Bioinform. 2016 Jul 3;

Authors: Che D, Wang Y, Bai W, Li L, Liu G, Zhang L, Zuo Y, Tao S, Hua J, Liao M

Abstract
Gametogenesis is a complex process, which includes mitosis and meiosis and results in the production of ovum and sperm. The development of gametogenesis is dynamic and needs many different genes to work synergistically, but it is lack of global perspective research about this process. In this study, we detected the dynamic process of gametogenesis from the perspective of systems biology based on protein-protein interaction networks (PPINs) and functional analysis. Results showed that gametogenesis genes have strong synergistic effects in PPINs within and between different phases during the development. Addition to the synergistic effects on molecular networks, gametogenesis genes showed functional consistency within and between different phases, which provides the further evidence about the dynamic process during the development of gametogenesis. At last, we detected and provided the core molecular modules of different phases about gametogenesis. The gametogenesis genes and related modules can be obtained from our Web site Gametogenesis Molecule Online (GMO, http://gametsonline.nwsuaflmz.com/index.php), which is freely accessible. GMO may be helpful for the reference and application of these genes and modules in the future identification of key genes about gametogenesis. Summary, this work provided a computational perspective and frame to the analysis of the gametogenesis dynamics and modularity in both human and mouse.

PMID: 27373733 [PubMed - as supplied by publisher]

[Using (1)H-nuclear magnetic resonance metabolomics and gene ontology to establish pathological staging model for esophageal cancer patients].

Zhonghua Wai Ke Za Zhi. 2016 Jul 1;54(7):540-545

Authors: Chen X, Wang K, Chen W, Jiang H, Deng PC, Li ZJ, Peng J, Zhou ZY, Yang H, Huang GX, Zeng J

Abstract
Objectives: By combining the metabolomics and computational biology, to explore the relationship between metabolic phenotype and pathological stage in esophageal cancer patients, to find the mechanism of metabolic network disturbance and develop a new method for fast preoperative clinical staging. Methods: A prospective cohort study (from April 2013 to January 2016) was conducted. The preoperative patients from Sichuan Provincial People's Hospital, who were diagnosed with esophageal cancer from May 2013 to April 2014 were included, and their serum samples were collected to detect (1)H-nuclear magnetic resonance (NMR) metabolomics for the purpose of drawing the metabolic fingerprinting in different stages of patients with esophageal cancer. The data were processed with these methods-principal components analysis: partial least squares regression and support vector machine, for the exploration of the enzyme-gene network regulatory mechanism in abnormal esophageal cancer metabolic network regulation and to build the quantitative prediction model of esophageal cancer staging in the end. All data were processed on high-performance computing platforms Matalab. The comparison of data had used Wilcoxon test, variance analysis, χ(2) test and Fisher exact test. Results: Twenty patients with different stages of esophageal cancer were included; and their serum metabolic fingerprinting could differentiate different tumor stages. There were no difference among the five teams in the age (F=1.086, P>0.05), the body mass index (F=1.035, P>0.05), the distance from the incisors to tumor (F=1.078, P>0.05). Among the patients with different TNM stages, there was a significant difference in plasma metabolome. Compared to ⅡB, ⅢA, Ⅳstage patients, increased levels of butanone, ethanol amine, homocysteine, hydroxy acids and estriol, together with decreased levels of glycoprotein, creatine, choline, isobutyricacid, alanine, leucine, valine, were observed inⅠB, ⅡA stage patients. Four metabolic markers (ethanol amine, hydroxy-propionic acid, homocysteine and estriol) were eventually selected. gene ontology analysis showed that 54 enzymes and genes regulated the 4 key metabolic markers. The quantitative prediction model of esophageal cancer staging based on esophageal cancer NMR spectrum were established. Cross-validation results showed that the predicted effect was good (root mean square error=5.3, R(2)=0.47, P=0.036). Conclusions: The systems biology approaches based on metabolomics and enzyme-gene regulatory network analysis can be used to quantify the metabolic network disturbance of patients with advanced esophageal cancer, and to predict preoperative clinical staging of esophageal cancer patients by plasma NMR metabolomics.

PMID: 27373482 [PubMed - as supplied by publisher]

Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics.

Biomed Inform Insights. 2016;8(Suppl 1):1-11

Authors: Torii M, Tilak SS, Doan S, Zisook DS, Fan JW

Abstract
In an era when most of our life activities are digitized and recorded, opportunities abound to gain insights about population health. Online product reviews present a unique data source that is currently underexplored. Health-related information, although scarce, can be systematically mined in online product reviews. Leveraging natural language processing and machine learning tools, we were able to mine 1.3 million grocery product reviews for health-related information. The objectives of the study were as follows: (1) conduct quantitative and qualitative analysis on the types of health issues found in consumer product reviews; (2) develop a machine learning classifier to detect reviews that contain health-related issues; and (3) gain insights about the task characteristics and challenges for text analytics to guide future research.

PMID: 27375358 [PubMed]

Coreference resolution improves extraction of Biological Expression Language statements from texts.

Database (Oxford). 2016;2016

Authors: Choi M, Liu H, Baumgartner W, Zobel J, Verspoor K

Abstract
We describe a system that automatically extracts biological events from biomedical journal articles, and translates those events into Biological Expression Language (BEL) statements. The system incorporates existing text mining components for coreference resolution, biological event extraction and a previously formally untested strategy for BEL statement generation. Although addressing the BEL track (Track 4) at BioCreative V (2015), we also investigate how incorporating coreference resolution might impact event extraction in the biomedical domain. In this paper, we report that our system achieved the best performance of 20.2 and 35.2 in F-score for the full BEL statement level on both stage 1, and stage 2 using provided gold standard entities, respectively. We also report that our results evaluated on the training dataset show benefit from integrating coreference resolution with event extraction.

PMID: 27374122 [PubMed - as supplied by publisher]

Mineral and metabolic profiles in tea leaves and flowers during flower development.

Plant Physiol Biochem. 2016 Jun 14;106:316-326

Authors: Jia S, Wang Y, Hu J, Ding Z, Liang Q, Zhang Y, Wang H

Abstract
Tea [Camellia sinensis (L.) O. Kuntze] is one of the most popular non-alcoholic beverage crops in the world, and the physiological processes and gene regulations involved in development in tea plants have been well characterized. However, relatively little is known about the metabolic changes combined with mineral distributions that occur during flower development. Here we detected the contents of 11 elements in tea leaves and flowers and found that, some of them, especially phosphorus, sulfur and copper, showed significant changes during tea flowering. We also detected 122 metabolites in tea leaves and flowers and found that, 72 of them showed significant differences between flowers and leaves, of which sugars, organic acids, and flavonoids dominated. The sugars, such as trehalose and galactose, all accumulated in tea flowers, and the organic acids, such as malic acid, citric acid and fumaric acid involved in TCA cycle. The flavonoids, like epicatechin, catechin gallate and epigallocatechin, were more abundant in leaves. Furthermore, we found that the contents of 33 metabolites changed during the development of flowers. Especially, citric acid, phenylalanine and most flavonoids decreased while fructose and galactose increased during flowering stages in flowers. We also analyzed the correlations between the ions and metabolites and found that, some mineral nutrients including phosphorus, sulfur, manganese and zinc had close relations to organic acids, flavonoids, sugars and several amino acids during flowering. We mapped the metabolic pathway according to the KEGG database. This work will serve as the foundation for a systems biology approach to the understanding of mineral metabolism.

PMID: 27372442 [PubMed - as supplied by publisher]

6 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/07/03

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Research and drug development activities in rare diseases: differences between Japan and Europe regarding influence of prevalence.

Drug Discov Today. 2016 Jun 28;

Authors: Mizoguchi H, Yamanaka T, Kano S

Abstract
Orphan drug legislation has contributed enormously to promote drug development for rare diseases but further effective and sustainable approaches are required. This study focused on the difference of rare disease prevalence between Japan and Europe, classified the rare diseases comprehensively using cluster analysis and analyzed the influence of prevalence on research activity and drug development. Although overall strong correlative progress of research was found and absolute numbers of values were greater in Europe than in Japan, the regional higher prevalent diseases demonstrated more progress of research and development relatively in the region by examining clusters. Our findings suggest potential optimal drug development in consideration of regional differences. Moreover, an in-depth analysis of diseases that showed exceptional research achievements compared with prevalence speculated important determinants of progress.

PMID: 27371505 [PubMed - as supplied by publisher]

9 new pubmed citations were retrieved for your search. Click on the search hyperlink below to display the complete search results:

("orphan disease" OR "rare disease" OR "orphan diseases" OR "rare diseases")

These pubmed results were generated on 2016/07/02

PubMed comprises more than 24 million citations for biomedical literature from MEDLINE, life science journals, and online books. Citations may include links to full-text content from PubMed Central and publisher web sites.

Methylene blue inhibits lumefantrine-resistant Plasmodium berghei.

J Infect Dev Ctries. 2016;10(6):635-642

Authors: Mwangi VI, Mumo RM, Kiboi DM, Omar SA, Ng'ang'a ZW, Ozwara HS

Abstract
INTRODUCTION: Chemotherapy still is the most effective way to control malaria, a major public health problem in sub-Saharan Africa. The large-scale use of the combination therapy artemether-lumefantrine for malaria treatment in Africa predisposes lumefantrine to emergence of resistance. There is need to identify drugs that can be used as substitutes to lumefantrine for use in combination therapy. Methylene blue, a synthetic anti-methemoglobinemia drug, has been shown to contain antimalarial properties, making it a candidate for drug repurposing. The present study sought to determine antiplasmodial effects of methylene blue against lumefantrine- and pyrimethamine-resistant strains of P. berghei.
METHODOLOGY: Activity of methylene blue was assessed using the classical four-day test on mice infected with lumefantrine-resistant and pyrimethamine-resistant P. berghei. A dose of 45 mg/kg/day was effective for testing ED90. Parasitemia and mice survival was determined.
RESULTS: At 45 mg/kg/day, methylene blue sustained significant parasite inhibition, over 99%, for at least 6 days post-treatment against lumefantrine-resistant and pyrimethamine-resistant P. berghei (p = 0.0086 and p = 0.0191, respectively). No serious adverse effects were observed.
CONCLUSIONS: Our results indicate that methylene blue at a concentration of 45 mg/kg/day confers over 99% inhibition against lumefantrine- and pyrimethamine-resistant P. berghei for six days. This shows the potential use methylene blue in the development of antimalarials against lumefantrine- and pyrimethamine-resistant parasites.

PMID: 27367013 [PubMed - as supplied by publisher]

Drug repositioning approaches to parasitic diseases: a medicinal chemistry perspective.

Drug Discov Today. 2016 Jun 27;

Authors: Ferreira LG, Andricopulo AD

Abstract
Identifying new indications for clinically useful drugs is a worthwhile approach for neglected tropical diseases. The number of successful repurposing cases in the field is growing as not-for-profit organizations, in association with academia and pharmaceutical companies, enable screening campaigns for the identification of new repositioning candidates. Current programs have delivered encouraging results as the use of state-of-the-art technologies, such as genomic and structural biology tools, and high-throughput screening platforms have become increasingly common in infectious disease research. Drug repositioning has played a key part in improving the lives of those suffering from these conditions, as evidenced by successful precedents and recent studies on preeminent parasitic disorders.

PMID: 27365271 [PubMed - as supplied by publisher]

Can you teach old drugs new tricks?

Nature. 2016 Jun 16;534(7607):314-6

Authors: Nosengo N

PMID: 27306171 [PubMed - indexed for MEDLINE]