Search

One common health problem in the US faced by colleges and universities is binge drinking. College students often post drinking related texts and images on social media as a socially desirable identity. Some public health and clinical research scholars have surveyed different social media sites manually to understand their behavior patterns. In this paper, we investigate the feasibility of mining the heterogeneous data scattered on social media to identify drinking-related contents, which is the first step towards unleashing the potential of social media in automatic detection of binge drinking users. We use the state-of-the-art algorithms such as Support Vector Machine and neural networks to classify drinking from non-drinking posts, which contain not only text, but also images and videos. Our results show that combining heterogeneous data types, we are able to identify drinking related posts with an overall accuracy of 82%. Prediction models based on text data is more reliable compared to the other two models built on image and video data for predicting drinking related contents.

Data on disease burden is often used for assessing population health, evaluating the effectiveness of interventions, formulating health policies, and planning future resource allocation. We investigated whether Internet usage data, particularly the search volume on Google and page view counts on Wikipedia, are correlated with the disease burden, measured by prevalence and treatment cost, for 1,633 diseases over an 11-year period. We also applied the method of least absolute shrinkage and selection operator (LASSO) to predict the burden of diseases, using those Internet data together with three other variables we quantified previously. We found a relatively strong correlation for 39 of 1,633 diseases, including viral hepatitis, diabetes mellitus, other headache syndromes, multiple sclerosis, sleep apnea, hemorrhoids, and disaccharidase deficiency. However, an accurate analysis must consider each condition’s characteristics, including acute/chronic nature, severity, familiarity to the public, and presence of stigma.

With the development of economy, commercial sector building energy consumption becomes more and more noticeable in China. The aim of this paper was to study the influencing factors governing commercial sector building energy consumption and the decoupling relation between building energy consumption and commercial sector economic development. The main results of this paper are as follows: (1) Commercial sector energy intensity decreased from 61.78 gce/yuan in 1991 to 36.70 gce/yuan in 2011, representing a decrease of 40.59 %. Since 2003, electricity has exceeded coal products and become the dominating energy. (2) The economic activity effect makes a positive contributor to the increase in commercial sector building energy consumption; however, the energy intensity effect plays an important role in decreasing energy consumption, followed by the energy structure effect. Same results have also been observed according to the relational degree. (3) Only four kinds of decoupling index occurred over 1991–2011. Strong decoupling occurred only in the years 1994, 1997, and 2008.

This study aims to examine how China's energy security has changed over 30 years of reform and the opening period. It constructs a 4-As quantitative evaluation framework—the availability of energy resources, the applicability of technology, the acceptability by society, and the affordability of energy resources. The quantitative results show that China's energy security was at its best during the sixth FYP period (1981–1985), but then deteriorated until it hit higher levels between 1995 and 2005. However, it was still lower than the level reached during the sixth FYP period. During the eleventh FYP period (2006–2010), the energy security situation deteriorated again. Differences in policy priority over the study period appear to affect the country's energy security status. This study suggests that China needs to develop renewable energy resources on a large scale and pay more attention to emissions control to reverse the downward trend in energy security.

Since bank employees are prone to high psychological pressure, it is key to explore the influencing mechanism of their emotional labor so as to relieve their pressure, as well as improve organizational performance and service quality. This study aimed to investigate the effects of emotional labor on bank employees&rsquo, well-being and to determine the mediating role of emotional disorder in this relationship. Employees responded to a survey regarding their use of emotional labor as well as perceptions of their well-being and emotional disorder. The results showed that employees&rsquo, use of emotional labor was related to their perceptions of well-being and confirmed the mediating role of emotional disorder in this relationship. The results indicated that surface acting has a significant negative impact on employee well-being, while deep acting has a significant positive impact. Moreover, emotional disorder played a role in mediating emotional labor and employee well-being, and emotional disorder was positively correlated with surface acting and negatively correlated with deep acting. The results revealed that developing deep-acting skills is important for increasing front-line bank staff&rsquo, s well-being in China, who are accustomed to repressing their emotions, and emotional disorder might occur more often than has been previously believed, which worsens their well-being.

Consolidating semantically rich annotation on digital histopathological images known as whole-slide images requires a software capable of handling such type of biomedical data with support for procedures which align with existing pathological protocols. Demands for large-scale annotated histopathological datasets are on the raise since they are needed for developments of artificial intelligence techniques to promote automated diagnosis, mass screening, phenotype-genotype association study, etc. This paper presents an open platform for efficient collaborative histopathological image annotation with standardised semantic enrichment at a pixel-level precision named OpenHI (Open Histopathological Image). The framework's responsive processing algorithm can perform large-scale histopathological image annotation and serve as biomedical data infrastructure for digital pathology. Its web-based design is highly configurable and could be extended to annotate histopathological image of various oncological types. The framework is open-source and fully documented.

A rapid and efficient synthesis of α-keto esters from β-ketonitriles using phenyliodine(III) diacetate is reported. This protocol gave α-keto esters in good yields. This is the first time to report the application of hypervalent iodine(III) reagents in the synthesis of α-keto esters. A plausible reaction mechanism is proposed.

Human organ, as the basic structural and functional unit in human body, is made of a large community of different cell types that organically bound together. Each organ usually exerts highly specified physiological function; while several related organs work smartly together to perform complicated body functions. In this study, we present a computational effort to understand the roles of genes in building functional connection between organs. More specifically, we mined multiple transcriptome datasets sampled from 36 human organs and tissues, and quantitatively identified 3,149 genes whose expressions showed consensus modularly patterns: specific to one organ/tissue, selectively expressed in several functionally related tissues and ubiquitously expressed. These pattern genes imply intrinsic connections between organs. According to the expression abundance of the 766 selective genes, we consistently cluster the 36 human organs/tissues into seven functional groups: adipose & gland, brain, muscle, immune, metabolism, mucoid and nerve conduction. The organs and tissues in each group either work together to form organ systems or coordinate to perform particular body functions. The particular roles of specific genes and selective genes suggest that they could not only be used to mechanistically explore organ functions, but also be designed for selective biomarkers and therapeutic targets.

Department of Medicine, Department of Human Genetics, Institute forGenomicsandSystemsBiology,UniversityofChicago,Chicago,IL60637,USAThe Publisher regrets that this article is an accidental duplication of anarticle that has already been published, DOI of original article: http://dx.doi.org/10.1016/j.tibtech.2010.01.004.The duplicate article has therefore been withdrawn.The fullElsevier PolicyonArticleWithdrawalcanbefoundathttp://www.elsevier.com/locate/withdrawalpolicy.TIBTEC789 No.ofPages1

The interaction of death-associated protein kinase 1 (DAPK1) with the 2B subunit (GluN2B) C-terminus of N-methyl-D-aspartate receptor (NMDAR) plays a critical role in the pathophysiology of depression and is considered a potential target for the structure-based discovery of new antidepressants. However, the 3D structures of C-terminus residues 1290&ndash, 1310 of GluN2B (GluN2B-CT1290-1310) remain elusive and the interaction between GluN2B-CT1290-1310 and DAPK1 is unknown. In this study, the mechanism of interaction between DAPK1 and GluN2B-CT1290-1310 was predicted by computational simulation methods including protein&ndash, peptide docking and molecular dynamics (MD) simulation. Based on the equilibrated MD trajectory, the total binding free energy between GluN2B-CT1290-1310 and DAPK1 was computed by the mechanics generalized born surface area (MM/GBSA) approach. The simulation results showed that hydrophobic, van der Waals, and electrostatic interactions are responsible for the binding of GluN2B-CT1290&ndash, 1310/DAPK1. Moreover, through per-residue free energy decomposition and in silico alanine scanning analysis, hotspot residues between GluN2B-CT1290-1310 and DAPK1 interface were identified. In conclusion, this work predicted the binding mode and quantitatively characterized the protein&ndash, peptide interface, which will aid in the discovery of novel drugs targeting the GluN2B-CT1290-1310 and DAPK1 interface.

The strain Rs-198 isolated from alkaline soil was investigated and identified as Pseudomonas putida on the basis of its physiological and biochemical properties as well as its 16S rDNA sequence analysis. The effects of the strain Rs-198 on cotton seedling growth under salt stress were studied. The results indicated that Rs-198 could protect against salt stress and promote cotton seedling growth. After treatment with Rs-198, the germination rate and healthy stand of cotton were increased by 23.8% and 30.7%, respectively. Individual plant height, fresh weight and dry weight of cotton seedling were increased by 12.8%, 30.7% and 10.0%, respectively as compared to the control (CK). In the fields, Rs-198 also exhibited the ability to increase the cotton's germination rate and healthy stand, which were 16.0% and 22.4% higher than that of CK. Further analysis showed that Rs-198 could increase the absorption of the Mg 2+ , K + and Ca 2+ and decrease the uptake of the Na + from the soil and also improve the production of endogenous indole acetic acid (IAA) content and reduce the abscisic acid (ABA) content of cotton seedling under salt stress.

What makes a successful drug target? A target molecule with an appropriate (druggable) tertiary structure is a necessary but not the sufficient condition for success. Here we analyzed specific properties of human genes and proteins targeted by 919 FDA-approved drugs and identified several quantitative measures that distinguish them from other genes and proteins at a highly significant level. Compared to an average gene and its encoded protein(s), successful drug targets are more highly connected (but far from being the most highly connected), have higher betweenness values, lower entropies of tissue expression, and lower ratios of nonsynonymous to synonymous single-nucleotide polymorphisms. Furthermore, we have identified human tissues that are significantly over- or undertargeted relative to the full spectrum of genes that are active in each tissue. Our study provides quantitative guidelines that could aid in the computational screening of new drug targets in human cells.

The small number of patients enrolled in clinical trials to test new drugs and the relatively short trial durations make it paramount to monitor drugs' effectiveness and risks after they are approved by the regulatory agency. A thorough evaluation of a drug's effectiveness, side effects, and social and economic influences can prevent serious health damage to the public and shed light on new drug discovery and development. Past research has examined spontaneous reporting systems and electronic health records systems as data sources to study medication outcomes. However, both data sources are not able to provide complete and unbiased pictures of patients' care, making it necessary to integrate new data sources, such as increasingly prevalent social media data. In this study, we compared and evaluated four social media sites, in terms of data coverage and quality using 11 disease-drug pairs of careful selection. We found some patients reported serendipitous new indications for the drugs they were using for comorbid conditions, which is truly valuable information for drug repositioning. We also identified five cases of informal use of English on social media that can be challenging for computers to process, including comparative sentiment, sarcasm, grammar errors, pronoun reference and semantic reference, and emoticons. Our study suggests that social media can be a complementary new data source for studying medication outcomes, and reliable natural language processing and text mining methods are needed to automatically mine social media data on a large scale.

This study applies both quantitative and qualitative analyses to the energy policies and energy security situation in China between 1980 and 2010. Two primary objectives are achieved in this study. The first objective is to examine how the energy security situation in China has evolved in the past 30 years. This study establishes a quantitative framework that shows that China’s energy security situation has not improved during the economic reform. The second objective of the study is to explore qualitatively why the energy security situation has not improved. To answer the “why” question, the study opens up a new perspective by analysing the relationship between energy security and energy policies from the macroeconomic reform perspective. This study discusses major reforms that took place over 30 years. It finds that China’s macroeconomic reform has restricted the formation of China’s energy policies and determined its energy security situation. In essence, China’s energy policies were only a reaction to the macroeconomic measures. They were not originally intended to improve energy security, but were passive reactions to China’s macroeconomic reform. This explains why China did not improve its energy security situation despite 30 years of reform.

Despite unprecedented energy consumption rates and environmental sustainability threats, China has seen fast economic growth in the past 30 years of reform. Within this context, this study applies both quantitative and qualitative analyses to the energy security situation in China between 1980 and 2010. Two primary objectives are achieved in this study. The first objective is to examine how the energy security situation in China has evolved in the past 30 years. This study establishes a quantitative 4-As framework and identifies 20 indicators incorporated in the framework based on a comprehensive concept of energy security which covers availability of resources, applicability of technologies, acceptability by society and affordability of prices (the 4-As). The framework analysis shows that China’s energy security situation has not improved during the economic reform. Its enlarging appetite for energy resources has made the quantitative value of the availability dimension decline. The acceptability dimension deteriorated along with the energy utilization, only improving a little recently. The affordability dimension improved after it hit the bottom in 1995. Although the country’s technology continued to progress, the improvement of the applicability dimension cannot restore the overall energy security status to the early reform period level. The emphasis on energy policies during the 30 years of reform appears to have affected the energy security status directly. The second objective of the study is to explore qualitatively why the energy security situation has not improved. To answer the “why” question, the study opens up a new perspective by analysing the relationship between energy security and energy policies from the macroeconomic reform perspective. This study discusses major reforms that took place over 30 years. It is found that China’s macroeconomic reform has restricted the formation of China’s energy policies and determined its energy security situation. In essence, China’s energy policies are only a reaction to the macroeconomic measures. In other words, China’s energy policies are not originally intended to improve energy security, but passive reactions to China’s macroeconomic reform. This explains why China did not improve its energy security situation despite 30 years of reform. Doctor of Philosophy

Klebsiella oxytoca Rs-5 isolated with ACC (1-aminocyclopropane-1-carboxylate) deaminase activity as the sole nitrogen source could obviously promote cotton seedling growth under salt stress and produce phytohormone indole-3-acetic acid (IAA). The amount of IAA produced by the strain Rs-5 was measured, and the effect of IAA on cotton growth under salt stress was studied. Different treatments were set to treat cotton seeds with fermentation broth containing strain Rs-5 (FB), strain Rs-5, fermentation broth with bacteria removed (FB-NB), fermentation broth without bacteria or IAA (FB-NB-NI) and single IAA solutions (SI) according to the IAA concentration after strain Rs-5 culturing of 48, 72 and 120 h. The germination rate, dry weight, plant height, root length and malondialdehyde (MDA), proline and endogenous IAA content in roots were determined. The results showed that both IAA produced by strain Rs-5 and the strain were effective in promoting cotton growth under salt stress. The growth and ability to resist salt stress of cotton seedlings were increased with the enhancement of IAA concentration. The treatment of FB containing bacteria and IAA at 120 h obtained the best state of cotton growth, when the IAA content was the highest in the fermentation broth (42.14 μg·L(-1)). The germination rate, dry weight, plant height and root length were increased by 29.4%, 24.3%, 27.2% and 27.2% , respectively, compared to the saline control. The strain Rs-5 and/or IAA could obviously reduce the MDA and proline content and increase the endogenous IAA content in cotton seedlings. However, the efficacy of other components in the fermentation broth was inconspicuous.

To observe the microscopic characteristic of the bonding interface between resin cement and dentin before and after using Nd: YAG laser, and evaluate the effects of the Nd: YAG laser on the shear bond strength of 3 resin cements to dentin.30 human premolars were cut into nearly equal halves, prepared to expose the dentin and randomly divided into two groups: Laser group and control group. The teeth in the laser group were processed with pulse Nd: YAG laser (0.8 W, 10 Hz) for 25 s, and then the teeth were bonded using 3 resin cements (RelyX ARC, Panavia F and RelyX Unicem) to dentin. All teeth in the control group were directly filled with 3 cements. The shear bond strength of specimens was measured. Each fractured specimen was examined under dental microscope. And 6 new human premolars were prepared into standard bonding specimens. After 3 teeth processed with pulse Nd: YAG laser, all teeth were bonded using 3 resin cements. To observe the microscopic characteristics of the bonding interface between resin cements and dentin.Nd: YAG laser on dentin surface could increase bond strength of Panavia F and RelyX Unicem (P0.05). And the laser could decrease bond strength of RelyX ARC (P0.05). Most of the fractured adhesive dentin surfaces were adhesive failure modes. Scanning electron microscope (SEM) observation demonstrated that Nd: YAG laser irradiation on dentin surface could decrease the hybrid layer and resin tags between the resin cements (RelyX ARC and Panavia F) and dentin. The interface between RelyX Unicem and dentin showed no obvious change, and resin tags were not observed.Nd: YAG laser on dentin surface can increase bond strength of Panavia F and RelyX Unicem. Therefore, they can be used together in clinic.

Application efficacy of the biological seed coating agent (BSCA) prepared with polyvinyl alcohol, sodium carboxymethyl celullose and Na-bentonite and having plant growth-promoting rhizobacteria (PGPR) Rs-5 and BCL-8, was investigated on cotton plants in the field. The results showed that the germination rate of cotton seeds treated with BSCA was increased by 11.3%, but the incidence rate of cotton seedlings was decreased by 10.8%, while other growth factors such as plant height, fresh weight, dry weight and leaf area of cotton seedlings were increased by 14.4%, 19.1%, 25.7% and 47.4% compared to treated with chemical seed coating agent (CSCA), respectively. Further analysis demonstrated that enzymatic activity of PAL, POD, PPO, SOD and photosynthetic pigment contents were improved to a certain extent as well, while MDA activity was declined. Moreover, the ionic uptake of Ca2+, Mg2+ and K+ in cotton was enhanced, while Na+ absorption was reduced in BSCA compared to CSCA. The yield of cotton seeds treated with BSCA was increased up to 10.5% in comparison with CSCA treatment. Hence, our results revealed that the use of BSCA could not only alleviate the adverse effects of salinity but also has a promising plant growth promoting potential in salinized soil.

This chapter presents a quantifiable definition of energy security and the various indicators generally used to measure it. These indicators are then applied to Association of Southeast Asian Nations (ASEAN) countries in order to evaluate their past energy security policies and activities, which include cross-border infrastructure building and hydropower exploitation projects in the region. ASEAN’s efforts at enhancing energy security are analysed relative to its efforts for climate change mitigation, as energy security and climate change have profound impacts on each other. The chapter also suggests alternative approaches for addressing issues related to energy security and climate change in the region.

Electronic health records (EHRs) have increased in popularity in many countries. Pushed by legal mandates, EHR systems have seen substantial progress recently, including increasing adoption of standards, improved medical vocabularies and enhancements in technical infrastructure for data sharing across healthcare providers. Although the progress is directly beneficial to patient care in a hospital or clinical setting, it can also aid drug discovery. In this article, we review three specific applications of EHRs in a drug discovery context: finding novel relationships between diseases, re-evaluating drug usage and discovering phenotype-genotype associations. We believe that in the near future EHR systems and related databases will impact significantly how we discover and develop safe and efficacious medicines.

A scientific ontology is a formal representation of knowledge within a domain, typically including central concepts, their properties, and relations. With the rise of computers and high-throughput data collection, ontologies have become essential to data mining and sharing across communities in the biomedical sciences. Powerful approaches exist for testing the internal consistency of an ontology, but not for assessing the fidelity of its domain representation. We introduce a family of metrics that describe the breadth and depth with which an ontology represents its knowledge domain. We then test these metrics using (1) four of the most common medical ontologies with respect to a corpus of medical documents and (2) seven of the most popular English thesauri with respect to three corpora that sample language from medicine, news, and novels. Here we show that our approach captures the quality of ontological representation and guides efforts to narrow the breach between ontology and collective discourse within a domain. Our results also demonstrate key features of medical ontologies, English thesauri, and discourse from different domains. Medical ontologies have a small intersection, as do English thesauri. Moreover, dialects characteristic of distinct domains vary strikingly as many of the same words are used quite differently in medicine, news, and novels. As ontologies are intended to mirror the state of knowledge, our methods to tighten the fit between ontology and domain will increase their relevance for new areas of biomedical science and improve the accuracy and power of inferences computed across them., Author Summary An ontology represents the concepts and their interrelation within a knowledge domain. Several ontologies have been developed in biomedicine, which provide standardized vocabularies to describe diseases, genes and gene products, physiological phenotypes, anatomical structures, and many other phenomena. Scientists use them to encode the results of complex experiments and observations and to perform integrative analysis to discover new knowledge. A remaining challenge in ontology development is how to evaluate an ontology's representation of knowledge within its scientific domain. Building on classic measures from information retrieval, we introduce a family of metrics including breadth and depth that capture the conceptual coverage and parsimony of an ontology. We test these measures using (1) four commonly used medical ontologies in relation to a corpus of medical documents and (2) seven popular English thesauri (ontologies of synonyms) with respect to text from medicine, news, and novels. Results demonstrate that both medical ontologies and English thesauri have a small overlap in concepts and relations. Our methods suggest efforts to tighten the fit between ontologies and biomedical knowledge.

Natural compounds represent a significant source for the development of novel medicines. Finding the target proteins for a natural compound is the most important step towards understanding its molecular mechanism for therapeutic usage. In fact, the search for target proteins could be considered the first step of the drug discovery and development pipeline. While experimental determination of compound-protein interactions remains very challenging, effective in silico approaches have been developed and have demonstrated appealing advantages, including their low-cost and capability to scale up easily. The goal of this article is to provide an introduction to in silico search for drug targets of natural compounds. I first review currently available natural compounds databases and human gene/protein databases, and the rapidly emerging databases for known drug-target interactions. These resources provide the 'materials' for in silico approaches and define the gold standard of 'positives' for evaluating them. I then introduce three classes of computational methods for target identification of natural compounds, namely molecular docking, quantitative structure-activity relationship (QSAR) modeling, and data mining and integrative analysis. Use of these methods is explained using real examples, and the advantages and disadvantages of each method are compared. As these state-of-the-art methods continue to mature amid significant challenges, this field appears poised for a period of significant growth, with untold benefits to drug discovery and natural product development.

The formulas were derived for the calculation of the permittivity of dielectric measured in TE 109 mode rectangular cavity and TE 011 mode cylindrical cavity. A testing system based on scalar network analyzer was adopted to measure the same sheet dielectric by the two cavities above respectively. The testing software based on LABVIEW was developed. The results showed that the measuring error was less than 3.8% and repeatability error was than 1.1% by perturbation method in rectangular cavity, while those are 0.4% and 0.3%, by using cylindrical cavity. The automatic measurement of permittivity of dielectric sheet can be achieved by perturbation method in cylindrical cavity.

Drug discovery today is impossible without sophisticated modeling and computation. In this review we touch on previous advances in computational biology and by tracing the steps involved in pharmaceutical development, we explore a range of novel, high value opportunities for computational innovation in modeling the biological process of disease and the social process of drug discovery. These opportunities include text mining for new drug leads, modeling molecular pathways and predicting the efficacy of drug cocktails, analyzing genetic overlap between diseases and predicting alternative drug use. Computation can also be used to model research teams and innovative regions and to estimate the value of academy-industry ties for scientific and human benefit. Attention to these opportunities could promise punctuated advance, and will complement the well-established computational work on which drug discovery currently relies.

Background: Modern drug discovery tends to understand disease processes at the molecular level and then determine optimal molecular targets for drug intervention. Inferences have made from all available drug targets, such as how many drug targets there are, or how many novel drug targets could be potentially found in the human genome, to what functional families these proteins belong, and what structural properties make them bind to small molecules tightly and specifically. But all these are very intuitive and qualitative. The key question of which gene or protein in a disease process could be a successful drug target remains unanswered. Results: We analyzed specific systems-level properties of human genes and proteins targeted by 919 FDA-approved drugs 1 and identified a number of quantitative measures that distinguish them from other genes and proteins at a highly significant level. Compared to an average gene and its encoded protein(s), successful drug targets are more highly connected in a molecular interaction network, but are far from being the most highly connected; they have higher betweenness values, lower entropies of tissue expression, and lower ratios of non-synonymous to synonymous single-nucleotide polymorphisms (see Figure 1). We also tested the performance of different classification algorithms (see Figure 2). Furthermore, we have identified human tissues significantly over- or under-targeted relative to the full spectrum of genes active in each tissue. Figure 1 (A) Connectivity distribution for the entire molecular network (A) Connectivity distribution for the entire molecular network; (B) Targets of the successful drugs are significantly more connected than an average gene in the network, but are not the most highly connected genes in the network; (C) Drug targets tend to have higher than average betweenness values; (D) The successful drug targets are not statistically different from the rest of the genes in terms of their clustering coefficients; (E-F): Analysis of the ratio (Cratio) of the number of non-synonymous to synonymous single-nucleotide polymorphisms (SNPs): (E) Successful drug targets have significantly smaller Cratio than human genes on average. (F) The value of Cratio tends to correlate negatively with the gene's connectivity in the network. Figure 2 Receiver operating characteristic (ROC) curves for the four classification algorithms that we used in this study Receiver operating characteristic (ROC) curves for the four classification algorithms that we used in this study. All four methods that we tested performed significantly better than baseline (ROC score of 0.5, corresponding to a random-guess method). The logistic regression performed best. We also built a machine-learning model to demonstrate the usefulness of these quantitative descriptors for predicting drug targets. With increasing availability of experimental data, we foresee that screening the whole human genome for potential novel drug targets could be feasible in near future. Conclusion: We found that genes associated with successful FDA-approved drugs have a number of properties at the network, sequence, and tissue-expression levels that significantly distinguish them from other human genes. Although the drug-target-selection guidelines that we suggest cannot replace expensive experiments, they can help pharmaceutical researchers narrow the prospective set of drug targets at the earliest stage of a drug development project. Specifically, when the pharmaceutical company must decide which target to pursue among pathologic pathways that are not fully understood, connectivity, betweenness, Cratio, and entropy might be useful quantitative estimates of each prospective target's expected success rate.

Summary Objective: The discovery of new targets that are sufficiently robust to yield marketable therapeutics is an enormous challenge. Conventional target identification approaches are disease-dependent, which require heavy experimental workload and comprehensive domain knowledge. In this work, we propose that a disease-independent property of proteins, “drug-target likeness”, can be explored to facilitate the genomic scale target screening in the post-genomic age. Methods: ASupport Vector Machine (SVM) classifier was trained to recognize target and non-target protein sequences compiled from the Therapeutic Target Database, Drug Bank, and PFam. Protein sequences are encoded by theircomposition, transition and distribution features of residues and Gaussian kernel function was used in SVM classification. Results: SVM with a fine-tuned kernel width records 66.4 ± 5.1% of sensitivity and 97.2 ± 0.6% of specificity, corresponding to an overall target prediction accuracy of 94.4 ± 0.8%. Conclusions: Though primitive, these results suggest that, similar to the “drug likeness” for small chemicals, their binding partners, drug targets, also display shared features which are reflected in their sequences and can be captured bystatistical learning approaches. Further research on how to accurately and interpretably measure the likeness of protein being a drug target is promising. Inspired bythe progress of “drug likeness” studies, advances in protein descriptors, statistical learning algorithms and more comprehensive and accurate gold-standard data set from disease biology research may help to further define the “drug-target likeness” property of proteins.

It has been demonstrated that numerous proteins interact with drugs or their metabolites. Knowledge of these proteins is necessary to understand the mechanisms of drug action and human response. Progress in modern genetics, molecular biology, biochemistry and pharmacology is generating a comprehensive mechanistic understanding of drug-target interaction on the molecular level. This is valuable for researchers and pharmaceutical companies in their efforts to improve the efficacy of existing drugs and to discover new ones. Most recently, the integration of a systems biology approach into drug discovery processes calls for more holistic knowledge and easily accessible resources of the proteins that are important in drug action and human response. We have reviewed many publicly accessible internet resources of these proteins, according to their roles in drug action and human response, such as therapeutic effect, adverse reaction, absorption, distribution, metabolism and excretion.

Understanding of cellular processes and underlying molecular events requires knowledge about different aspects of molecular interactions, networks of molecules and pathways in addition to the sequence, structure and function of individual molecules involved. Databases of interacting molecules, pathways and related chemical reaction equations have been developed. The kinetic data for these interactions, which is important for mechanistic investigation, quantitative study and simulation of cellular processes and events, is not provided in the existing databases. We introduce a new database of Kinetic Data of Bio-molecular Interactions (KDBI) aimed at providing experimentally determined kinetic data of protein-protein, protein-RNA, protein-DNA, protein-ligand, RNA-ligand, DNA-ligand binding or reaction events described in the literature. KDBI contains information about binding or reaction event, participating molecules (name, synonyms, molecular formula, classification, SWISS-PROT AC or CAS number), binding or reaction equation, kinetic data and related references. The kinetic data is in terms of one or a combination of the following quantities as given in the literature of a particular event: association/dissociation or on/off rate constant, first/second/third/. order rate constant, equilibrium rate constant, catalytic rate constant, equilibrium association/dissociation constant, inhibition constant and binding affinity constant. Each entry can be retrieved through protein or nucleic acid or ligand name, SWISS-PROT AC number, ligand CAS number and full-text search of a binding or reaction event. KDBI currently contains 8273 entries of biomolecular binding or reaction events involving 1380 proteins, 143 nucleic acids and 1395 small molecules. Hyperlinks are provided for accessing references in Medline and available 3D structures in PDB and NDB. This database can be accessed at http://xin.cz3.nus.edu.sg/group/kdbi/kdbi.asp.