PSOVina: The hybrid particle swarm optimization algorithm for protein-ligand docking

Protein-ligand docking is an essential step in modern drug discovery process. The challenge here is to accurately predict and efficiently optimize the position and orientation of ligands in the binding pocket of a target protein. In this paper, we present a new method called PSOVina which combined the particle swarm optimization (PSO) algorithm with the efficient Broyden-Fletcher-Goldfarb-Shannon (BFGS) local search method adopted in AutoDock Vina to tackle the conformational search problem in docking. Using a diverse data set of 201 protein-ligand complexes from the PDBbind database and a full set of ligands and decoys for four representative targets from the directory of useful decoys (DUD) virtual screening data set, we assessed the docking performance of PSOVina in comparison to the original Vina program. Our results showed that PSOVina achieves a remarkable execution time reduction of 51-60% without compromising the prediction accuracies in the docking and virtual screening experiments. This improvement in time efficiency makes PSOVina a better choice of a docking tool in large-scale protein-ligand docking applications. Our work lays the foundation for the future development of swarm-based algorithms in molecular docking programs. PSOVina is freely available to non-commercial users at http://cbbio.cis.umac.mo .

Advances of Applying Metaheuristics to Data Mining Techniques

Metaheuristics have lately gained popularity among researchers. Their underlying designs are inspired by biological entities and their behaviors, e.g. schools of fish, colonies of insects, and other land animals etc. They have been used successfully in optimization applications ranging from financial modeling, image processing, resource allocations, job scheduling to bioinformatics. In particular, metaheuristics have been proven in many combinatorial optimization problems. So that it is not necessary to attempt all possible candidate solutions to a problem via exhaustive enumeration and evaluation which is computationally intractable. The aim of this paper is to highlight some recent research related to metaheuristics and to discuss how they can enhance the efficacy of data mining algorithms. An upmost challenge in Data Mining is combinatorial optimization that, often lead to performance degradation and scalability issues. Two case studies are presented, where metaheuristics improve the accuracy of classification and clustering by avoiding local optima.

A biometric authentication model using hand gesture images

A novel hand biometric authentication method based on measurements of the user’s stationary hand gesture of hand sign language is proposed. The measurement of hand gestures could be sequentially acquired by a low-cost video camera. There could possibly be another level of contextual information, associated with these hand signs to be used in biometric authentication. As an analogue, instead of typing a password ‘iloveu’ in text which is relatively vulnerable over a communication network, a signer can encode a biometric password using a sequence of hand signs, ‘i’ , ‘l’ , ‘o’ , ‘v’ , ‘e’ , and ‘u’. Subsequently the features from the hand gesture images are extracted which are integrally fuzzy in nature, to be recognized by a classification model for telling if this signer is who he claimed himself to be, by examining over his hand shape and the postures in doing those signs. It is believed that everybody has certain slight but unique behavioral characteristics in sign language, so are the different hand shape compositions. Simple and efficient image processing algorithms are used in hand sign recognition, including intensity profiling, color histogram and dimensionality analysis, coupled with several popular machine learning algorithms. Computer simulation is conducted for investigating the efficacy of this novel biometric authentication model which shows up to 93.75% recognition accuracy.

Classifying human voices by using hybrid SFX time-series preprocessing and ensemble feature selection

Voice biometrics is one kind of physiological characteristics whose voice is different for each individual person. Due to this uniqueness, voice classification has found useful applications in classifying speakers' gender, mother tongue or ethnicity (accent), emotion states, identity verification, verbal command control, and so forth. In this paper, we adopt a new preprocessing method named Statistical Feature Extraction (SFX) for extracting important features in training a classification model, based on piecewise transformation treating an audio waveform as a time-series. Using SFX we can faithfully remodel statistical characteristics of the time-series; together with spectral analysis, a substantial amount of features are extracted in combination. An ensemble is utilized in selecting only the influential features to be used in classification model induction. We focus on the comparison of effects of various popular data mining algorithms on multiple datasets. Our experiment consists of classification tests over four typical categories of human voice data, namely, Female and Male, Emotional Speech, Speaker Identification, and Language Recognition. The experiments yield encouraging results supporting the fact that heuristically choosing significant features from both time and frequency domains indeed produces better performance in voice classification than traditional signal processing techniques alone, like wavelets and LPC-to-CC.

Attitudes and practices of healthcare providers regarding gestational diabetes: results of a survey conducted at the 2010 meeting of the International Association of Diabetes in Pregnancy Study Group (IADPSG)

OBJECTIVE

To investigate the attitudes and practices of healthcare providers regarding gestational diabetes mellitus and to identify differences between provider types and geographical locations.

RESEARCH DESIGN AND METHODS

A survey was distributed to participants at the 2010 GDM Pasadena II: International Conference regarding management of gestational diabetes.

RESULTS

Of 250 distributed surveys, there was a 60.8% (n = 152) response rate. Most responders believed it was cost beneficial to treat and screen for gestational diabetes. Approximately 2/3 accepted that the diagnosis of Type 2 diabetes could be made in pregnancy. For the diagnosis of gestational diabetes, c. 60% opted for a one-step screen while 30% opted for a two-step screen. Most responders were comfortable treating a non-pregnant woman with Type 2 diabetes. Ninety per cent of responders initiated antenatal testing at 32-34 weeks for medication-treated gestational diabetes, with a wider variation for diet-treated gestational diabetes. Obstetricians were less comfortable than other providers in treating a non-pregnant woman with Type 2 diabetes, but were more likely to prescribe glyburide. Providers in the USA, compared with those from other countries, were more likely to perform a Caesarean section based on fetal weight, use glibenclamide (glyburide), initiate exercise post-delivery and less likely to check 2-h postprandial glucose levels. At least 80% of responders routinely screened for Type 2 diabetes at 6-12 weeks post-delivery.

CONCLUSION

There are differences internationally and by provider types in attitudes and management regarding gestational diabetes. These findings may be useful in developing strategies for implementing the recent guidelines by the International Association of the Diabetes and Pregnancy Study Groups.

A humanized monoclonal antibody targeting the β7 integrin selectively blocks intestinal homing of T lymphocytes

BACKGROUND AND PURPOSE

rhuMAb Beta7 is a humanized anti-human β7 monoclonal antibody currently in phase I in inflammatory bowel disease. rhuMAb Beta7 binds the β7 subunit of the integrins α4β7 and αEβ7, blocking interaction with their ligands. These integrins play key roles in immune cell homing to and retention in mucosal sites, and are associated with chronic inflammatory diseases of the gastrointestinal tract. The goal of this study was to evaluate the mucosal specificity of rhuMAb Beta7.

EXPERIMENTAL APPROACH

We assessed the effect of murine anti-Beta7 on lymphocyte homing in mouse models of autoimmune disease. We also compared the effect of rhuMAb Beta7 on circulating mucosal-homing versus peripheral-homing T cells in naïve non-human primates.

KEY RESULTS

In cynomolgus monkeys, occupancy of β7 integrin receptors by rhuMAb Beta7 correlated with an increase in circulating β7(+) mucosal-homing lymphocytes, with no apparent effect on levels of circulating β7(-) peripheral-homing lymphocytes. rhuMAb Beta7 also inhibited lymphocyte homing to the inflamed colons of severe combined immunodeficient mice in CD45RB(high) CD4(+) T-cell transfer models. Consistent with a lack of effect on peripheral homing, in a mouse model of experimental autoimmune encephalomyelitis, anti-β7 treatment resulted in no amelioration of CNS inflammation.

CONCLUSIONS AND IMPLICATIONS

The results presented here suggest that rhuMAb Beta7 selectively blocks lymphocyte homing to the gastrointestinal tract without affecting lymphocyte trafficking to non-mucosal tissues. rhuMAb Beta7 provides a targeted therapeutic approach with the potential for a more attractive benefit:risk ratio than currently available inflammatory bowel disease therapies.