Virtual reality for neurorehabilitation: A bibliometric analysis of knowledge structure and theme trends.

Background: As an emerging technology, virtual reality (VR) has been broadly applied in the medical field, especially in neurorehabilitation. The growing application of VR therapy promotes an increasing amount of clinical studies. In this paper, we present a bibliometric analysis of the existing studies to reveal the current research hotspots and guide future research directions. Methods: Articles and reviews on the related topic were retrieved from the Science Citation Index Expanded of Web of Science Core Collection database. VOSviewer and Citespace software were applied to systematically analyze information about publications, countries, institutions, authors, journals, citations, and keywords from the included studies. Results: A total of 1,556 papers published between 1995 and 2021 were identified. The annual number of papers increased gradually over the past three decades, with a peak publication year in 2021 (n = 276). Countries and institutions from North America and Western European were playing leading roles in publications and total citations. Current hotspots were focused on the effectiveness of VR therapy in cognitive and upper limb motor rehabilitation. The clusters of keywords contained the four targeted neurological diseases of VR, while the burst keywords represented that the latest studies were directed toward more defined types of VR therapy and greater study design. Conclusions: Our study offers information regarding to the current hotspots and emerging trends in the VR for rehabilitation field. It could guide future research and application of VR therapy in neurorehabilitation.

Prediction of functional class of proteins and peptides irrespective of sequence homology by support vector machines.

Various computational methods have been used for the prediction of protein and peptide function based on their sequences. A particular challenge is to derive functional properties from sequences that show low or no homology to proteins of known function. Recently, a machine learning method, support vector machines (SVM), have been explored for predicting functional class of proteins and peptides from amino acid sequence derived properties independent of sequence similarity, which have shown promising potential for a wide spectrum of protein and peptide classes including some of the low- and non-homologous proteins. This method can thus be explored as a potential tool to complement alignment-based, clustering-based, and structure-based methods for predicting protein function. This article reviews the strategies, current progresses, and underlying difficulties in using SVM for predicting the functional class of proteins. The relevant software and web-servers are described. The reported prediction performances in the application of these methods are also presented.

Derivation of stable microarray cancer-differentiating signatures using consensus scoring of multiple random sampling and gene-ranking consistency evaluation.

Microarrays have been explored for deriving molecular signatures to determine disease outcomes, mechanisms, targets, and treatment strategies. Although exhibiting good predictive performance, some derived signatures are unstable due to noises arising from measurement variability and biological differences. Improvements in measurement, annotation, and signature selection methods have been proposed. We explored a new signature selection method that incorporates consensus scoring of multiple random sampling and multistep evaluation of gene-ranking consistency for maximally avoiding erroneous elimination of predictor genes. This method was tested by using a well-studied 62-sample colon cancer data set and two other cancer data sets (86-sample lung adenocarcinoma and 60-sample hepatocellular carcinoma). For the colon cancer data set, the derived signatures of 20 sampling sets, composed of 10,000 training test sets, are fairly stable with 80% of top 50 and 69% to 93% of all predictor genes shared by all 20 signatures. These shared predictor genes include 48 cancer-related and 16 cancer-implicated genes, as well as 50% of the previously derived predictor genes. The derived signatures outperform all previously derived signatures in predicting colon cancer outcomes from an independent data set collected from the Stanford Microarray Database. Our method showed similar performance for the other two data sets, suggesting its usefulness in deriving stable signatures for biomarker and target discovery.

Support vector machines approach for predicting druggable proteins: recent progress in its exploration and investigation of its usefulness.

Identification and validation of viable targets is an important first step in drug discovery and new methods, and integrated approaches are continuously explored to improve the discovery rate and exploration of new drug targets. An in silico machine learning method, support vector machines, has been explored as a new method for predicting druggable proteins from amino acid sequence independent of sequence similarity, thereby facilitating the prediction of druggable proteins that exhibit no or low homology to known targets.

Computer prediction of allergen proteins from sequence-derived protein structural and physicochemical properties.

BACKGROUND: Computational methods have been developed for predicting allergen proteins from sequence segments that show identity, homology, or motif match to a known allergen. These methods achieve good prediction accuracies, but are less effective for novel proteins with no similarity to any known allergen. METHODS: This work tests the feasibility of using a statistical learning method, support vector machines, as such a method. The prediction system is trained and tested by using 1005 allergen proteins from the Allergome database and 22,469 non-allergen proteins from 7871 Pfam families. RESULTS: Testing results by an independent set of 229 allergen and 6717 non-allergen proteins from 7871 Pfam families show that 93.0% and 99.9% of these are correctly predicted, which are comparable to the best results of other methods. Of the 18 novel allergen proteins non-homologous to any other proteins in the Swissprot database, 88.9% is correctly predicted. A further screening of 168,128 proteins in the Swissprot database finds that 2.9% of the proteins are predicted as allergen proteins, which is consistent with the estimated numbers from motif-based methods. CONCLUSIONS: Our study suggests that SVM is a potentially useful method for predicting allergen proteins and it has certain capability for predicting novel allergen proteins. Our software can be accessed at .

MHC-BPS: MHC-binder prediction server for identifying peptides of flexible lengths from sequence-derived physicochemical properties.

Major histocompatibility complex (MHC)-binding peptides are essential for antigen recognition by T-cell receptors and are being explored for vaccine design. Computational methods have been developed for predicting MHC-binding peptides of fixed lengths, based on the training of relatively few non-binders. It is desirable to introduce methods applicable for peptides of flexible lengths and trained by using more diverse sets of non-binders. MHC-BPS is a web-based MHC-binder prediction server that uses support vector machines for predicting peptide binders of flexible lengths for 18 MHC class I and 12 class II alleles from sequence-derived physicochemical properties, which were trained by using 4,208 approximately 3,252 binders and 234,333 approximately 168,793 non-binders, and evaluated by an independent set of 545 approximately 476 binders and 110,564 approximately 84,430 non-binders. The binder prediction accuracies are 86 approximately 99% for 25 and 70 approximately 80% for five alleles, and the non-binder accuracies are 96 approximately 99% for 30 alleles. A screening of HIV-1 genome identifies 0.01 approximately 5% and 5 approximately 8% of the constituent peptides as binders for 24 and 6 alleles, respectively, including 75 approximately 100% of the known epitopes. This method correctly predicts 73.3% of the 15 newly published epitopes in the last 4 months of 2005. MHC-BPS is available at http://bidd.cz3.nus.edu.sg/mhc/ .

In silico search of putative adverse drug reaction related proteins as a potential tool for facilitating drug adverse effect prediction.

Adverse drug reaction (ADR) is a significant issue in drug development and post-market applications. Different experimental and computational approaches need to be explored for predicting ADRs due to the complexity of their molecular mechanisms. One approach for predicting ADRs of a drug is to search for its interaction with ADR-related proteins (ADRRPs). In this work, this approach is tested on 11 marketed anti-HIV drugs covering protease inhibitors (PIs), nucleoside reverse transcriptase inhibitors (NRTIs), and non-nucleoside reverse transcriptase inhibitors (NNRTIs). An in silico drug target search method, INVDOCK, is used for searching the ADRRPs of each of these drugs. The corresponding ADRs of the predicted ADRRPs of each of these drugs are compared to clinically observed ADRs reported in the literature. It is found that 86-89% of the INVDOCK predicted ADRs of these drugs are consistent with the literature reported ADRs, and about 67-100% of the literature-reported ADRs of these drugs to various degrees is agreed with INVDOCK predictions. These results suggest that it is feasible to explore in silico ADRRP search methods for facilitating drug toxicity prediction.

Computer prediction of drug resistance mutations in proteins.

Drug resistance is of increasing concern in the treatment of infectious diseases and cancer. Mutation in drug-interacting disease proteins is one of the primary causes for resistance particularly against anti-infectious drugs. Prediction of resistance mutations in these proteins is valuable both for the molecular dissection of drug resistance mechanisms and for predicting features that guide the design of new agents to counter resistant strains. Several protein structure- and sequence-based computer methods have been explored for mechanistic study and prediction of resistance mutations. These methods and their usefulness are reviewed here.

Prediction of RNA-binding proteins from primary sequence by a support vector machine approach.

Elucidation of the interaction of proteins with different molecules is of significance in the understanding of cellular processes. Computational methods have been developed for the prediction of protein-protein interactions. But insufficient attention has been paid to the prediction of protein-RNA interactions, which play central roles in regulating gene expression and certain RNA-mediated enzymatic processes. This work explored the use of a machine learning method, support vector machines (SVM), for the prediction of RNA-binding proteins directly from their primary sequence. Based on the knowledge of known RNA-binding and non-RNA-binding proteins, an SVM system was trained to recognize RNA-binding proteins. A total of 4011 RNA-binding and 9781 non-RNA-binding proteins was used to train and test the SVM classification system, and an independent set of 447 RNA-binding and 4881 non-RNA-binding proteins was used to evaluate the classification accuracy. Testing results using this independent evaluation set show a prediction accuracy of 94.1%, 79.3%, and 94.1% for rRNA-, mRNA-, and tRNA-binding proteins, and 98.7%, 96.5%, and 99.9% for non-rRNA-, non-mRNA-, and non-tRNA-binding proteins, respectively. The SVM classification system was further tested on a small class of snRNA-binding proteins with only 60 available sequences. The prediction accuracy is 40.0% and 99.9% for snRNA-binding and non-snRNA-binding proteins, indicating a need for a sufficient number of proteins to train SVM. The SVM classification systems trained in this work were added to our Web-based protein functional classification software SVMProt, at http://jing.cz3.nus.edu.sg/cgi-bin/svmprot.cgi. Our study suggests the potential of SVM as a useful tool for facilitating the prediction of protein-RNA interactions.

Drug Adverse Reaction Target Database (DART) : proteins related to adverse drug reactions.

An adverse drug reaction (ADR) often results from interaction of a drug or its metabolites with specific protein targets important in normal cellular function. Knowledge about these targets is both important in facilitating the study of the mechanisms of ADRs and in new drug discovery. It is also useful in the development and testing of rational drug design and safety evaluation tools. The Drug Adverse Reaction Database (DART) is intended to provide comprehensive information about adverse effect targets of drugs described in the literature. Moreover, proteins involved in adverse effect targets of chemicals not yet confirmed as ADR targets are also included as potential targets. This database gives physiological function of each target, binding drugs/agonists/antagonists/activators/inhibitors, IC(50) values of the inhibitors, corresponding adverse effects, and type of ADR induced by drug binding to a target. Cross-links to other databases are also introduced to facilitate the access of information about the sequence, 3-dimensional structure, function, and nomenclature of each target along with drug/ligand binding properties, and related literature. The database currently contains entries for 147 ADR targets and 89 potential targets. A total of 187 adverse reaction conditions, 257 drugs, and 1080 ligands known to bind to each of these targets are also currently described. Each entry can be retrieved through multiple search methods including target name, target physiological function, adverse effect, ligand name, and biological pathways. A special page is provided for contribution of new or additional information. This database can be accessed at http://xin.cz3.nus.edu.sg/group/drt/dart.asp.