Software For Classification Research Articles

Genetic Evolutionary Learning Fitness Function (GELFF) for Feature Diagnosis to Software Fault Prediction

Nowadays, proper feature selection f+orFault prediction is very perplexing task. Improper feature selection may lead to bad result. To avoid this, there is a need to find the aridity of software fault. This is achieved by finding the fitness of the evolutionaryAlgorithmic function. In this paper, we finalize the Genetic evolutionarynature of our Feature set with the help of Fitness Function. Feature Selection is the objective of the prediction model tocreate the underlying process of generalized data. The wide range of data like fault dataset, need the better objective function is obtained by feature selection, ranking, elimination and construction. In this paper, we focus on finding the fitness of the machine learning function which is used in the diagnostics of fault in the software for the better classification.

Intelligent Software for Classification of Regional Inequality in Banten Province Using the Williamson Index

The process of development in some areas occur quickly, but several areas experienced a very slow process. In this case, the local government has some problems in the process of implementing of local development, whether in policy making or proposing a strategy for local development, as well as improving the environment in order to support the development of their region's economy. Banten Province is a province located at the west of Java. The administrative conditions which is directly bordered with Jakarta as a capital city and West Java province makes Banten province become the most strategic regions. Based on economic growth data of Banten Province in 2010 - 2015, it can be seen that economic growth of Banten Province is increasing positively in five years. Although the growth of economic development in Banten province tend to be positive, it can be seen significantly that there is still a level of development that has an index of inequality in some districts or cites in Banten. In the process of identification the inequality level among the districts in Banten province, the government usually analyze the case by using manual calculations to determine the policy that should be taken in addressing the inequality that occur in those districts. This process takes quite a long time to take the policy in structuring regional areas. In determining the area of inequality, the government requires Gross Regional Domestic Product (GRDP) data and the data of the population which can be calculated by using several inequality area methods. The method that often used is the Shift Share, Index Gain, Williamson Index and Klassen Typology. In some cases, Williamson index is often used in calculating the inequality between regions or territories. Several studies that had used Williamason index in identifying inequality development usually used the data analysis without particular tools such as software. It complicates the process of inequality analysis if the amount of the data is quite big. Therefore, this study aims to develop the software as a supporting tool for the identification of inequality development using Williamson index. The application is based-website to make it easier for the identification process.

Abstract 4690: Multi-sample automation of the CLARITY technology for the processing of 3D volumes of tissue

Abstract The current technologies utilized for preclinical and clinical drug development in cancer is largely dependent upon the 2-dimensional (2D) analysis of thin formalin-fixed paraffin embedded (FFPE) tissue sections (5-10 µm). However, the importance of understanding cellular phenotypic information combined with three-dimensional (3D) spatial analysis of tissues has recently evolved. In recent years, several clearing techniques, such as CLARITY, have been developed and modified as a means to image and evaluate these volumetric tissues. Most of these techniques have employed chemical approaches to improve tissue clearing, while inadvertently affecting the tissue integrity on a macroscopic or microscopic level. Our previous work with CLARITY has demonstrated how the tissue-hydrogel matrix (HM) is able to maintain its structural integrity overall. Yet, some of the most noted caveats to employing this technique has been the lengthy processing times, and the lack of robust 3D spatial analysis software. We sought to address these issues through the development of an automated clearing and staining platform for CLARITY processed tissues with a proprietary 3D image analysis employing artificial intelligence and machine learning techniques. All experiments were performed with the CLARITY technique using HM-embedded tissues that were clearing with a SDS/borate clearing buffer. Evaluation of the clearing module was assessed using a passive staining (diffusion-based) approach before sample imaging. The effectiveness of the staining module was assessed using passively cleared tissues that were “actively” stained using the developed respective module, followed by standard imaging. The imaging data was then uploaded into our proprietary 3D software for segmentation, classification, and quantitative spatial analysis. We were able to demonstrate successful clearing and staining in both normal and cancerous tissue samples in a total time of less than one day. Consistent results are obtained for both fresh and formalin-fixed tissues. This was a significant reduction in the time associated with the standard passive clearing and staining procedure. In short, the development of our end-to-end multi-sample clearing and staining platform not only removes the laborious sectioning and sample registration for sample reconstruction, but also maintains the benefits of multiple interrogation of a single sample. Although volumetric clearing and 3D analysis are still in their infancy from a technology perspective, one tissue sample using these novel approaches provides as much volumetric information as 200 FFPE sections, while also maintaining key spatial information. Citation Format: Sharla L. White, Yi Chen, Qi Shen, Laurie J. Goodman. Multi-sample automation of the CLARITY technology for the processing of 3D volumes of tissue [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4690.

Prediction of distant melanoma recurrence from primary tumor digital H&E images using deep learning.

9577 Background: Patients with resected melanoma are at high risk for systemic recurrence. Quantifying the risk of recurrence can help identify the need for adjuvant immunotherapies, and accelerate the acquisition of survival statistics in clinical trials. Methods: 75 patients with stages I, II, and III melanoma seen at Columbia University Medical Center between 2001 and 2014 and The Icahn School of Medicine at Mount Sinai between 2000 and 2010 were included based on availability of tissue and 24 months of clinical follow-up. Images were scanned into tiff files (Aperio Biosystems). Deep neural net (DNN) architecture was designed consisting of convolutional and recurrent neural networks (CNN, RNN). Using QuPath open source software for nuclear segmentation and cell classification, we generated cell location, density, and clustering features to identify tissue areas for training of network parameters. DNN analyzes image and feature information locally within an H&E image, generating a prediction vote per region, and votes are averaged. Results: The model was validated on two independent external sets of stage I-III primary melanomas. Cohort 1 (Yale Medical Center), had n = 86 patients, of whom 49 were alive or had no evidence of disease at death (no DMR) and 37 died from melanoma. The second set, Cohort 2 (Geisnger Health Systems), had n = 29 patients, 15 without DMR and 14 with DMR. Prediction scores correlated with DMR status in both sets (AUC = 0.94 and 0.77 for Cohorts 1 and 2, respectively). A multivariable Cox proportional hazard model showed DNN recurrence prediction to be an independent prognostic factor for both Cohort 1 (HR = 2.54, 95% CI: 1.54-4.19, p = .0004***) and Cohort 2 (HR = 8.43, 95% CI: 2.58-27.51, p = .001**). Conclusions: We designed a DNN for quantitative prediction of melanoma recurrence from a H&E stained tissue. The prediction score warrants further study in larger patient cohorts and may constitute a novel digital pathology tool for the selection of melanoma patients for adjuvant immunotherapy. [Table: see text]

Mapeamento da mudança do uso e cobertura das terras do município de Britânia do ano de 1985 até 2015 e sua relação com o avanço do setor agropecuário

O bioma Cerrado vem sendo desmatado, de maneira intensa desde a década de 1970 até os dias atuais, principalmente para a expansão da agricultura e pecuária. Neste intuito foi escolhido o município de Britânia - GO, que faz parte deste bioma, com o objetivo de fazer um mapeamento do uso e cobertura das terras deste município do ano de 1985, 1995, 2005 e 2015, com a perspectiva de analisar a correlação entre o uso das terras e o efetivo bovino no município. Para mapeamento do uso e cobertura das terras foi utilizado as imagens do Landsat 5, sensor (TM) de 1985, 1995 e 2005 e a imagem do Landsat 8 de 2015 sensor (OLI), onde foi usado o software ENVI 5 para segmentação das imagens e o software ArcGis 10 para a fotointerpretação das imagens. Por meio do mapeamento de uso e cobertura das terras, identificou-se que houve o desmatamento de aproximadamente 18% da área total de Britânia para plantio de pastagem, do ano de 1985 até 2015, e que o processo de desmatamento está intimamente ligado a produção agropecuária, com um aumento de 67.716 cabeças de gado no intervalo destes anos.

О повышении эффективности вычислений при классификации изображений

Automatic classification of images using the Bayes classifier with integer coefficients is considered. It is shown that by representing the polynomials used in the classifier that differ from the generally accepted ones through indicating only nonzero coefficients, it is possible to save memory and reduce the number of operations while keeping the same classification quality. The polynomial coefficients are rounded and represented using integers (fixed-point numbers). A polynomial storing method that makes it possible to store only nonzero coefficients of the polynomial is analyzed. In using this method, the vectors and numbers used for carrying out classification are stored in the usual way. The MNIST database was used for carrying out the experiments. Seven bits were taken to represent the polynomial coefficients. For storing coefficients without rounding, 11.77 MB of memory is needed; for storing the rounded coefficients in the usual form, 2.93 MB of memory is needed; and for storing only nonzero rounded coefficients, 1.3 MB is needed. The number of addition and multiplication operations required to make a decision about the image class has been reduced significantly. An economical method for storing matrices with polynomial coefficients on a disk is presented. In the experiment with the MNIST database, only 0.35 MB of memory had to be used for storing the polynomials in the database. The study results can be used in elaborating software and hardware for automatic classification of images. It is also of interest to study similar techniques to improve the efficiency of computations in other classifiers.

Rapid Classification and Identification of Multiple Microorganisms with Accurate Statistical Significance via High-Resolution Tandem Mass Spectrometry

Rapid and accurate identification and classification of microorganisms is of paramount importance to public health and safety. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is complicating correct microbial identification even in a simple sample due to the large number of candidates present. To properly untwine candidate microbes in samples containing one or more microbes, one needs to go beyond apparent morphology or simple “fingerprinting”; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptide-centric representations of microbes to better separate them and by augmenting our earlier analysis method that yields accurate statistical significance. Here, we present an updated analysis workflow that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using 226 MS/MS publicly available data files (each containing from 2500 to nearly 100,000 MS/MS spectra) and 4000 additional MS/MS data files, that the updated workflow can correctly identify multiple microbes at the genus and often the species level for samples containing more than one microbe. We have also shown that the proposed workflow computes accurate statistical significances, i.e., E values for identified peptides and unified E values for identified microbes. Our updated analysis workflow MiCId, a freely available software for Microorganism Classification and Identification, is available for download at https://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.Graphical ᅟ

Inventory of Close-to-Nature Forests Based on the Combination of Airborne LiDAR Data and Aerial Multispectral Images Using a Single-Tree Approach

This study is concerned with the assessment of application possibilities for remote sensing data within a forest inventory in close-to-nature forests. A combination of discrete airborne laser scanning data and multispectral aerial images separately evaluated main tree and forest stand characteristics (i.e., the number of trees, mean height and diameter, tree species, tree height, tree diameter, and tree volume). We used eCognition software (Trimble GeoSpatial, Munich, Germany) for tree species classification and reFLex software (National Forest Centre, Zvolen, Slovakia) for individual tree detection as well as for forest inventory attribute estimations. The accuracy assessment was conducted at the ProSilva demo site Smolnícka Osada (Eastern Slovakia, Central Europe), which has been under selective management for more than 60 years. The remote sensing data were taken using a scanner (Leica ALS70-CM) and camera (Leica RCD30) from an average height of 1034 m, and the ground reference data contained the measured positions and dimensions of 1151 trees in 45 plots distributed across the region. This approach identified 73% of overstory and 28% of understory trees. Tree species classification within overstory trees resulted in an overall accuracy slightly greater than 65%. We also found that the mean difference between the remote-based results and ground data was −0.3% for tree height, 1.1% for tree diameter, and 1.9% for stem volume. At the stand level, the mean difference reached values of 0.4%, 17.9%, and −21.4% for mean height, mean diameter, and growing stock, respectively.

MetaCache: context-aware classification of metagenomic reads using minhashing.

Metagenomic shotgun sequencing studies are becoming increasingly popular with prominent examples including the sequencing of human microbiomes and diverse environments. A fundamental computational problem in this context is read classification, i.e. the assignment of each read to a taxonomic label. Due to the large number of reads produced by modern high-throughput sequencing technologies and the rapidly increasing number of available reference genomes corresponding software tools suffer from either long runtimes, large memory requirements or low accuracy. We introduce MetaCache-a novel software for read classification using the big data technique minhashing. Our approach performs context-aware classification of reads by computing representative subsamples of k-mers within both, probed reads and locally constrained regions of the reference genomes. As a result, MetaCache consumes significantly less memory compared to the state-of-the-art read classifiers Kraken and CLARK while achieving highly competitive sensitivity and precision at comparable speed. For example, using NCBI RefSeq draft and completed genomes with a total length of around 140 billion bases as reference, MetaCache's database consumes only 62 GB of memory while both Kraken and CLARK fail to construct their respective databases on a workstation with 512 GB RAM. Our experimental results further show that classification accuracy continuously improves when increasing the amount of utilized reference genome data. MetaCache is open source software written in C ++ and can be downloaded at http://github.com/muellan/metacache. bertil.schmidt@uni-mainz.de. Supplementary data are available at Bioinformatics online.

Android Malware Classification Using K-Means Clustering Algorithm

Malware was designed to gain access or damage a computer system without user notice. Besides, attacker exploits malware to commit crime or fraud. This paper proposed Android malware classification approach based on K-Means clustering algorithm. We evaluate the proposed model in terms of accuracy using machine learning algorithms. Two datasets were selected to demonstrate the practicing of K-Means clustering algorithms that are Virus Total and Malgenome dataset. We classify the Android malware into three clusters which are ransomware, scareware and goodware. Nine features were considered for each types of dataset such as Lock Detected, Text Detected, Text Score, Encryption Detected, Threat, Porn, Law, Copyright and Moneypak. We used IBM SPSS Statistic software for data classification and WEKA tools to evaluate the built cluster. The proposed K-Means clustering algorithm shows promising result with high accuracy when tested using Random Forest algorithm.

Fully embedded myoelectric control for a wearable robotic hand orthosis.

To prevent learned non-use of the affected hand in chronic stroke survivors, rehabilitative training should be continued after discharge from the hospital. Robotic hand orthoses are a promising approach for home rehabilitation. When combined with intuitive control based on electromyography, the therapy outcome can be improved. However, such systems often require extensive cabling, experience in electrode placement and connection to external computers. This paper presents the framework for a stand-alone, fully wearable and real-time myoelectric intention detection system based on the Myo armband. The hard and software for real-time gesture classification were developed and combined with a routine to train and customize the classifier, leading to a unique ease of use. The system including training of the classifier can be set up within less than one minute. Results demonstrated that: (1) the proposed algorithm can classify five gestures with an accuracy of 98%, (2) the final system can online classify three gestures with an accuracy of 94.3% and, in a preliminary test, (3) classify three gestures from data acquired from mildly to severely impaired stroke survivors with an accuracy of over 78.8%. These results highlight the potential of the presented system for electromyography-based intention detection for stroke survivors and, with the integration of the system into a robotic hand orthosis, the potential for a wearable platform for all day robot-assisted home rehabilitation.

Residents\u2019 satisfaction with primary medical and health services in Western China

BackgroundCurrently, China is in the process of medical and health care reform, and the establishment of primary medical and health services covering urban and rural residents is an important aspect of this process. Studying the satisfaction of residents of underdeveloped areas with their primary medical and health services and identifying the factors that can increase the satisfaction of different groups may improve patient compliance and ultimately improve health. Moreover, such research may provide a reference for the development of medical and health undertakings in similarly underdeveloped areas.MethodsA face-to-face survey was conducted on a stratified random sample of 2200 residents in Gansu by using structured questionnaires. Demographic characteristics were collated, and questionnaires were factor-analysed and weighted using SPSS software to obtain scores for each factor, as well as total satisfaction scores. The characteristics of poorly satisfied populations were determined by a multiple linear regression analysis using SAS software. A cluster analysis was performed using SAS software for classification and a separate discussion of populations.ResultsThe hypertension self-awareness rate (11.29%) of the sampled population was lower than the average hypertension prevalence (23.85%), as recorded in the 2014 Health Statistical Yearbook of the region. The disease knowledge awareness factor was the lowest factor (2.857), whereas the policy awareness factor was the highest factor (4.772). The overall satisfaction was moderate (3.898).The multivariate linear regression model was significant (p <0.05). The regression coefficients were -0.041 for minors; 0.065 for unemployed people; and 0.094 for people with an elementary school educational level, a value lower than that of other population groups.A cluster analysis was used to divide the respondents into five groups. The overall satisfaction was lowest in the second population group (rural, middle-aged)(Fz = 3.64) and was highest in the fourth population group(minors) (Fz = 4.13). Different population groups showed different satisfaction rates in F1 to F6.ConclusionHypertensive patients had low self-awareness, and residents had a poor grasp of disease and limited health knowledge. Their overall satisfaction was moderate. Residents expressed comparatively high satisfaction with the current policy. Minors, adults with low level of education, unemployed people and other vulnerable groups expressed low overall satisfaction. The degree of satisfaction varied greatly among the different groups. Targeted medical and health practices should be implemented for different groups; additionally, the public health practice should be strengthened.

IMPACT OF IMAGE RESOLUTION ON PAVEMENT DISTRESS DETECTION USING PICUCHA METHODOLOGY

An accurate and regular survey of the road surface distresses is a key factor for pavementrehabilitation design and management, allowing public managers to maximize the value of thecontinuously limited budgets for road improvements and maintenance. Manual pavement distresssurveys are labor-intensive, expensive and unsafe for highly-trafficked highways. Over the years,automated surveys using various hardware devices have been developed and improved forpavement field data collection to solve the problems associated with manual surveys. However,the reliable distress detection software and the data analysis remain challenging. This studyfocused on the analysis of a newly-developed pavement distress classification algorithm, called thePICture Unsupervised Classification with Human Analysis (PICUCHA) method, particularly theimpact of image resolutions on its classification accuracy. The results show that a non-linearrelationship exists between the classification accuracy and the image resolution, suggesting thatimages with a resolution around 1.24 mm/pixel may provide the optimal classification accuracywhen using the PICUCHA method. The findings of this study can help to improve more effectiveuses of the specialize software for pavement distress classification, to support decision makers tochoose cameras according to their budgets and desired survey accuracy, and to evaluate howexisting cameras will perform if used with PICUCHA.

Bayesian Survival Analysis of High-Dimensional Microarray Data for Mantle Cell Lymphoma Patients.

Survival time of lymphoma patients can be estimated with the help of microarray technology. In this study, with the use of iterative Bayesian Model Averaging (BMA) method, survival time of Mantle Cell Lymphoma patients (MCL) was estimated and in reference to the findings, patients were divided into two high- risk and low-risk groups. In this study, gene expression data of MCL patients were used in order to select a subset of genes for survival analysis with microarray data, using the iterative BMA method. To evaluate the performance of the method, patients were divided into high-risk and low-risk based on their scores. Performance prediction was investigated using the log-rank test. The bioconductor package "iterativeBMAsurv" was applied with R statistical software for classification and survival analysis. In this study, 25 genes associated with survival for MCL patients were identified across 132 selected models. The maximum likelihood estimate coefficients of the selected genes and the posterior probabilities of the selected models were obtained from training data. Using this method, patients could be separated into high-risk and low-risk groups with high significance (p<0.001). The iterative BMA algorithm has high precision and ability for survival analysis. This method is capable of identifying a few predictive variables associated with survival, among many variables in a set of microarray data. Therefore, it can be used as a low-cost diagnostic tool in clinical research.

Abstract B134: Profiling compounds in human primary cell BioMAP® systems for drug discovery and development in cancer immunotherapy

Abstract The emerging class of novel drugs, including targeted biologics, shows compelling efficacy in oncology. The ability to test new agents, alone or in combination, in predictive human disease models supports the identification, optimization and application of new therapeutic strategies. Human primary cell-based BioMAP® Systems are designed to recapitulate the complex signals and phenotypic responses of diseased tissues and thus provide broad biological coverage of multiple and diverse indications. The BioMAP Diversity Plus panel consists of 12 systems with combinations of human primary endothelial cells, blood leukocytes, macrophages, bronchial epithelial cells, smooth muscle cells, fibroblasts, and keratinocytes relevant for inflammation, oncology and fibrosis. BioMAP Systems are capable of detecting and distinguishing activities of a very broad range of mechanistically diverse compound classes. We have generated a proprietary reference database of BioMAP profiles of &amp;gt;4,500 bioactive agents (drugs, biologics, experimental and environmental chemicals) and employ proprietary software and data mining tools for classification and similarity analyses of bioactivity profiles. The activities captured in the BioMAP profile are consistent with biological activities observed in patients. We have recently expanded the platform to include tumor microenvironment model systems to interrogate the phenotypic impact of compounds on inflammation, immune-function, tissue-matrix remodeling/metastasis, vascular/angiogenesis and/or tumor biology. BioMAP oncology systems consist of co-cultures of human primary fibroblasts (stromal) or endothelial cells (vascular) and PBMC (immune) with the HT29 (CRC) or NCI-H1299 cell lines (NSCLC) to model the inflamed host-tumor stromal and vascular microenvironments, respectively. Profiling clinical and development checkpoint modulators including antibodies, small molecules and combinations resulted in increased immune-related activity as measured by the resulting cytokine profile and cell markers of immune modulation, consistent both with the levels of CTLA4 in the system and with clinical results from patients. BioMAP® Systems thus provide a highly useful platform to (1) identify anti-inflammatory and anti-cancer effects of various agents and combinations and (2) identify different phenotypic outcomes of these agents that influence disease progression and resolution. Together these BioMAP data will support the discovery and development of safer and more effective therapies. Citation Format: Alison O'Mahony, Jason Ptacek, Jennifer Melrose, Karen Matta, Ellen Berg. Profiling compounds in human primary cell BioMAP® systems for drug discovery and development in cancer immunotherapy. [abstract]. In: Proceedings of the CRI-CIMT-EATI-AACR Inaugural International Cancer Immunotherapy Conference: Translating Science into Survival; September 16-19, 2015; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2016;4(1 Suppl):Abstract nr B134.

KNIME an Open Source Solution for Predictive Analytics in the Geosciences [Software and Data Sets

KNIME (Konstanz Information Miner) is a modular computational environment, which allowseasy visual assembly, interactive data analysis, and data processing. It is an open source predictive analytics platform (released under the GNU General Public License v3) suited to process a variety of data formats, from basic csv or xlsx files, to more complex data structures such as xml, url and relational databases (e.g., db2, Oracle, MySQL). Surprisingly, it has not seen wide application in the earth sciences. A number of case studies providing examples of geoscience data processing will benefit both the academia and industry, very few geoscience applications are currently reported and these are dominantly in geoinformatics. In particular, the Energy and Mineral Exploration sectors, which make extensive use of Exploratory Data Analysis, Machine Learning (ML) and Data Mining (DM) software for data classification, pattern recognition and predictive modelling, will benefit significantly from KNIME. In contrast to other predictive analytics platforms (e.g., Orange, R, Rapid- Miner, Scikit-learn), what makes KNIME particularly appealing to geoscience applications is its ability to integrate different programming languages in the same workflow environment, some of them like the statistical software R or Matlab are well known in the geoscience community. KNIME is supported by an extensive community of users and developers. Since KNIME is built on top of Eclipse it shares the benefit of a plugin architecture that makes it easily extensible, many custom-built nodes are available and easily accessible through the Community Contributions area.

Identification of Microorganisms by High Resolution Tandem Mass Spectrometry with Accurate Statistical Significance.

Correct and rapid identification of microorganisms is the key to the success of many important applications in health and safety, including, but not limited to, infection treatment, food safety, and biodefense. With the advance of mass spectrometry (MS) technology, the speed of identification can be greatly improved. However, the increasing number of microbes sequenced is challenging correct microbial identification because of the large number of choices present. To properly disentangle candidate microbes, one needs to go beyond apparent morphology or simple ‘fingerprinting’; to correctly prioritize the candidate microbes, one needs to have accurate statistical significance in microbial identification. We meet these challenges by using peptidome profiles of microbes to better separate them and by designing an analysis method that yields accurate statistical significance. Here, we present an analysis pipeline that uses tandem MS (MS/MS) spectra for microbial identification or classification. We have demonstrated, using MS/MS data of 81 samples, each composed of a single known microorganism, that the proposed pipeline can correctly identify microorganisms at least at the genus and species levels. We have also shown that the proposed pipeline computes accurate statistical significances, i.e., E-values for identified peptides and unified E-values for identified microorganisms. The proposed analysis pipeline has been implemented in MiCId, a freely available software for Microorganism Classification and Identification. MiCId is available for download at http://www.ncbi.nlm.nih.gov/CBBresearch/Yu/downloads.html.Graphical ᅟElectronic supplementary materialThe online version of this article (doi:10.1007/s13361-015-1271-2) contains supplementary material, which is available to authorized users.

Automatic semantic classification of scientific literature according to the hallmarks of cancer.

The hallmarks of cancer have become highly influential in cancer research. They reduce the complexity of cancer into 10 principles (e.g. resisting cell death and sustaining proliferative signaling) that explain the biological capabilities acquired during the development of human tumors. Since new research depends crucially on existing knowledge, technology for semantic classification of scientific literature according to the hallmarks of cancer could greatly support literature review, knowledge discovery and applications in cancer research. We present the first step toward the development of such technology. We introduce a corpus of 1499 PubMed abstracts annotated according to the scientific evidence they provide for the 10 currently known hallmarks of cancer. We use this corpus to train a system that classifies PubMed literature according to the hallmarks. The system uses supervised machine learning and rich features largely based on biomedical text mining. We report good performance in both intrinsic and extrinsic evaluations, demonstrating both the accuracy of the methodology and its potential in supporting practical cancer research. We discuss how this approach could be developed and applied further in the future. The corpus of hallmark-annotated PubMed abstracts and the software for classification are available at: http://www.cl.cam.ac.uk/∼sb895/HoC.html. simon.baker@cl.cam.ac.uk.

Quality assessment program for EuroFlow protocols: summary results of four-year (2010-2013) quality assurance rounds.

Flow cytometric immunophenotyping has become essential for accurate diagnosis, classification, and disease monitoring in hemato-oncology. The EuroFlow Consortium has established a fully standardized "all-in-one" pipeline consisting of standardized instrument settings, reagent panels, and sample preparation protocols and software for data analysis and disease classification. For its reproducible implementation, parallel development of a quality assurance (QA) program was required. Here, we report on the results of four consecutive annual rounds of the novel external QA EuroFlow program. The novel QA scheme aimed at monitoring the whole flow cytometric analysis process (cytometer setting, sample preparation, acquisition and analysis) by reading the median fluorescence intensities (MedFI) of defined lymphocytes' subsets. Each QA participant applied the predefined reagents' panel on blood cells of local healthy donors. A uniform gating strategy was applied to define lymphocyte subsets and to read MedFI values per marker. The MedFI values were compared with reference data and deviations from reference values were quantified using performance score metrics. In four annual QA rounds, we analyzed 123 blood samples from local healthy donors on 14 different instruments in 11 laboratories from nine European countries. The immunophenotype of defined cellular subsets appeared sufficiently standardized to permit unified (software) data analysis. The coefficient of variation of MedFI for 7 of 11 markers performed repeatedly below 30%, average MedFI in each QA round ranged from 86 to 125% from overall median. Calculation of performance scores was instrumental to pinpoint standardization failures and their causes. Overall, the new EuroFlow QA system for the first time allowed to quantify the technical variation that is introduced in the measurement of fluorescence intensities in a multicentric setting over an extended period of time. EuroFlow QA is a proficiency test specific for laboratories that use standardized EuroFlow protocols. It may be used to complement, but not replace, established proficiency tests. © 2014 International Society for Advancement of Cytometry.

CLAP: a web-server for automatic classification of proteins with special reference to multi-domain proteins.

BackgroundThe function of a protein can be deciphered with higher accuracy from its structure than from its amino acid sequence. Due to the huge gap in the available protein sequence and structural space, tools that can generate functionally homogeneous clusters using only the sequence information, hold great importance. For this, traditional alignment-based tools work well in most cases and clustering is performed on the basis of sequence similarity. But, in the case of multi-domain proteins, the alignment quality might be poor due to varied lengths of the proteins, domain shuffling or circular permutations. Multi-domain proteins are ubiquitous in nature, hence alignment-free tools, which overcome the shortcomings of alignment-based protein comparison methods, are required. Further, existing tools classify proteins using only domain-level information and hence miss out on the information encoded in the tethered regions or accessory domains. Our method, on the other hand, takes into account the full-length sequence of a protein, consolidating the complete sequence information to understand a given protein better.ResultsOur web-server, CLAP (Classification of Proteins), is one such alignment-free software for automatic classification of protein sequences. It utilizes a pattern-matching algorithm that assigns local matching scores (LMS) to residues that are a part of the matched patterns between two sequences being compared. CLAP works on full-length sequences and does not require prior domain definitions.Pilot studies undertaken previously on protein kinases and immunoglobulins have shown that CLAP yields clusters, which have high functional and domain architectural similarity. Moreover, parsing at a statistically determined cut-off resulted in clusters that corroborated with the sub-family level classification of that particular domain family.ConclusionsCLAP is a useful protein-clustering tool, independent of domain assignment, domain order, sequence length and domain diversity. Our method can be used for any set of protein sequences, yielding functionally relevant clusters with high domain architectural homogeneity. The CLAP web server is freely available for academic use at http://nslab.mbu.iisc.ernet.in/clap/.