Gold Standard Dataset Research Articles

Biomedical data and computational models for drug repositioning: a comprehensive review

Drug repositioning can drastically decrease the cost and duration taken by traditional drug research and development while avoiding the occurrence of unforeseen adverse events. With the rapid advancement of high-throughput technologies and the explosion of various biological data and medical data, computational drug repositioning methods have been appealing and powerful techniques to systematically identify potential drug-target interactions and drug-disease interactions. In this review, we first summarize the available biomedical data and public databases related to drugs, diseases and targets. Then, we discuss existing drug repositioning approaches and group them based on their underlying computational models consisting of classical machine learning, network propagation, matrix factorization and completion, and deep learning based models. We also comprehensively analyze common standard data sets and evaluation metrics used in drug repositioning, and give a brief comparison of various prediction methods on the gold standard data sets. Finally, we conclude our review with a brief discussion on challenges in computational drug repositioning, which includes the problem of reducing the noise and incompleteness of biomedical data, the ensemble of various computation drug repositioning methods, the importance of designing reliable negative samples selection methods, new techniques dealing with the data sparseness problem, the construction of large-scale and comprehensive benchmark data sets and the analysis and explanation of the underlying mechanisms of predicted interactions.

DeepHIT: a deep learning framework for prediction of hERG-induced cardiotoxicity.

Blockade of the human ether-à-go-go-related gene (hERG) channel by small compounds causes a prolonged QT interval that can lead to severe cardiotoxicity and is a major cause of the many failures in drug development. Thus, evaluating the hERG-blocking activity of small compounds is important for successful drug development. To this end, various computational prediction tools have been developed, but their prediction performances in terms of sensitivity and negative predictive value (NPV) need to be improved to reduce false negative predictions. We propose a computational framework, DeepHIT, which predicts hERG blockers and non-blockers for input compounds. For the development of DeepHIT, we generated a large-scale gold-standard dataset, which includes 6632 hERG blockers and 7808 hERG non-blockers. DeepHIT is designed to contain three deep learning models to improve sensitivity and NPV, which, in turn, produce fewer false negative predictions. DeepHIT outperforms currently available tools in terms of accuracy (0.773), MCC (0.476), sensitivity (0.833) and NPV (0.643) on an external test dataset. We also developed an in silico chemical transformation module that generates virtual compounds from a seed compound, based on the known chemical transformation patterns. As a proof-of-concept study, we identified novel urotensin II receptor (UT) antagonists without hERG-blocking activity derived from a seed compound of a previously reported UT antagonist (KR-36676) with a strong hERG-blocking activity. In summary, DeepHIT will serve as a useful tool to predict hERG-induced cardiotoxicity of small compounds in the early stages of drug discovery and development. https://bitbucket.org/krictai/deephit and https://bitbucket.org/krictai/chemtrans. Supplementary data are available at Bioinformatics online.

Enterovirus D68 outbreak detection through a syndromic disease epidemiology network

BackgroundIn 2014, enterovirus D68 (EV-D68) was responsible for an outbreak of severe respiratory illness in children, with 1,153 EV-D68 cases reported across 49 states. Despite this, there is no commercial assay for its detection in routine clinical care. BioFire® Syndromic Trends (Trend) is an epidemiological network that collects, in near real-time, deidentified. BioFire test results worldwide, including data from the BioFire® Respiratory Panel (RP). ObjectivesUsing the RP version 1.7 (which was not explicitly designed to differentiate EV-D68 from other picornaviruses), we formulate a model, Pathogen Extended Resolution (PER), to distinguish EV-D68 from other human rhinoviruses/enteroviruses (RV/EV) tested for in the panel. Using PER in conjunction with Trend, we survey for historical evidence of EVD68 positivity and demonstrate a method for prospective real-time outbreak monitoring within the network. Study designPER incorporates real-time polymerase chain reaction metrics from the RPRV/EV assays. Six institutions in the United States and Europe contributed to the model creation, providing data from 1,619 samples spanning two years, confirmed by EV-D68 gold-standard molecular methods. We estimate outbreak periods by applying PER to over 600,000 historical Trend RP tests since 2014. Additionally, we used PER as a prospective monitoring tool during the 2018 outbreak. ResultsThe final PER algorithm demonstrated an overall sensitivity and specificity of 87.1% and 86.1%, respectively, among the gold-standard dataset. During the 2018 outbreak monitoring period, PER alerted the research network of EV-D68 emergence in July. One of the first sites to experience a significant increase, Nationwide Children's Hospital, confirmed the outbreak and implemented EV-D68 testing at the institution in response. Applying PER to the historical Trend dataset to determine rates among RP tests, we find three potential outbreaks with predicted regional EV-D68 rates as high as 37% in 2014, 16% in 2016, and 29% in 2018. ConclusionsUsing PER within the Trend network was shown to both accurately predict outbreaks of EV-D68 and to provide timely notifications of its circulation to participating clinical laboratories

FLAG-PDFe: Features Oriented Metadata Extraction Framework for Scientific Publications

The unprecedented growth of the research publications in diversified domains has overwhelmed the research community. It requires a cumbersome process to extract this enormous information by manually analyzing these research documents. To automatically extract content of a document in a structured way, metadata and content must be annotated. Scientific community has been focusing on automatic extraction of content by forming different heuristics and applying different machine learning techniques. One of the renowned conference organizers, ESWC organizes state-of-the-art challenge to extract metadata like authors, affiliations, countries in affiliations, supplementary material, sections, table, figures, funding agencies, and EU funded projects from PDF files of research articles. We have proposed a feature centric technique that can be used to extract logical layout structure of articles from publishers with diversified composition styles. To extract unique metadata from a research article placed in logical layout structure, we have developed a four-staged novel approach “FLAG-PDFe”. The approach is built upon distinct and generic features based on the textual and the geometric information from the raw content of research documents. At the first stage, the distinct features are used to identify different physical layout components of an individual article. Since research journals follow their unique publishing styles and layout formats, therefore, we develop generic features to handle these diversified publishing patterns. We employ support vector classification (SVC) in the third stage to extract the logical layout structure (LLS)/ sections of an article, after performing comprehensive evaluation of generic features and machine learning models. Finally, we further apply heuristics on LLS to extract the desired metadata of an article. The outcomes of the study are obtained using the gold standard data set. The results yields 0.877 recall, precision 0.928 and 0.897 F-measure. Our approach has achieved a 16% gain on f-measure when compared to the best approach of the ESWC challenge.

The Ontologies Community of Practice: An Initiative by the CGIAR Platform for Big Data in Agriculture

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labelling to be interoperable. As recommended by the FAIR principles, data, labels and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no agreed full set of ontologies recommended for data annotation across agricultural research disciplines, which may span genetics, environment, agroecology, biology and socioeconomics. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant ontology expertise. This CoP aims to stimulate knowledge sharing and directly support platform development teams by producing ontologies or contributing missing concepts, recommending best practices and identifying mitigation solutions when gold standard datasets are difficult to attain.

NLPEI: A Novel Self-Interacting Protein Prediction Model Based on Natural Language Processing and Evolutionary Information

The study of protein self-interactions (SIPs) can not only reveal the function of proteins at the molecular level, but is also crucial to understand activities such as growth, development, differentiation, and apoptosis, providing an important theoretical basis for exploring the mechanism of major diseases. With the rapid advances in biotechnology, a large number of SIPs have been discovered. However, due to the long period and high cost inherent to biological experiments, the gap between the identification of SIPs and the accumulation of data is growing. Therefore, fast and accurate computational methods are needed to effectively predict SIPs. In this study, we designed a new method, NLPEI, for predicting SIPs based on natural language understanding theory and evolutionary information. Specifically, we first understand the protein sequence as natural language and use natural language processing algorithms to extract its features. Then, we use the Position-Specific Scoring Matrix (PSSM) to represent the evolutionary information of the protein and extract its features through the Stacked Auto-Encoder (SAE) algorithm of deep learning. Finally, we fuse the natural language features of proteins with evolutionary features and make accurate predictions by Extreme Learning Machine (ELM) classifier. In the SIPs gold standard data sets of human and yeast, NLPEI achieved 94.19% and 91.29% prediction accuracy. Compared with different classifier models, different feature models, and other existing methods, NLPEI obtained the best results. These experimental results indicated that NLPEI is an effective tool for predicting SIPs and can provide reliable candidates for biological experiments.

Enhanced Ontology Matching for Big Data Integration

ontology matching (OM) is a critical process for many disciplines. It aims at identifying the semantic correspondences among different ontologies that are merged for data integration. Unfortunately, OM still faces challenges, especially, in the big data integration (BDI) area. The high degree of semantic heterogeneity problem prevents the integration of relevant data and increased with large-scale ontologies of BDI. The quality of OM still needs more improvements to cope with BDI applications. So, this paper proposes a semantic OM approach called semantic matcher. It achieves the goals of semantic heterogeneity resolving and the quality improvement. It exploits the semantic similarity based on the word embedding model. The word embedding model provides efficient distribution semantic representation of domain words as vectors based on their context. The applicability of the proposed semantic matcher is evaluated through an experiment. The conference and anatomy gold standard datasets are evaluating the experimental results. Accuracy evaluates the quality through the precision, recall, and F-measure measures. Based on the experimental results evaluation, the proposed semantic matcher is promising and efficient in the semantic OM for BDI.

RCMF: a robust collaborative matrix factorization method to predict miRNA-disease associations

BackgroundPredicting miRNA-disease associations (MDAs) is time-consuming and expensive. It is imminent to improve the accuracy of prediction results. So it is crucial to develop a novel computing technology to predict new MDAs. Although some existing methods can effectively predict novel MDAs, there are still some shortcomings. Especially when the disease matrix is processed, its sparsity is an important factor affecting the final results.ResultsA robust collaborative matrix factorization (RCMF) is proposed to predict novel MDAs. The L2,1-norm are introduced to our method to achieve the highest AUC value than other advanced methods.Conclusions5-fold cross validation is used to evaluate our method, and simulation experiments are used to predict novel associations on Gold Standard Dataset. Finally, our prediction accuracy is better than other existing advanced methods. Therefore, our approach is effective and feasible in predicting novel MDAs.

Validity of Natural Language Processing for Ascertainment of EGFR and ALK Test Results in SEER Cases of Stage IV Non-Small-Cell Lung Cancer.

SEER registries do not report results of epidermal growth factor receptor (EGFR) and anaplastic lymphoma kinase (ALK) mutation tests. To facilitate population-based research in molecularly defined subgroups of non-small-cell lung cancer (NSCLC), we assessed the validity of natural language processing (NLP) for the ascertainment of EGFR and ALK testing from electronic pathology (e-path) reports of NSCLC cases included in two SEER registries: the Cancer Surveillance System (CSS) and the Kentucky Cancer Registry (KCR). We obtained 4,278 e-path reports from 1,634 patients who were diagnosed with stage IV nonsquamous NSCLC from September 1, 2011, to December 31, 2013, included in CSS. We used 855 CSS reports to train NLP systems for the ascertainment of EGFR and ALK test status (reported v not reported) and test results (positive v negative). We assessed sensitivity, specificity, and positive and negative predictive values in an internal validation sample of 3,423 CSS e-path reports and repeated the analysis in an external sample of 1,041 e-path reports from 565 KCR patients. Two oncologists manually reviewed all e-path reports to generate gold-standard data sets. NLP systems yielded internal validity metrics that ranged from 0.95 to 1.00 for EGFR and ALK test status and results in CSS e-path reports. NLP showed high internal accuracy for the ascertainment of EGFR and ALK in CSS patients-F scores of 0.95 and 0.96, respectively. In the external validation analysis, NLP yielded metrics that ranged from 0.02 to 0.96 in KCR reports and F scores of 0.70 and 0.72, respectively, in KCR patients. NLP is an internally valid method for the ascertainment of EGFR and ALK test information from e-path reports available in SEER registries, but future work is necessary to increase NLP external validity.

Accurate, scalable and integrative haplotype estimation

The number of human genomes being genotyped or sequenced increases exponentially and efficient haplotype estimation methods able to handle this amount of data are now required. Here we present a method, SHAPEIT4, which substantially improves upon other methods to process large genotype and high coverage sequencing datasets. It notably exhibits sub-linear running times with sample size, provides highly accurate haplotypes and allows integrating external phasing information such as large reference panels of haplotypes, collections of pre-phased variants and long sequencing reads. We provide SHAPEIT4 in an open source format and demonstrate its performance in terms of accuracy and running times on two gold standard datasets: the UK Biobank data and the Genome In A Bottle.

Train-O-Matic: Supervised Word Sense Disambiguation with no (manual) effort

Word Sense Disambiguation (WSD) is the task of associating the correct meaning with a word in a given context. WSD provides explicit semantic information that is beneficial to several downstream applications, such as question answering, semantic parsing and hypernym extraction. Unfortunately, WSD suffers from the well-known knowledge acquisition bottleneck problem: it is very expensive, in terms of both time and money, to acquire semantic annotations for a large number of sentences. To address this blocking issue we present Train-O-Matic, a knowledge-based and language-independent approach that is able to provide millions of training instances annotated automatically with word meanings. The approach is fully automatic, i.e., no human intervention is required, and the only type of human knowledge used is a task-independent WordNet-like resource.Moreover, as the sense distribution in the training set is pivotal to boosting the performance of WSD systems, we also present two unsupervised and language-independent methods that automatically induce a sense distribution when given a simple corpus of sentences. We show that, when the learned distributions are taken into account for generating the training sets, the performance of supervised methods is further enhanced. Experiments have proven that Train-O-Matic on its own, and also coupled with word sense distribution learning methods, lead a supervised system to achieve state-of-the-art performance consistently across gold standard datasets and languages. Importantly, we show how our sense distribution learning techniques aid Train-O-Matic to scale well over domains, without any extra human effort. To encourage future research, we release all the training sets in 5 different languages and the sense distributions for each domain of SemEval-13 and SemEval-15 at http://trainomatic.org.

An instrumental variable approach to estimation of match probabilities or precision in linked data

Background with rationaleWhile probabilistic linkage methods are ostensibly based on the probabilities of record pairs being matches (i.e. their marginal precision or positive predictive value), in practice they are used principally for ranking candidate links and fall short of supporting estimation of absolute probabilities. A few variations on Fellegi and Sunter’s framework have been proposed to better accommodate the dependencies that limit transformation of match weights into match probabilities, but there are almost no alternative frameworks for match probability estimation.
 Main AimTo explore the feasibility, accuracy and limitations of a novel instrumental variable approach to estimation of match probabilities for use in either probabilistic record linkage or evaluation of linkage error.
 Methods/ApproachUsing both simulated data and a gold standard (labelled) dataset derived from real-world linked data, I assessed the accuracy of match probability estimation for a range of potential instruments and compared results to estimates produced using conventional probabilistic techniques.
 ResultsThe technique involves trading the potential value of one matching variable in discriminating between candidate links for improved estimation of match probabilities within groups of otherwise similar candidates. Analysis of simulated data confirmed the theoretical validity of the approach in supporting unbiased estimation of match probabilities despite dependencies between other matching variables. Analysis of real-world data demonstrated feasibility in terms of the availability of real-world instruments that provided sufficiently accurate estimation in groups of candidate links above a minimum size. Invalid instruments produced estimates that could be strongly biased.
 ConclusionThese early results are promising but the general availability of valid instruments, their ‘affordability’ in terms of sacrificed discrimination, and means for identifying valid instruments remain unclear. However, this approach represents a new variety of tool for the data linker’s toolkit, which may provide a useful angle on an otherwise difficult-to-estimate parameter and have applications yet to be envisaged.

An alignment-free method for bulk comparison of protein sequences from different species

The available number of protein sequences rapidly increased with the development of new sequencing techniques. This in turn led to an urgent need for the development of new computational methods utilizing these data for the solution of different biological problems. One of these problems is the comparison of protein sequences from different species to reveal their evolutional relationship. Recently, several alignment-free methods proposed for this purpose. Here in this study, we also proposed an alignment-free method for the same purpose. Different from the existing methods, the proposed method not only allows for a pairwise comparison of two protein sequences, but also it allows for a bulk comparison of multiple protein sequences simultaneously. Computational results performed on gold-standard datasets showed that, bulk comparison of multiple sequences is much faster than its pairwise counterpart and the proposed method achieves a performance which is quite competitive with the state-of-the-art alignment-based method, ClustalW.0000-0003-4810-1970

Designing a standard protocol for manually reviewing patient data demographics for record linkage

Background and Hypothesis: Accurate record linkage is essential to address fragmentation of patient data across independent healthcare organizations. To accurately evaluate record linkage methods, so-called “gold standard” data sets with labeled true matches and non-matches are needed. Human review, the process of manually assessing potentially linked patient demographic records and determining whether the record pair belongs to an idiosyncratic individual, is needed to create these datasets. However, the human review process is susceptible to bias and human error. Consequently, record linkage accuracy evaluations are prone to be biased by inaccurate gold standards. Consistent and scientifically rigorous methods for creating gold standard record linkage data sets must be developed, as none have yet been described. In this study, we describe a repeatable process for developing consistent manually reviewed datasets and analyze the results obtained from 15 human reviews of 200 record pairs following our protocol.
 Experimental Design/Methods: We obtained patient records from the Indiana Network for Patient Care and Marion County Health Department. We created record pairs for manual reviews by probabilistically linking datasets using multiple blocking schemes. Two-hundred record pairs were then manually reviewed by 15 different individuals and the results were analyzed.
 Results: Across the 200 record pairs reviewed by 15 reviewers, 155 were nondiscordant pairs whereas 45 were discordant, 40 among which were the result of outliers.
 Conclusion and Potential Impact: From the record pair evaluation results, some empirical rules can be established for the process of manual review, though the nuances of evaluation reasoning require more discussion and a larger sample size. Nonetheless, establishing a standard for manual reviewing is a step towards better health care and complete patient records.

Mining Purchase Intent in Twitter

Most social media platforms allow users to freely express their beliefs, opinions, thoughts, and intents. Twitter is one of the most popular social media platforms where users’ post their intent to purchase. A purchase intent can be defined as measurement of the probability that a consumer will purchase a product or service in future. Identification of purchase intent in Twitter sphere is of utmost interest as it is one of the most long-standing and widely used measures in marketing research. In this paper, we present a supervised learning strategy to identify users’ purchase intent from the language they use in Twitter. Recurrent Neural Networks (RNNs), in particular with Long Short-Term Memory (LSTM) hidden units, are powerful and increasingly popular models for text classification. They effectively encode sequences with varying length and capture long range dependencies. We present the first study to apply LSTM for purchase intent identification task. We train the LSTM network on semi-automatically created dataset. Our model achieves competent classification accuracy (F1 = 83%) over a gold-standard dataset. Further, we demonstrate the efficacy of the LSTM network by comparing its performance with different classical classification algorithms taking this purchase intent identification task into account.

Implementing a hash-based privacy-preserving record linkage tool in the OneFlorida clinical research network.

ObjectiveTo implement an open-source tool that performs deterministic privacy-preserving record linkage (RL) in a real-world setting within a large research network.Materials and MethodsWe learned 2 efficient deterministic linkage rules using publicly available voter registration data. We then validated the 2 rules’ performance with 2 manually curated gold-standard datasets linking electronic health records and claims data from 2 sources. We developed an open-source Python-based tool—OneFL Deduper—that (1) creates seeded hash codes of combinations of patients’ quasi-identifiers using a cryptographic one-way hash function to achieve privacy protection and (2) links and deduplicates patient records using a central broker through matching of hash codes with a high precision and reasonable recall.ResultsWe deployed the OneFl Deduper (https://github.com/ufbmi/onefl-deduper) in the OneFlorida, a state-based clinical research network as part of the national Patient-Centered Clinical Research Network (PCORnet). Using the gold-standard datasets, we achieved a precision of 97.25∼99.7% and a recall of 75.5%. With the tool, we deduplicated ∼3.5 million (out of ∼15 million) records down to 1.7 million unique patients across 6 health care partners and the Florida Medicaid program. We demonstrated the benefits of RL through examining different disease profiles of the linked cohorts.ConclusionsMany factors including privacy risk considerations, policies and regulations, data availability and quality, and computing resources, can impact how a RL solution is constructed in a real-world setting. Nevertheless, RL is a significant task in improving the data quality in a network so that we can draw reliable scientific discoveries from these massive data resources.

Lexicon-Grammar based open information extraction from natural language sentences in Italian

In the last decade, the quantity of readily accessible text has grown rapidly and enormously, long exceeding the capacity of humans to read and understand it. One of the most interesting strategies proposed to fulfill this need is known as Open Information Extraction (OIE). It is essentially devised to read in sentences and rapidly extract one or more domain-independent coherent propositions, each represented by a verb relation and its arguments. Even though many OIE approaches exist for English, no significant research has been conducted about OIE on Italian texts. Due to the usage of language-specific features, OIE systems operating in other languages are not directly applicable for Italian. Therefore, this paper proposes, as first contribution, a novel approach to perform OIE for Italian language, based on standard linguistic structures to analyze sentences and on a set of verbal behavior patterns to extract information from them. These patterns are built combining a solid linguistic theoretical framework, i.e. Lexicon-Grammar (LG), and distributional profiles extracted from a contemporary Italian corpus, i.e. itWaC. Starting from simple sentences, the approach is able to determine elementary tuples, then, all their permutations, by adding complements and adverbials, and, finally, n-ary propositions, by granting syntactic invariance, preserving the overall grammaticality and also respecting some syntactic constraints and selection preferences, thus approximating a first level of semantic acceptability. As second contribution of this work, a gold standard dataset for the Italian language has been built from the itWaC corpus, aimed at being widely used to enable the experimental validation of OIE solutions. It has been manually and independently labeled by four Italian native speakers with all the n-ary propositions that can be extracted, following the criteria of grammaticality and acceptability, i.e. granting syntactic well-formedness and meaningfulness in the context. Finally, the proposed approach has been experimented and quantitatively validated on this gold standard dataset, also in comparison with an indirect approach translating input sentences and output propositions from Italian to English and vice versa and embedding an OIE approach for English, as well as with an OIE system for Italian previously presented by the authors. The results obtained have shown the effectiveness of the proposed approach in generating propositions with respect to these criteria of grammaticality and acceptability. Even if the approach has been evaluated for the Italian language, it is essentially based on linguistic resources produced by LG, which exist for many languages besides Italian and a representative corpus for the language under consideration. Given these premises, it has a general basis from a methodological perspective and can be proficiently extended also to other languages.

Identifying Diabetes in Clinical Notes in Hebrew: A Novel Text Classification Approach Based on Word Embedding.

NimbleMiner is a word embedding-based, language-agnostic natural language processing system for clinical text classification. Previously, NimbleMiner was applied in English and this study applied NimbleMiner on a large sample of inpatient clinical notes in Hebrew to identify instances of diabetes mellitus. The study data included 521,278 clinical notes (one admission and one discharge note per patient) for 268,664 hospital admissions to medical-surgical units of a large hospital in Israel. NimbleMiner achieved overall good performance (F-score =.94) when tested on a gold standard human annotated dataset of 800 clinical notes. We found 15% more patients with diabetes mentioned in the clinical notes compared with diagnoses data. Our findings about underreporting of diabetes in the coded diagnoses data highlight the urgent need for tools and algorithms that will help busy providers identify a range of useful information, like having a diabetes.

Geologist-level wireline log shape identification with recurrent neural networks

The identification of wireline log shapes is of great significance for research in paleogeography and sedimentology, and further for natural resources characterization. Generally, identification of log shapes is manually achieved by geologists through careful observation of the morphological features of log curves. This process is subjective and tedious, and it may introduce artefacts depending on the bias of the interpreters. Therefore, we developed a model which exceeds average performance of experienced geologists in identifying basic log shapes. The excellence of the model mainly results from a large annotated dataset and a specially designed Recurrent Neural Network (RNN) architecture. The dataset contains 2676 Spontaneous Potential (SP) log segments regarding four basic log shapes (bell, funnel, egg and cylinder shapes). There are six layers in the model's architecture, and the core is a layer of Long Short Term Memory (LSTM) units which are designed to extract the morphological features of the log curve. A committee of four experienced geologists annotated a gold standard testing dataset, based on which different approaches can be compared in regard to their accuracies. We also tried standard artificial neural network (ANN), Support Vector Machine (SVM) and an analytical method (Trendline Matching) for this problem. It turns out the proposed RNN model is more accurate than geologists' manual labelling and also the above three methods. The research for this problem is still at the initial stage and expecting more work and ideas to further improve the robustness of the model. Therefore, we also put forward some ideas for improving the model and enlarging the dataset.

Detection of dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach

The accessibility of a huge amount of protein-protein interaction (PPI) data has allowed to do research on biological networks that reveal the structure of a protein complex, pathways and its cellular organization. A key demand in computational biology is to recognize the modular structure of such biological networks. The detection of protein complexes from the PPI network, is one of the most challenging and significant problems in the post-genomic era. In Bioinformatics, the frequently employed approach for clustering the networks is Markov Clustering (MCL). Many of the researches for protein complex detection were done on the static PPI network, which suffers from a few drawbacks. To resolve this problem, this paper proposes an approach to detect the dynamic protein complexes through Markov Clustering based on Elephant Herd Optimization Approach (DMCL-EHO). Initially, the proposed method divides the PPI network into a set of dynamic subnetworks under various time points by combining the gene expression data and secondly, it employs the clustering analysis on every subnetwork using the MCL along with Elephant Herd Optimization approach. The experimental analysis was employed on different PPI network datasets and the proposed method surpasses various existing approaches in terms of accuracy measures. This paper identifies the common protein complexes that are expressively enriched in gold-standard datasets and also the pathway annotations of the detected protein complexes using the KEGG database.