Explorative search of distributed bio-data to answer complex biomedical questions

Masseroli, Marco; Picozzi, Matteo; Ghisalberti, Giorgio; Ceri, Stefano

doi:10.1186/1471-2105-15-s1-s3

Cited by 12 publications

(7 citation statements)

References 34 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…On the validation side, we intend to take advantage of literature-based validation software, as made in [ 34 ], and implement useful statistical coefficient for ROC analysis, as made by Robin et al [ 35 ]. Finally, we want to integrate our software into the on-line Web platform of Bio Search Computing [ 36 ] and make it publicly available to the scientific community.…”

Section: Discussionmentioning

confidence: 99%

Computational algorithms to predict Gene Ontology annotations

2015

Self Cite

View full text Add to dashboard Cite

BackgroundGene function annotations, which are associations between a gene and a term of a controlled vocabulary describing gene functional features, are of paramount importance in modern biology. Datasets of these annotations, such as the ones provided by the Gene Ontology Consortium, are used to design novel biological experiments and interpret their results. Despite their importance, these sources of information have some known issues. They are incomplete, since biological knowledge is far from being definitive and it rapidly evolves, and some erroneous annotations may be present. Since the curation process of novel annotations is a costly procedure, both in economical and time terms, computational tools that can reliably predict likely annotations, and thus quicken the discovery of new gene annotations, are very useful.MethodsWe used a set of computational algorithms and weighting schemes to infer novel gene annotations from a set of known ones. We used the latent semantic analysis approach, implementing two popular algorithms (Latent Semantic Indexing and Probabilistic Latent Semantic Analysis) and propose a novel method, the Semantic IMproved Latent Semantic Analysis, which adds a clustering step on the set of considered genes. Furthermore, we propose the improvement of these algorithms by weighting the annotations in the input set.ResultsWe tested our methods and their weighted variants on the Gene Ontology annotation sets of three model organism genes (Bos taurus, Danio rerio and Drosophila melanogaster ). The methods showed their ability in predicting novel gene annotations and the weighting procedures demonstrated to lead to a valuable improvement, although the obtained results vary according to the dimension of the input annotation set and the considered algorithm.ConclusionsOut of the three considered methods, the Semantic IMproved Latent Semantic Analysis is the one that provides better results. In particular, when coupled with a proper weighting policy, it is able to predict a significant number of novel annotations, demonstrating to actually be a helpful tool in supporting scientists in the curation process of gene functional annotations.

show abstract

Section: Discussionmentioning

confidence: 99%

Computational algorithms to predict Gene Ontology annotations

2015

Self Cite

View full text Add to dashboard Cite

show abstract

“…Public availability in the GPKB of all new annotations detected complements our work in the construction of the GPKB as an updated integrated collection of relevant biomedical-molecular data sparsely available. It makes the GPKB an even more valuable data source for integrated bio-searches on several types of annotation data, aimed at answering complex biomedical questions that can lead to biomedical knowledge discovery, as we showed in [ 30 ]. Furthermore, the provenance tracking implemented in GPKB allows users to exactly know the origin of each data integrated in the GPKB, as well as of all the annotations detect by transitive relationship and the data used for their detection.…”

Section: Discussionmentioning

confidence: 99%

Detection of gene annotations and protein-protein interaction associated disorders through transitive relationships between integrated annotations

2015

Self Cite

View full text Add to dashboard Cite

BackgroundIncreasingly high amounts of heterogeneous and valuable controlled biomolecular annotations are available, but far from exhaustive and scattered in many databases. Several annotation integration and prediction approaches have been proposed, but these issues are still unsolved. We previously created a Genomic and Proteomic Knowledge Base (GPKB) that efficiently integrates many distributed biomolecular annotation and interaction data of several organisms, including 32,956,102 gene annotations, 273,522,470 protein annotations and 277,095 protein-protein interactions (PPIs).ResultsBy comprehensively leveraging transitive relationships defined by the numerous association data integrated in GPKB, we developed a software procedure that effectively detects and supplement consistent biomolecular annotations not present in the integrated sources. According to some defined logic rules, it does so only when the semantic type of data and of their relationships, as well as the cardinality of the relationships, allow identifying molecular biology compliant annotations. Thanks to controlled consistency and quality enforced on data integrated in GPKB, and to the procedures used to avoid error propagation during their automatic processing, we could reliably identify many annotations, which we integrated in GPKB. They comprise 3,144 gene to pathway and 21,942 gene to biological function annotations of many organisms, and 1,027 candidate associations between 317 genetic disorders and 782 human PPIs. Overall estimated recall and precision of our approach were 90.56 % and 96.61 %, respectively. Co-functional evaluation of genes with known function showed high functional similarity between genes with new detected and known annotation to the same pathway; considering also the new detected gene functional annotations enhanced such functional similarity, which resembled the one existing between genes known to be annotated to the same pathway. Strong evidence was also found in the literature for the candidate associations detected between Cystic fibrosis disorder and the PPIs between the CFTR_HUMAN, DERL1_HUMAN, RNF5_HUMAN, AHSA1_HUMAN and GOPC_HUMAN proteins, and between the CHIP_HUMAN and HSP7C_HUMAN proteins.ConclusionsAlthough identified gene annotations and PPI-genetic disorder candidate associations require biological validation, our approach intrinsically provides their in silico evidence based on available data. Public availability within the GPKB (http://www.bioinformatics.deib.polimi.it/GPKB/) of all identified and integrated annotations offers a valuable resource fostering new biomedical-molecular knowledge discoveries.

show abstract

“…By selecting the "NCBI Blast: Protein sequence alignment search by Protein ID" service and inserting all NCBI BLAST [32] parameters (as shown in Fig. 3 of [15]), or using their default values, the user can run a final query that leads to a result table containing all proteins that satisfy the original query. Accordingly, the original multi-topic complex query is answered; then, the user may decide to further expand the final results by using another service in Bio-SeCo, or start a new query.…”

Section: Case Studiesmentioning

confidence: 99%

“…We registered and leveraged it within the Bio Search Computing system (Bio-SeCo): http://www.bioinformatics.deib.polimi.it/bioseco/seco/ [15], which uses the Search Computing technology [16] to build answers to complex biomedical search queries. It does so by interacting with a collection of cooperating search services and using the ranking and joining of results as the dominant factors for service composition [25].…”

Section: Semsim Web Servicementioning

confidence: 99%

“…LSI belongs to the category of the vector-based approaches, which work on a ndimensional vector space where every considered entity identifies a dimension. We leveraged it for the computation of gene semantic similarities within two novel software components, named SimilBio and SemSim; the latter one, is a new Web service, here introduced for the first time, which we recently added to the online Bio Search Computing platform (Bio-SeCo) [15].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Software Suite for Gene and Protein Annotation Prediction and Similarity Search

Chicco

Masseroli

2015

IEEE/ACM Trans. Comput. Biol. and Bioinf.

Self Cite

View full text Add to dashboard Cite

In the computational biology community, machine learning algorithms are key instruments for many applications, including the prediction of gene-functions based upon the available biomolecular annotations. Additionally, they may also be employed to compute similarity between genes or proteins. Here, we describe and discuss a software suite we developed to implement and make publicly available some of such prediction methods and a computational technique based upon Latent Semantic Indexing (LSI), which leverages both inferred and available annotations to search for semantically similar genes. The suite consists of three components. BioAnnotationPredictor is a computational software module to predict new gene-functions based upon Singular Value Decomposition of available annotations. SimilBio is a Web module that leverages annotations available or predicted by BioAnnotationPredictor to discover similarities between genes via LSI. The suite includes also SemSim, a new Web service built upon these modules to allow accessing them programmatically. We integrated SemSim in the Bio Search Computing framework (http://www.bioinformatics.deib. polimi.it/bio-seco/seco/), where users can exploit the Search Computing technology to run multi-topic complex queries on multiple integrated Web services. Accordingly, researchers may obtain ranked answers involving the computation of the functional similarity between genes in support of biomedical knowledge discovery.

show abstract

Explorative search of distributed bio-data to answer complex biomedical questions

Cited by 12 publications

References 34 publications

Computational algorithms to predict Gene Ontology annotations

Computational algorithms to predict Gene Ontology annotations

Detection of gene annotations and protein-protein interaction associated disorders through transitive relationships between integrated annotations

Software Suite for Gene and Protein Annotation Prediction and Similarity Search

Contact Info

Product

Resources

About