Encontrados 13 documentos, a visualizar página 1 de 2

A machine learning and chemometrics assisted interpretation of spectroscopic da...

In this work, a metabolomics dataset from 1H nuclear magnetic resonance spectroscopy of Brazilian propolis was analyzed using machine learning algorithms, including feature selection and classification methods. Partial least square-discriminant analysis (PLS-DA), random forest (RF), and wrapper methods combining decision trees and rules with evolutionary algorithms (EA) showed to be complementary approaches, al...

Investigating metabolic responses in recombinant E. coli cultures using metabol...

Semantic annotation of biological concepts interplaying microbial cellular resp...

Background Automated extraction systems have become a time saving necessity in Systems Biology. Considerable human effort is needed to model, analyse and simulate biological networks. Thus, one of the challenges posed to Biomedical Text Mining tools is that of learning to recognise a wide variety of biological concepts with different functional roles to assist in these processes. Results Here, we present ...

BioDR : semantic indexing networks for biomedical document retrieval

In Biomedical research, retrieving documents that match an interesting query is a task performed quite frequently. Typically, the set of obtained results is extensive containing many non-interesting documents and consists in a flat list, i.e., not organized or indexed in any way. This work proposes BioDR, a novel approach that allows the semantic indexing of the results of a query, by identifying relevant terms...

Combining syntactic and ontological knowledge to extract biologically relevant ...

Bringing biologists and text miners closer together is a major aim towards the general usage of literature mining tools. Our contribution to this aim is an end-user tool for the extraction of problem-specific biologically relevant relations. Development efforts are being focused on easy-to-use text mining workflows including commonly available entity recognisers and syntactic processors, and the construction of...

@Note : a workbench for biomedical text mining

Biomedical Text Mining (BioTM) is providing valuable approaches to the automated curation of scientific literature. However, most efforts have addressed the benchmarking of new algorithms rather than user operational needs. Bridging the gap between BioTM researchers and biologists’ needs is crucial to solve real-world problems and promote further research. We present @Note, a platform for BioTM that aims at th...

Uncovering mechanisms underlying the Escherichia coli stringent response : appl...

Biomedical text mining applied to document retrieval and semantic indexing

In Biomedical research, the ability to retrieve the adequate information from the ever growing literature is an extremely important asset. This work provides an enhanced and general purpose approach to the process of document retrieval that enables the filtering of PubMed query results. The system is based on semantic indexing providing, for each set of retrieved documents, a network that links documents and re...

Bringing text miners and biologists closer together

Digging out evidences on escherichia coli stringent response from scientific li...