Background: Molecular experiments using multiplex strategies such as cDNA microarrays or proteomic approaches generate large datasets requiring biological interpretation. Text based data mining tools have recently been developed to query large biological datasets of this type of data. PubMatrix is a web-based tool that allows simple text based mining of the NCBI literature search service PubMed using any two lists of keywords terms, resulting in a frequency matrix of term co-occurrence.

Results: For example, a simple term selection procedure allows automatic pair-wise comparisons of approximately 1-100 search terms versus approximately 1-10 modifier terms, resulting in up to 1,000 pair wise comparisons. The matrix table of pair-wise comparisons can then be surveyed, queried individually, and archived. Lists of keywords can include any terms currently capable of being searched in PubMed. In the context of cDNA microarray studies, this may be used for the annotation of gene lists from clusters of genes that are expressed coordinately. An associated PubMatrix public archive provides previous searches using common useful lists of keyword terms.

Conclusions: In this way, lists of terms, such as gene names, or functional assignments can be assigned genetic, biological, or clinical relevance in a rapid flexible systematic fashion. http://pubmatrix.grc.nia.nih.gov/

Download full-text PDF

Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC317283PMC
http://dx.doi.org/10.1186/1471-2105-4-61DOI Listing

Publication Analysis

Top Keywords

text based
8
lists keywords
8
pair-wise comparisons
8
lists
5
terms
5
pubmatrix tool
4
tool multiplex
4
multiplex literature
4
literature mining
4
mining background
4

Similar Publications

Diabetes Mellitus combined with Mild Cognitive Impairment (DM-MCI) is a high incidence disease among the elderly. Patients with DM-MCI have considerably higher risk of dementia, whose daily self-care and life management (i.e.

View Article and Find Full Text PDF

Aluminum alloys have promising characteristics which make them more useful in industrial applications for thermal management and entropy of the fluidic system. Hence, the current research deals with the analysis of entropy and thermal performance of (CHO-HO)/50:50% saturated by (AA7072/AA7076/TiAIV) alloys. Traditional problem modified using enhanced characteristics of ternary alloys and hydrocarbon 50:50% base fluid.

View Article and Find Full Text PDF

Early prediction of patient responses to neoadjuvant chemotherapy (NACT) is essential for the precision treatment of early breast cancer (EBC). Therefore, this study aims to noninvasively and early predict pathological complete response (pCR). We used dynamic ultrasound (US) imaging changes acquired during NACT, along with clinicopathological features, to create a nomogram and construct a machine learning model.

View Article and Find Full Text PDF

Quantum computers now encounter the significant challenge of scalability, similar to the issue that classical computing faced previously. Recent results in high-fidelity spin qubits manufactured with a Si CMOS technology, along with demonstrations that cryogenic CMOS-based control/readout electronics can be integrated into the same chip or die, opens up an opportunity to break out the challenges of qubit size, I/O, and integrability. However, the power consumption of cryogenic CMOS-based control/readout electronics cannot support thousands or millions of qubits.

View Article and Find Full Text PDF

This study aims to enhance the classification accuracy of adverse events associated with the da Vinci surgical robot through advanced natural language processing techniques, thereby ensuring medical device safety and protecting patient health. Addressing the issues of incomplete and inconsistent adverse event records, we employed a deep learning model that combines BERT and BiLSTM to predict whether adverse event reports resulted in patient harm. We developed the Bert-BiLSTM-Att_dropout model specifically for text classification tasks with small datasets, optimizing the model's generalization ability and key information capture through the integration of dropout and attention mechanisms.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!