Understanding Medical Text Related to Breast Cancer: A Review

Ali, Noha; Amer, Eslam; Zayed, Hala H.

doi:10.1007/978-3-319-64861-3_26

Cited by 2 publications

(3 citation statements)

References 11 publications

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“…(5) Step 5: With the node with the highest combined influence value with the existing seed nodes, add it to the set of seed nodes. (6) Step 6: Repeat steps 4 and 5 until all s seeds are selected. 7Step 7: Return s seed nodes selected from the list.…”

Section: The Influence Maximization Problemmentioning

confidence: 99%

“…These biological networks provide a wealth of raw materials for further understanding of biological systems, the discovery of complex diseases, and the search for therapeutic drugs [5]. Both text mining and network analysis have been applied to find the hidden biological knowledge and gene regulation rules behind the huge amount of information [6]. Biomedical text mining for extracting biomedical facts from biomedical literature has improved considerably [7].…”

Section: Introductionmentioning

confidence: 99%

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Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization

Wang

Jin

Chang

et al. 2020

BMC Med Inform Decis Mak

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Background: Gastrointestinal (GI) cancer including colorectal cancer, gastric cancer, pancreatic cancer, etc., are among the most frequent malignancies diagnosed annually and represent a major public health problem worldwide. Methods: This paper reports an aided curation pipeline to identify potential influential genes for gastrointestinal cancer. The curation pipeline integrates biomedical literature to identify named entities by Bi-LSTM-CNN-CRF methods. The entities and their associations can be used to construct a graph, and from which we can compute the sets of cooccurring genes that are the most influential based on an influence maximization algorithm. Results: The sets of co-occurring genes that are the most influential that we discover include RARA-CRBP1, CASP3-BCL2, BCL2-CASP3-CRBP1, RARA-CASP3-CRBP1, FOXJ1-RASSF3-ESR1, FOXJ1-RASSF1A-ESR1, FOXJ1-RASS F1A-TNFAIP8-ESR1. With TCGA and functional and pathway enrichment analysis, we prove the proposed approach works well in the context of gastrointestinal cancer. Conclusions: Our pipeline that uses text mining to identify objects and relationships to construct a graph and uses graph-based influence maximization to discover the most influential co-occurring genes presents a viable direction to assist knowledge discovery for clinical applications.

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Section: The Influence Maximization Problemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization

Wang

Jin

Chang

et al. 2020

BMC Med Inform Decis Mak

View full text Add to dashboard Cite

show abstract

“…Second, the normal (natural) text differs from the biomedical text in the following characteristics, a medical term may be written abbreviated like this cell type name called (OR) means outer root cell type, not that proposition letter. Also, a very important characteristic, the medical term may consist of phrases or compound-words like this protein name (hypoxia-inducible) or symptom like high-blood-pressure all of these characteristics may cause dispersion problems in classification [4].…”

Section: Introductionmentioning

confidence: 99%

Enhancing the performance of cancer text classification model based on cancer hallmarks

Ali

AbuEl-Atta

Zayed

2021

IJ-AI

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<span id="docs-internal-guid-cb130a3a-7fff-3e11-ae3d-ad2310e265f8"><span>Deep learning (DL) algorithms achieved state-of-the-art performance in computer vision, speech recognition, and natural language processing (NLP). In this paper, we enhance the convolutional neural network (CNN) algorithm to classify cancer articles according to cancer hallmarks. The model implements a recent word embedding technique in the embedding layer. This technique uses the concept of distributed phrase representation and multi-word phrases embedding. The proposed model enhances the performance of the existing model used for biomedical text classification. The result of the proposed model overcomes the previous model by achieving an F-score equal to 83.87% using an unsupervised technique that trained on PubMed abstracts called PMC vectors (PMCVec) embedding. Also, we made another experiment on the same dataset using the recurrent neural network (RNN) algorithm with two different word embeddings Google news and PMCVec which achieving F-score equal to 74.9% and 76.26%, respectively.</span></span>

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Understanding Medical Text Related to Breast Cancer: A Review

Cited by 2 publications

References 11 publications

Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization

Identification of most influential co-occurring gene suites for gastrointestinal cancer using biomedical literature mining and graph-based influence maximization

Enhancing the performance of cancer text classification model based on cancer hallmarks

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