Cancer adjuvant chemotherapy strategic classification by artificial neural network with gene expression data: An example for non-small cell lung cancer

Chen, Yen‐Chen; Chang, Yo-Cheng; Ke, Wan-Chi; Chiu, Hung Wen

doi:10.1016/j.jbi.2015.05.006

Cited by 24 publications

(10 citation statements)

References 32 publications

(44 reference statements)

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“…For this study, we wondered whether expression profiling of genes previously associated with infection may help identify PJI in periprosthetic tissue, especially when synovial fluid is not available but tissues are available. For feature selection and classification of gene expression data in our study, we used a neural network approach shown to have excellent properties for the analysis of gene expression patterns ( 17 – 20 ). These microarray studies proved that a neural network takes into account the intrinsic characteristics of gene expression data, confirms the most informative gene subsets, and improves the classification accuracy with the best parameters based on data sets.…”

Section: Discussionmentioning

confidence: 99%

Excellent Diagnostic Characteristics for Ultrafast Gene Profiling ofDEFA1-IL1B-LTFin Detection of Prosthetic Joint Infections

et al. 2017

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The timely and exact diagnosis of prosthetic joint infection (PJI) is crucial for surgical decision-making. Intraoperatively, delivery of the result within an hour is required. Alpha-defensin lateral immunoassay of joint fluid (JF) is precise for the intraoperative exclusion of PJI; however, for patients with a limited amount of JF and/or in cases where the JF is bloody, this test is unhelpful. Important information is hidden in periprosthetic tissues that may much better reflect the current status of implant pathology. We therefore investigated the utility of the gene expression patterns of 12 candidate genes (TLR1, -2, -4, -6, and 10, DEFA1, LTF, IL1B, BPI, CRP, IFNG, and DEFB4A) previously associated with infection for detection of PJI in periprosthetic tissues of patients with total joint arthroplasty (TJA) (n = 76) reoperated for PJI (n = 38) or aseptic failure (n = 38), using the ultrafast quantitative reverse transcription-PCR (RT-PCR) Xxpress system (BJS Biotechnologies Ltd.). Advanced data-mining algorithms were applied for data analysis. For PJI, we detected elevated mRNA expression levels of DEFA1 (P < 0.0001), IL1B (P < 0.0001), LTF (P < 0.0001), TLR1 (P = 0.02), and BPI (P = 0.01) in comparison to those in tissues from aseptic cases. A feature selection algorithm revealed that the DEFA1-IL1B-LTF pattern was the most appropriate for detection/exclusion of PJI, achieving 94.5% sensitivity and 95.7% specificity, with likelihood ratios (LRs) for positive and negative results of 16.3 and 0.06, respectively. Taken together, the results show that DEFA1-IL1B-LTF gene expression detection by use of ultrafast qRT-PCR linked to an electronic calculator allows detection of patients with a high probability of PJI within 45 min after sampling. Further testing on a larger cohort of patients is needed.

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Section: Discussionmentioning

confidence: 99%

Excellent Diagnostic Characteristics for Ultrafast Gene Profiling ofDEFA1-IL1B-LTFin Detection of Prosthetic Joint Infections

et al. 2017

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“…For example, Chen et al used artificial neural networks (ANN) to model NSCLC survival. To do this, the group identified the ACT‐correlated gene signatures and performed several types of ANN algorithms to construct the optimal ANN architecture for ACT benefit classification . Then they assessed the reliability of their method by cross‐data set validation.…”

Section: Treatment Selection and Outcome Prediction For Systemic Thermentioning

confidence: 99%

“…To do this, the group identified the ACT-correlated gene signatures and performed several types of ANN algorithms to construct the optimal ANN architecture for ACT benefit classification. 41 Then they assessed the reliability of their method by cross-data set validation. The 10-fold cross validation classification offered an accuracy of 65.71%.…”

Section: Outcome Prediction For Systemic Therapiesmentioning

confidence: 99%

Role of artificial intelligence in the care of patients with nonsmall cell lung cancer

Rabbani

Kanevsky

Kafi

et al. 2018

Eur J Clin Investigation

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“…Because of the development of sequencing technology, precision medicine has become a popular field in cancer research. Omics data have been widely used for cancer classification based on identified gene signatures, gene pathways, and protein-protein interaction networks, among others [3][4][5]. Such classifications can help oncologists provide more accurate treatment regimens for individuals.…”

Section: Introductionmentioning

confidence: 99%

“…Such classifications can help oncologists provide more accurate treatment regimens for individuals. Gene expression data are among the most widely analyzed types of omics data and can be used for such endeavors as biomarker identification, patient classification, and prognostic prediction [4,[6][7][8]. In addition, one published classification organized CRC into four consensus molecular subtypes using gene expression data, and this classification represents the best description of CRC heterogeneity at the gene expression level and shows the potential of merging additional scale data in the future [9].…”

Section: Introductionmentioning

confidence: 99%

Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data

Tong

Tian

Zhou

et al. 2020

BMC Med Inform Decis Mak

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Background: Colon cancer is common worldwide and is the leading cause of cancer-related death. Multiple levels of omics data are available due to the development of sequencing technologies. In this study, we proposed an integrative prognostic model for colon cancer based on the integration of clinical and multi-omics data. Methods: In total, 344 patients were included in this study. Clinical, gene expression, DNA methylation and miRNA expression data were retrieved from The Cancer Genome Atlas (TCGA). To accommodate the high dimensionality of omics data, unsupervised clustering was used as dimension reduction method. The bias-corrected Harrell's concordance index was used to verify which clustering result provided the best prognostic performance. Finally, we proposed a prognostic prediction model based on the integration of clinical data and multi-omics data. Uno's concordance index with cross-validation was used to compare the discriminative performance of the prognostic model constructed with different covariates. Results: Combinations of clinical and multi-omics data can improve prognostic performance, as shown by the increase of the bias-corrected Harrell's concordance of the prognostic model from 0.7424 (clinical features only) to 0.7604 (clinical features and three types of omics features). Additionally, 2-year, 3-year and 5-year Uno's concordance statistics increased from 0.7329, 0.7043, and 0.7002 (clinical features only) to 0.7639, 0.7474 and 0.7597 (clinical features and three types of omics features), respectively. Conclusion:In conclusion, this study successfully combined clinical and multi-omics data for better prediction of colon cancer prognosis.

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Cancer adjuvant chemotherapy strategic classification by artificial neural network with gene expression data: An example for non-small cell lung cancer

Abstract: Using gene signature profiles to predict ACT benefit in NSCLC is feasible. The key to this analysis was identifying the pertinent genes and classification. This study maybe helps reduce the ineffective medical practices to avoid the waste of medical resources.

Cited by 24 publications

References 32 publications

Excellent Diagnostic Characteristics for Ultrafast Gene Profiling ofDEFA1-IL1B-LTFin Detection of Prosthetic Joint Infections

Excellent Diagnostic Characteristics for Ultrafast Gene Profiling ofDEFA1-IL1B-LTFin Detection of Prosthetic Joint Infections

Role of artificial intelligence in the care of patients with nonsmall cell lung cancer

Improving prediction performance of colon cancer prognosis based on the integration of clinical and multi-omics data

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