scholarly journals Finding Colon Cancer- and Colorectal Cancer-Related Microbes Based on Microbe–Disease Association Prediction

2021 ◽  
Vol 12 ◽  
Author(s):  
Yu Chen ◽  
Hongjian Sun ◽  
Mengzhe Sun ◽  
Changguo Shi ◽  
Hongmei Sun ◽  
...  
Keyword(s):  
Colon Cancer ◽  
Colorectal Carcinoma ◽  
Information Integration ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Disease Association ◽  
Biological Data ◽  
Label Propagation ◽  
Close Link ◽  
Stable Performance

Microbes are closely associated with the formation and development of diseases. The identification of the potential associations between microbes and diseases can boost the understanding of various complex diseases. Wet experiments applied to microbe–disease association (MDA) identification are costly and time-consuming. In this manuscript, we developed a novel computational model, NLLMDA, to find unobserved MDAs, especially for colon cancer and colorectal carcinoma. NLLMDA integrated negative MDA selection, linear neighborhood similarity, label propagation, information integration, and known biological data. The Gaussian association profile (GAP) similarity of microbes and GAPs similarity and symptom similarity of diseases were firstly computed. Secondly, linear neighborhood method was then applied to the above computed similarity matrices to obtain more stable performance. Thirdly, negative MDA samples were selected, and the label propagation algorithm was used to score for microbe–disease pairs. The final association probabilities can be computed based on the information integration method. NLLMDA was compared with the other five classical MDA methods and obtained the highest area under the curve (AUC) value of 0.9031 and 0.9335 on cross-validations of diseases and microbe–disease pairs. The results suggest that NLLMDA was an effective prediction method. More importantly, we found that Acidobacteriaceae may have a close link with colon cancer and Tannerella may densely associate with colorectal carcinoma.

scholarly journals A Prediction Model for Metachronous Peritoneal Carcinomatosis in Patients with Stage T4 Colon Cancer after Curative Resection

Cancers ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2808
Author(s):  
Tzong-Yun Tsai ◽  
Jeng-Fu You ◽  
Yu-Jen Hsu ◽  
Jing-Rong Jhuang ◽  
Yih-Jong Chern ◽  
...  
Keyword(s):  
Colon Cancer ◽  
High Risk ◽  
Prediction Model ◽  
Prediction Error ◽  
Area Under The Curve ◽  
Low Risk ◽  
Brier Score ◽  
Individual Risk ◽  
T4 Colon Cancer ◽  
Testing Set

(1) Background: The aim of this study was to develop a prediction model for assessing individual mPC risk in patients with pT4 colon cancer. Methods: A total of 2003 patients with pT4 colon cancer undergoing R0 resection were categorized into the training or testing set. Based on the training set, 2044 Cox prediction models were developed. Next, models with the maximal C-index and minimal prediction error were selected. The final model was then validated based on the testing set using a time-dependent area under the curve and Brier score, and a scoring system was developed. Patients were stratified into the high- or low-risk group by their risk score, with the cut-off points determined by a classification and regression tree (CART). (2) Results: The five candidate predictors were tumor location, preoperative carcinoembryonic antigen value, histologic type, T stage and nodal stage. Based on the CART, patients were categorized into the low-risk or high-risk groups. The model has high predictive accuracy (prediction error ≤5%) and good discrimination ability (area under the curve >0.7). (3) Conclusions: The prediction model quantifies individual risk and is feasible for selecting patients with pT4 colon cancer who are at high risk of developing mPC.

scholarly journals Ischemic Stroke Prediction by Exploring Sleep Related Features

2021 ◽  
Vol 11 (5) ◽  
pp. 2083
Author(s):  
Jia Xie ◽  
Zhu Wang ◽  
Zhiwen Yu ◽  
Bin Guo ◽  
Xingshe Zhou
Keyword(s):  
Ischemic Stroke ◽  
Life Quality ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Neural System ◽  
Human Beings ◽  
Filter Model ◽  
Fine Grained ◽  
Proposed Model ◽  
Precision Recall Curve

Ischemic stroke is one of the typical chronic diseases caused by the degeneration of the neural system, which usually leads to great damages to human beings and reduces life quality significantly. Thereby, it is crucial to extract useful predictors from physiological signals, and further diagnose or predict ischemic stroke when there are no apparent symptoms. Specifically, in this study, we put forward a novel prediction method by exploring sleep related features. First, to characterize the pattern of ischemic stroke accurately, we extract a set of effective features from several aspects, including clinical features, fine-grained sleep structure-related features and electroencephalogram-related features. Second, a two-step prediction model is designed, which combines commonly used classifiers and a data filter model together to optimize the prediction result. We evaluate the framework using a real polysomnogram dataset that contains 20 stroke patients and 159 healthy individuals. Experimental results demonstrate that the proposed model can predict stroke events effectively, and the Precision, Recall, Precision Recall Curve and Area Under the Curve are 63%, 85%, 0.773 and 0.919, respectively.

scholarly journals IIMLP: integrated information-entropy-based method for LncRNA prediction

2021 ◽  
Vol 22 (S3) ◽  
Author(s):  
Junyi Li ◽  
Huinian Li ◽  
Xiao Ye ◽  
Li Zhang ◽  
Qingzhe Xu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Dna Sequences ◽  
Information Entropy ◽  
Area Under The Curve ◽  
Prediction Method ◽  
Machine Learning Algorithms ◽  
Reading Frame ◽  
Non Coding Rna ◽  
The One ◽  
Long Non Coding Rna

Abstract Background The prediction of long non-coding RNA (lncRNA) has attracted great attention from researchers, as more and more evidence indicate that various complex human diseases are closely related to lncRNAs. In the era of bio-med big data, in addition to the prediction of lncRNAs by biological experimental methods, many computational methods based on machine learning have been proposed to make better use of the sequence resources of lncRNAs. Results We developed the lncRNA prediction method by integrating information-entropy-based features and machine learning algorithms. We calculate generalized topological entropy and generate 6 novel features for lncRNA sequences. By employing these 6 features and other features such as open reading frame, we apply supporting vector machine, XGBoost and random forest algorithms to distinguish human lncRNAs. We compare our method with the one which has more K-mer features and results show that our method has higher area under the curve up to 99.7905%. Conclusions We develop an accurate and efficient method which has novel information entropy features to analyze and classify lncRNAs. Our method is also extendable for research on the other functional elements in DNA sequences.

scholarly journals Impact of Grade Groups on Prostate Cancer-Specific and Other-Cause Mortality: Competing Risk Analysis from a Large Single Institution Series

Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1963
Author(s):  
Daimantas Milonas ◽  
Tomas Ruzgas ◽  
Zilvinas Venclovas ◽  
Mindaugas Jievaltas ◽  
Steven Joniau
Keyword(s):  
Clinical Decision Making ◽  
Area Under The Curve ◽  
Clinical Decision ◽  
Competing Risk ◽  
Grade Group ◽  
Tertiary Center ◽  
Stable Performance ◽  
Cancer Specific Mortality ◽  
Risk Of Cancer

Objective: To assess the risk of cancer-specific mortality (CSM) and other-cause mortality (OCM) using post-operative International Society of Urological Pathology Grade Group (GG) model in patients after radical prostatectomy (RP). Patients and Methods: Overall 1921 consecutive men who underwent RP during 2001 to 2017 in a single tertiary center were included in the study. Multivariate competing risk regression analysis was used to identify significant predictors and quantify cumulative incidence of CSM and OCM. Time-depending area under the curve (AUC) depicted the performance of GG model on prediction of CSM. Results: Over a median follow-up of 7.9-year (IQR 4.4-11.7) after RP, 235 (12.2%) deaths were registered, and 52 (2.7%) of them were related to PCa. GG model showed high and stable performance (time-dependent AUC 0.88) on prediction of CSM. Cumulative 10-year CSM in GGs 1 to 5 was 0.9%, 2.3%, 7.6%, 14.7%, and 48.6%, respectively; 10-year OCM in GGs was 15.5%, 16.1%, 12.6%, 17.7% and 6.5%, respectively. The ratio between 10-year CSM/OCM in GGs 1 to 5 was 1:17, 1:7, 1:2, 1:1, and 7:1, respectively. Conclusions: Cancer-specific and other-cause mortality differed widely between GGs. Presented findings could aid in personalized clinical decision making for active treatment.

scholarly journals Colonoscopic Review and Surgical Approach in the Management of Colorectal Carcinoma–A Retrospective Study of 50 cases

2019 ◽  
Vol 11 (2) ◽  
pp. 36-40
Author(s):  
Md Ershad Ul Quadir ◽  
Munshi Md Mojibur Rahman ◽  
Md Mahbubur Rahman
Keyword(s):  
Colon Cancer ◽  
Weight Loss ◽  
Retrospective Study ◽  
Early Detection ◽  
Colorectal Carcinoma ◽  
Rectal Carcinoma ◽  
Palliative Therapy ◽  
Treatment Modalities ◽  
Definitive Treatment ◽  
Female Ratio

Introduction: There is no exact statistics about the incidence of colorectal cancer in Bangladesh. According to National Cancer Institute, London, it is the 2nd most common cancer affecting more than 30,000 people in each year. As many patients with colon cancer do not develop symptoms until it is advanced and detection in early stage can only be achieved by screening of asymptomatic person. Maximum patients present lately with distance metastases when there is nothing to treat except palliative therapy. Objectives: To identify the risk factors, early symptoms, signs, treatment modalities, operative outcome, morbidity and mortality rate. Materials and Methods: This retrospective study was carried out at CMH Dhaka during August 2002 to August 2004. A total of 50 patients were taken as study sample. All the patients were admitted in different surgical units of CMH Dhaka for surgical treatment. Detailed history were taken on admission by a questionnaire and examined thoroughly and findings regarding Anaemia, Jaundice, Dehydration, Oedema, Lymphadenopathy, Nutritional status and abnormal signs like ascites, distension, rigidity, organomegaly recorded. Digital rectal examination were done in all cases and finally examined by Proctoscope, Sigmoidoscope and with Colonoscope. FOBT (Fecal Occult Blood Test), serum tumour marker was also assessed. Results: Out of 50 cases 22 were rectal carcinoma and next common site was caecum and number was 10. There was a variation in the sex ratio. Out of 50 cases 33 were male and 17 were female. The highest incidence was among people of 6th decade (28%) and next highest was in 4th decade (24%). Majority of patient with right colon cancer presented with abdominal pain 12 out of 22 cases (56%) and weight loss 15 cases (68%). For left colon cancer commonest symptom was weight loss and weakness and altered bowel habit. Almost all cases with rectal carcinoma presented with bleeding per rectum. Conclusion: About 50% of lesions were found in recto-sigmoid junction and male: female ratio was 1.9:1. All efforts and modern technology should be applied for early detection and treatment. The survival rate is usually very poor in rectal carcinoma. In this study most of the cases were subjected to post operative Chemo and Radiotherapy, but more were treated with neoadjuvant chemoradiation for down staging. The need for early detection of Colorectal Carcinoma (CRC) should be stressed in the form of screening patient awareness and understanding about symptomatology. Early diagnosis and definitive treatment are thereby increasing expectation of higher survival and better prognosis in patient of colorectal carcinoma. Journal of Armed Forces Medical College Bangladesh Vol.11(2) 2015: 36-40

MDAPlatform: a Component-based Platform for Constructing and Assessing miRNA-disease association Prediction Methods

2021 ◽  
Vol 16 ◽  
Author(s):  
Yayan Zhang ◽  
Guihua Duan ◽  
Cheng Yan ◽  
Haolun Yi ◽  
Fang-Xiang Wu ◽  
...  
Keyword(s):  
Computational Models ◽  
Critical Role ◽  
Prediction Method ◽  
Disease Association ◽  
Prediction Methods ◽  
Comparison Results ◽  
Disease Associations ◽  
Pros And Cons ◽  
Clinical Drugs ◽  
Validation Experiments

Background: Increasing evidence has indicated that miRNA-disease association prediction plays a critical role in the study of clinical drugs. Researchers have proposed many computational models for miRNA-disease prediction. However, there is no unified platform to compare and analyze the pros and cons or share the code and data of these models. Objective: In this study, we develop an easy-to-use platform (MDAPlatform) to construct and assess miRNA-disease association prediction method. Methods: MDAPlatform integrates the relevant data of miRNA, disease and miRNA-disease associations that are used in previous miRNA-disease association prediction studies. Based on the componentized model, it develops differet components of previous computational methods. Results: Users can conduct cross validation experiments and compare their methods with other methods, and the visualized comparison results are also provided. Conclusion: Based on the componentized model, MDAPlatform provides easy-to-operate interfaces to construct the miRNA-disease association method, which is beneficial to develop new miRNA-disease association prediction methods in the future.

MELPMDA: A New Method Based on Matrix Enhancement and Label Propagation for Predicting miRNA-Disease Association

2021 ◽  
pp. 536-548
Author(s):  
Zhen-Wei Zhang ◽  
Zhen Gao ◽  
Chun-Hou Zheng ◽  
Yu-Tian Wang ◽  
Su-Min Qi
Keyword(s):  
Disease Association ◽  
Label Propagation ◽  
New Method ◽  
Matrix Enhancement

scholarly journals Tannic acid-stabilized gold nano-particles are superior to native tannic acid in inducing ROS-dependent mitochondrial apoptosis in colorectal carcinoma cells via the p53/AKT axis

RSC Advances ◽  
2019 ◽  
Vol 9 (14) ◽  
pp. 8025-8038 ◽  
Author(s):  
Sayoni Nag ◽  
Krishnendu Manna ◽  
Krishna Das Saha
Keyword(s):  
Colon Cancer ◽  
Colorectal Carcinoma ◽  
Cancer Cells ◽  
Tannic Acid ◽  
Carcinoma Cells ◽  
Nano Particles ◽  
Akt Pathway ◽  
Mitochondrial Apoptosis ◽  
Colon Cancer Cells ◽  
Gold Nano Particles

Tannic acid and AuNP-TA lead to death of colon cancer cells via the ROS/p53/Akt pathway, and AuNP-TA is more potent.

scholarly journals Active Semisupervised Clustering Algorithm with Label Propagation for Imbalanced and Multidensity Datasets

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Mingwei Leng ◽  
Jianjun Cheng ◽  
Jinjin Wang ◽  
Zhengquan Zhang ◽  
Hanhai Zhou ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
Clustering Algorithms ◽  
Label Propagation ◽  
Data Selection ◽  
Imbalanced Datasets ◽  
Active Mechanism ◽  
Real World Applications ◽  
Stable Performance ◽  
Active Data ◽  
Semisupervised Clustering

The accuracy of most of the existing semisupervised clustering algorithms based on small size of labeled dataset is low when dealing with multidensity and imbalanced datasets, and labeling data is quite expensive and time consuming in many real-world applications. This paper focuses on active data selection and semisupervised clustering algorithm in multidensity and imbalanced datasets and proposes an active semisupervised clustering algorithm. The proposed algorithm uses an active mechanism for data selection to minimize the amount of labeled data, and it utilizes multithreshold to expand labeled datasets on multidensity and imbalanced datasets. Three standard datasets and one synthetic dataset are used to demonstrate the proposed algorithm, and the experimental results show that the proposed semisupervised clustering algorithm has a higher accuracy and a more stable performance in comparison to other clustering and semisupervised clustering algorithms, especially when the datasets are multidensity and imbalanced.

scholarly journals Photodynamic Therapy of Presumed Choroidal Metastasis Secondary to Colorectal Carcinoma: Literature Review

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Laurentino Biccas Neto ◽  
José Z. Pulido ◽  
Gustavo B. Melo ◽  
Luiz H. Lima ◽  
Eduardo B. Rodrigues
Keyword(s):  
Colorectal Cancer ◽  
Colon Cancer ◽  
Visual Acuity ◽  
Photodynamic Therapy ◽  
Colorectal Carcinoma ◽  
Literature Review ◽  
Systemic Chemotherapy ◽  
Local Treatment ◽  
Subretinal Fluid ◽  
Choroidal Metastasis

Colorectal cancer may yield metastasis to the choroid. Its management may be challenging, since there is no consensus about treatment. We describe a case of a 70-year-old male with colon cancer who complained of worsening visual acuity of his better-seeing eye to 20/40 secondary to a nonpigmented choroidal mass of medium reflectivity under the inferior temporal arcade and neurosensory foveal detachment. Besides systemic chemotherapy, local treatment with verteporfin photodynamic therapy (vPDT) was performed. After one month, visual acuity improved to 20/25 and subretinal fluid faded. In conclusion, vPDT may be a useful adjuvant treatment modality for choroidal metastasis secondary to colorectal cancer.

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