scholarly journals Predicting Tryptic Cleavage from Proteomics Data Using Decision Tree Ensembles

2013 ◽  
Vol 12 (5) ◽  
pp. 2253-2259 ◽  
Author(s):  
Thomas Fannes ◽  
Elien Vandermarliere ◽  
Leander Schietgat ◽  
Sven Degroeve ◽  
Lennart Martens ◽  
...  
Keyword(s):  
Decision Tree ◽  
Proteomics Data ◽  
Tryptic Cleavage

scholarly journals Cytokines and Chemokines in Pediatric Appendicitis: A Multiplex Analysis of Inflammatory Protein Mediators

2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
S. Ali Naqvi ◽  
Graham C. Thompson ◽  
Ari R. Joffe ◽  
Jaime Blackwood ◽  
Dori-Ann Martin ◽  
...  
Keyword(s):  
Abdominal Pain ◽  
Decision Tree ◽  
Severity Of Illness ◽  
Primary Objective ◽  
Future Research ◽  
Proteomics Data ◽  
Monocyte Chemoattractant Protein 1 ◽  
Suspected Appendicitis ◽  
Amyloid A ◽  
Inflammatory Protein

Objectives. We aimed to demonstrate the potential of precision medicine to describe the inflammatory landscape present in children with suspected appendicitis. Our primary objective was to determine levels of seven inflammatory protein mediators previously associated with intra-abdominal inflammation (C-reactive protein—CRP, procalcitonin—PCT, interleukin-6 (IL), IL-8, IL-10, monocyte chemoattractant protein-1—MCP-1, and serum amyloid A—SAA) in a cohort of children with suspected appendicitis. Subsequently, using a multiplex proteomics approach, we examined an expansive array of novel candidate cytokine and chemokines within this population. Methods. We performed a secondary analysis of targeted proteomics data from Alberta Sepsis Network studies. Plasma mediator levels, analyzed by Luminex multiplex assays, were evaluated in children aged 5-17 years with nonappendicitis abdominal pain (NAAP), acute appendicitis (AA), and nonappendicitis sepsis (NAS). We used multivariate regression analysis to evaluate the seven target proteins, followed by decision tree and heat mapping analyses for all proteins evaluated. Results. 185 children were included: 83 with NAAP, 79 AA, and 23 NAS. Plasma levels of IL-6, CRP, MCP-1, PCT, and SAA were significantly different in children with AA compared to those with NAAP (p<0.001). Expansive proteomic analysis demonstrated 6 patterns in inflammatory mediator profiles based on severity of illness. A decision tree incorporating the proteins CRP, ferritin, SAA, regulated on activation normal T-cell expressed and secreted (RANTES), monokine induced by gamma interferon (MIG), and PCT demonstrated excellent specificity (0.920) and negative predictive value (0.882) for children with appendicitis. Conclusions. Multiplex proteomic analyses described the inflammatory landscape of children presenting to the ED with suspected appendicitis. We have demonstrated the feasibility of this approach to identify potential novel candidate cytokines/chemokine patterns associated with a specific illness (appendicitis) amongst those with a broad ED presentation (abdominal pain). This approach can be modelled for future research initiatives in pediatric emergency medicine.

Automated Development of Clinical Strategies Using Multistage Decision Analysis

1986 ◽  
Vol 25 (04) ◽  
pp. 207-214 ◽  
Author(s):  
P. Glasziou
Keyword(s):  
Decision Tree ◽  
Decision Analysis ◽  
Decision Trees ◽  
Optimal Strategy ◽  
Cholestatic Jaundice ◽  
Clinical Strategies ◽  
Simultaneous Study

SummaryThe development of investigative strategies by decision analysis has been achieved by explicitly drawing the decision tree, either by hand or on computer. This paper discusses the feasibility of automatically generating and analysing decision trees from a description of the investigations and the treatment problem. The investigation of cholestatic jaundice is used to illustrate the technique.Methods to decrease the number of calculations required are presented. It is shown that this method makes practical the simultaneous study of at least half a dozen investigations. However, some new problems arise due to the possible complexity of the resulting optimal strategy. If protocol errors and delays due to testing are considered, simpler strategies become desirable. Generation and assessment of these simpler strategies are discussed with examples.

KLASIFIKASI SELEKSI ATRIBUT PADA SERANGAN SPAM MENGGUNAKAN METODE ALGORITMA DECISION TREE

2018 ◽  
Vol 14 (2) ◽  
pp. 145
Author(s):  
Aji Sudibyo ◽  
Taufik Asra ◽  
Bakhtiar Rifai
Keyword(s):  
Decision Tree

internet sangat biasa untuk sekarang ini, penggunaaan internetnya tak lepas dari penggunaan email, salah satu ancaman yang terjadi ketika menggunakan email adalah spam, spam  merupakan pesan atau email yang tidak diinginkan oleh penerimanya dan dikirimkan secara massa.        Penelitian tentang serangan spam didapat dari dataset spam sebanyak 4601 record yang terdiri 1813 record dianggap spam dan 278 data bukan spam dengan atribut awal sebanyak 57 atribute dengan 1 atribute class, pada ekperimen yang dilakukan menggunakan select attribute dengan decision tree menjadi 15 atribute dengan 1 atribute class dilakukan 3 percobaan pengujian dengan persentase atribute 30%, 50% dan 70% select atribute didapat hasil fitur select atribute sebesar 70% didapat hasil lebih baik dari 30% ataupun 50% dengan nilai accuracy sebesar 92.469%.

KLASIFIKASI SMS SPAM MENGGUNAKAN SUPPORT VECTOR MACHINE

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede
Keyword(s):  
Data Mining ◽  
Support Vector Machine ◽  
Decision Tree ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Spam Detection ◽  
Support Vector Machine Algorithm ◽  
Data Mining Techniques ◽  
To Receive

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.

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