scholarly journals Subnetwork-Based Analysis of Chronic Lymphocytic Leukemia Identifies Pathways That Associate with Disease Progression,

Blood ◽  
2011 ◽  
Vol 118 (21) ◽  
pp. 3564-3564 ◽  
Author(s):  
Han-Yu Chuang ◽  
Laura Z. Rassenti ◽  
Michelle Salcedo ◽  
Kate Licon ◽  
Alexander Kohlmann ◽  
...  
Keyword(s):  
Disease Progression ◽  
Cross Validation ◽  
Risk Groups ◽  
Lymphocytic Leukemia ◽  
Survival Curves ◽  
Survival Analyses ◽  
Expression Levels ◽  
Aggressive Disease ◽  
Indolent Disease ◽  
Fold Cross Validation

Abstract Abstract 3564 The clinical course of patients with chronic lymphocytic leukemia (CLL) is heterogeneous. Whereas some patients develop aggressive disease requiring early treatment, others can have highly indolent disease and not require therapy for many years. Several prognostic factors have been identified that can stratify patients into groups that differ in their relative tendency for disease progression and/or survival. Microarray studies have highlighted differences in mRNA levels found between such CLL subgroups. Here, we pursued a subnetwork-based analysis of gene expression profiles to discriminate between groups of patients with disparate risks for disease progression. The clinical characterization of patients, blood-sample preparation, and microarray processing all follow the unified protocol implemented by the Microarray Innovations in LEukemia (MILE) program, which proposed standards for microarray-based assays in the diagnosis and sub-classification of leukemia. From an initial cohort of 130 patients, we identified 38 prognostic subnetworks that could predict the relative risk for disease progression requiring therapy from the time of sample collection (Fig. 1A). The prognostic power of these subnetworks then was validated on a second cohort of patients in the MILE study and on another set of CLL patients evaluated outside the MILE program (Fig. 1B). The identified subnetworks could assess the risk for requiring therapy at the time of tissue collection more accurately than established markers (Fig. 1C). Statistical analyses of these and the microarray data collected in prior studies revealed the greatest divergence in gene expression was observed using samples collected within 1 year of diagnosis. Thereafter there was increasing congruence in the expression levels of some subnetworks between patients over time. Moreover, the expression levels of such predictive subnetworks could evolve in patients with otherwise indolent disease characteristics to resemble those associated with patients found to have aggressive disease at diagnosis. These analyses suggest that degenerate pathways apparently converge into common pathways that are associated with disease progression. We conclude that, in addition to having predictive power, these identified subnetworks represent an array of pathways associated with disease progression. As such, these results have implications for understanding cancer evolution and for the development of novel treatment strategies for patients with CLL.Figure 1Use of expression levels of genes versus subnetworks to stratify patient samples. (A) Five-fold cross validation on the 130 patients from UCSD. Survival analyses on SC→TX are shown for both the low (dashed lines) and high (solid lines) risk groups predicted by subnetwork signatures (red lines) or by gene signatures (green lines). (B-C) Survival curves on SC→TX for the 17 European patients (B) or for the patient cohort in Friedman et al (2009) (C). The two risk groups are predicted by two sets of markers developed on the UCSD cohort, including the 38 subnetworks (red lines) and the top 230 genes (green lines).Figure 1. Use of expression levels of genes versus subnetworks to stratify patient samples. (A) Five-fold cross validation on the 130 patients from UCSD. Survival analyses on SC→TX are shown for both the low (dashed lines) and high (solid lines) risk groups predicted by subnetwork signatures (red lines) or by gene signatures (green lines). (B-C) Survival curves on SC→TX for the 17 European patients (B) or for the patient cohort in Friedman et al (2009) (C). The two risk groups are predicted by two sets of markers developed on the UCSD cohort, including the 38 subnetworks (red lines) and the top 230 genes (green lines). Disclosures: Foa: Roche: Consultancy, Speakers Bureau.

Differences in oncogenic potency but not target cell specificity distinguish the two forms of the BCR/ABL oncogene

1991 ◽  
Vol 11 (9) ◽  
pp. 4710-4716
Author(s):  
M Kelliher ◽  
A Knott ◽  
J McLaughlin ◽  
O N Witte ◽  
N Rosenberg
Keyword(s):  
Philadelphia Chromosome ◽  
Lymphocytic Leukemia ◽  
Myelogenous Leukemia ◽  
Hematopoietic Stem ◽  
Hematological Diseases ◽  
Aggressive Disease ◽  
Disease Patterns ◽  
Cell Specificity ◽  
Different Types ◽  
Indolent Disease

Two forms of activated BCR/ABL proteins, P210 and P185, that differ in BCR-derived sequences, are associated with Philadelphia chromosome-positive leukemias. One of these diseases is chronic myelogenous leukemia, an indolent disease arising in hematopoietic stem cells that is almost always associated with the P210 form of BCR/ABL. Acute lymphocytic leukemia, a more aggressive malignancy, can be associated with both forms of BCR/ABL. While it is virtually certain that BCR/ABL plays a central role in both of these diseases, the features that determine the association of a particular form with a given disease have not been elucidated. We have used the bone marrow reconstitution leukemogenesis model to test the hypothesis that BCR sequences influence the ability of activated ABL to transform different types of hematopoietic cells. Our studies reveal that both P185 and P210 induce a similar spectrum of hematological diseases, including granulocytic, myelomonocytic, and lymphocytic leukemias. Despite the similarity of the disease patterns, animals given P185-infected marrow developed a more aggressive disease after a shorter latent period than those given P210-infected marrow. These data demonstrate that the structure of the BCR/ABL oncoprotein does not affect the type of disease induced by each form of the oncogene but does control the potency of the oncogenic signal.

scholarly journals High-level ROR1 associates with accelerated disease progression in chronic lymphocytic leukemia

Blood ◽  
2016 ◽  
Vol 128 (25) ◽  
pp. 2931-2940 ◽  
Author(s):  
Bing Cui ◽  
Emanuela M. Ghia ◽  
Liguang Chen ◽  
Laura Z. Rassenti ◽  
Christopher DeBoever ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Disease Progression ◽  
Lymphocytic Leukemia ◽  
Expression Level ◽  
Aggressive Disease ◽  
Key Points ◽  
High Level ◽  
Cell Expression

Key Points The expression level of ROR1 on CLL cells varies between patients. High-level CLL-cell expression of ROR1 associates with more aggressive disease.

Genomic Analysis of Serial Chronic Lymphocytic Leukemia Samples Suggests That Epigenetic Changes, Rather Than Clonal Evolution, May Drive the Progression of This Leukemia

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 4563-4563
Author(s):  
Emanuela M. Ghia ◽  
Erin Smith ◽  
Christopher DeBoever ◽  
Laura Z. Rassenti ◽  
Sophie Rozenzhak ◽  
...  
Keyword(s):  
Chronic Lymphocytic Leukemia ◽  
Somatic Mutations ◽  
Conflicts Of Interest ◽  
Clonal Evolution ◽  
Lymphocytic Leukemia ◽  
Epigenetic Changes ◽  
Sample Collection ◽  
Aggressive Disease ◽  
Indolent Disease ◽  
Time Point

Abstract Abstract 4563 The clinical course of patients with chronic lymphocytic leukemia (CLL) is heterogeneous, with some patients requiring treatment relatively soon after diagnosis and others having indolent disease for many years. Some patients with indolent disease, however may develop more aggressive disease over time that requires therapy. To identify genetic and epigenetic changes that associate with the transition from indolent to aggressive disease, we used genomic methods to analyze sequential samples obtained from 19 CLL patients evaluated at the UC San Diego Moores Cancer Center who ultimately required treatment, as per iwCLL guidelines. For all patients, the first time point sample collection (SC1) was obtained within 1 year post-diagnosis and the second time point sample collection (SC2) was obtained within 1 year before treatment. We performed whole-exome sequencing (Agilent 50Mb capture, 100×) and methylation (450K) array analyses on leukemia cells and germline DNA. Somatic allele frequencies ranged from < 10% to 50%, suggesting heterogeneity within the tumor. When comparing SC1 versus SC2, we observed changes in somatic allele frequency for 6 (32%) of 19 patients, however 13 (68%) of 19 patients did not have evidence for clonal evolution at the somatic level, suggesting that the acquisition of additional somatic mutations did not drive CLL progression and that the clonal population structure remains stable throughout disease progression with multiple clones per patient. Using 450K CpG methylation arrays, we identified 52,409 sites (FDR=0.05) that changed consistently between SC1 and SC2 across 19 patients, suggesting that epigenetic changes were widespread, even without detectable somatic mutations. In summary, our results imply that CLL progression can occur in the absence of somatic mutations, but rather may reflect non-stochastic alterations in the epigenome altering RNA expression. Disclosures: No relevant conflicts of interest to declare.

scholarly journals Differences in oncogenic potency but not target cell specificity distinguish the two forms of the BCR/ABL oncogene.

1991 ◽  
Vol 11 (9) ◽  
pp. 4710-4716 ◽  
Author(s):  
M Kelliher ◽  
A Knott ◽  
J McLaughlin ◽  
O N Witte ◽  
N Rosenberg
Keyword(s):  
Philadelphia Chromosome ◽  
Lymphocytic Leukemia ◽  
Myelogenous Leukemia ◽  
Hematopoietic Stem ◽  
Hematological Diseases ◽  
Aggressive Disease ◽  
Disease Patterns ◽  
Cell Specificity ◽  
Different Types ◽  
Indolent Disease

Two forms of activated BCR/ABL proteins, P210 and P185, that differ in BCR-derived sequences, are associated with Philadelphia chromosome-positive leukemias. One of these diseases is chronic myelogenous leukemia, an indolent disease arising in hematopoietic stem cells that is almost always associated with the P210 form of BCR/ABL. Acute lymphocytic leukemia, a more aggressive malignancy, can be associated with both forms of BCR/ABL. While it is virtually certain that BCR/ABL plays a central role in both of these diseases, the features that determine the association of a particular form with a given disease have not been elucidated. We have used the bone marrow reconstitution leukemogenesis model to test the hypothesis that BCR sequences influence the ability of activated ABL to transform different types of hematopoietic cells. Our studies reveal that both P185 and P210 induce a similar spectrum of hematological diseases, including granulocytic, myelomonocytic, and lymphocytic leukemias. Despite the similarity of the disease patterns, animals given P185-infected marrow developed a more aggressive disease after a shorter latent period than those given P210-infected marrow. These data demonstrate that the structure of the BCR/ABL oncoprotein does not affect the type of disease induced by each form of the oncogene but does control the potency of the oncogenic signal.

Combination of Support Vector Machine and K-Fold cross-validation for prediction of long-term degradation of the compressive strength of marine concrete

2018 ◽  
Vol 1 (1) ◽  
pp. 120-130 ◽  
Author(s):  
Chunxiang Qian ◽  
Wence Kang ◽  
Hao Ling ◽  
Hua Dong ◽  
Chengyao Liang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Environmental Factors ◽  
Cross Validation ◽  
Concrete Strength ◽  
Simulation Method ◽  
Support Vector ◽  
Svm Model ◽  
Artificial Neural Network Ann ◽  
Influence Degree ◽  
Fold Cross Validation

Support Vector Machine (SVM) model optimized by K-Fold cross-validation was built to predict and evaluate the degradation of concrete strength in a complicated marine environment. Meanwhile, several mathematical models, such as Artificial Neural Network (ANN) and Decision Tree (DT), were also built and compared with SVM to determine which one could make the most accurate predictions. The material factors and environmental factors that influence the results were considered. The materials factors mainly involved the original concrete strength, the amount of cement replaced by fly ash and slag. The environmental factors consisted of the concentration of Mg2+, SO42-, Cl-, temperature and exposing time. It was concluded from the prediction results that the optimized SVM model appeared to perform better than other models in predicting the concrete strength. Based on SVM model, a simulation method of variables limitation was used to determine the sensitivity of various factors and the influence degree of these factors on the degradation of concrete strength.

Rancang Bangun Sistem Informasi Untuk Menentukan Kapabilitas Konsumen Dalam Mengambil Pinjaman KPR

2016 ◽  
Vol 7 (2) ◽  
pp. 75-80
Author(s):  
Adhi Kusnadi ◽  
Risyad Ananda Putra
Keyword(s):  
Data Mining ◽  
Low Income ◽  
Cross Validation ◽  
Classification Tree ◽  
Large Population ◽  
Housing Development ◽  
Good Precision ◽  
Index Terms ◽  
The Government ◽  
Fold Cross Validation

Indonesia is one country that has a relatively large population . The government in the period of 5 years, annually hold a procurement program 1 million FLPP house units. This program is held in an effort to provide a decent home for low income people. FLPP housing development requires good precision and speed of development on the part of the developer, this is often hampered by the bank process, because it is difficult to predict the results and speed of data processing in the bank. Knowing the ability of consumers to get subsidized credit, has many advantages, among others, developers can plan a better cash flow, and developers can replace consumers who will be rejected before entering the bank process. For that reason built a system that can help developers. There are many methods that can be used to create this application. One of them is data mining with Classification tree. The results of 10-fold-cross-validation applications have an accuracy of 92%. Index Terms-Data Mining, Classification Tree, Housing, FLPP, 10-fold-cross Validation, Consumer Capability

Klasifikasi Berita Kriminal Menggunakan Naïve Bayes Classifier (NBC) dengan Pengujian K-Fold Cross Validation

2019 ◽  
Vol 5 (2) ◽  
pp. 108-117
Author(s):  
Herfia Rhomadhona ◽  
Jaka Permadi
Keyword(s):  
Cross Validation ◽  
Online Media ◽  
Bayes Classifier ◽  
Ve Bayes ◽  
Fold Cross Validation

Berita kriminalitas merupakan berita yang selalu menjadi trending topik di setiap media massa, khususnya media massa online. Media massa online terlah menyediakan beberapa fasilitas untuk mempermudah masyarakan dalam mencari sebuah berita berdasarkan topik. Media massa online melabeli suatu berita berdasarkan kategorinya. Namun, media massa online tidak memberikan sub kategori pada berita tersebut. Sebagai contoh jika seorang pengguna membuka kategori kriminal, maka yang ditampilkan adalah semua jenis berita kriminal tanpa memberikan informasi yang spesifik dari jenis kriminalitasnya. Permasalahan tersebut dapat diatasi dengan mengklasifikasikan berita kriminalitas berdasarkan subkategori. Penelitian ini menggunakan metode Naïve Bayes Classifier (NBC)  untuk mengklasifikasi berita berdasarkan sub kategorinya. Adapun subkategori terbagi kedalam 5 kategori yaitu korupsi, narkoba, pencurian, pemerkosaan dan pembunuhan. Penelitian ini bertujuan untuk mengetahui kemampuan NBC dalam mengklasifikasi berita dengan melakukan pengujian menggunakan teknik K-Fold Cross Validation dengan nilai K dari 3 sampai 10. Hasil pengujian menyatakan bahwa NBC memiliki kemampuan dalam klasifikasi berita kriminal dengan nilai precision sebesar 98,53 %, nilai recall sebesar 98,44 % dan nilai accuracy sebesar 99,38 %.

Prediction of K562 Cells Functional Inhibitors Based on Machine Learning Approaches

2020 ◽  
Vol 25 (40) ◽  
pp. 4296-4302 ◽  
Author(s):  
Yuan Zhang ◽  
Zhenyan Han ◽  
Qian Gao ◽  
Xiaoyi Bai ◽  
Chi Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Inclusion Bodies ◽  
Cross Validation ◽  
Independent Set ◽  
K562 Cells ◽  
Machine Learning Algorithms ◽  
Learning Approaches ◽  
Validation Test ◽  
Excess Number ◽  
Fold Cross Validation

Background: β thalassemia is a common monogenic genetic disease that is very harmful to human health. The disease arises is due to the deletion of or defects in β-globin, which reduces synthesis of the β-globin chain, resulting in a relatively excess number of α-chains. The formation of inclusion bodies deposited on the cell membrane causes a decrease in the ability of red blood cells to deform and a group of hereditary haemolytic diseases caused by massive destruction in the spleen. Methods: In this work, machine learning algorithms were employed to build a prediction model for inhibitors against K562 based on 117 inhibitors and 190 non-inhibitors. Results: The overall accuracy (ACC) of a 10-fold cross-validation test and an independent set test using Adaboost were 83.1% and 78.0%, respectively, surpassing Bayes Net, Random Forest, Random Tree, C4.5, SVM, KNN and Bagging. Conclusion: This study indicated that Adaboost could be applied to build a learning model in the prediction of inhibitors against K526 cells.

scholarly journals Environmental Sensitivity and Awareness as Differentiating Factors in the Purchase Decision-Making Process in the Smartphone Industry—Case of Polish Consumers

2021 ◽  
Vol 13 (1) ◽  
pp. 348
Author(s):  
Lukasz Skowron ◽  
Monika Sak-Skowron
Keyword(s):  
Expectation Maximization ◽  
Cross Validation ◽  
Expectation Maximization Algorithm ◽  
Decision Making Process ◽  
Environmental Sensitivity ◽  
Significance Level ◽  
On Line ◽  
Purchase Process ◽  
The Impact ◽  
Fold Cross Validation

The first of the research objectives discussed in this article was to analyze the differences related to the valuation of particular factors influencing the purchase process in the smartphone industry, expressed by respondents with different sensitivity and environmental awareness, as well as the assessment of their knowledge about the impact of smartphones on the natural environment. The second objective of the research was to determine whether the level of environmental sensitivity, awareness and knowledge about the impact of smartphones on the environment has a statistically significant influence on the respondents’ choice of smartphone brand. The survey was conducted using an on-line questionnaire, distributed by a specialized research agency on a representative sample of over 1000 Polish residents. In order to identify the various customers clusters, the expectation-maximization algorithm and the v-fold cross-validation were used. Additionally, in order to analyze the significance level of differences between clusters the nonparametric Mann-Whitney U-test was carried out. The results show unequivocally that people with a different approach to ecological issues demonstrate statistically significant differences in their purchasing behaviors in the smartphone industry. Furthermore, it was noticed that in the case of comparing some smartphones brands, there is a statistically confirmed difference in the environmental sensitivity and awareness of the customers who use them. Moreover, the research has shown that in Polish customers’ consciousness smartphones are mistakenly considered to be relatively safe and environmentally friendly products.

scholarly journals Interclass Interference Suppression in Multi-Class Problems

2021 ◽  
Vol 11 (1) ◽  
pp. 450
Author(s):  
Jinfu Liu ◽  
Mingliang Bai ◽  
Na Jiang ◽  
Ran Cheng ◽  
Xianling Li ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Cross Validation ◽  
Selection Process ◽  
Interference Suppression ◽  
Generalization Ability ◽  
Suppression Effect ◽  
Binary Classifiers ◽  
The One ◽  
Fold Cross Validation ◽  
Validation Experiments

Multi-classifiers are widely applied in many practical problems. But the features that can significantly discriminate a certain class from others are often deleted in the feature selection process of multi-classifiers, which seriously decreases the generalization ability. This paper refers to this phenomenon as interclass interference in multi-class problems and analyzes its reason in detail. Then, this paper summarizes three interclass interference suppression methods including the method based on all-features, one-class classifiers and binary classifiers and compares their effects on interclass interference via the 10-fold cross-validation experiments in 14 UCI datasets. Experiments show that the method based on binary classifiers can suppress the interclass interference efficiently and obtain the best classification accuracy among the three methods. Further experiments were done to compare the suppression effect of two methods based on binary classifiers including the one-versus-one method and one-versus-all method. Results show that the one-versus-one method can obtain a better suppression effect on interclass interference and obtain better classification accuracy. By proposing the concept of interclass inference and studying its suppression methods, this paper significantly improves the generalization ability of multi-classifiers.

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