Hybrid biogeography based simultaneous feature selection and MHC class I peptide binding prediction using support vector machines and random forests

Srivastava, Atulji; Ghosh, Shameek; Anantharaman, N.; Jayaraman, Vaidyanathan

doi:10.1016/j.jim.2012.09.013

Cited by 12 publications

(10 citation statements)

References 43 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, the hybrid filter-wrapper algorithm was employed to select a subset of potential prognostic variables 24. The proposed FSS process was conducted on 64 IC/CCRT, 72 IC/RT, and overall patients.…”

Section: Resultsmentioning

confidence: 99%

“…Here, we addressed these problems by employing a rigorous processing framework. This framework consist of two well-founded steps, a hybrid filter-wrapper FSS to select a concise yet informative biomarker panel and a prognostic classification through the AdaBoost algorithm 24. In the FSS step, a recently reported model of LH-RELIEF was used to find the potential candidate variables 25.…”

Section: Discussionmentioning

confidence: 99%

“…Here, we conducted a FSS method to select a pool of informative biomarkers, which were called feature herein, that were able to dichotomize the individuals into high and low risk to recurrence. To achieve the task of FSS, a hybrid filter-wrapper algorithm was utilized 24. Specifically, we firstly ranked the importance of feature by LH-RELIEF model to select the top 20 variables 25.…”

Section: Methodsmentioning

confidence: 99%

See 2 more Smart Citations

Molecular Decision Tree Algorithms Predict Individual Recurrence Pattern for Locally Advanced Nasopharyngeal Carcinoma

et al. 2019

View full text Add to dashboard Cite

Background : Recurrence remains one of the key reasons of relapse after the radical radiation for locally advanced nasopharyngeal carcinoma (NPC). Here, the multiple molecular and clinical variables integrated decision tree algorithms were designed to predict individual recurrence patterns (with VS without recurrence) for locally advanced NPC. Methods : A total of 136 locally advanced NPC patients retrieved from a randomized controlled phase III trial, were included. For each patient, the expression levels of 33 clinicopathological biomarkers in tumor specimen, 3 Epstein-Barr virus related serological antibody titer and 5 clinicopathological variables, were detected and collected to construct the decision tree algorithm. The expression level of 33 clinicopathological biomarkers in tumor specimen was evaluated by immunohistochemistry staining. Results : Three algorithm classifiers, augmented by the adaptive boosting algorithm for variable selection and classification, were designed to predict individual recurrence pattern. The classifiers were trained in the training subset and further tested using a 10-fold cross-validation scheme in the validation subset. In total, 13 molecules expression level in tumor specimen, including AKT1, Aurora-A, Bax, Bcl-2, N-Cadherin, CENP-H, HIF-1α, LMP-1, C-Met, MMP-2, MMP-9, Pontin and Stathmin, and N stage were selected to construct three 10-fold cross-validation decision tree classifiers. These classifiers showed high predictive sensitivity (87.2-93.3%), specificity (69.0-100.0%), and overall accuracy (84.5-95.2%) to predict recurrence pattern individually. Multivariate analyses confirmed the decision tree classifier was an independent prognostic factor to predict individual recurrence (algorithm 1: hazard ration (HR) 0.07, 95% confidence interval (CI) 0.03-0.16, P < 0.01; algorithm 2: HR 0.13, 95% CI 0.04-0.44, P < 0.01; algorithm 3: HR 0.13, 95% CI 0.03-0.68, P = 0.02). Conclusion : Multiple molecular and clinicopathological variables integrated decision tree algorithms may individually predict the recurrence pattern for locally advanced NPC. This decision tree algorism provides a potential tool to select patients with high recurrence risk for intensive follow-up, and to diagnose recurrence at an earlier stage for salvage treatment in the NPC endemic region.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Molecular Decision Tree Algorithms Predict Individual Recurrence Pattern for Locally Advanced Nasopharyngeal Carcinoma

et al. 2019

View full text Add to dashboard Cite

show abstract

“…SMM not only predicted HLA binding but also evaluated peptide transport as a function of antigen presentation and proteasomal cleavage with the TAP algorithm. Subsequent efforts to develop new class I binding affinity prediction softwares have included the use of combined support vector machine-based (SVM) and random forest machine-learning approaches (Srivastava et al, 2013), or combining the information obtained from amino acid pairwise contact potentials and quantum topology molecular similarity descriptors (Saethang et al, 2013) to better model HLA class I peptide interactions.…”

Section: Somatic Mutations Generate Neoantigensmentioning

confidence: 99%

Applications of Immunogenomics to Cancer

Liu

Mardis

2017

Cell

189

124

View full text Add to dashboard Cite

“…The final step in neoantigen discovery is the prediction of binding affinities for each novel somatic mutation-translated set of peptides to the patient’s HLA proteins. The development of modeling algorithms that calculate HLA binding affinities has been an active area of research that has resulted in several approaches such as analysis by neural network-based algorithms that are trained on measured binding affinities (e.g., NetMHC), scattering-matrix method (SMM)-based approaches, and others ( Peters and Sette 2005 ; Lundegaard et al 2008 ; Srivastava et al 2013 ). The large number of algorithms indicates the attendant difficulty of identifying neoantigens, although precision has improved over time, especially for class I HLA proteins, for which more experimental data exist, even for the more rare haplotypes.…”

Section: Immunogenomics and Neoantigen Predictionmentioning

confidence: 99%

Cancer Immunogenomics: Computational Neoantigen Identification and Vaccine Design

Hundal

Miller

Griffith

et al. 2016

Cold Spring Harb Symp Quant Biol

View full text Add to dashboard Cite

The application of modern high-throughput genomics to the study of cancer genomes has exploded in the past few years, yielding unanticipated insights into the myriad and complex combinations of genomic alterations that lead to the development of cancers. Coincident with these genomic approaches have been computational analyses that are capable of multiplex evaluations of genomic data toward specific therapeutic end points. One such approach is called “immunogenomics” and is now being developed to interpret protein-altering changes in cancer cells in the context of predicted preferential binding of these altered peptides by the patient’s immune molecules, specifically human leukocyte antigen (HLA) class I and II proteins. One goal of immunogenomics is to identify those cancer-specific alterations that are likely to elicit an immune response that is highly specific to the patient’s cancer cells following stimulation by a personalized vaccine. The elements of such an approach are outlined herein and constitute an emerging therapeutic option for cancer patients.

show abstract

Hybrid biogeography based simultaneous feature selection and MHC class I peptide binding prediction using support vector machines and random forests

Cited by 12 publications

References 43 publications

Molecular Decision Tree Algorithms Predict Individual Recurrence Pattern for Locally Advanced Nasopharyngeal Carcinoma

Molecular Decision Tree Algorithms Predict Individual Recurrence Pattern for Locally Advanced Nasopharyngeal Carcinoma

Applications of Immunogenomics to Cancer

Cancer Immunogenomics: Computational Neoantigen Identification and Vaccine Design

Contact Info

Product

Resources

About