iMulti-HumPhos: a multi-label classifier for identifying human phosphorylated proteins using multiple kernel learning based support vector machines

2017 ◽  
Vol 13 (8) ◽  
pp. 1608-1618 ◽  
Author(s):  
Md. Al Mehedi Hasan ◽  
Shamim Ahmad ◽  
Md. Khademul Islam Molla
Keyword(s):  
Support Vector Machines ◽  
Multiple Kernel Learning ◽  
Kernel Learning ◽  
Support Vector ◽  
Phosphorylated Proteins ◽  
Multiple Kernel ◽  
Vector Machines

An efficient multi-label classifier for identifying human phosphorylated proteins has been developed by introducing multiple kernel learning based support vector machines.

scholarly journals A Multiple Kernel Learning Model Based on p-Norm

2018 ◽  
Vol 2018 ◽  
pp. 1-7 ◽  
Author(s):  
Jinshan Qi ◽  
Xun Liang ◽  
Rui Xu
Keyword(s):  
Multiple Kernel Learning ◽  
Research Society ◽  
Kernel Functions ◽  
Support Vector ◽  
Kernel Weights ◽  
Multiple Kernel ◽  
Vector Machines ◽  
Norm Constraint ◽  
Special Case ◽  
Elastic Constraint

By utilizing kernel functions, support vector machines (SVMs) successfully solve the linearly inseparable problems. Subsequently, its applicable areas have been greatly extended. Using multiple kernels (MKs) to improve the SVM classification accuracy has been a hot topic in the SVM research society for several years. However, most MK learning (MKL) methods employ L1-norm constraint on the kernel combination weights, which forms a sparse yet nonsmooth solution for the kernel weights. Alternatively, the Lp-norm constraint on the kernel weights keeps all information in the base kernels. Nonetheless, the solution of Lp-norm constraint MKL is nonsparse and sensitive to the noise. Recently, some scholars presented an efficient sparse generalized MKL (L1- and L2-norms based GMKL) method, in which L1  L2 established an elastic constraint on the kernel weights. In this paper, we further extend the GMKL to a more generalized MKL method based on the p-norm, by joining L1- and Lp-norms. Consequently, the L1- and L2-norms based GMKL is a special case in our method when p=2. Experiments demonstrated that our L1- and Lp-norms based MKL offers a higher accuracy than the L1- and L2-norms based GMKL in the classification, while keeping the properties of the L1- and L2-norms based on GMKL.

scholarly journals Efficient Multiple Kernel Learning Algorithms Using Low-Rank Representation

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
Wenjia Niu ◽  
Kewen Xia ◽  
Baokai Zu ◽  
Jianchuan Bai
Keyword(s):  
Recognition Accuracy ◽  
Multiple Kernel Learning ◽  
Low Rank ◽  
Kernel Learning ◽  
Support Vector ◽  
Distribution Characteristics ◽  
Kernel Weights ◽  
Multiple Kernel ◽  
Kernel Approximation ◽  
Low Rank Representation

Unlike Support Vector Machine (SVM), Multiple Kernel Learning (MKL) allows datasets to be free to choose the useful kernels based on their distribution characteristics rather than a precise one. It has been shown in the literature that MKL holds superior recognition accuracy compared with SVM, however, at the expense of time consuming computations. This creates analytical and computational difficulties in solving MKL algorithms. To overcome this issue, we first develop a novel kernel approximation approach for MKL and then propose an efficient Low-Rank MKL (LR-MKL) algorithm by using the Low-Rank Representation (LRR). It is well-acknowledged that LRR can reduce dimension while retaining the data features under a global low-rank constraint. Furthermore, we redesign the binary-class MKL as the multiclass MKL based on pairwise strategy. Finally, the recognition effect and efficiency of LR-MKL are verified on the datasets Yale, ORL, LSVT, and Digit. Experimental results show that the proposed LR-MKL algorithm is an efficient kernel weights allocation method in MKL and boosts the performance of MKL largely.

General Dimensional Multiple-Output Support Vector Regressions and Their Multiple Kernel Learning

2015 ◽  
Vol 45 (11) ◽  
pp. 2572-2584 ◽  
Author(s):  
Wooyong Chung ◽  
Jisu Kim ◽  
Heejin Lee ◽  
Euntai Kim
Keyword(s):  
Multiple Kernel Learning ◽  
Kernel Learning ◽  
Support Vector ◽  
Multiple Kernel

scholarly journals A Multiple Kernel Learning Approach for Air Quality Prediction

2018 ◽  
Vol 2018 ◽  
pp. 1-15 ◽  
Author(s):  
Hong Zheng ◽  
Haibin Li ◽  
Xingjian Lu ◽  
Tong Ruan
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Arima Model ◽  
Multiple Kernel Learning ◽  
Parametric Models ◽  
Kernel Learning ◽  
Support Vector ◽  
Quality Prediction ◽  
Air Quality Prediction ◽  
Multiple Kernel

Air quality prediction is an important research issue due to the increasing impact of air pollution on the urban environment. However, existing methods often fail to forecast high-polluting air conditions, which is precisely what should be highlighted. In this paper, a novel multiple kernel learning (MKL) model that embodies the characteristics of ensemble learning, kernel learning, and representative learning is proposed to forecast the near future air quality (AQ). The centered alignment approach is used for learning kernels, and a boosting approach is used to determine the proper number of kernels. To demonstrate the performance of the proposed MKL model, its performance is compared to that of classical autoregressive integrated moving average (ARIMA) model; widely used parametric models like random forest (RF) and support vector machine (SVM); popular neural network models like multiple layer perceptron (MLP); and long short-term memory neural network. Datasets acquired from a coastal city Hong Kong and an inland city Beijing are used to train and validate all the models. Experiments show that the MKL model outperforms the other models. Moreover, the MKL model has better forecast ability for high health risk category AQ.

A spectral clustering with self-weighted multiple kernel learning method for single-cell RNA-seq data

2020 ◽  
Author(s):  
Ren Qi ◽  
Jin Wu ◽  
Fei Guo ◽  
Lei Xu ◽  
Quan Zou
Keyword(s):  
Single Cell ◽  
Spectral Clustering ◽  
Kernel Method ◽  
Multiple Kernel Learning ◽  
Superior Performance ◽  
Kernel Learning ◽  
Support Vector ◽  
Multiple Kernel ◽  
Proposed Model ◽  
Similarity Information

Abstract Single-cell RNA-sequencing (scRNA-seq) data widely exist in bioinformatics. It is crucial to devise a distance metric for scRNA-seq data. Almost all existing clustering methods based on spectral clustering algorithms work in three separate steps: similarity graph construction; continuous labels learning; discretization of the learned labels by k-means clustering. However, this common practice has potential flaws that may lead to severe information loss and degradation of performance. Furthermore, the performance of a kernel method is largely determined by the selected kernel; a self-weighted multiple kernel learning model can help choose the most suitable kernel for scRNA-seq data. To this end, we propose to automatically learn similarity information from data. We present a new clustering method in the form of a multiple kernel combination that can directly discover groupings in scRNA-seq data. The main proposition is that automatically learned similarity information from scRNA-seq data is used to transform the candidate solution into a new solution that better approximates the discrete one. The proposed model can be efficiently solved by the standard support vector machine (SVM) solvers. Experiments on benchmark scRNA-Seq data validate the superior performance of the proposed model. Spectral clustering with multiple kernels is implemented in Matlab, licensed under Massachusetts Institute of Technology (MIT) and freely available from the Github website, https://github.com/Cuteu/SMSC/.

Path2Surv: Pathway/gene set-based survival analysis using multiple kernel learning

2019 ◽  
Vol 35 (24) ◽  
pp. 5137-5145 ◽  
Author(s):  
Onur Dereli ◽  
Ceyda Oğuz ◽  
Mehmet Gönen
Keyword(s):  
Gene Expression ◽  
Survival Analysis ◽  
Multiple Kernel Learning ◽  
Kernel Learning ◽  
Supplementary Information ◽  
Support Vector ◽  
Gene Set ◽  
Multiple Kernel ◽  
Pathway Gene ◽  
Gene Sets

Abstract Motivation Survival analysis methods that integrate pathways/gene sets into their learning model could identify molecular mechanisms that determine survival characteristics of patients. Rather than first picking the predictive pathways/gene sets from a given collection and then training a predictive model on the subset of genomic features mapped to these selected pathways/gene sets, we developed a novel machine learning algorithm (Path2Surv) that conjointly performs these two steps using multiple kernel learning. Results We extensively tested our Path2Surv algorithm on 7655 patients from 20 cancer types using cancer-specific pathway/gene set collections and gene expression profiles of these patients. Path2Surv statistically significantly outperformed survival random forest (RF) on 12 out of 20 datasets and obtained comparable predictive performance against survival support vector machine (SVM) using significantly fewer gene expression features (i.e. less than 10% of what survival RF and survival SVM used). Availability and implementation Our implementations of survival SVM and Path2Surv algorithms in R are available at https://github.com/mehmetgonen/path2surv together with the scripts that replicate the reported experiments. Supplementary information Supplementary data are available at Bioinformatics online.

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