We propose Twin SVM, a binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM. The Twin SVM formulation is in the spirit of proximal SVMs via generalized eigenvalues. On several benchmark data sets, Twin SVM is not only fast, but shows good generalization. Twin SVM is also useful for automatically discovering two-dimensional projections of the data.

Single cell messenger RNA sequencing (scRNA-seq) provides a window into transcriptional landscapes in complex tissues. The recent introduction of droplet based transcriptomics platforms has enabled the parallel screening of thousands of cells. Large-scale single cell transcriptomics is advantageous as it promises the discovery of a number of rare cell sub-populations. Existing algorithms to find rare cells scale unbearably slowly or terminate, as the sample size grows to the order of tens of thousands. We propose Finder of Rare Entities (FiRE), an algorithm that, in a matter of seconds, assigns a rareness score to every individual expression profile under study. We demonstrate how FiRE scores can help bioinformaticians focus the downstream analyses only on a fraction of expression profiles within ultra-large scRNA-seq data. When applied to a large scRNA-seq dataset of mouse brain cells, FiRE recovered a novel sub-type of the pars tuberalis lineage.

Designing new glasses requires a priori knowledge of how the composition of a glass dictates its properties such as stiffness, density, or processability. Developing multi-property design charts, namely, glass selection charts, using deep learning can enable discovery of novel glasses with targeted properties.

Surface electromyogram (sEMG) is a measure of the muscle activity from the skin surface, and is an excellent indicator of the strength of muscle contraction. It is an obvious choice for control of prostheses and identification of body gestures. Using sEMG to identify posture and actions that are a result of overlapping multiple active muscles is rendered difficult by interference between different muscle activities. In the literature, attempts have been made to apply independent component analysis to separate sEMG into components corresponding to the activities of different muscles, but this has not been very successful, because some muscles are larger and more active than the others. We address the problem of how to learn to separate each gesture or activity from all others. Multicategory classification problems are usually solved by solving many one-versus-rest binary classification tasks. These subtasks naturally involve unbalanced datasets. Therefore, we require a learning methodology that can take into account unbalanced datasets, as well as large variations in the distributions of patterns corresponding to different classes. This paper reports the use of twin support vector machine for gesture classification based on sEMG, and shows that this technique is eminently suited to such applications.

Inter and intra-tumoral heterogeneity are major stumbling blocks in the treatment of cancer and are responsible for imparting differential drug responses in cancer patients. Recently, the availability of high-throughput screening datasets has paved the way for machine learning based personalized therapy recommendations using the molecular profiles of cancer specimens. In this study, we introduce Precily, a predictive modeling approach to infer treatment response in cancers using gene expression data. In this context, we demonstrate the benefits of considering pathway activity estimates in tandem with drug descriptors as features. We apply Precily on single-cell and bulk RNA sequencing data associated with hundreds of cancer cell lines. We then assess the predictability of treatment outcomes using our in-house prostate cancer cell line and xenografts datasets exposed to differential treatment conditions. Further, we demonstrate the applicability of our approach on patient drug response data from The Cancer Genome Atlas and an independent clinical study describing the treatment journey of three melanoma patients. Our findings highlight the importance of chemo-transcriptomics approaches in cancer treatment selection.

In twin support vector machines (TWSVMs), we determine pair of non-parallel planes by solving two related SVM-type problems, each of which is smaller than the one in a conventional SVM. However, similar to other classification methods, the performance of the TWSVM classifier depends on the choice of the kernel. In this paper we treat the kernel selection problem for TWSVM as an optimization problem over the convex set of finitely many basic kernels, and formulate the same as an iterative alternating optimization problem. The efficacy of the proposed classification algorithm is demonstrated with some UCI machine learning benchmark datasets.

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