Plasmodium falciparum (Pf)-mediated malaria is one of the most devastating diseases in the world, and the search for suitable antimalarial drugs remains an extraordinary challenge for scientists working in this area. Novel unconventional approaches could reveal new potential targets that may be useful for the treatment of malaria. We used a bioinformatics approach to analyze the entire genome of the Pf3D7 strain. Because the carbon (C-) content is a pivotal parameter that determines the hydrophobicity of a protein, which in turn controls protein folding and function, we analyzed the entire Pf3D7 proteome based on the gene's thymine (T)-controlled amino acid expression. Our data disclose a total of 14 proteins encoded by chromosome-4 and chromosome-9 that have an outstanding T-encoded and C-controlled hydrophobic character. The identification of these proteins could open new pivotal drug-targeting avenues.

F-measure is a popular performance metric used in classification when the dataset is unbalanced. Optimizing this measure directly is often challenging since no closed form solution exists. Current algorithms use approximations to the F-measure and design classifiers using maximum margin or logistic regression framework. These algorithms are not scalable and the classifiers designed are not robust to outliers. In this work, we propose a general framework for approximate F-measure maximization. We also propose a non-convex loss function which is robust to outliers. Use of elastic net regularizer in the problem formulation enables us to do simultaneous classifier design and feature selection. We present an efficient algorithm to solve the proposed problem formulation. The proposed algorithm is simple and is easy to implement. Numerical experiments on real-world benchmark datasets demonstrate that the proposed algorithm is fast and gives better generalization performance compared to some existing approaches. Thus, it is a powerful alternative for optimizing F-measure and designing a sparse classifier.

Structural Support Vector Machines (SSVMs) have recently gained wide prominence in classifying structured and complex objects like parse-trees, image segments and Part-of-Speech (POS) tags. Typical learning algorithms used in training SSVMs result in model parameters which are vectors residing in a large-dimensional feature space. Such a high-dimensional model parameter vector contains many non-zero components which often lead to slow prediction and storage issues. Hence there is a need for sparse parameter vectors which contain a very small number of non-zero components. L1-regularizer and elastic net regularizer have been traditionally used to get sparse model parameters. Though L1-regularized structural SVMs have been studied in the past, the use of elastic net regularizer for structural SVMs has not been explored yet. In this work, we formulate the elastic net SSVM and propose a sequential alternating proximal algorithm to solve the dual formulation. We compare the proposed method with existing methods for L1-regularized Structural SVMs. Experiments on large-scale benchmark datasets show that the proposed dual elastic net SSVM trained using the sequential alternating proximal algorithm scales well and results in highly sparse model parameters while achieving a comparable generalization performance. Hence the proposed sequential alternating proximal algorithm is a competitive method to achieve sparse model parameters and a comparable generalization performance when elastic net regularized Structural SVMs are used on very large datasets.