An automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant pathogens

Fungal plant-pathogens promote infection of their hosts through the release of ‘effectors’—a broad class of cytotoxic or virulence-promoting molecules. Effectors may be recognised by resistance or sensitivity receptors in the host, which can determine disease outcomes. Accurate prediction of effectors remains a major challenge in plant pathology, but if achieved will facilitate rapid improvements to host disease resistance. This study presents a novel tool and pipeline for the ranking of predicted effector candidates—Predector—which interfaces with multiple software tools and methods, aggregates disparate features that are relevant to fungal effector proteins, and applies a pairwise learning to rank approach. Predector outperformed a typical combination of secretion and effector prediction methods in terms of ranking performance when applied to a curated set of confirmed effectors derived from multiple species. We present Predector (https://github.com/ccdmb/predector) as a useful tool for the ranking of predicted effector candidates, which also aggregates and reports additional supporting information relevant to effector and secretome prediction in a simple, efficient, and reproducible manner.

Online regularized pairwise learning with non-i.i.d. observations

In this paper, we consider the online regularized pairwise learning (ORPL) algorithm with least squares loss function for non-independently and identically distribution (non-i.i.d.) observations. We first establish new Bennett’s inequalities for [Formula: see text]-mixing sequence, geometrically [Formula: see text]-mixing sequence, [Formula: see text]-geometrically ergodic Markov chain and uniformly ergodic Markov chain. Then we establish the convergence rates for the last iterate of the ORPL algorithm with the polynomially decaying step sizes and varying regularization parameters for non-i.i.d. observations. These established results in this paper extend the previously known results of ORPL from i.i.d. observations to the case of non-i.i.d. observations, and the established result of ORPL for [Formula: see text]-mixing can be nearly optimal rate of ORPL for i.i.d. observations with [Formula: see text]-norm.

Item Response Ranking for Cognitive Diagnosis

Cognitive diagnosis, a fundamental task in education area, aims at providing an approach to reveal the proficiency level of students on knowledge concepts. Actually, monotonicity is one of the basic conditions in cognitive diagnosis theory, which assumes that student's proficiency is monotonic with the probability of giving the right response to a test item. However, few of previous methods consider the monotonicity during optimization. To this end, we propose Item Response Ranking framework (IRR), aiming at introducing pairwise learning into cognitive diagnosis to well model the monotonicity between item responses. Specifically, we first use an item specific sampling method to sample item responses and construct response pairs based on their partial order, where we propose the two-branch sampling methods to handle the unobserved responses. After that, we use a pairwise objective function to exploit the monotonicity in the pair formulation. In fact, IRR is a general framework which can be applied to most of contemporary cognitive diagnosis models. Extensive experiments demonstrate the effectiveness and interpretability of our method.

Differentially Private Pairwise Learning Revisited

Instead of learning with pointwise loss functions, learning with pairwise loss functions (pairwise learning) has received much attention recently as it is more capable of modeling the relative relationship between pairs of samples. However, most of the existing algorithms for pairwise learning fail to take into consideration the privacy issue in their design. To address this issue, previous work studied pairwise learning in the Differential Privacy (DP) model. However, their utilities (population errors) are far from optimal. To address the sub-optimal utility issue, in this paper, we proposed new pure or approximate DP algorithms for pairwise learning. Specifically, under the assumption that the loss functions are Lipschitz, our algorithms could achieve the optimal expected population risk for both strongly convex and general convex cases. We also conduct extensive experiments on real-world datasets to evaluate the proposed algorithms, experimental results support our theoretical analysis and show the priority of our algorithms.

Pairwise deep learning to rank for top-N recommendation

Recent researches indicate that pairwise learning to rank methods could achieve high performance in dealing with data sparsity and long tail distribution in item recommendation, although suffering from problems such as high computational complexity and insufficient samples, which may cause low convergence and inaccuracy. To further improve the performance in computational capability and recommendation accuracy, in this article, a novel deep neural network based recommender architecture referred to as PDLR is proposed, in which the item corpus will be partitioned into two collections of positive instances and negative items respectively, and pairwise comparison will be performed between the positive instances and negative samples to learn the preference degree for each user. With the powerful capability of neural network, PDLR could capture rich interactions between each user and items as well as the intricate relations between items. As a result, PDLR could minimize the ranking loss, and achieve significant improvement in ranking accuracy. In practice, experimental results over four real world datasets also demonstrate the superiority of PDLR in contrast to state-of-the-art recommender approaches, in terms of Rec@N, Prec@N, AUC and NDCG@N.

Predector: an automated and combinative method for the predictive ranking of candidate effector proteins of fungal plant-pathogens

‘Effectors’ are a broad class of cytotoxic or virulence-promoting molecules that are released from plant-pathogen cells to cause disease in their host. Fungal effectors are a core research area for improving host disease resistance; however, because they generally lack common distinguishing features or obvious sequence similarity, discovery of effectors remains a major challenge. This study presents a novel tool and pipeline for effector prediction - Predector - which interfaces with multiple software tools and methods, aggregates disparate features that are relevant to fungal effector proteins, and ranks effector candidate proteins using a pairwise learning to rank approach. Predector outperformed alternative effector prediction methods that were applied to a curated set of confirmed effectors derived from multiple species. We present Predector as a useful tool for the prediction and ranking of effector candidates, which aggregates and reports additional supporting information relevant to effector and secretome prediction in a simple, efficient, and reproducible manner. Predector is available from https://github.com/ccdmb/predector and associated data from https://github.com/ccdmb/predector-data.