MOTIVATION: Survival risk prediction using gene expression data is important in making treatment decisions in cancer. Standard neural network (NN) survival analysis models are black boxes with a lack of interpretability. More interpretable visible neural network architectures are designed using biological pathway knowledge. But they do not model how pathway structures can change for particular cancer types. RESULTS: We propose a novel Mutated Pathway Visible Neural Network (MPVNN) architecture, designed using prior signaling pathway knowledge and random replacement of known pathway edges using gene mutation data simulating signal flow disruption. As a case study, we use the PI3K-Akt pathway and demonstrate overall improved cancer-specific survival risk prediction of MPVNN over other similar-sized NN and standard survival analysis methods. We show that trained MPVNN architecture interpretation, which points to smaller sets of genes connected by signal flow within the PI3K-Akt pathway that is important in risk prediction for particular cancer types, is reliable. AVAILABILITY AND IMPLEMENTATION: The data and code are available at https://github.com/gourabghoshroy/MPVNN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MOTIVATION: Inferring gene regulatory networks (GRNs) from expression data is a significant systems biology problem. A useful inference algorithm should not only unveil the global structure of the regulatory mechanisms but also the details of regulatory interactions such as edge direction (from regulator to target) and sign (activation/inhibition). Many popular GRN inference algorithms cannot infer edge signs, and those that can infer signed GRNs cannot simultaneously infer edge directions or network cycles. RESULTS: To address these limitations of existing algorithms, we propose Polynomial Lasso Bagging (PoLoBag) for signed GRN inference with both edge directions and network cycles. PoLoBag is an ensemble regression algorithm in a bagging framework where Lasso weights estimated on bootstrap samples are averaged. These bootstrap samples incorporate polynomial features to capture higher-order interactions. Results demonstrate that PoLoBag is consistently more accurate for signed inference than state-of-the-art algorithms on simulated and real-world expression datasets. AVAILABILITY AND IMPLEMENTATION: Algorithm and data are freely available at https://github.com/gourabghoshroy/PoLoBag. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Several variants of the Particle Swarm Optimization (PSO) algorithm have been proposed in recent past to tackle the multi-objective optimization problems based on the concept of Pareto optimality. Although a plethora of significant research articles have so far been published on analysis of the stability and convergence properties of PSO as a single-objective optimizer, till date, to the best of our knowledge, no such analysis exists for the multi-objective PSO (MOPSO) algorithms. This paper presents a first, simple analysis of the general Pareto-based MOPSO and finds conditions on its most important control parameters (the inertia factor and acceleration coefficients) that control the convergence behavior of the algorithm to the Pareto front in the objective function space. Limited simulation supports have also been provided to substantiate the theoretical derivations.

Invasive Weed Optimization (IWO) is a recently developed derivative-free metaheuristic algorithm that mimics the robust process of weeds colonization and distribution in an ecosystem. On the other hand central to an ecosystem is the foraging behavior that pertains to the act of searching for food and forms an integral part of the daily life of most of the living creatures. For over past two decades, a few significant optimization algorithms were developed by emulating the foraging behavior of creatures like ants, bacteria, fish, bees etc. This article presents a hybrid real-parameter optimizer developed by incorporating the principles of Optimal Foraging Theory (OFT) in IWO, with a view to improving the search mechanism of the latter over discontinuous and multi-modal fitness landscapes, riddled with local optima. The hybridization does not impose any serious computational burden on IWO in terms of increasing number of Function Evaluations (FEs). The performance of the resulting hybrid algorithm has been compared with eleven other state-of-the-art metaheuristic algorithms over a test-suite of 16 numerical benchmarks taken from the CEC (Congress on Evolutionary Computation) 2005 competition and special session on real parameter optimization. Our simulation experiments indicate that the proposed algorithm is able to attain comparable results against the nine other optimizers. Owing to its promising performance on benchmarks and ease of implementation (without requiring much programming overhead), the proposed algorithm may serve as an attractive alternative for a plethora of practical optimization problems.

Harmony Search (HS) is a recently developed stochastic algorithm which imitates the music improvisation process. In this process, the musicians improvise their instrument pitches searching for the perfect state of harmony. Practical experiences, however, suggest that the algorithm suffers from the problems of slow and/or premature convergence over multimodal and rough fitness landscapes. This paper presents an attempt to improve the search performance of HS by hybridizing it with Differential Evolution (DE) algorithm. The performance of the resulting hybrid algorithm has been compared with classical HS, the global best HS, and a very popular variant of DE over a test-suite of six well known benchmark functions and one interesting practical optimization problem. The comparison is based on the following performance indices - (i) accuracy of final result, (ii) computational speed, and (iii) frequency of hitting the optima.