ComplexContact (http://raptorx2.uchicago.edu/ComplexContact/) is a web server for sequence-based interfacial residue-residue contact prediction of a putative protein complex. Interfacial residue-residue contacts are critical for understanding how proteins form complex and interact at residue level. When receiving a pair of protein sequences, ComplexContact first searches for their sequence homologs and builds two paired multiple sequence alignments (MSA), then it applies co-evolution analysis and a CASP-winning deep learning (DL) method to predict interfacial contacts from paired MSAs and visualizes the prediction as an image. The DL method was originally developed for intra-protein contact prediction and performed the best in CASP12. Our large-scale experimental test further shows that ComplexContact greatly outperforms pure co-evolution methods for inter-protein contact prediction, regardless of the species.

Based on a bio-heuristic algorithm, this paper proposes a novel path planner called obstacle avoidance beetle antennae search (OABAS) algorithm, which is applied to the global path planning of unmanned aerial vehicles (UAVs). Compared with the previous bio-heuristic algorithms, the algorithm proposed in this paper has advantages of a wide search range and breakneck search speed, which resolves the contradictory requirements of the high computational complexity of the bio-heuristic algorithm and real-time path planning of UAVs. Besides, the constraints used by the proposed algorithm satisfy various characteristics of the path, such as shorter path length, maximum allowed turning angle, and obstacle avoidance. Ignoring the z-axis optimization by combining with the minimum threat surface (MTS), the resultant path meets the requirements of efficiency and safety. The effectiveness of the algorithm is substantiated by applying the proposed path planning algorithm on the UAVs. Moreover, comparisons with other existing algorithms further demonstrate the superiority of the proposed OABAS algorithm.

As the volume of data available for analysis grows, feature selection is becoming a vital part of ensuring accurate classification results. In classification problems, selecting a small number of features reduces computational complexity, but selecting the right features is important to maintain a high level of accuracy. In this paper, we present a feature selection method based on hybrid improved quantum-behavior particle swarm optimization, called HI-BQPSO. The HI-BQPSO combines a filtering method with an improved quantum-behavior particle swarm optimization algorithm to greatly reduce the dimensionality of the data so as to overcome some of the shortcomings of BQPSO. Tests were conducted on nine gene expression datasets and 36 UCI datasets to evaluate and compare the classification accuracy of the HI-BQPSO's selected feature subsets against four other algorithms. The results, using a variety of different classifiers, show that the HI-BQPSO significantly reduces the number of features required for classification while maintaining higher levels of accuracy in many cases.

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