Protein classification is a crucial field in bioinformatics. The development of a comprehensive tool that can perform feature evaluation, visualization, automated machine learning, and model interpretation would significantly advance research in protein classification. However, there is a significant gap in the literature regarding tools that integrate all these essential functionalities. This paper presents iProps, a novel Python-based software package, meticulously crafted to fulfill these multifaceted requirements. iProps is distinguished by its proficiency in feature extraction, evaluation, automated machine learning, and interpretation of classification models. Firstly, iProps fully leverages evolutionary information and amino acid reduction information to propose or extend several numerical protein features that are independent of sequence length, including SC-PSSM, ORDip, TRC, CTDC-E, CKSAAGP-E, and so forth; at the same time, it also implements the calculation of 17 other numerical features within the software. iProps also provides feature combination operations for the aforementioned features to generate more hybrid features, and has added data balancing sampling processing as well as built-in classifier settings, among other functionalities. Thus, It can discern the most effective protein class recognition feature from a multitude of candidates, utilizing three automated machine learning algorithms to identify the most optimal classifiers and parameter settings. Furthermore, iProps generates a detailed explanatory report that includes 23 informative graphs derived from three interpretable models. To assess the performance of iProps, a series of numerical experiments were conducted using two well-established datasets. The results demonstrated that our software achieved superior recognition performance in every case. Beyond its contributions to bioinformatics, iProps broadens its applicability by offering robust data analysis tools that are beneficial across various disciplines, capitalizing on its automated machine learning and model interpretation capabilities. As an open-source platform, iProps is readily accessible and features an intuitive user interface, ensuring ease of use for individuals, even those without a background in programming.

Composed image retrieval is a challenging task in the field of multi-modal learning, aiming at measuring the similarities between target images and query images with modification sentences. Most previous methods either construct feature composition for the query image and modification text or concentrate on extracting cross-modal alignments. However, these methods are prone to neglect the negative impacts of the mismatched correspondences between the hybrid-modal query and target, which could be discriminative when comparing similar instances. Besides, localized textual representations are not fully explored when learning similarities between the query and the target. To overcome the above issues, we propose a Negative-Sensitive Framework with Semantic Enhancement (NSFSE) for mining the adaptive boundaries between matched and mismatched samples with comprehensive consideration of positive and negative correspondences. It can optimize the threshold dynamically based on distributions to explore the intrinsic characteristics of positive and negative correlations, which could further facilitate accurate similarity learning. A text-guided attention mechanism after infusing cross-modal affinities on localized word features is exploited in NSFSE to explore latent semantic-related visual similarity and cross-modal similarity simultaneously. The performance of extensive experiments and comprehensive analysis on three representative datasets CIRR, FashionIQ, and Fashion200 K demonstrate the effectiveness of negative mining of similarity with semantic enhancement in the proposed NSFSE.