Identifying interactions between two or more proteins is crucial as it helps understand living organisms' cellular behaviour and the underlying molecular mechanisms of various diseases. However, most existing computational algorithms in the field model this as a binary interaction between any two proteins, instead of conserving the evolutionary regions of protein function and interactions. This is important for predicting potential interaction sites, vital for drug design, target identification, and understanding disease progression and pathogenic mechanisms. Position-aware encoding provides a way to incorporate the order of amino acids in a protein sequence into the model, thus capturing folding patterns, leading to more accurate predictions of protein structures and their interactions. This is crucial because the sequence order can affect the structure and function of proteins. The proposed DensePPI-2 model is a novel bio-inspired substitution matrix-based sequence encoding with deep learning for identifying interacting protein pairs. It demonstrates an AUC of 97.13% on the S. cerevisiae dataset, improving by 1.4% over the best existing methods. Furthermore, DensePPI-2 outperforms recent sequence-based approaches on the human benchmark dataset, addressing the complexities of protein-protein interaction test classes. DensePPI-2 has been successfully applied for (i) identifying pathogen-host interactions and (ii) predicting near-residue-level interaction, even though the model was not trained on residue-level data. The enhanced performance on diverse test sets proves the efficiency of the bio-inspired sequence-to-image colour encoding strategy using the substitution matrices. The dataset and the developed models are available at https://github.com/CMATERJU-BIOINFO/DensePPI-2 for academic use only.

BACKGROUND: Quantitative analysis of dendritic spine morphology and density is crucial for understanding synaptic plasticity and its role in neuropsychiatric disorders, including Alzheimer's disease and schizophrenia. While both 3D and 2D approaches exist for spine analysis, 2D methods offer advantages in computational efficiency, rapid assessment, and more reasonable to use in case of limited z-resolution images acquired through confocal and previous generation super-resolution microscopy. In this work, we developed a modality-agnostic spine segmentation approach based on 2D skeletonization. Specifically, we implemented two analytical workflows, viz., 2dSpAn-Auto.b, that implements binary skeletonization alogrithm and 2dSpAn-Auto.f, that generates fuzzy skeletons directly from gray-scale images. Our developed method enables fast and automatic segmentation and morphological analysis of 2D maximum intensity projection images of dendritic spines. Expert users can fine-tune parameters when needed, though default settings prove robust across various imaging conditions. The developed 2dSpAn-Auto software tool is most suitable for automated batch processing while maintaining user flexibility through an intuitive graphical interface. RESULTS: 2dSpAn-Auto is validated across multiple imaging modalities (in vitro, ex vivo, and in vivo) for automatic assessment of dendritic spine parameters including spine density, morphometry (spine area, spine length, head width, minimum and average neck width), and total dendrite length. Validation studies demonstrate high accuracy and reproducibility across varying imaging protocols and experimental conditions. Multiple images from similar experimental setups can be processed seamlessly in the batch mode. CONCLUSIONS: 2dSpAn-Auto provides a robust, interpretable solution for fast analysis of dendritic spines, a critical need in neurological research and clinical assessment. The combination of automated processing with optional expert oversight makes it suitable for both routine analysis and specialized research applications. The software, complete with the source code and comprehensive documentation, is available to the research community for non-commercial use under GNU General Public License (GPL) v3.

Abstract Scene text image super-resolution (STISR), often considered a preliminary step for scene text recognition, refers to the task of enhancing the resolution of text embedded in natural scene images and plays a vital role in various applications. Most of the existing STISR methods either leverage deep convolutional neural networks by regarding text images as natural scene images or use a text recognizer’s feedback as guidance to the STISR process. However, since the text recognition is initially done on low-resolution images, it is mostly inaccurate, more so as the length of the words increases, thus degrading the super-resolution process. In this paper, we introduce DEPP which utilizes dictionary embedding (DE) based probabilistic priors calculated from a large English text corpus consisting of both alphabets and digits. The initial state and the bigram probabilities obtained are fused with the probability obtained from the recognizer, before passing it onto a single image super-resolution (SISR) block. By integrating DE as a prior and implementing a modified perceptual loss, the method effectively captures the contextual information of text, enabling more accurate super-resolution and visually pleasing results. Experimental results on the benchmark TextZoom dataset demonstrate that our DEPP framework achieves superior performance compared to most existing approaches, particularly for medium and long-length words, as measured by text recognition accuracy. Since DEPP uses the text recognition attributes to rectify or guide the super-resolution process, it makes our method more domain-inspired and task-aware, compared to usual black box deep learners.

Biclustering of biologically meaningful binary information is essential in many applications related to drug discovery, like protein-protein interactions and gene expressions. However, for robust performance in recently emerging large health datasets, it is important for new biclustering algorithms to be scalable and fast. We present a rapid unsupervised biclustering (RUBic) algorithm that achieves this objective with a novel encoding and search strategy. RUBic significantly reduces the computational overhead on both synthetic and experimental datasets shows significant computational benefits, with respect to several state-of-the-art biclustering algorithms. In 100 synthetic binary datasets, our method took [Formula: see text] s to extract 494,872 biclusters. In the human PPI database of size [Formula: see text], our method generates 1840 biclusters in [Formula: see text] s. On a central nervous system embryonic tumor gene expression dataset of size 712,940, our algorithm takes 101 min to produce 747,069 biclusters, while the recent competing algorithms take significantly more time to produce the same result. RUBic is also evaluated on five different gene expression datasets and shows significant speed-up in execution time with respect to existing approaches to extract significant KEGG-enriched bi-clustering. RUBic can operate on two modes, base and flex, where base mode generates maximal biclusters and flex mode generates less number of clusters and faster based on their biological significance with respect to KEGG pathways. The code is available at ( https://github.com/CMATERJU-BIOINFO/RUBic ) for academic use only.

This letter introduces the LOOP binary descriptor (local optimal-oriented pattern) that encodes rotation invariance into the main formulation itself. This makes any post processing stage for rotation invariance redundant and improves on both accuracy and time complexity. We consider fine-grained lepidoptera (moth/butterfly) species recognition as the representative problem since it involves repetition of localized patterns and textures that may be exploited for discrimination. We evaluate the performance of LOOP against its predecessors as well as few other popular descriptors. Besides experiments on standard benchmarks, we also introduce a new small image dataset on NZ Lepidoptera. LOOP performs as well or better on all datasets evaluated compared to previous binary descriptors. The new dataset and demo code of the proposed method are available through the lead author's academic webpage and GitHub.

In printed stylized documents, text lines may be curved in shape and as a result characters of a single line may be multi-oriented. This paper presents a multi-scale and multi-oriented character recognition scheme using foreground as well as background information. Here each character is partitioned into multiple circular zones. For each zone, three centroids are computed by grouping the constituent character segments (components) of each zone into two clusters. As a result, we obtain one global centroid for all the components in the zone, and further two centroids for the two generated clusters. The above method is repeated for both foreground as well as background information. The features are generated by encoding the spatial distribution of these centroids by computing their relative angular information. These features are then fed into a SVM classifier. A PCA based feature selection phase has also been applied. Detailed experiments on Bangla and Devanagari datasets have been performed. It has been seen that the proposed methodology outperforms a recent competing method.

Abstract The present paper proposes a supervised learning based automated human facial emotion recognition strategy with a feature selection scheme employing a novel variation of the gravitational search algorithm (GSA). The initial feature set is generated from the facial images by using the 2‐D discrete cosine transform (DCT) and then the proposed modified binary quantum GSA with differential mutation (MBQGSA‐DM) is utilized to select a sub‐set of features with high discriminative power. This is achieved by minimising the cost function formulated as the ratio of the within class and interclass distances. The overall system performs its final classification task based on selected feature inputs, utilising a back propagation based artificial neural network (ANN). Extensive experimental evaluations are carried out utilising a standard, benchmark emotion database, that is, Japanese Female Facial Expresssion (JAFFE) database and the results clearly indicate that the proposed method outperforms several existing techniques, already known in literature for solving similar problems. Further validation has also been carried out on a facial expression database developed at Jadavpur University, Kolkata, India and the results obtained further strengthen the notion of superiority of the proposed method.