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.

Large-scale protein-protein interaction (PPI) network of an organism provides key insights into its cellular and molecular functionalities, signaling pathways and underlying disease mechanisms. For any organism, the total unexplored protein interactions significantly outnumbers all known positive and negative interactions. For Human, all known PPI datasets contain only ∼ 5.61 million positive and ∼ 0.76 million negative interactions, which is ∼ 3.1% of potential interactions. We have implemented a distributed algorithm in Apache Spark that evaluates a Human PPI network of ∼ 180 million potential interactions resulting from 18 994 reviewed proteins for which Gene Ontology (GO) annotations are available. The computed scores have been validated against state-of-the-art methods on benchmark datasets.FuzzyPPI performed significantly better with an average F1 score of 0.62 compared to GOntoSim (0.39), GOGO (0.38), and Wang (0.38) when tested with the Gold Standard PPI Dataset. The resulting scores are published with a web server for non-commercial use athttp://fuzzyppi.mimuw.edu.pl/. Moreover, conventional PPI prediction methods produce binary results, but in fact this is just a simplification as PPIs have strengths or probabilities and recent studies show that protein binding affinities may prove to be effective in detecting protein complexes, disease association analysis, signaling network reconstruction, etc. Keeping these in mind, our algorithm is based on a fuzzy semantic scoring function and produces probabilities of interaction.

Proteins interact with other proteins and bio-molecules to form a complex and execute key biological functions in a living organism, and respond to several environmental signals. Designing efficient predictive models for protein complexes is a challenging task with limited coverage in the contemporary literature. With this motivation, we have developed a novel method, PCPredG, for 3-node protein complex prediction from PPI networks using 5-node graphlet features. CORUM protein complex repository has been used to curate positive and negative data samples with the help of MCODE and MCL clustering algorithms. During experiments, Random Forest(RF) and SVM classifiers are trained with 1000 positive 3-node complexes in 10-fold cross-validation setup and with 1:1 to 1:10 positive-negative proportions. In parallel, we have implemented the state-of-the-art GCN with polarised message-passing, GAT and an ensemble of GCN and GAT in both balanced and imbalanced setups. We also introduced a 10-fold quality consensus on the hold-out set across all the experiments. We have achieved the best performances with the RF classifier in both balanced and imbalanced experiments.

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.

Over the years, several methods have been proposed for the computational PPI prediction with different performance evaluation strategies. While attempting to benchmark performance scores, most of these methods often suffer with ill-treated cross-validation strategies, adhoc selection of positive/negative samples etc. To address these issues, in our proposed multi-level feature based PPI prediction approach (JUPPI), using sequence, domain and GO information as features, a refined evaluation strategy has been introduced. During the evaluation process, we first extract high quality negative data using three-stage filtering, and then introduce a pair-input based cross validation strategy with three difficulty levels for test-set predictions. Our proposed evaluation strategy reduces the component-level overlapping issue in test sets. Performance of JUPPI is compared with those of the state-of-the-art approaches in this domain and tested on six independent PPI datasets. In almost all the datasets, JUPPI outperforms the state-of-the-art not only at human proteome level for PPI prediction, but also for prediction of interactors for intrinsic disordered human proteins. https://figshare.com/projects/JUPPI_A_Multi-level_Feature_Based_Method_for_PPI_Prediction_and_a_Refined_Strategy_for_Performance_Assessment/81656 JUPPI tool and the developed datasets (JUPPId) are available in public domain for academic use along with supplementary materials, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TCBB.2020.3004970.

BACKGROUND: Bioimaging techniques offer a robust tool for studying molecular pathways and morphological phenotypes of cell populations subjected to various conditions. As modern high-resolution 3D microscopy provides access to an ever-increasing amount of high-quality images, there arises a need for their analysis in an automated, unbiased, and simple way. Segmentation of structures within the cell nucleus, which is the focus of this paper, presents a new layer of complexity in the form of dense packing and significant signal overlap. At the same time, the available segmentation tools provide a steep learning curve for new users with a limited technical background. This is especially apparent in the bulk processing of image sets, which requires the use of some form of programming notation. RESULTS: In this paper, we present PartSeg, a tool for segmentation and reconstruction of 3D microscopy images, optimised for the study of the cell nucleus. PartSeg integrates refined versions of several state-of-the-art algorithms, including a new multi-scale approach for segmentation and quantitative analysis of 3D microscopy images. The features and user-friendly interface of PartSeg were carefully planned with biologists in mind, based on analysis of multiple use cases and difficulties encountered with other tools, to offer an ergonomic interface with a minimal entry barrier. Bulk processing in an ad-hoc manner is possible without the need for programmer support. As the size of datasets of interest grows, such bulk processing solutions become essential for proper statistical analysis of results. Advanced users can use PartSeg components as a library within Python data processing and visualisation pipelines, for example within Jupyter notebooks. The tool is extensible so that new functionality and algorithms can be added by the use of plugins. For biologists, the utility of PartSeg is presented in several scenarios, showing the quantitative analysis of nuclear structures. CONCLUSIONS: In this paper, we have presented PartSeg which is a tool for precise and verifiable segmentation and reconstruction of 3D microscopy images. PartSeg is optimised for cell nucleus analysis and offers multi-scale segmentation algorithms best-suited for this task. PartSeg can also be used for the bulk processing of multiple images and its components can be reused in other systems or computational experiments.

We present a human protein cluster analysis by combining: 1) n-gram based amino acid frequency features, 2) optimal feature selection, 3) hierarchical clustering, and 4) advanced partitioning techniques. Our method qualitatively and quantitatively groups proteins with increasing sequence similarity into similar clusters by calculating the frequency model of amino acids using n-grams. We experiment with n = 1, i.e., unigrams, n = 2, i.e., bigrams, and finally n = 3, i.e., trigrams for optimal selection of features to design the 3gClust algorithm. The benchmarking results on 20,105 manually curated human proteins show that 3gClust ensures better cluster compactness in the case of proteins with similar functional groups, biological processes, structural alignment, and shared domains (e.g., aquaporins, keratins). Quantitative analysis of non singleton clusters shows significant improvement in their compactness in comparison to other state-of-the art methodologies. 3gClust is available at https://sites.google.com/site/bioinfoju/projects/3gclust for academic use along with supplementary materials, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TCBB.2018.2840996, and datasets.

The semantic similarity between two interacting proteins can be estimated by combining the similarity scores of the GO terms associated with the proteins. Greater number of similar GO annotations between two proteins indicates greater interaction affinity. Existing semantic similarity measures make use of the GO graph structure, the information content of GO terms, or a combination of both. In this paper, we present a hybrid approach which utilizes both the topological features of the GO graph and information contents of the GO terms. More specifically, we 1) consider a fuzzy clustering of the GO graph based on the level of association of the GO terms, 2) estimate the GO term memberships to each cluster center based on the respective shortest path lengths, and 3) assign weightage to GO term pairs on the basis of their dissimilarity with respect to the cluster centers. We test the performance of our semantic similarity measure against seven other previously published similarity measures using benchmark protein-protein interaction datasets of Homo sapiens and Saccharomyces cerevisiae based on sequence similarity, Pfam similarity, area under ROC curve, and measure.

MOTIVATION: Accurate and effective dendritic spine segmentation from the dendrites remains as a challenge for current neuroimaging research community. In this article, we present a new method (2dSpAn) for 2-d segmentation, classification and analysis of structural/plastic changes of hippocampal dendritic spines. A user interactive segmentation method with convolution kernels is designed to segment the spines from the dendrites. Formal morphological definitions are presented to describe key attributes related to the shape of segmented spines. Spines are automatically classified into one of four classes: Stubby, Filopodia, Mushroom and Spine-head Protrusions. RESULTS: The developed method is validated using confocal light microscopy images of dendritic spines from dissociated hippocampal cultures for: (i) quantitative analysis of spine morphological changes, (ii) reproducibility analysis for assessment of user-independence of the developed software and (iii) accuracy analysis with respect to the manually labeled ground truth images, and also with respect to the available state of the art. The developed method is monitored and used to precisely describe the morphology of individual spines in real-time experiments, i.e. consequent images of the same dendritic fragment. AVAILABILITY AND IMPLEMENTATION: The software and the source code are available at https://sites.google.com/site/2dspan/ under open-source license for non-commercial use. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Theoretical properties of a multi-scale opening (MSO) algorithm for two conjoined fuzzy objects are established, and its extension to separating two conjoined fuzzy objects with different intensity properties is introduced. Also, its applications to artery/vein (A/V) separation in pulmonary CT imaging and carotid vessel segmentation in CT angiograms (CTAs) of patients with intracranial aneurysms are presented. The new algorithm accounts for distinct intensity properties of individual conjoined objects by combining fuzzy distance transform (FDT), a morphologic feature, with fuzzy connectivity, a topologic feature. The algorithm iteratively opens the two conjoined objects starting at large scales and progressing toward finer scales. Results of application of the method in separating arteries and veins in a physical cast phantom of a pig lung are presented. Accuracy of the algorithm is quantitatively evaluated in terms of sensitivity and specificity on patients' CTA data sets and its performance is compared with existing methods. Reproducibility of the algorithm is examined in terms of volumetric agreement between two users' carotid vessel segmentation results. Experimental results using this algorithm on patients' CTA data demonstrate a high average accuracy of 96.3% with 95.1% sensitivity and 97.5% specificity and a high reproducibility of 94.2% average agreement between segmentation results from two mutually independent users. Approximately, twenty-five to thirty-five user-specified seeds/separators are needed for each CTA data through a custom designed graphical interface requiring an average of thirty minutes to complete carotid vascular segmentation in a patient's CTA data set.

Predicting residues that participate in protein-protein interactions (PPI) helps to identify, which amino acids are located at the interface. In this paper, we show that the performance of the classical support vector machine (SVM) algorithm can further be improved with the use of a custom-designed fuzzy membership function, for the partner-specific PPI interface prediction problem. We evaluated the performances of both classical SVM and fuzzy SVM (F-SVM) on the PPI databases of three different model proteomes of Homo sapiens, Escherichia coli and Saccharomyces Cerevisiae and calculated the statistical significance of the developed F-SVM over classical SVM algorithm. We also compared our performance with the available state-of-the-art fuzzy methods in this domain and observed significant performance improvements. To predict interaction sites in protein complexes, local composition of amino acids together with their physico-chemical characteristics are used, where the F-SVM based prediction method exploits the membership function for each pair of sequence fragments. The average F-SVM performance (area under ROC curve) on the test samples in 10-fold cross validation experiment are measured as 77.07, 78.39, and 74.91 percent for the aforementioned organisms respectively. Performances on independent test sets are obtained as 72.09, 73.24 and 82.74 percent respectively. The software is available for free download from http://code.google.com/p/cmater-bioinfo.

BACKGROUND: We present here the recent update of AMS algorithm for identification of post-translational modification (PTM) sites in proteins based only on sequence information, using artificial neural network (ANN) method. The query protein sequence is dissected into overlapping short sequence segments. Ten different physicochemical features describe each amino acid; therefore nine residues long segment is represented as a point in a 90 dimensional space. The database of sequence segments with confirmed by experiments post-translational modification sites are used for training a set of ANNs. RESULTS: The efficiency of the classification for each type of modification and the prediction power of the method is estimated here using recall (sensitivity), precision values, the area under receiver operating characteristic (ROC) curves and leave-one-out tests (LOOCV). The significant differences in the performance for differently optimized neural networks are observed, yet the AMS 3.0 tool integrates those heterogeneous classification schemes into the single consensus scheme, and it is able to boost the precision and recall values independent of a PTM type in comparison with the currently available state-of-the art methods. CONCLUSIONS: The standalone version of AMS 3.0 presents an efficient way to identify post-translational modifications for whole proteomes. The training datasets, precompiled binaries for AMS 3.0 tool and the source code are available at http://code.google.com/p/automotifserver under the Apache 2.0 license scheme.

This paper introduces a novel BIST-quality metric termed as the FiF -- FoF (Fan-in-Factor & Fan-out-Factor) defined on FSM-states. Based on the FiF -- FoF analysis, an efficient scheme is presented that ensures all state codes appear with uniform likelyhood at the present state (PS) lines during the test phase. This results in higher fault efficiency in a BIST structure. Experimental results on MCNC benchmarks show that the scheme improves fault efficiency of sequential circuits significantly, with marginal area overhead.