Alignment Block Research Articles

AA-RGTCN: reciprocal global temporal convolution network with adaptive alignment for video-based person re-identification.

Person re-identification(Re-ID) aims to retrieve pedestrians under different cameras. Compared with image-based Re-ID, video-based Re-ID extracts features from video sequences that contain both spatial features and temporal features. Existing methods usually focus on the most attractive image parts, and this will lead to redundant spatial description and insufficient temporal description. Other methods that take temporal clues into consideration usually ignore misalignment between frames and only focus on a fixed length of one given sequence. In this study, we proposed a Reciprocal Global Temporal Convolution Network with Adaptive Alignment(AA-RGTCN). The structure could address the drawback of misalignment between frames and model discriminative temporal representation. Specifically, the Adaptive Alignment block is designed to shift each frame adaptively to its best position for temporal modeling. Then, we proposed the Reciprocal Global Temporal Convolution Network to model robust temporal features across different time intervals along both normal and inverted time order. The experimental results show that our AA-RGTCN can achieve 85.9% mAP and 91.0% Rank-1 on MARS, 90.6% Rank-1 on iLIDS-VID, and 96.6% Rank-1 on PRID-2011, indicating we could gain better performance than other state-of-the-art approaches.

A layout framework for genome-wide multiple sequence alignment graphs.

Sequence alignments are often used to analyze genomic data. However, such alignments are often only calculated and compared on small sequence intervals for analysis purposes. When comparing longer sequences, these are usually divided into shorter sequence intervals for better alignment results. This usually means that the order context of the original sequence is lost. To prevent this, it is possible to use a graph structure to represent the order of the original sequence on the alignment blocks. The visualization of these graph structures can provide insights into the structural variations of genomes in a semi-global context. In this paper, we propose a new graph drawing framework for representing gMSA data. We produce a hierarchical graph layout that supports the comparative analysis of genomes. Based on a reference, the differences and similarities of the different genome orders are visualized. In this work, we present a complete graph drawing framework for gMSA graphs together with the respective algorithms for each of the steps. Additionally, we provide a prototype and an example data set for analyzing gMSA graphs. Based on this data set, we demonstrate the functionalities of the framework using two examples.

FAMF-Net: Feature Alignment Mutual Attention Fusion with Region Awareness for Breast Cancer Diagnosis via Imbalanced Data.

Automatic and accurate classification of breast cancer in multimodal ultrasound images is crucial to improve patients' diagnosis and treatment effect and save medical resources. Methodologically, the fusion of multimodal ultrasound images often encounters challenges such as misalignment, limited utilization of complementary information, poor interpretability in feature fusion, and imbalances in sample categories. To solve these problems, we propose a feature alignment mutual attention fusion method (FAMF-Net), which consists of a region awareness alignment (RAA) block, a mutual attention fusion (MAF) block, and a reinforcement learning-based dynamic optimization strategy(RDO). Specifically, RAA achieves region awareness through class activation mapping and performs translation transformation to achieve feature alignment. When MAF utilizes a mutual attention mechanism for feature interaction fusion, it mines edge and color features separately in B-mode and shear wave elastography images, enhancing the complementarity of features and improving interpretability. Finally, RDO uses the distribution of samples and prediction probabilities during training as the state of reinforcement learning to dynamically optimize the weights of the loss function, thereby solving the problem of class imbalance. The experimental results based on our clinically obtained dataset demonstrate the effectiveness of the proposed method. Our code will be available at: https://github.com/Magnety/Multi_modal_Image.

Cross-Attention-Guided Feature Alignment Network for Road Crack Detection

Road crack detection is one of the important issues in the field of traffic safety and urban planning. Currently, road damage varies in type and scale, and often has different sizes and depths, making the detection task more challenging. To address this problem, we propose a Cross-Attention-guided Feature Alignment Network (CAFANet) for extracting and integrating multi-scale features of road damage. Firstly, we use a dual-branch visual encoder model with the same structure but different patch sizes (one large patch and one small patch) to extract multi-level damage features. We utilize a Cross-Layer Interaction (CLI) module to establish interaction between the corresponding layers of the two branches, combining their unique feature extraction capability and contextual understanding. Secondly, we employ a Feature Alignment Block (FAB) to align the features from different levels or branches in terms of semantics and spatial aspects, which significantly improves the CAFANet’s perception of the damage regions, reduces background interference, and achieves more precise detection and segmentation of damage. Finally, we adopt multi-layer convolutional segmentation heads to obtain high-resolution feature maps. To validate the effectiveness of our approach, we conduct experiments on the public CRACK500 dataset and compare it with other mainstream methods. Experimental results demonstrate that CAFANet achieves excellent performance in road crack detection tasks, which exhibits significant improvements in terms of F1 score and accuracy, with an F1 score of 73.22% and an accuracy of 96.78%.

Learning Motion-Robust Remote Photoplethysmography through Arbitrary Resolution Videos

Remote photoplethysmography (rPPG) enables non-contact heart rate (HR) estimation from facial videos which gives significant convenience compared with traditional contact-based measurements. In the real-world long-term health monitoring scenario, the distance of the participants and their head movements usually vary by time, resulting in the inaccurate rPPG measurement due to the varying face resolution and complex motion artifacts. Different from the previous rPPG models designed for a constant distance between camera and participants, in this paper, we propose two plug-and-play blocks (i.e., physiological signal feature extraction block (PFE) and temporal face alignment block (TFA)) to alleviate the degradation of changing distance and head motion. On one side, guided with representative-area information, PFE adaptively encodes the arbitrary resolution facial frames to the fixed-resolution facial structure features. On the other side, leveraging the estimated optical flow, TFA is able to counteract the rPPG signal confusion caused by the head movement thus benefit the motion-robust rPPG signal recovery. Besides, we also train the model with a cross-resolution constraint using a two-stream dual-resolution framework, which further helps PFE learn resolution-robust facial rPPG features. Extensive experiments on three benchmark datasets (UBFC-rPPG, COHFACE and PURE) demonstrate the superior performance of the proposed method. One highlight is that with PFE and TFA, the off-the-shelf spatio-temporal rPPG models can predict more robust rPPG signals under both varying face resolution and severe head movement scenarios. The codes are available at https://github.com/LJWGIT/Arbitrary_Resolution_rPPG.

Transferring Image-CLIP to Video-Text Retrieval via Temporal Relations

We present a novel network to transfer the image-language pre-trained model to video-text retrieval in an end-to-end manner. Leading approaches in the domain of video-and-language learning try to distill the spatio-temporal video features and multi-modal interaction between videos and language from a large-scale video-text dataset. Differently, we leverage the pre-trained image-language model, and simplify it as a two-stage framework including co-learning of image and text, and enhancing temporal relations between video frames and video-text respectively. Specifically, based on the spatial semantics captured by Contrastive Language-Image Pre-training (CLIP) model, our model involves a Temporal Difference Block (TDB) to capture motions at fine temporal video frames, and a Temporal Alignment Block (TAB) to re-align the tokens of video clips and phrases and enhance the cross-modal correlation. These two temporal blocks efficiently realize video-language learning and enable the proposed model to scale well on comparatively small datasets. We conduct extensive experimental studies including ablation studies and comparisons with existing SOTA methods, and our proposed approach outperforms them on the popularly-employed text-to-video and video-to-text retrieval benchmarks, including MSR-VTT, MSVD, LSMDC, and VATEX.

Deep learning architecture with transformer and semantic field alignment for voxel-level dose prediction on brain tumors.

The use of convolution neural networks (CNN) to accurately predict dose distributions can accelerate intensity-modulated radiation therapy (IMRT) planning. The purpose of our study is to develop a novel deep learning architecture for precise voxel-level dose prediction on brain tumors. A dataset of 120 patients with brain tumors is built for the retrospective study. The dose distributions are predicted by a designed end-to-end model called TS-Net, in which the transformer encoder module is utilized to obtain abundant global features by learning long-range correlations of the input sequence. In addition, semantic field alignment (SFA) block is proposed in decoding path to ensure effective propagation of strong semantic information from deep to shallow. Five images from different channels are fed into the architecture, including a computed tomography (CT) image, a planning target volumes (PTV) image, an organs-at-risk (OARs) image, a beam configuration image, and a distance image, and the predicted dose distributions are taken as outputs. We use different evaluation metrics to evaluate the performance of the model and discuss the role of the auxiliary beam configuration information provided by non-modulated dose distributions. The TS-Net prediction accuracies in terms of mean absolute error (MAE) are 2.98% for PTV, 7.19% for brainstem, 1.88% for left len, 2.48% for right len, 9.61% for left optic nerve, 9.10% for right optic nerve, 8.99% for optic chiasma, and 8.28% for pituitary. There is no statistically significant difference between the predicted results and clinical dose distributions for clinical indexes including homogeneity index (HI), D50, and D95 for PTV; V40, mean dose, and max dose for OARs; except for conformation index (CI) and D2 for PTV. The model has dice similarity coefficient (DSC) values of above 0.91 for most isodose volumes, clearly outperforming HD U-Net, and being slightly better than U-Net and DCNN. The proposed TS-Net with beam configuration input can achieve accurate voxel-level dose prediction for brain tumors, and is a usable tool for improving the efficiency and quality of radiotherapy.

AANet: an attention-based alignment semantic segmentation network for high spatial resolution remote sensing images

ABSTRACT In this paper, we present an efficient network to tackle three critical problems in high spatial resolution (HSR) remote sensing image segmentation: feature misalignment, insufficient contextual information extraction and various class imbalance issues. In detail, we propose a novel Feature Alignment Block (FAB) to suppress misalignment issues with the guide of an anchor map. Further, to extract sufficient information, we design a Contextual Augmentation Block (CAB) to augment features of different semantic levels. Finally, we present an Annealing Online Hard Example Mining (AOHEM) strategy to handle the various class imbalance issues with a view to dynamically adjust the focus of the network. We apply the above proposed designs to FPN to form our Attention-based Alignment Network (AANet). Experimental results demonstrate that the proposed method achieves promising results on the challenging iSAID and Vaihingen datasets with a better trade-off between accuracy and complexity.

$\text{A}^{3}$Seg: An Annealing Augmented and Aligned Semantic Segmentation Network for High Spatial Resolution Remote Sensing Images

Semantic segmentation of high spatial resolution (HSR) remote sensing images (RSIs) plays an important role in many applications. However, HSR RSIs have significantly larger spatial sizes than typical natural images, which results in fewer valuable samples when training models. In addition, fusing multiscale features is the key step in obtaining features with strong semantic and high spatial information. However, current feature fusion methods are too straightforward to address misalignment issues. To handle these two problems, we propose an annealing augmented and aligned segmentation network, named <inline-formula><tex-math notation="LaTeX">$\text{A}^{3}$</tex-math></inline-formula>Seg. Specifically, we propose an annealing online hard example mining (AOHEM) strategy to automatically select more valuable samples during the training stage. Based on AOHEM, a contextual augmentation block is proposed to extract sufficient contextual information using three different attention mechanisms in consideration of three different feature properties. Finally, we propose a novel feature alignment block to fuse features at different levels by alignment with the guidance of a salient feature. Experimental results on three different HSR RSIs datasets demonstrate that the proposed method outperforms the state-of-the-art general semantic segmentation methods with a better tradeoff between accuracy and complexity.

Feature-Aligned Single-Stage Rotation Object Detection With Continuous Boundary

Recently, rotation detection has gained much attention and shown its potential for accurate localization in remote sensing scenes. However, the objects in remote sensing images have a variety of directions, sizes, and aspect ratios, which makes it difficult to locate and classify objects. Therefore, the object detection task is still facing great challenges in the field of remote sensing. In this paper, we propose a novel single-stage detector, which includes feature alignment block (FAB), double regression branches (DRBs), and a circumcircle rotation box (CRB). FAB utilizes the deformable convolution to flexibly obtain the features of different aspect ratio objects, and aligns the regression features with the corresponding classification features through fusion. Consequently, it can make the extracted feature information have stronger discrimination, which is conducive to improving the object position and classification accuracy. DRBs consist of a main regression branch and an auxiliary regression branch. The main regression branch is used to fine-tune the result of the auxiliary regression branch to obtain a more accurate regression result. Moreover, to eliminate the boundary discontinuity problem faced by regression-based detectors, we construct CRB through designing an angle of rotation and the radius of the circumcircle. Extensive experiments and visual analysis are conducted on three public benchmarks, i.e., DOTA, HRSC2016, and DIOR-R. The results show that our proposed method has excellent localization and classification performance for oriented objects on all the representative datasets.

Power Transformations and Feature Alignment Guided Network for SAR Ship Detection

Due to the capacity of full-time and full-weather working, synthetic aperture radar (SAR) images have been frequently applied to ship detection. However, the interference of speckle noise and shores poses enormous challenges to the accuracy of detection. Extracting multi-scale features is regarded as a good way to detect ships of different sizes, but features at different scales are not strictly aligned, which may further affect the detection results. Therefore, this letter proposes an anchor-free method, namely power transformations and feature alignment guided network (Pow-FAN), to solve the above problems. In Pow-FAN, we first utilize a power-based convolution block called PCB to extract features, which can suppress speckle noise and shores and enhance the ship targets. Furthermore, a novel feature alignment block named FAB is put forward to avoid the dislocation problems when integrating features of different scales. Compared with other state-of-the-art methods, Pow-FAN can achieve competitive detection results on the SAR ship detection dataset (SSDD) and the High-Resolution SAR Images Dataset (HRSID). The ablation study and discussion on the backbone and different situations further demonstrate the superiority of our network structure.

Band offset engineering at C2N/MSe2 (M = Mo, W) interfaces.

Stacking layered two-dimensional materials in a type-II band alignment block has provided a high-performance method in photocatalytic water-splitting technology. The key parameters in such heterostructure configurations are the valence and conduction band offsets at the interface, which determine the device performance. Here, based on density functional theory calculations, the bandgap and band offsets at C2N/MSe2 (M = Mo, W) interfaces have been engineered. The main findings demonstrate that the C2N monolayer interacts with both MoSe2 and WSe2 monolayers through weak van der Waals interactions. These heterostructures possess a narrower indirect bandgap and a typical type-II heterostructure feature, being suitable for promoting the separation of photogenerated electron–hole pairs. The calculated Gibbs free energy of hydrogen adsorption demonstrates a reduction in the overpotential, towards the hydrogen evolution reaction, upon forming heterostructures. To further tune the bandgap values and band offsets of heterostructures, the external perturbations are included through a vertical strain and finite electric field. It is found that both the vertical strain and electric field strongly modulate the bandgap values and the magnitude of the band offsets, while the typical type-II band alignment remains preserved. It is noticeable that the band offset magnitudes of the C2N/MoSe2 and C2N/WSe2 heterostructures are more sensitive to an external electric field than to a vertical interlayer strain.

Scalable multiple whole-genome alignment and locally collinear block construction with SibeliaZ

Multiple whole-genome alignment is a challenging problem in bioinformatics. Despite many successes, current methods are not able to keep up with the growing number, length, and complexity of assembled genomes, especially when computational resources are limited. Approaches based on compacted de Bruijn graphs to identify and extend anchors into locally collinear blocks have potential for scalability, but current methods do not scale to mammalian genomes. We present an algorithm, SibeliaZ-LCB, for identifying collinear blocks in closely related genomes based on analysis of the de Bruijn graph. We further incorporate this into a multiple whole-genome alignment pipeline called SibeliaZ. SibeliaZ shows run-time improvements over other methods while maintaining accuracy. On sixteen recently-assembled strains of mice, SibeliaZ runs in under 16 hours on a single machine, while other tools did not run to completion for eight mice within a week. SibeliaZ makes a significant step towards improving scalability of multiple whole-genome alignment and collinear block reconstruction algorithms on a single machine.

HiC-Hiker: a probabilistic model to determine contig orientation in chromosome-length scaffolds with Hi-C.

Motivation De novo assembly of reference-quality genomes used to require enormously laborious tasks. In particular, it is extremely time-consuming to build genome markers for ordering assembled contigs along chromosomes; thus, they are only available for well-established model organisms. To resolve this issue, recent studies demonstrated that Hi-C could be a powerful and cost-effective means to output chromosome-length scaffolds for non-model species with no genome marker resources, because the Hi-C contact frequency between a pair of two loci can be a good estimator of their genomic distance, even if there is a large gap between them. Indeed, state-of-the-art methods such as 3D-DNA are now widely used for locating contigs in chromosomes. However, it remains challenging to reduce errors in contig orientation because shorter contigs have fewer contacts with their neighboring contigs. These orientation errors lower the accuracy of gene prediction, read alignment, and synteny block estimation in comparative genomics.ResultsTo reduce these contig orientation errors, we propose a new algorithm, named HiC-Hiker, which has a firm grounding in probabilistic theory, rigorously models Hi-C contacts across contigs, and effectively infers the most probable orientations via the Viterbi algorithm. We compared HiC-Hiker and 3D-DNA using human and worm genome contigs generated from short reads, evaluated their performances, and observed a remarkable reduction in the contig orientation error rate from 4.3% (3D-DNA) to 1.7% (HiC-Hiker). Our algorithm can consider long-range information between distal contigs and precisely estimates Hi-C read contact probabilities among contigs, which may also be useful for determining the ordering of contigs.Availability and implementationHiC-Hiker is freely available at: https://github.com/ryought/hic_hiker.

Genetic relationships and polyploid origins in the Lippia alba complex

Plant genomes vary in size and complexity due in part to polyploidization. Latitudinal analyses of polyploidy are biased toward floras of temperate regions, with much less research done in the tropics. Lippia alba has been described as a tropical polyploid complex with diploid, triploid, tetraploid, and hexaploid accessions. However, no data regarding relationships among the ploidal levels and their origins have been reported. Our goals are to clarify the relationships among accessions of Lippia alba and the origins of each ploidal level. We investigated 98 samples representing all five geographical regions of Brazil and all ploidal levels using microsatellite (SSR) allelic variation and DNA sequences of ITS and trnL-F. Nine morphological structures were analyzed from 33 herbarium samples, and the chemical compounds of 78 accessions were analyzed by GC-MS. Genetic distance analysis, the alignment block pattern, as well as RAxML and Bayesian trees showed that accessions grouped by ploidal level. The triploids form a well-defined group that originated from a single group of diploids. The tetraploids and hexaploid grouped together in SSR and trnL-F analyses. The recovered groups agree with chemical data and morphology. The accessions grouped by ploidal level. Only one origin of triploids from a single group of diploids was observed. The tetraploid origin is uncertain; however, it appears to have contributed to the origin of the hexaploid. This framework reveals linkages among the ploidal levels, providing new insights into the evolution of a polyploid complex of tropical plants.

Insight into Evolution and Conservation Patterns of B1-Subfamily Members of GPCR.

The diverse, evolutionary architectures of proteins can be regarded as molecular fossils, tracing a historical path that marks important milestones across life. The B1-subfamily of GPCRs (G-protein-coupled receptors) are medically significant proteins that comprise 15 transmembrane receptor proteins in Homo sapiens. These proteins control the intracellular concentration of cyclic AMP as well as various vital processes in the body. However, little is known about the evolutionary correlation and conservational blueprint of this GPCR subfamily. We performed a comprehensive analysis to understand the evolutionary architecture among 13 members of the B1-subfamily. Multiple sequence alignment analysis exhibited six multiple sequence aligned blocks and five highly aligned blocks. Molecular phylogenetics indicated that CRHR1 and CRHR2 share a typical ancestral relationship and are siblings in 100% bootstrap replications with a total of 24 nodes observed in the cladogram. CRHR2 has the maximum number of extremely conserved amino acids followed by ADCYAP1R1. The longest continuous number sequence logos (74) were found between sequence location 349 and 423, and consequently, the maximum and minimum logo height recorded was 3.6 bits and 0.18 bits, respectively. Finally, to understand the model and pattern of evolutionary relatedness, the conservation blueprint, and the diversification among the members of a protein family, GPCR distribution from several species throughout the animal kingdom was analysed. Together, the study provides an evolutionary insight and offers a rapid method to explore the potential of depicting the evolutionary relationship, conservation blueprint, and diversification among the B1-subfamily of GPCRs using bioinformatics, algorithm analysis, and mathematical models.Electronic supplementary materialThe online version of this article (10.1007/s10989-020-10043-5) contains supplementary material, which is available to authorized users.

Diversity, structure, and synteny of the cutinase gene of Colletotrichum species.

Colletotrichum species complexes are among the top 10 economically important fungal plant pathogens worldwide because they can infect climacteric and nonclimacteric fruit at the pre and/or postharvest stages. C. truncatum is the major pathogen responsible for anthracnose of green and red bell pepper fruit worldwide. C. brevisporum was recently reported to be a minor pathogen of red bell pepper fruit in Trinidad, but has recently been reported as pathogenic to other host species in other countries. The ability of these phytopathogens to produce and secrete cutinase is required for dismantling the cuticle of the host plant and, therefore, crucial to the necrotrophic phase of their infection strategy. In vitro bioassays using different lipid substrates confirmed the ability of C. truncatum and C. brevisporum isolates from green and red bell peppers to secrete cutinase. The diversity, structure and organization and synteny of the cutinase gene were determined among different Colletotrichum species. Cluster analysis indicated a low level of nucleotide variation among C. truncatum sequences. Nucleotide sequences of C. brevisporum were more related to C. truncatum cutinase nucleotide sequences than to C. gloeosporioides. Cluster patterns coincided with haplotype and there was evidence of significant positive selection with no recombination signatures. The structure of the cutinase gene included two exons with one intervening intron and, therefore, one splice variant. Although amino acid sequences were highly conserved among C. truncatum isolates, diversity “hot spots” were revealed when the 66‐amino acid coding region of 200 fungal species was compared. Twenty cutinase orthologues were detected among different fungal species, whose common ancestor is Pezizomycotina and it is purported that these orthologues arose through a single gene duplication event prior to speciation. The cutinase domain was retained both in structure and arrangement among 34 different Colletotrichum species. The order of aligned genomic blocks between species and the arrangement of flanking protein domains were also conserved and shared for those domains immediately located at the N‐ and C‐terminus of the cutinase domain. Among these were an RNA recognition motif, translation elongation factor, signal peptide, pentatricopeptide repeat, and Hsp70 family of chaperone proteins, all of which support the expression of the cutinase gene. The findings of this study are important to understanding the evolution of the cutinase gene in C. truncatum as a key component of the biotrophic–necrotrophic switch which may be useful in developing gene‐targeting strategies to decrease the pathogenic potential of Colletotrichum species.

Aligning molecular sequences using hybrid bioinspired algorithm in GPU

To explicate the functionality of the basic cell, there is a need for the study of bioinformatics. To better understand the structural and functional information of molecules, sequence analysis is considered as the root domain. In this, aligning the sequence is the first step, an NP-complete problem like all biological problems. Owing to the increased molecular data in biology, there is a demand for the development of efficient approaches to this sequence alignment problem. From the study it is concluded that there is trade-off between accuracy and computational time. Focusing on the latter in this paper, a new parallel hybridised bio-inspired approach (PGWOGO) is proposed without sacrificing the accuracy. A grey wolf optimiser technique is hybridised with the genetic operators and the parallel phases are implemented in Quadro 4,000 graphics processing unit. A new crossover and mutation operator's namely horizontal crossover and local gaps shuffle mutation operator between aligned blocks are employed. The performance of proposed algorithm is evaluated using the cells update per second (CUPS) and compared with the state-of-the-art techniques. The results show that the proposed algorithm yields better alignment than other techniques.

Aligning molecular sequences using hybrid bioinspired algorithm in GPU

To explicate the functionality of the basic cell, there is a need for the study of bioinformatics. To better understand the structural and functional information of molecules, sequence analysis is considered as the root domain. In this, aligning the sequence is the first step, an NP-complete problem like all biological problems. Owing to the increased molecular data in biology, there is a demand for the development of efficient approaches to this sequence alignment problem. From the study it is concluded that there is trade-off between accuracy and computational time. Focusing on the latter in this paper, a new parallel hybridised bio-inspired approach (PGWOGO) is proposed without sacrificing the accuracy. A grey wolf optimiser technique is hybridised with the genetic operators and the parallel phases are implemented in Quadro 4,000 graphics processing unit. A new crossover and mutation operator's namely horizontal crossover and local gaps shuffle mutation operator between aligned blocks are employed. The performance of proposed algorithm is evaluated using the cells update per second (CUPS) and compared with the state-of-the-art techniques. The results show that the proposed algorithm yields better alignment than other techniques.

Amino acid substitution scoring matrices specific to intrinsically disordered regions in proteins

An amino acid substitution scoring matrix encapsulates the rates at which various amino acid residues in proteins are substituted by other amino acid residues, over time. Database search methods make use of substitution scoring matrices to identify sequences with homologous relationships. However, widely used substitution scoring matrices, such as BLOSUM series, have been developed using aligned blocks that are mostly devoid of disordered regions in proteins. Hence, these substitution-scoring matrices are mostly inappropriate for homology searches involving proteins enriched with disordered regions as the disordered regions have distinct amino acid compositional bias, and therefore expected to have undergone amino acid substitutions that are distinct from those in the ordered regions. We, therefore, developed a novel series of substitution scoring matrices referred to as EDSSMat by exclusively considering the substitution frequencies of amino acids in the disordered regions of the eukaryotic proteins. The newly developed matrices were tested for their ability to detect homologs of proteins enriched with disordered regions by means of SSEARCH tool. The results unequivocally demonstrate that EDSSMat matrices detect more number of homologs than the widely used BLOSUM, PAM and other standard matrices, indicating their utility value for homology searches of intrinsically disordered proteins.