Antibody Clustering Using a Machine Learning Pipeline that Fuses Genetic, Structural, and Physicochemical Properties.

Antibody V domain clustering is of paramount importance to a repertoire of immunology-related areas. Although several approaches have been proposed for antibody clustering, still no consensus has been reached. Numerous attempts use information from genes, protein sequences, 3D structures, and 3D surfaces in an effort to elucidate unknown action mechanisms directly related to their function and to either link them directly to diseases or drive the discovery of new medicines, such as antibody drug conjugates (ADC). Herein, we describe a new V domain antibody clustering method based on the comparison of the interaction sites between each antibody and its antigen. A more specific clustering analysis of the antibody's V domain was provided using deep learning and data mining techniques. The multidimensional information was extracted from the structural resolved antibodies when they were captured to interact with other proteins. The available 3D structures of protein antigen-antibody (Ag-Ab) interfaces contain information about how antibody V domains recognize antigens as well as about which amino acids are involved in the recognition. As such, the antibody surface holds information about antigens' folding that reside with the Ab-Ag interface residues and how they interact. In order to gain insight into the nature of such interactions, we propose a new simple philosophy to transform the conserved framework (fragment regions, complementarity-determining regions) of antibody V domain in a binary form using structural features of antibody-antigen interactions, toward identifying new antibody signatures in V domain binding activity. Finally, an advanced three-level hybrid classification scheme has been set for clustering antibodies in subgroups, which can combine the information from the protein sequences, the three-dimensional structures, and specific "key patterns" of recognized interactions. The clusters provide multilevel information about antibodies and antibody-antigen complexes.

Drugena: A Fully Automated Immunoinformatics Platform for the Design of Antibody-Drug Conjugates Against Neurodegenerative Diseases.

Antibodies are proteins that are the first line of defense in the adaptive immune response of vertebrates. Thereby, they are involved in a multitude of biochemical mechanisms and clinical manifestations with significant medical interest, such as autoimmunity, the regulation of infection, and cancer. An emerging field in antibody science that is of huge medicinal interest is the development of novel antibody-interacting drugs. Such entities are the antibody-drug conjugates (ADCs), which are a new type of targeted therapy, which consist of an antibody linked to a payload drug. Overall, the underlying principle of ADCs is the discerning delivery of a drug to a target, hoping to increase the potency of the original drug. Drugena suite is a pioneering platform that employs state-of-the-art computational biology methods in the fight against neurodegenerative diseases using ADCs. Drugena encompasses an up-to-date structural database of specialized antibodies for neurological disorders and the NCI database with over 96 million entities for the in silico development of ADCs. The pipeline of the Drugena suite has been divided into several steps and modules that are closely related with a synergistic fashion under a user-friendly graphical user interface.

M3G: maximum margin microarray gridding.

BACKGROUND: Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding. METHODS: In this paper we propose M3G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots. RESULTS: The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M3G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels. CONCLUSIONS: The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots.

Variable background active contour model for computer-aided delineation of nodules in thyroid ultrasound images.

This paper presents a computer-aided approach for nodule delineation in thyroid ultrasound (US) images. The developed algorithm is based on a novel active contour model, named variable background active contour (VBAC), and incorporates the advantages of the level set region-based active contour without edges (ACWE) model, offering noise robustness and the ability to delineate multiple nodules. Unlike the classic active contour models that are sensitive in the presence of intensity inhomogeneities, the proposed VBAC model considers information of variable background regions. VBAC has been evaluated on synthetic images, as well as on real thyroid US images. From the quantification of the results, two major impacts have been derived: 1) higher average accuracy in the delineation of hypoechoic thyroid nodules, which exceeds 91%; and 2) faster convergence when compared with the ACWE model.

MIGS-GPU: Microarray Image Gridding and Segmentation on the GPU.

Complementary DNA (cDNA) microarray is a powerful tool for simultaneously studying the expression level of thousands of genes. Nevertheless, the analysis of microarray images remains an arduous and challenging task due to the poor quality of the images that often suffer from noise, artifacts, and uneven background. In this study, the MIGS-GPU [Microarray Image Gridding and Segmentation on Graphics Processing Unit (GPU)] software for gridding and segmenting microarray images is presented. MIGS-GPU's computations are performed on the GPU by means of the compute unified device architecture (CUDA) in order to achieve fast performance and increase the utilization of available system resources. Evaluation on both real and synthetic cDNA microarray images showed that MIGS-GPU provides better performance than state-of-the-art alternatives, while the proposed GPU implementation achieves significantly lower computational times compared to the respective CPU approaches. Consequently, MIGS-GPU can be an advantageous and useful tool for biomedical laboratories, offering a user-friendly interface that requires minimum input in order to run.

An intelligent system for automatic detection of gastrointestinal adenomas in video endoscopy.

Today 95% of all gastrointestinal carcinomas are believed to arise from adenomas. The early detection of adenomas could prevent their evolution to cancer. A novel system for the support of the detection of adenomas in gastrointestinal video endoscopy is presented. Unlike other systems, it accepts standard low-resolution video input thus requiring less computational resources and facilitating both portability and the potential to be used in telemedicine applications. It combines intelligent processing techniques of SVMs and color-texture analysis methodologies into a sound pattern recognition framework. Concerning the system's accuracy this was measured using ROC analysis and found to exceed 94%.

A custom grow-cut based scheme for 2D-gel image segmentation.

This work introduces a novel method for the detection and segmentation of protein spots in 2D-gel images. A multi-thresholding approach is utilized for the detection of protein spots, while a custom grow-cult algorithm combined with region growing and morphological operators is used for the segmentation process. The experimental evaluation against four state-of-the-art 2D-gel image segmentation algorithms demonstrates the superiority of the proposed approach and indicates that it constitutes an advantageous and reliable solution for 2D-gel image analysis.

Grow-cut based automatic cDNA microarray image segmentation.

Complementary DNA (cDNA) microarray is a well-established tool for simultaneously studying the expression level of thousands of genes. Segmentation of microarray images is one of the main stages in a microarray experiment. However, it remains an arduous and challenging task due to the poor quality of images. Images suffer from noise, artifacts, and uneven background, while spots depicted on images can be poorly contrasted and deformed. In this paper, an original approach for the segmentation of cDNA microarray images is proposed. First, a preprocessing stage is applied in order to reduce the noise levels of the microarray image. Then, the grow-cut algorithm is applied separately to each spot location, employing an automated seed selection procedure, in order to locate the pixels belonging to spots. Application on datasets containing synthetic and real microarray images shows that the proposed algorithm performs better than other previously proposed methods. Moreover, in order to exploit the independence of the segmentation task for each separate spot location, both a multithreaded CPU and a graphics processing unit (GPU) implementation were evaluated.

Computer-aided tumor detection in endoscopic video using color wavelet features.

We present an approach to the detection of tumors in colonoscopic video. It is based on a new color feature extraction scheme to represent the different regions in the frame sequence. This scheme is built on the wavelet decomposition. The features named as color wavelet covariance (CWC) are based on the covariances of second-order textural measures and an optimum subset of them is proposed after the application of a selection algorithm. The proposed approach is supported by a linear discriminant analysis (LDA) procedure for the characterization of the image regions along the video frames. The whole methodology has been applied on real data sets of color colonoscopic videos. The performance in the detection of abnormal colonic regions corresponding to adenomatous polyps has been estimated high, reaching 97% specificity and 90% sensitivity.

An effective approach for detection and segmentation of protein spots on 2-D gel images.

Two-dimensional gel image analysis is widely recognized as a particularly challenging and arduous process in proteomics field. The detection and segmentation of protein spots are two significant stages of this process as they can considerably affect the final biological conclusions of a proteomic experiment. The available techniques and commercial software packages deal with the existing challenges of 2-D gel images in a different degree of success. Furthermore, they require extensive human intervention which not only limits the throughput but unavoidably questions the objectivity and reproducibility of results. This paper introduces a novel approach for the detection and segmentation of protein spots on 2-D gel images. The proposed approach is based on 2-D image histograms as well as on 3-D spots morphology. It is automatic and capable to deal with the most common deficiencies of existing software programs and techniques in an effective manner. Experimental evaluation includes tests on several real and synthetic 2-D gel images produced by different technology setups, containing a total of ∼ 21,400 spots. Furthermore, the proposed approach has been compared with two commercial software packages as well as with two state-of-the-art techniques. Results have demonstrated the effectiveness of the proposed approach and its superiority against compared software packages and techniques.

Self-parameterized active contours based on regional edge structure for medical image segmentation.

This work introduces a novel framework for unsupervised parameterization of region-based active contour regularization and data fidelity terms, which is applied for medical image segmentation. The work aims to relieve MDs from the laborious, time-consuming task of empirical parameterization and bolster the objectivity of the segmentation results. The proposed framework is inspired by an observed isomorphism between the eigenvalues of structure tensors and active contour parameters. Both may act as descriptors of the orientation coherence in regions containing edges. The experimental results demonstrate that the proposed framework maintains a high segmentation quality without the need of trial-and-error parameter adjustment.

Automated adjustment of region-based active contour parameters using local image geometry.

A principled method for active contour (AC) parameterization remains a challenging issue in segmentation research, with a potential impact on the quality, objectivity, and robustness of the segmentation results. This paper introduces a novel framework for automated adjustment of region-based AC regularization and data fidelity parameters. Motivated by an isomorphism between the weighting factors of AC energy terms and the eigenvalues of structure tensors, we encode local geometry information by mining the orientation coherence in edge regions. In this light, the AC is repelled from regions of randomly oriented edges and guided toward structured edge regions. Experiments are performed on four state-of-the-art AC models, which are automatically adjusted and applied on benchmark datasets of natural, textured and biomedical images and two image restoration models. The experimental results demonstrate that the obtained segmentation quality is comparable to the one obtained by empirical parameter adjustment, without the cumbersome and time-consuming process of trial and error.

ΤND: a thyroid nodule detection system for analysis of ultrasound images and videos.

In this paper, we present a computer-aided-diagnosis (CAD) system prototype, named TND (Thyroid Nodule Detector), for the detection of nodular tissue in ultrasound (US) thyroid images and videos acquired during thyroid US examinations. The proposed system incorporates an original methodology that involves a novel algorithm for automatic definition of the boundaries of the thyroid gland, and a novel approach for the extraction of noise resilient image features effectively representing the textural and the echogenic properties of the thyroid tissue. Through extensive experimental evaluation on real thyroid US data, its accuracy in thyroid nodule detection has been estimated to exceed 95%. These results attest to the feasibility of the clinical application of TND, for the provision of a second more objective opinion to the radiologists by exploiting image evidences.

A computer-based technique for automated spot detection in proteomics images.

This paper introduces a novel computer-based technique for automated detection of protein spots in proteomics images. The proposed technique is based on the localization of regional intensity maxima associated with protein spots and is formulated so as to ignore rectangular-shaped streaks, minimize the detection of false negatives, and allow the detection of multiple overlapping spots. Regional intensity constraints are imposed on the localized maxima in order to cope with the presence of noise and artifacts. The experimental evaluation of the proposed technique on real proteomics images demonstrates that it: 1) achieves a predictive value ( PV) and detection sensitivity (DS ) which exceed 90%; 2) outperforms Melanie software package in terms of PV , specificity, and DS; 3) ignores artifacts; 4) distinguishes multiple overlapping spots; 5) locates spots within streaks; and 6) is automated and efficient.

3-D spot modeling for automatic segmentation of cDNA microarray images.

Spot segmentation--the second essential stage of cDNA microarray image analysis--constitutes a challenging process. At present, several up-to-date spot-segmentation techniques or software programs--proposed in the literature--are often characterized as "automatic." On the contrary, they are in effect not fully automatic since they require human intervention in order to specify mandatory input parameters or to correct their results. Human intervention, however, can inevitably modify the actual results of the cDNA microarray experiment and lead to erroneous biological conclusions. Therefore, the development of an automated spot-segmentation process becomes of exceptional interest. In this paper, an original and fully automatic approach to accurately segmenting the spots in a cDNA microarray image is presented. In order for the segmentation to be accomplished, each real spot of the cDNA microarray image is represented in a three-dimensional (3-D) space by a 3-D spot model. Each 3-D spot model is determined via an optimization problem, which is solved by using a genetic algorithm. The segmentation of real spots is conducted by drawing the contours of their 3-D spot models. The proposed method has been compared with various published and established techniques, using several synthetic and real cDNA microarray images that contain thousands of spots. The outcome has shown that the proposed method outperforms prevalent existing techniques. It is also noise resistant and yields excellent results even under adverse conditions such as the appearance of spots of various sizes and shapes.

Unsupervised SVM-based gridding for DNA microarray images.

This paper presents a novel method for unsupervised DNA microarray gridding based on support vector machines (SVMs). Each spot is a small region on the microarray surface where chains of known DNA sequences are attached. The goal of microarray gridding is the separation of the spots into distinct cells. The positions of the spots on a DNA microarray image are first detected using image analysis operations and then a set of soft-margin linear SVM classifiers is used to estimate the optimal layout of the grid lines in the image. Each grid line is the separating line produced by one of the SVM classifiers, which maximizes the margin between two consecutive rows or columns of spots. The classifiers are trained using the spot locations as training vectors. The proposed method was evaluated on reference microarray images containing more than two million spots in total. The results illustrate its robustness in the presence of artifacts, noise and weakly expressed spots, as well as image rotation. The comparison to state of the art methods for microarray gridding reveals the superior performance of the proposed method. In 96.4% of the cases, the spots reside completely inside their respective grid cells.

Fusion of fuzzy statistical distributions for classification of thyroid ultrasound patterns.

OBJECTIVE: This paper proposes a novel approach for thyroid ultrasound pattern representation. Considering that texture and echogenicity are correlated with thyroid malignancy, the proposed approach encodes these sonographic features via a noise-resistant representation. This representation is suitable for the discrimination of nodules of high malignancy risk from normal thyroid parenchyma. MATERIALS AND METHODS: The material used in this study includes a total of 250 thyroid ultrasound patterns obtained from 75 patients in Greece. The patterns are represented by fused vectors of fuzzy features. Ultrasound texture is represented by fuzzy local binary patterns, whereas echogenicity is represented by fuzzy intensity histograms. The encoded thyroid ultrasound patterns are discriminated by support vector classifiers. RESULTS: The proposed approach was comprehensively evaluated using receiver operating characteristics (ROCs). The results show that the proposed fusion scheme outperforms previous thyroid ultrasound pattern representation methods proposed in the literature. The best classification accuracy was obtained with a polynomial kernel support vector machine, and reached 97.5% as estimated by the area under the ROC curve. CONCLUSIONS: The fusion of fuzzy local binary patterns and fuzzy grey-level histogram features is more effective than the state of the art approaches for the representation of thyroid ultrasound patterns and can be effectively utilized for the detection of nodules of high malignancy risk in the context of an intelligent medical system.

A computer-aided system for malignancy risk assessment of nodules in thyroid US images based on boundary features.

In this paper, a novel computer-based approach is proposed for malignancy risk assessment of thyroid nodules in ultrasound images. The proposed approach is based on boundary features and is motivated by the correlation which has been addressed in medical literature between nodule boundary irregularity and malignancy risk. In addition, local echogenicity variance is utilized so as to incorporate information associated with local echogenicity distribution within nodule boundary neighborhood. Such information is valuable for the discrimination of high-risk nodules with blurred boundaries from medium-risk nodules with regular boundaries. Analysis of variance is performed, indicating that each boundary feature under study provides statistically significant information for the discrimination of thyroid nodules in ultrasound images, in terms of malignancy risk. k-nearest neighbor and support vector machine classifiers are employed for the classification tasks, utilizing feature vectors derived from all combinations of features under study. The classification results are evaluated with the use of the receiver operating characteristic. It is derived that the proposed approach is capable of discriminating between medium-risk and high-risk nodules, obtaining an area under curve, which reaches 0.95.

Active contours guided by echogenicity and texture for delineation of thyroid nodules in ultrasound images.

Thyroid nodules are solid or cystic lumps formed in the thyroid gland and may be caused by a variety of thyroid disorders. This paper presents a novel active contour model for precise delineation of thyroid nodules of various shapes according to their echogenicity and texture, as displayed in ultrasound (US) images. The proposed model, named joint echogenicity-texture (JET), is based on a modified Mumford-Shah functional that, in addition to regional image intensity, incorporates statistical texture information encoded by feature distributions. The distributions are aggregated within the functional through new log-likelihood goodness-of-fit terms. The JET model requires only a rough region of interest within the thyroid gland as input and automatically proceeds with precise delineation of the nodules, revealing their shape and size. The performance of the JET model was validated on a range of US images displaying hypoechoic and isoechoic nodules of various shapes. The quantification of the results shows that the JET model: 1) provides precise delineations of thyroid nodules as compared to "ground truth" delineations obtained by experts and 2) copes with the limitations of the previous thyroid US delineation approaches as it is capable of delineating thyroid nodules regardless of their echogenicity or shape.

An original genetic approach to the fully automatic gridding of microarray images.

Gridding microarray images remains, at present, a major bottleneck. It requires human intervention which causes variations of the gene expression results. In this paper, an original and fully automatic approach for accurately locating a distorted grid structure in a microarray image is presented. The gridding process is expressed as an optimization problem which is solved by using a genetic algorithm (GA). The GA determines the line-segments constituting the grid structure. The proposed method has been compared with existing software tools as well as with a recently published technique. For this purpose, several real and artificial microarray images containing more than one million spots have been used. The outcome has shown that the accuracy of the proposed method achieves the high value of 94% and it outperforms the existing approaches. It is also noise-resistant and yields excellent results even under adverse conditions such as arbitrary grid rotations, and the appearance of various spot sizes.