MOTIVATION: Cancer remains a leading cause of mortality globally. Recent improvements in survival have been facilitated by the development of targeted and less toxic immunotherapies, such as chimeric antigen receptor (CAR)-T cells and antibody-drug conjugates (ADCs). These therapies, effective in treating both pediatric and adult patients with solid and hematological malignancies, rely on the identification of cancer-specific surface protein targets. While technologies like RNA sequencing and proteomics exist to survey these targets, identifying optimal targets for immunotherapies remains a challenge in the field. RESULTS: To address this challenge, we developed ImmunoTar, a novel computational tool designed to systematically prioritize candidate immunotherapeutic targets. ImmunoTar integrates user-provided RNA-sequencing or proteomics data with quantitative features from multiple public databases, selected based on predefined criteria, to generate a score representing the gene's suitability as an immunotherapeutic target. We validated ImmunoTar using three distinct cancer datasets, demonstrating its effectiveness in identifying both known and novel targets across various cancer phenotypes. By compiling diverse data into a unified platform, ImmunoTar enables comprehensive evaluation of surface proteins, streamlining target identification and empowering researchers to efficiently allocate resources, thereby accelerating the development of effective cancer immunotherapies. AVAILABILITY AND IMPLEMENTATION: Code and data to run and test ImmunoTar are available at https://github.com/sacanlab/immunotar.

In this paper, we computationally predicted the interactions between HIV-1 and human proteins, based on the hypothesis that proteins with similar interface architecture share similar interaction partners. Evolution - aware protein structural alignment method UniAlign was used to calculate the similarity between two protein interface architectures. Using experimentally verified HIV-1, human protein-protein interactions data, we first selected 12 features, including geometric similarity, conversion similarity etc.; then trained a support vector machine (SVM) with Gaussian kernel for the binary classification problem: whether a given protein pairs `interact' or `no interact'. We used the trained and tuned SVM classifier to discover potential novel HIV-1 interacting partners for human proteins. Many predicted interactions had significant literature support, and we modeled the novel 3D interacting complex for HIV-1 envelope gp120 and gp41 proteins. We provided the first structural evidence for those interactions.

MOTIVATION: During the evolution, functional sites on the surface of the protein as well as the hydrophobic core maintaining the structural integrity are well-conserved. However, available protein structure alignment methods align protein structures based solely on the 3D geometric similarity, limiting their ability to detect functionally relevant correspondences between the residues of the proteins, especially for distantly related homologous proteins. RESULTS: In this article, we propose a new protein pairwise structure alignment algorithm (UniAlign) that incorporates additional evolutionary information captured in the form of sequence similarity, sequence profiles and residue conservation. We define a per-residue score (UniScore) as a weighted sum of these and other features and develop an iterative optimization procedure to search for an alignment with the best overall UniScore. Our extensive experiments on CDD, HOMSTRAD and BAliBASE benchmark datasets show that UniAlign outperforms commonly used structure alignment methods. We further demonstrate UniAlign's ability to develop family-specific models to drastically improve the quality of the alignments. AVAILABILITY AND IMPLEMENTATION: UniAlign is available as a web service at: http://sacan.biomed.drexel.edu/unialign CONTACT: [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Microarray data can be analyzed by observing the activity of groups of genes; this approach can provide more relevant information than the analysis of individual gene expression. There are numerous statistical methods available for the enrichment of gene sets; however, these methods often fail to identify relevant gene sets due to the noisy nature of the data. We present weighted hypergeometric and weighted chi-squared methods that rank each gene's contribution to enrichment by the absolute value of the logarithm of its fold change. We demonstrate that these methods can produce more biologically relevant results than the standard hypergeometric test, despite being more conservative and enriching fewer pathways with significant p-values.

A system that can automatically and accurately identify the region of a chronic wound could largely improve conventional clinical practice for the wound diagnosis and treatment. We designed a system that uses color wound photographs taken from the patients, and is capable of automatic image segmentation and wound region identification. Several commonly used segmentation methods are utilized with their parameters fine-tuned automatically to obtain a collection of candidate wound regions. Two different types of Artificial Neural Networks (ANNs), the Multi-Layer Perceptron (MLP) and the Radial Basis Function (RBF) with parameters determined by a cross-validation approach, are then applied with supervised learning in the prediction procedure for the wound identification, and their results are compared. The satisfactory results obtained by this system make it a promising tool to assist in the field of clinical wound evaluation.

Availability of an effective tool for protein multiple structural alignment (MSTA) is essential for discovery and analysis of biologically significant structural motifs that can help solve functional annotation and drug design problems. Existing MSTA methods collect residue correspondences mostly through pairwise comparison of consecutive fragments, which can lead to suboptimal alignments, especially when the similarity among the proteins is low. We introduce a novel strategy based on: building a contact-window based motif library from the protein structural data, discovery and extension of common alignment seeds from this library, and optimal superimposition of multiple structures according to these alignment seeds by an enhanced partial order curve comparison method. The ability of our strategy to detect multiple correspondences simultaneously, to catch alignments globally, and to support flexible alignments, endorse a sensitive and robust automated algorithm that can expose similarities among protein structures even under low similarity conditions. Our method yields better alignment results compared to other popular MSTA methods, on several protein structure data sets that span various structural folds and represent different protein similarity levels. A web-based alignment tool, a downloadable executable, and detailed alignment results for the data sets used here are available at http://sacan.biomed. drexel.edu/Smolign and http://bio.cse.ohio-state.edu/Smolign.

Among the biological macromolecules, proteins have attracted special attention from the scientific community due to their rich functional roles. The ability to visualize and manipulate macromolecular structures on graphical display devices has facilitated the identification and analysis of these macromolecules. Structural analyses of the proteins often provide important insights into their biochemical functions. However, such analysis is often limited by the representation of protein structures and the corresponding computational resource requirements. In this study, we focus on the molecular surface of the proteins and investigate computationally and visually effective representations to serve a number of visualization and analysis purposes. Specifically, we "unfold" the protein surface onto a planar space, while preserving the local surface features as much as possible. In contrast to classical cartographic projections, our approach is able to preserve local shape features. Several biochemical properties associated with each surface point are mapped to generate a two dimensional map of these features. The 3D-2D mapping of the surface vertices has also been utilized to texture-map an arbitrary image back onto the protein structure to facilitate the visualization of the 3D structure.

Within the scope of information retrieval, efficient similarity search in large document or multimedia collections is a critical task. In this paper, we present a rigorous comparison of three different approaches to the image retrieval problem, including cluster-based indexing, distance-based indexing, and multidimensional scaling methods. The time and accuracy trade-offs for each of these methods are demonstrated on a large Corel image database. Similarity of images is obtained via a feature-based similarity measure using four MPEG-7 low-level descriptors. We show that an optimization of feature contributions to the distance measure can identify irrelevant features and is necessary to obtain the maximum accuracy. We further show that using multidimensional scaling can achieve comparable accuracy, while speeding-up the query times significantly by allowing the use of spatial access methods.

This study presents IDE_A-ITCP, a Turkish nationwide Information Technologies Certificate Program, which is based on synchronous and asynchronous communication methods over the Internet, offered by cooperation of the Middle East Technical University Computer Engineering Department and Continuing Education Center. This online certificate program started in May 1998 and is still active with its 13th group of participants. The program includes eight fundamental courses of the Computer Engineering Department curriculum and is comprised of four semesters spanning nine months. The main aim of this program is to train the participants in the IT field to meet the demands in the field of computer technologies in Turkey. In the present study, the history, infrastructure, and educational aspects of IDE_A are discussed in detail.

Traditional bitmap indexes are utilized as a special type of primary or clustered indexes where the queries are answered by performing fast logical operations supported by hardware. Answers are mapped to the physical data by using the row id of each tuple. Bitmaps represent the i-th tuple in the original table with the i-th bit position of the index. Run-length compression is used to reduce the size of the bitmaps and it has been shown that ordered data is significantly better compressed. However, for large-scale and dynamic datasets it is infeasible to keep the data always sorted. Partitioning can be used to keep the data in smaller and manageable chunks, where a different bitmap index is built for each chunk. We propose a novel bitmap index design with partitioning which serves as basis for non-clustered bitmap indexes. Individual bitmaps are not stored, only an Existence Bitmap (EB) for the existing ranks of the full table is maintained. This approach improves update performance of sorted bitmaps and does not require maintaining a heap as the underlying table, nor the same ordering for all the partitions. A one dimensional index is used over the ranks to map the bits in the EB to the physical order of the data, which allows queries to run even faster. The proposed approach, called ranked Non-Clustered Bitmaps (rNCB), is compared against traditional bitmaps using FastBit and shows significant performance gains. 1.

BACKGROUND: Dual-channel microarray experiments are commonly employed for inference of differential gene expressions across varying organisms and experimental conditions. The design of dual-channel microarray experiments that can help minimize the errors in the resulting inferences has recently received increasing attention. However, a general and scalable search tool and a corresponding database of optimal designs were still missing. DESCRIPTION: An efficient and scalable search method for finding near-optimal dual-channel microarray designs, based on a greedy hill-climbing optimization strategy, has been developed. It is empirically shown that this method can successfully and efficiently find near-optimal designs. Additionally, an improved interwoven loop design construction algorithm has been developed to provide an easily computable general class of near-optimal designs. Finally, in order to make the best results readily available to biologists, a continuously evolving catalog of near-optimal designs is provided. CONCLUSION: A new search algorithm and database for near-optimal microarray designs have been developed. The search tool and the database are accessible via the World Wide Web at http://db.cse.ohio-state.edu/MicroarrayDesigner. Source code and binary distributions are available for academic use upon request.

New protein structures are continuously being determined with the hope of deriving insights into the function and mechanisms of proteins, and consequently, protein structure repositories are growing by leaps and bounds. However, we are still far from having the right methods for sensitive and effective use of the available structural data. The fact that current structural analysis tools are impractical for large-scale applications have given rise to several approaches that try to quickly identify candidate proteins worthy of further analysis. Nonetheless, these approaches do not provide the desired sensitivity of identifying important structural similarities. In this study, we propose a new protein structure retrieval method (RCIndex: Residue-Contacts Index) that is based on accurate and efficient identification of similar residue contacts from a database of available protein structures. By defining a metric distance function for biologically meaningful comparison of residue contacts, distance-based indexing is made applicable for quick retrieval of similar residue contact seeds. These seeds are extended into high scoring segment pairs, which induce structural superpositions. The results show that RCIndex is effective in not only identifying related proteins, but also producing remarkably high quality structural alignments that are comparable to or better than those produced by popular pairwise alignment tools. To the best of our knowledge, this is the first time the protein structure retrieval and alignment tasks are successfully handled together.

MOTIVATION: Cell motility is a critical part of many important biological processes. Automated and sensitive cell tracking is essential to cell motility studies where the tracking results can be used for diagnostic or curative decisions and where mathematical models can be developed to deepen our understanding of the mechanisms underlying cell motility. RESULTS: We have developed CellTrack: a self-contained, extensible, and cross-platform software package for cell tracking and motility analysis. Besides the general purpose image enhancement, object segmentation and tracking algorithms, we have implemented a novel edge-based method for sensitive tracking of the cell boundaries, and constructed an ensemble of methods that achieves refined tracking results even under large displacements or deformations of the cells. AVAILABILITY: CellTrack is an Open Source project and is freely available at http://db.cse.ohio-state.edu/CellTrack.

MOTIVATION: Identification and comparison of similar three-dimensional (3D) protein structures has become an even greater challenge in the face of the rapidly growing structure databases. Here, we introduce Vorometric, a new method that provides efficient search and alignment of a query protein against a database of protein structures. Voronoi contacts of the protein residues are enriched with the secondary structure information and a metric substitution matrix is developed to allow efficient indexing. The contact hits obtained from a distance-based indexing method are extended to obtain high-scoring segment pairs, which are then used to generate structural alignments. RESULTS: Vorometric is the first to address both search and alignment problems in the protein structure databases. The experimental results show that Vorometric is simultaneously effective in retrieving similar protein structures, producing high-quality structure alignments, and identifying cross-fold similarities. Vorometric outperforms current structure retrieval methods in search accuracy, while requiring com-parable running times. Furthermore, the structural superpositions produced are shown to have better quality and coverage, when compared with those of the popular structure alignment tools. AVAILABILITY: Vorometric is available as a web service at http://bio.cse.ohio-state.edu/Vorometric

Sequence similarity search of proteins is one of the basic and most common steps followed in bioninformatics research and is used in making evolutionary, structural, and functional inferences. The quality of the search and the alignment of the protein sequences depend crucially on the underlying amino-acid substitution matrix. We present a method for deriving amino acid substitution matrices from 4-body contact propensities of amino-acids in 3D protein structures. Unlike current popular methods, our method does not rely on mutational analysis, evolutionary arguments, or alignment of protein sequences or structures. The alignment accuracy of our derived matrices is evaluated using the BAliBASE reference alignment set and is found to be comparable to that of popular matrices from the literature. Notably, the metric subset of our matrices outperform other available metric matrices. Our matrices will be useful especially in the development of empirical potential energy functions and in distance-based sequence indexing. Supplementary Material: Our substitution matrices and detailed alignment data can be obtained from http://www.ceng.metu.edu.tr/~ahmet/bioinfo/distmat

MOTIVATION: The rapidly growing protein structure repositories have opened up new opportunities for discovery and analysis of functional and evolutionary relationships among proteins. Detecting conserved structural sites that are unique to a protein family is of great value in identification of functionally important atoms and residues. Currently available methods are computationally expensive and fail to detect biologically significant local features. RESULTS: We propose Local Feature Mining in Proteins (LFM-Pro) as a framework for automatically discovering family-specific local sites and the features associated with these sites. Our method uses the distance field to backbone atoms to detect geometrically significant structural centers of the protein. A feature vector is generated from the geometrical and biochemical environment around these centers. These features are then scored using a statistical measure, for their ability to distinguish a family of proteins from a background set of unrelated proteins, and successful features are combined into a representative set for the protein family. The utility and success of LFM-Pro are demonstrated on trypsin-like serine proteases family of proteins and on a challenging classification dataset via comparison with DALI. The results verify that our method is successful both in identifying the distinctive sites of a given family of proteins, and in classifying proteins using the extracted features. AVAILABILITY: The software and the datasets are freely available for academic research use at http://bioinfo.ceng.metu.edu.tr/Pub/LFMPro.