SEGCECO: Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication.

Recent advances in single-cell RNA sequencing technology have eased analyses of signaling networks of cells. Recently, cell-cell interaction has been studied based on various link prediction approaches on graph-structured data. These approaches have assumptions about the likelihood of node interaction, thus showing high performance for only some specific networks. Subgraph-based methods have solved this problem and outperformed other approaches by extracting local subgraphs from a given network. In this work, we present a novel method, called Subgraph Embedding of Gene expression matrix for prediction of CEll-cell COmmunication (SEGCECO), which uses an attributed graph convolutional neural network to predict cell-cell communication from single-cell RNA-seq data. SEGCECO captures the latent and explicit attributes of undirected, attributed graphs constructed from the gene expression profile of individual cells. High-dimensional and sparse single-cell RNA-seq data make converting the data into a graphical format a daunting task. We successfully overcome this limitation by applying SoptSC, a similarity-based optimization method in which the cell-cell communication network is built using a cell-cell similarity matrix which is learned from gene expression data. We performed experiments on six datasets extracted from the human and mouse pancreas tissue. Our comparative analysis shows that SEGCECO outperforms latent feature-based approaches, and the state-of-the-art method for link prediction, WLNM, with 0.99 ROC and 99% prediction accuracy. The datasets can be found at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE84133 and the code is publicly available at Github https://github.com/sheenahora/SEGCECO and Code Ocean https://codeocean.com/capsule/8244724/tree.

Computationally repurposing drugs for breast cancer subtypes using a network-based approach.

'De novo' drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called 'in silico' drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging. We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes. Indeed, further clinical analysis is needed to confirm the therapeutic effects of identified drugs on each breast cancer subtype.

Radiological and histopathological assessment of bone infiltration in soft tissue sarcomas.

BACKGROUND: Deep soft tissue sarcomas are frequently in contact with bone. The therapeutic decision of a composite resection strategy may be challenging, which is usually based on clinical and radiological criteria. The aims of the study were to evaluate the overall frequency of bone and periosteal infiltration in these patients in whom composite resection was indicated, and evaluate the role of magnetic resonance imaging and bone scintigraphy in this scenario. METHODS: Forty-nine patients with a composite surgical resection (soft tissue sarcoma and bone), treated at a single institution between 2006 and 2018, were retrospectively included. Presurgical planning of the resection limits was based on clinical and imaging findings (magnetic resonance imaging and bone scintigraphy). Magnetic resonance imaging was performed in all patients (100%) and bone scintigraphy in 41 (83.7% of the cases). According to magnetic resonance imaging results, patients were divided into two groups: Group A, in which the tumor is adjacent to the bone without evidence of infiltration (n = 24, 48,9%), and Group B, patients with evidence of bone involvement by magnetic resonance imaging (n = 25, 51,1%). BS showed a pathological deposit in 28 patients (68.3%). Histological analysis of the resection specimen was preceded to identify bone and periosteal infiltration. For the analysis of the diagnostic validity of imaging tests, histological diagnosis was considered as the gold standard in the evaluation of STS bone infiltration. RESULTS: Histological bone infiltration was identified in 49% of patients and isolated periosteal infiltration in 14.3%. In terms of diagnostic accuracy, magnetic resonance imaging and bone scintigraphy sensitivity values were 92% and 90%, and their specificity values were 91.7% and 52.4%, respectively. CONCLUSIONS: The incidence of bone and periosteal infiltration of soft tissue sarcomas in contact with bone is high. Presurgical bone assessment by MRI has proven to be a sensitive and specific tool in the diagnosis of bone infiltration. Due to its high negative predictive value, BS is a useful test to rule out it. In those cases, in which there is suspicion of bone infiltration not confirmed by MRI, new diagnostic protocols should be established in order to avoid inappropriate resections.

iSOM-GSN: an integrative approach for transforming multi-omic data into gene similarity networks via self-organizing maps.

MOTIVATION: One of the main challenges in applying graph convolutional neural networks (CNNs) on gene-interaction data is the lack of understanding of the vector space to which they belong, and also the inherent difficulties involved in representing those interactions on a significantly lower dimension, viz Euclidean spaces. The challenge becomes more prevalent when dealing with various types of heterogeneous data. We introduce a systematic, generalized method, called iSOM-GSN, used to transform 'multi-omic' data with higher dimensions onto a 2D grid. Afterwards, we apply a CNN to predict disease states of various types. Based on the idea of Kohonen's self-organizing map, we generate a 2D grid for each sample for a given set of genes that represent a gene similarity network. RESULTS: We have tested the model to predict breast and prostate cancer using gene expression, DNA methylation and copy number alteration. Prediction accuracies in the 94-98% range were obtained for tumor stages of breast cancer and calculated Gleason scores of prostate cancer with just 14 input genes for both cases. The scheme not only outputs nearly perfect classification accuracy, but also provides an enhanced scheme for representation learning, visualization, dimensionality reduction and interpretation of multi-omic data. AVAILABILITY AND IMPLEMENTATION: The source code and sample data are available via a Github project at https://github.com/NaziaFatima/iSOM_GSN. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Prediction of tumor location in prostate cancer tissue using a machine learning system on gene expression data.

BACKGROUND: Finding the tumor location in the prostate is an essential pathological step for prostate cancer diagnosis and treatment. The location of the tumor - the laterality - can be unilateral (the tumor is affecting one side of the prostate), or bilateral on both sides. Nevertheless, the tumor can be overestimated or underestimated by standard screening methods. In this work, a combination of efficient machine learning methods for feature selection and classification are proposed to analyze gene activity and select them as relevant biomarkers for different laterality samples. RESULTS: A data set that consists of 450 samples was used in this study. The samples were divided into three laterality classes (left, right, bilateral). The aim of this work is to understand the genomic activity in each class and find relevant genes as indicators for each class with nearly 99% accuracy. The system identified groups of differentially expressed genes (RTN1, HLA-DMB, MRI1) that are able to differentiate samples among the three classes. CONCLUSION: The proposed method was able to detect sets of genes that can identify different laterality classes. The resulting genes are found to be strongly correlated with disease progression. HLA-DMB and EIF4G2, which are detected in the set of genes can detect the left laterality, were reported earlier to be in the same pathway called Allograft rejection SuperPath.

The predictive performance of short-linear motif features in the prediction of calmodulin-binding proteins.

BACKGROUND: The prediction of calmodulin-binding (CaM-binding) proteins plays a very important role in the fields of biology and biochemistry, because the calmodulin protein binds and regulates a multitude of protein targets affecting different cellular processes. Computational methods that can accurately identify CaM-binding proteins and CaM-binding domains would accelerate research in calcium signaling and calmodulin function. Short-linear motifs (SLiMs), on the other hand, have been effectively used as features for analyzing protein-protein interactions, though their properties have not been utilized in the prediction of CaM-binding proteins. RESULTS: We propose a new method for the prediction of CaM-binding proteins based on both the total and average scores of known and new SLiMs in protein sequences using a new scoring method called sliding window scoring (SWS) as features for the prediction module. A dataset of 194 manually curated human CaM-binding proteins and 193 mitochondrial proteins have been obtained and used for testing the proposed model. The motif generation tool, Multiple EM for Motif Elucidation (MEME), has been used to obtain new motifs from each of the positive and negative datasets individually (the SM approach) and from the combined negative and positive datasets (the CM approach). Moreover, the wrapper criterion with random forest for feature selection (FS) has been applied followed by classification using different algorithms such as k-nearest neighbors (k-NN), support vector machines (SVM), naive Bayes (NB) and random forest (RF). CONCLUSIONS: Our proposed method shows very good prediction results and demonstrates how information contained in SLiMs is highly relevant in predicting CaM-binding proteins. Further, three new CaM-binding motifs have been computationally selected and biologically validated in this study, and which can be used for predicting CaM-binding proteins.

A novel model used to detect differential splice junctions as biomarkers in prostate cancer from RNA-Seq data.

BACKGROUND: In cancer alternative RNA splicing represents one mechanism for flexible gene regulation, whereby protein isoforms can be created to promote cell growth, division and survival. Detecting novel splice junctions in the cancer transcriptome may reveal pathways driving tumorigenic events. In this regard, RNA-Seq, a high-throughput sequencing technology, has expanded the study of cancer transcriptomics in the areas of gene expression, chimeric events and alternative splicing in search of novel biomarkers for the disease. RESULTS: In this study, we propose a new two-dimensional peak finding method for detecting differential splice junctions in prostate cancer using RNA-Seq data. We have designed an integrative process that involves a new two-dimensional peak finding algorithm to combine junctions and then remove irrelevant introns across different samples within a population. We have also designed a scoring mechanism to select the most common junctions. CONCLUSIONS: Our computational analysis on three independent datasets collected from patients diagnosed with prostate cancer reveals a small subset of junctions that may potentially serve as biomarkers for prostate cancer. AVAILABILITY: The pipeline, along with their corresponding algorithms, are available upon request.

PPPCT: Privacy-Preserving framework for Parallel Clustering Transcriptomics data.

Single-cell transcriptomics data provides crucial insights into patients' health, yet poses significant privacy concerns. Genomic data privacy attacks can have deep implications, encompassing not only the patients' health information but also extending widely to compromise their families'. Moreover, the permanence of leaked data exacerbates the challenges, making retraction an impossibility. While extensive efforts have been directed towards clustering single-cell transcriptomics data, addressing critical challenges, especially in the realm of privacy, remains pivotal. This paper introduces an efficient, fast, privacy-preserving approach for clustering single-cell RNA-sequencing (scRNA-seq) datasets. The key contributions include ensuring data privacy, achieving high-quality clustering, accommodating the high dimensionality inherent in the datasets, and maintaining reasonable computation time for big-scale datasets. Our proposed approach utilizes the map-reduce scheme to parallelize clustering, addressing intensive calculation challenges. Intel Software Guard eXtension (SGX) processors are used to ensure the security of sensitive code and data during processing. Additionally, the approach incorporates a logarithm transformation as a preprocessing step, employs non-negative matrix factorization for dimensionality reduction, and utilizes parallel k-means for clustering. The approach fully leverages the computing capabilities of all processing resources within a secure private cloud environment. Experimental results demonstrate the efficacy of our approach in preserving patient privacy while surpassing state-of-the-art methods in both clustering quality and computation time. Our method consistently achieves a minimum of 7% higher Adjusted Rand Index (ARI) than existing approaches, contingent on dataset size. Additionally, due to parallel computations and dimensionality reduction, our approach exhibits efficiency, converging to very good results in less than 10 seconds for a scRNA-seq dataset with 5000 genes and 6000 cells when prioritizing privacy and under two seconds without privacy considerations. Availability and implementation Code and datasets availability: https://github.com/University-of-Windsor/PPPCT.

The role of electrostatic energy in prediction of obligate protein-protein interactions.

BACKGROUND: Prediction and analysis of protein-protein interactions (PPI) and specifically types of PPIs is an important problem in life science research because of the fundamental roles of PPIs in many biological processes in living cells. In addition, electrostatic interactions are important in understanding inter-molecular interactions, since they are long-range, and because of their influence in charged molecules. This is the main motivation for using electrostatic energy for prediction of PPI types. RESULTS: We propose a prediction model to analyze protein interaction types, namely obligate and non-obligate, using electrostatic energy values as properties. The prediction approach uses electrostatic energy values for pairs of atoms and amino acids present in interfaces where the interaction occurs. The main features of the complexes are found and then the prediction is performed via several state-of-the-art classification techniques, including linear dimensionality reduction (LDR), support vector machine (SVM), naive Bayes (NB) and k-nearest neighbor (k-NN). For an in-depth analysis of classification results, some other experiments were performed by varying the distance cutoffs between atom pairs of interacting chains, ranging from 5Å to 13Å. Moreover, several feature selection algorithms including gain ratio (GR), information gain (IG), chi-square (Chi2) and minimum redundancy maximum relevance (mRMR) are applied on the available datasets to obtain more discriminative pairs of atom types and amino acid types as features for prediction. CONCLUSIONS: Our results on two well-known datasets of obligate and non-obligate complexes confirm that electrostatic energy is an important property to predict obligate and non-obligate protein interaction types on the basis of all the experimental results, achieving accuracies of over 98%. Furthermore, a comparison performed by changing the distance cutoff demonstrates that the best values for prediction of PPI types using electrostatic energy range from 9Å to 12Å, which show that electrostatic interactions are long-range and cover a broader area in the interface. In addition, the results on using feature selection before prediction confirm that (a) a few pairs of atoms and amino acids are appropriate for prediction, and (b) prediction performance can be improved by eliminating irrelevant and noisy features and selecting the most discriminative ones.

Mutations in two adjacent novel genes are associated with epidermodysplasia verruciformis.

Epidermodysplasia verruciformis (OMIM 226400) is a rare autosomal recessive genodermatosis associated with a high risk of skin carcinoma that results from an abnormal susceptibility to infection by specific human papillomaviruses (HPVs). We recently mapped a susceptibility locus for epidermodysplasia verruciformis (EV1) to chromosome 17q25. Here we report the identification of nonsense mutations in two adjacent novel genes, EVER1 and EVER2, that are associated with the disease. The gene products EVER1 and EVER2 have features of integral membrane proteins and are localized in the endoplasmic reticulum.

Cell Type Annotation Model Selection: General-Purpose vs. Pattern-Aware Feature Gene Selection in Single-Cell RNA-Seq Data.

With the advances in high-throughput sequencing technology, an increasing amount of research in revealing heterogeneity among cells has been widely performed. Differences between individual cells' functionality are determined based on the differences in the gene expression profiles. Although the observations indicate a great performance of clustering methods, manual annotation of the clusters of cells is a challenge yet to be addressed more scalable and faster. On the other hand, due to the lack of enough labelled datasets, just a few supervised techniques have been used in cell type identification, and they obtained more robust results compared to clustering methods. A recent study showed that a complementary step of feature selection helped support vector machine (SVM) to outperform other classifiers in different scenarios. In this article, we compare and evaluate the performance of two state-of-the-art supervised methods, XGBoost and SVM, with information gain as a feature selection method. The results of the experiments on three standard scRNA-seq datasets indicate that XGBoost automatically annotates cell types in a simpler and more scalable framework. Additionally, it sheds light on the potential use of boosting tree approaches combined with deep neural networks to capture underlying information of single-cell RNA-Seq data more effectively. It can be used to identify marker genes and other applications in biological studies.

A Fecal-Microbial-Extracellular-Vesicles-Based Metabolomics Machine Learning Framework and Biomarker Discovery for Predicting Colorectal Cancer Patients.

Colorectal cancer (CRC) is one of the most common and lethal diseases among all types of cancer, and metabolites play a significant role in the development of this complex disease. This study aimed to identify potential biomarkers and targets in the diagnosis and treatment of CRC using high-throughput metabolomics. Metabolite data extracted from the feces of CRC patients and healthy volunteers were normalized with the median normalization and Pareto scale for multivariate analysis. Univariate ROC analysis, the t-test, and analysis of fold changes (FCs) were applied to identify biomarker candidate metabolites in CRC patients. Only metabolites that overlapped the two different statistical approaches (false-discovery-rate-corrected p-value < 0.05 and AUC > 0.70) were considered in the further analysis. Multivariate analysis was performed with biomarker candidate metabolites based on linear support vector machines (SVM), partial least squares discrimination analysis (PLS-DA), and random forests (RF). The model identified five biomarker candidate metabolites that were significantly and differently expressed (adjusted p-value < 0.05) in CRC patients compared to healthy controls. The metabolites were succinic acid, aminoisobutyric acid, butyric acid, isoleucine, and leucine. Aminoisobutyric acid was the metabolite with the highest discriminatory potential in CRC, with an AUC equal to 0.806 (95% CI = 0.700-0.897), and was down-regulated in CRC patients. The SVM model showed the most substantial discrimination capacity for the five metabolites selected in the CRC screening, with an AUC of 0.985 (95% CI: 0.94-1).

Discovering cell types using manifold learning and enhanced visualization of single-cell RNA-Seq data.

Identifying relevant disease modules such as target cell types is a significant step for studying diseases. High-throughput single-cell RNA-Seq (scRNA-seq) technologies have advanced in recent years, enabling researchers to investigate cells individually and understand their biological mechanisms. Computational techniques such as clustering, are the most suitable approach in scRNA-seq data analysis when the cell types have not been well-characterized. These techniques can be used to identify a group of genes that belong to a specific cell type based on their similar gene expression patterns. However, due to the sparsity and high-dimensionality of scRNA-seq data, classical clustering methods are not efficient. Therefore, the use of non-linear dimensionality reduction techniques to improve clustering results is crucial. We introduce a method that is used to identify representative clusters of different cell types by combining non-linear dimensionality reduction techniques and clustering algorithms. We assess the impact of different dimensionality reduction techniques combined with the clustering of thirteen publicly available scRNA-seq datasets of different tissues, sizes, and technologies. We further performed gene set enrichment analysis to evaluate the proposed method's performance. As such, our results show that modified locally linear embedding combined with independent component analysis yields overall the best performance relative to the existing unsupervised methods across different datasets.

Identification of Enriched Regions in ChIP-Seq Data via a Linear-Time Multi-Level Thresholding Algorithm.

Chromatin immunoprecipitation (ChIP-Seq) has emerged as a superior alternative to microarray technology as it provides higher resolution, less noise, greater coverage and wider dynamic range. While ChIP-Seq enables probing of DNA-protein interaction over the entire genome, it requires the use of sophisticated tools to recognize hidden patterns and extract meaningful data. Over the years, various attempts have resulted in several algorithms making use of different heuristics to accurately determine individual peaks corresponding to unique DNA-protein. However, finding all the significant peaks with high accuracy in a reasonable time is still a challenge. In this work, we propose the use of Multi-level thresholding algorithm, which we call LinMLTBS, used to identify the enriched regions on ChIP-Seq data. Although various suboptimal heuristics have been proposed for multi-level thresholding, we emphasize on the use of an algorithm capable of obtaining an optimal solution, while maintaining linear-time complexity. Testing various algorithm on various ENCODE project datasets shows that our approach attains higher accuracy relative to previously proposed peak finders while retaining a reasonable processing speed.

Cardiac Cement Embolism After Thoracic Kyphoplasty: Successful Conservative Treatment With 4-Year Follow-Up.

BACKGROUND: The present case report describes a complication after a percutaneous spine surgery technique that is highly uncommon in clinical practice: a bone cement cardiac embolism. This rare complication emphasizes the importance of this case, which is also interesting considering the midterm follow-up. Documented cardiac embolisms published in the literature (which are scarce) describe the acute phase of these cases but lack follow-up. There are no systematic reviews on this topic, only case-by-case presentations, and surgeons are not aware of its real implications. CASE: We report a case of an 84-year-old man who developed sudden thoracic and spinal pain associated with 82% saturation and dyspnea a few hours after 4-level thoracic spine vertebroplasty and kyphoplasty. Imaging revealed multiple bone cement embolisms in his lung and heart. Because the patient was hemodynamically stable, cardiologists recommended conservative treatment with low molecular weight heparin, without embolus removal. At 4-year follow-up, the patient remained asymptomatic. CONCLUSION: Cardiac cement embolization following percutaneous techniques represents a life-threatening situation that should be ruled out if the patient presents symptoms during the early postoperative period. Treatment may vary from conservative to emergency open-heart surgery.

Relevance of Fluorodopa PET Scan in Dopamine Responsive Dystonia and Juvenile Parkinsonism: A Systematic Review.

BACKGROUND: Dopamine Responsive Dystonia (DRD) and Juvenile Parkinsonism (JP) are two diseases commonly presenting with parkinsonian symptoms in young patients. Current clinical guidelines offer a diagnostic approach based on molecular analysis. However, developing countries have limitations in terms of accessibility to these tests. We aimed to assess the utility of imaging equipment, usually more available worldwide, to help diagnose and improve patients' quality of life with these diseases. METHODS: We performed a systematic literature review in English using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) and meta-analysis of observational studies in epidemiology (MOOSE) protocols. We only used human clinical trials about dopamine responsive dystonia and juvenile parkinsonism patients in which a fluorodopa (FD) positron emission tomography (PET) scan was performed to identify its use in these diseases. RESULTS: We included six studies that fulfilled our criteria. We found a clear pattern of decreased uptake in the putamen and caudate nucleus in JP cases. At the same time, the results in DRD were comparable to normal subjects, with only a slightly decreased marker uptake in the previously mentioned regions by the FD PET scan. CONCLUSIONS: We found a distinctive pattern for each of these diseases. Identifying these findings with FD PET scans can shorten the delay in making a definitive diagnosis when genetic testing is unavailable, a common scenario in developing countries.

Prediction of biological protein-protein interactions using atom-type and amino acid properties.

Identification and analysis of types of biological protein-protein interactions and their interfaces to predict obligate and non-obligate complexes is a problem that has drawn the attention of the research community in the past few years. In this paper, we propose a prediction approach to predict these two types of complexes. We use desolvation energies - amino acid and atom type - of the residues present in the interface. The prediction is performed via two state-of-the-art classification techniques, namely linear dimensionality reduction (LDR) and support vector machines (SVM). The results on a newly compiled data set, namely BPPI, which is a joint and modified version of two well-known data sets consisting of 213 obligate and 303 non-obligate complexes, show that the best prediction is achieved with SVM (76.94% accuracy) when using desolvation energies of atom-type features. Also, the proposed approach outperforms the previous solvent accessible area-based approaches using SVM (75% accuracy) and LDR (73.06% accuracy). Moreover, a visual analysis of desolvation energies in obligate and non-obligate complexes shows that a few atom-type pairs are good descriptors for these types of complexes.

A fully automatic gridding method for cDNA microarray images.

BACKGROUND: Processing cDNA microarray images is a crucial step in gene expression analysis, since any errors in early stages affect subsequent steps, leading to possibly erroneous biological conclusions. When processing the underlying images, accurately separating the sub-grids and spots is extremely important for subsequent steps that include segmentation, quantification, normalization and clustering. RESULTS: We propose a parameterless and fully automatic approach that first detects the sub-grids given the entire microarray image, and then detects the locations of the spots in each sub-grid. The approach, first, detects and corrects rotations in the images by applying an affine transformation, followed by a polynomial-time optimal multi-level thresholding algorithm used to find the positions of the sub-grids in the image and the positions of the spots in each sub-grid. Additionally, a new validity index is proposed in order to find the correct number of sub-grids in the image, and the correct number of spots in each sub-grid. Moreover, a refinement procedure is used to correct possible misalignments and increase the accuracy of the method. CONCLUSIONS: Extensive experiments on real-life microarray images and a comparison to other methods show that the proposed method performs these tasks fully automatically and with a very high degree of accuracy. Moreover, unlike previous methods, the proposed approach can be used in various type of microarray images with different resolutions and spot sizes and does not need any parameter to be adjusted.

Multiple gene expression profile alignment for microarray time-series data clustering.

MOTIVATION: Clustering gene expression data given in terms of time-series is a challenging problem that imposes its own particular constraints. Traditional clustering methods based on conventional similarity measures are not always suitable for clustering time-series data. A few methods have been proposed recently for clustering microarray time-series, which take the temporal dimension of the data into account. The inherent principle behind these methods is to either define a similarity measure appropriate for temporal expression data, or pre-process the data in such a way that the temporal relationships between and within the time-series are considered during the subsequent clustering phase. RESULTS: We introduce pairwise gene expression profile alignment, which vertically shifts two profiles in such a way that the area between their corresponding curves is minimal. Based on the pairwise alignment operation, we define a new distance function that is appropriate for time-series profiles. We also introduce a new clustering method that involves multiple expression profile alignment, which generalizes pairwise alignment to a set of profiles. Extensive experiments on well-known datasets yield encouraging results of at least 80% classification accuracy.

Cast metal bases as an economical alternative for the severely resorbed mandible.

Resorption of the alveolar ridge is a common problem in edentulous patients and can compromise the stability and function of dentures. Resorption and its consequences can be minimized when strategically placed implants are used; however, this option is financially out of reach for many patients. The article discusses a more cost-effective alternative (metalbased dentures) for patients with ridge resorption. In certain environments, like a dental school, where patients are looking for solutions to their dental problems at a reasonable price, cast metal bases can be a feasible economical alternative for edentulous patients. Both cases presented here demonstrated a significant improvement in stability, phonation, and mastication.