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SignalML: metaformat for description of biomedical time series.

This paper introduces a complete and elegant solution to the problem of inherent incompatibility of different formats used for digital storage of biomedical time series (in particular EEG) and their annotations. We define a simple XML-based language, in which information on the structure of binary data files can be simply and efficiently coded. In most cases, description of an existing format takes relatively few lines of XML code. Once written, this information can be used by any software, which, owing to this meta-description, may read the original data files, thus eliminating the need for conversions and duplication of data. This proposition is hereby submitted to an open discussion within the community involved in relevant research, clinical and commercial applications. Links to the current version of the XML Schema defining the language and pilot implementation of a compliant viewer/annotator are located at http://eeg.pl/SignalML/.

Database Management Systems↗

Significance analysis of qualitative mammographic features, using linear classifiers, neural networks and support vector machines.

Advances in modern technologies and computers have enabled digital image processing to become a vital tool in conventional clinical practice, including mammography. However, the core problem of the clinical evaluation of mammographic tumors remains a highly demanding cognitive task. In order for these automated diagnostic systems to perform in levels of sensitivity and specificity similar to that of human experts, it is essential that a robust framework on problem-specific design parameters is formulated. This study is focused on identifying a robust set of clinical features that can be used as the base for designing the input of any computer-aided diagnosis system for automatic mammographic tumor evaluation. A thorough list of clinical features was constructed and the diagnostic value of each feature was verified against current clinical practices by an expert physician. These features were directly or indirectly related to the overall morphological properties of the mammographic tumor or the texture of the fine-scale tissue structures as they appear in the digitized image, while others contained external clinical data of outmost importance, like the patient's age. The entire feature set was used as an annotation list for describing the clinical properties of mammographic tumor cases in a quantitative way, such that subsequent objective analyses were possible. For the purposes of this study, a mammographic image database was created, with complete clinical evaluation descriptions and positive histological verification for each case. All tumors contained in the database were characterized according to the identified clinical features' set and the resulting dataset was used as input for discrimination and diagnostic value analysis for each one of these features. Specifically, several standard methodologies of statistical significance analysis were employed to create feature rankings according to their discriminating power. Moreover, three different classification models, namely linear classifiers, neural networks and support vector machines, were employed to investigate the true efficiency of each one of them, as well as the overall complexity of the diagnostic task of mammographic tumor characterization. Both the statistical and the classification results have proven the explicit correlation of all the selected features with the final diagnosis, qualifying them as an adequate input base for any type of similar automated diagnosis system. The underlying complexity of the diagnostic task has justified the high value of sophisticated pattern recognition architectures.

Analysis of Variance↗

AI-enabled viral genomics: from virus discovery to host prediction and emerging variant forecasting.

The rapid expansion of metagenomic sequencing has generated vast repositories of viral sequence data that far outpace our capacity to interpret them using conventional approaches. Highly divergent sequences, sparse functional annotation, and taxonomically uneven sampling present fundamental challenges for reference-dependent methods, which lose sensitivity precisely for novel and understudied viruses with high public health relevance. Artificial intelligence (AI) provides a new avenue to address these challenges by enabling predictive inference from viral genomes and proteins while reducing dependence on sequence similarity. In this Review, we discuss representative advances in AI for virus discovery, taxonomic classification and functional annotation, prediction of host range and zoonotic potential, and efforts toward forecasting emerging variants. These advances are transforming viral genomics from a largely descriptive discipline into one with increasing predictive capability. We also critically assess the major challenges that constrain current approaches, including the availability of high-quality and representative datasets, rigorous model evaluation, biological interpretability and responsible governance for increasingly capable AI models.

Artificial Intelligence↗

Extended angiography in diagnosis and treatment of renal carcinoma.

New and highly sophisticated advances in uro-angiography need to be systematically analyzed and presented for evaluation. Our experience with these techniques applied to renal carcinoma is reviewed. This series demonstrates the important features of angiography in the diagnosis and treatment of renal carcinoma although the number of patients was not large enough to permit statistical treatment of the data. We anticipate that close cooperation between radiologist and urologist will continue to produce advances such as annotated in this report.

Adenocarcinoma↗

Estimating local backbone structural deviation in homology models.

After the atomic coordinates themselves, the most important data in a homology model are the spatial reliability estimates associated with each of the atoms (atom annotation). Recent blind homology modeling predictions have demonstrated that principally correct sequence-structure alignments are achievable to sequence identities as low as 25% [Martin, A.C., MacArthur, M.W., Thornton, J.M., 1997. Assessment of comparative modeling in CASP2. Proteins Suppl(1), 14-28]. The locations and extent of spatial deviations in the backbone between correctly aligned homologous protein structures remained very poorly estimated however, and these errors were the cause of errant loop predictions [Abagyan, R., Batalov, S., Cardozo, T., Totrov, M., Webber, J., Zhou, Y., 1997. Homology modeling with internal coordinate mechanics: deformation zone mapping and improvements of models via conformational search. Proteins Suppl(1), 29-37]. In order to derive accurate measures for local backbone deviations, we made a systematic study of static local backbone deviations between homologous pairs of protein structures. We found that 'through space' proximity to gaps and chain termini, local three-dimensional 'density', three-dimensional environment conservation, and B-factor of the template contribute to local deviations in the backbone in addition to local sequence identity. Based on these finding, we have identified the meaningful ranges of values within which each of these parameters correlates with static local backbone deviation and produced a combined scoring function to greatly improve the estimation of local backbone deviations. The optimized function has more than twice the accuracy of local sequence identity or B-factor alone and was validated in a recent blind structure prediction experiment. This method may be used to evaluate the utility of a preliminary homology model for a particular biological investigation (e.g. drug design) or to provide an improved starting point for molecular mechanics loop prediction methods.

Computer Simulation↗

Whole-genome analysis: annotations and updates.

The most important advances in the field of genome annotation over the past two years involve the use of cDNA sequences, protein structures and gene expression data to predict genes. These types of information not only improve gene identification, but they also give insights into variation in gene structure and function.

Alternative Splicing↗

Linking pathway gene expressions to the growth inhibition response from the National Cancer Institute's anticancer screen and drug mechanism of action.

Novel strategies are proposed to quantitatively analyze and relate biological pathways to drug responses using gene expression and small-molecule growth inhibition data (GI(50)) derived from the National Cancer Institute's 60 cancer cells (NCI(60)). We have annotated groups of drug GI(50) responses with pathways defined by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and BioCarta, and functional categories defined by Gene Ontology (GO), through correlations between pathway gene expression patterns and drug GI(50) profiles. Drug-gene-pathway relationships may then be utilized to find drug targets or target-specific drugs. Significantly correlated pathways and the gene products involved represent interesting targets for further exploration, whereas drugs that are significantly correlated with only certain pathways are more likely to be target specific. Separate pathway clustering finds that pathways engaged in the same biological process tend to have similar drug correlation patterns. The biological and statistical significances of our method are established by comparison to known small-molecule inhibitor-gene target relationships reported in the literature and by standard randomization procedures. The results of our pathway, gene expression and drug-induced growth inhibition associations, can serve as a basis for proposing testable hypotheses about potential anticancer drugs, their targets, and mechanisms of action.

Antineoplastic Agents↗

An automatic method to discriminate malignant masses from normal tissue in digital mammograms.

Specificity levels of automatic mass detection methods in mammography are generally rather low, because suspicious looking normal tissue is often hard to discriminate from real malignant masses. In this work a number of features were defined that are related to image characteristics that radiologists use to discriminate real lesions from normal tissue. An artificial neural network was used to map the computed features to a measure of suspiciousness for each region that was found suspicious by a mass detection method. Two data sets were used to test the method. The first set of 72 malignant cases (132 films) was a consecutive series taken from the Nijmegen screening programme, 208 normal films were added to improve the estimation of the specificity of the method. The second set was part of the new DDSM data set from the University of South Florida. A total of 193 cases (772 films) with 372 annotated malignancies was used. The measure of suspiciousness that was computed using the image characteristics was successful in discriminating tumours from false positive detections. Approximately 75% of all cancers were detected in at least one view at a specificity level of 0.1 false positive per image.

Breast Neoplasms↗

Knowledge acquisition, consistency checking and concurrency control for Gene Ontology (GO).

MOTIVATION: A critical element of the computational infrastructure required for functional genomics is a shared language for communicating biological data and knowledge. The Gene Ontology (GO; http://www.geneontology.org) provides a taxonomy of concepts and their attributes for annotating gene products. As GO increases in size, its ongoing construction and maintenance becomes more challenging. In this paper, we assess the applicability of a Knowledge Base Management System (KBMS), Protégé-2000, to the maintenance and development of GO. RESULTS: We transferred GO to Protégé-2000 in order to evaluate its suitability for GO. The graphical user interface supported browsing and editing of GO. Tools for consistency checking identified minor inconsistencies in GO and opportunities to reduce redundancy in its representation. The Protégé Axiom Language proved useful for checking ontological consistency. The PROMPT tool allowed us to track changes to GO. Using Protégé-2000, we tested our ability to make changes and extensions to GO to refine the semantics of attributes and classify more concepts. AVAILABILITY: Gene Ontology in Protégé-2000 and the associated code are located at http://smi.stanford.edu/projects/helix/gokbms/. Protégé-2000 is available from http://protege.stanford.edu.

Artificial Intelligence↗

Interactive gene-order comparison for multiple small genomes.

UNLABELLED: The Genome Organization Analysis Tool (GOAT) is a program that performs comparative sequence analysis on ordered gene lists from annotated genomes, provides visual and tabular output, and provides means of accessing and analyzing related gene sequence data, for the purpose of comparing genome organization at the gene-order level. GOAT can be used to compare any two or more genomes or chromosomes on demand, or configured to provide access to precomputed comparisons of a specific group of genome sequences. AVAILABILITY: Demonstration web server and software download, subject to the Virginia Tech Noncommercial License are available at http://gaia.biotech.vt.edu/goat/. SUPPLEMENTARY INFORMATION: Updates, installation and configuration information are available at http://gaia.biotech.vt.edu/goat.

Algorithms↗

Identification of function-associated loop motifs and application to protein function prediction.

MOTIVATION: The detection of function-related local 3D-motifs in protein structures can provide insights towards protein function in absence of sequence or fold similarity. Protein loops are known to play important roles in protein function and several loop classifications have been described, but the automated identification of putative functional 3D-motifs in such classifications has not yet been addressed. This identification can be used on sequence annotations. RESULTS: We evaluated three different scoring methods for their ability to identify known motifs from the PROSITE database in ArchDB. More than 500 new putative function-related motifs not reported in PROSITE were identified. Sequence patterns derived from these motifs were especially useful at predicting precise annotations. The number of reliable sequence annotations could be increased up to 100% with respect to standard BLAST. CONTACT: boliva@imim.es SUPPLEMENTARY INFORMATION: Supplementary Data are available at Bioinformatics online.

Amino Acid Sequence↗

AnoXcel: an Anopheles gambiae protein database.

The proteome of the mosquito Anopheles gambiae was organized on a hyperlinked spreadsheet format containing one protein per row and several pieces of information in each column. The information for each protein ranges from the presence or absence of signal peptide indicative of secretion, presence of transmembrane domains, similarities to several databases, chromosomal location, and relatedness to other An. gambiae proteins, etc. Hosted by AnoBase (http://www.anobase.org/), the whole spreadsheet or segments of it can be downloaded or searched from http://www.anobase.org/AnoBase/Genes/Ano-Xcel by the scientist dealing with the annotation of proteome subsets such as those deriving from transcriptomes, nucleotide microarrays or high throughput mass spectrometry data.

Animals↗

Microarray expression analysis of meiosis and microsporogenesis in hexaploid bread wheat.

BACKGROUND: Our understanding of the mechanisms that govern the cellular process of meiosis is limited in higher plants with polyploid genomes. Bread wheat is an allohexaploid that behaves as a diploid during meiosis. Chromosome pairing is restricted to homologous chromosomes despite the presence of homoeologues in the nucleus. The importance of wheat as a crop and the extensive use of wild wheat relatives in breeding programs has prompted many years of cytogenetic and genetic research to develop an understanding of the control of chromosome pairing and recombination. The rapid advance of biochemical and molecular information on meiosis in model organisms such as yeast provides new opportunities to investigate the molecular basis of chromosome pairing control in wheat. However, building the link between the model and wheat requires points of data contact. RESULTS: We report here a large-scale transcriptomics study using the Affymetrix wheat GeneChip(R) aimed at providing this link between wheat and model systems and at identifying early meiotic genes. Analysis of the microarray data identified 1,350 transcripts temporally-regulated during the early stages of meiosis. Expression profiles with annotated transcript functions including chromatin condensation, synaptonemal complex formation, recombination and fertility were identified. From the 1,350 transcripts, 30 displayed at least an eight-fold expression change between and including pre-meiosis and telophase II, with more than 50% of these having no similarities to known sequences in NCBI and TIGR databases. CONCLUSION: This resource is now available to support research into the molecular basis of pairing and recombination control in the complex polyploid, wheat.

Chromosome Pairing↗

The MYC/TXNIP axis mediates NCL-Suppressed CD8+T cell immune response in lung adenocarcinoma.

BACKGROUND: Lung adenocarcinoma is a deadly malignancy with immune evasion playing a key role in tumor progression. Glucose metabolism is crucial for T cell function, and the nucleolar protein NCL may influence T cell glucose metabolism. This study aims to investigate NCL's role in T cell glucose metabolism and immune evasion by lung adenocarcinoma cells. METHODS: Utilizing single-cell RNA sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we analyzed cell clustering, annotation, and prognosis. In vitro experiments involved manipulating NCL expression in CD8+ T cells to study immune function and glucose metabolism. In vivo studies using an orthotopic transplant mouse model monitored NCL's impact on CD8+ T cell glucose metabolism and anti-tumor immune function. RESULTS: NCL was associated with T cell dysfunction and glucose metabolism. NCL silencing enhanced CD8+ T cell glucose metabolism, cytotoxicity, and infiltration, while NCL overexpression had the opposite effect. NCL overexpression relieved MYC-mediated transcriptional repression of TXNIP, reducing CD8+ T cell glucose metabolism. In vivo, NCL inhibited CD8+ T cell glucose metabolism through the MYC/TXNIP axis, hindering anti-tumor immune function. CONCLUSIONS: NCL overexpression suppresses CD8+ T cell glucose metabolism and anti-tumor immune function, promoting lung adenocarcinoma progression via the MYC/TXNIP axis.

CD8-Positive T-Lymphocytes↗

Bioinformatics analysis of miR-2861 and miR-5011-5p that function as potential tumor suppressors in colorectal carcinogenesis.

BACKGROUND: The study aimed to was to investigate the relationship between miR-2861, miR-5011-5p, and colorectal carcinogenesis. METHOD: In the present study, it was isolated RNA from both the tumor and non-tumor tissue of a total of 80 CRC patients and after synthesizing the cDNA, it was performed qRT-PCR to determine the expression levels of miR‑2861 and miR‑5011-5p. In addition, it was predicted that dysregulated miRNAs targets, pathways and functional gene annotations that may be important in colorectal carcinogenesis using KEGG pathway and GO analysis. RESULTS: The resulting data revealed that both expression levels of miR-2861 and miR-5011-5p were significantly decreased in tumor tissues compared with non-tumor tissues of CRC patients. The GO and KEGG pathway analysis showed that miR-2861 and miR-5011-5p may participate in multiple the biological process, cellular components, and molecular function subcategories such as mitotic cell cycle, regulation of small GTPase mediated signal transduction, cell death, and acid binding transcription factor activity. It was also revealed that target genes of miRNAs can be found in signaling pathways such as TGF-beta, Rap1, Ras, cAMP, Wnt, mTOR and, PI3K-Akt signaling pathways. CONCLUSION: These findings imply that miR-2861 and miR-5011-5p might function as tumor suppressors in the development of CRC.

MicroRNAs↗

KISS: the kinetoplastid RNA editing sequence search tool.

Kinetoplastid mitochondrial mRNA editing is a post-transcriptional process of uridine insertion and deletion. Editing is mediated by small RNA molecules termed guide RNAs (gRNAs). Most gRNAs are encoded by numerous small circular DNA minicircles, while the protein coding mitochondrial genes are encoded on a separate, larger genome called the maxicircle. In order to provide a workbench for the analysis of RNA editing in kinetoplastids and a well-annotated set of guide RNAs for Trypanosoma brucei, we generated the kinetoplastid RNA editing sequence search tool (KISS) (http://gmod.mbl.edu/kiss/). KISS is a pipeline and database that uses BLAST comparisons and minicircle sequence motifs to annotate potential gRNAs and cognate mRNA editing sequence. KISS 1.0 contains all previously known minicircle and maxicircle data from Trypanosoma brucei plus >400 new minicircle sequences. Using an online format, KISS 1.0 allows the mapping and visualization of all known T. brucei gRNAs to minicircle genes and to potential mRNA substrates for RNA editing.

Animals↗

Prospective molecular profiling of melanoma metastases suggests classifiers of immune responsiveness.

We amplified RNAs from 63 fine needle aspiration (FNA) samples from 37 s.c. melanoma metastases from 25 patients undergoing immunotherapy for hybridization to a 6108-gene human cDNA chip. By prospectively following the history of the lesions, we could correlate transcript patterns with clinical outcome. Cluster analysis revealed a tight relationship among autologous synchronously sampled tumors compared with unrelated lesions (average Pearson's r = 0.83 and 0.7, respectively, P < 0.0003). As reported previously, two subgroups of metastatic melanoma lesions were identified that, however, had no predictive correlation with clinical outcome. Ranking of gene expression data from pretreatment samples identified approximately 30 genes predictive of clinical response (P < 0.001). Analysis of their annotations denoted that approximately half of them were related to T-cell regulation, suggesting that immune responsiveness might be predetermined by a tumor microenvironment conducive to immune recognition.

Adult↗

Challenges in real-life emotion annotation and machine learning based detection.

Since the early studies of human behavior, emotion has attracted the interest of researchers in many disciplines of Neurosciences and Psychology. More recently, it is a growing field of research in computer science and machine learning. We are exploring how the expression of emotion is perceived by listeners and how to represent and automatically detect a subject's emotional state in speech. In contrast with most previous studies, conducted on artificial data with archetypal emotions, this paper addresses some of the challenges faced when studying real-life non-basic emotions. We present a new annotation scheme allowing the annotation of emotion mixtures. Our studies of real-life spoken dialogs from two call center services reveal the presence of many blended emotions, dependent on the dialog context. Several classification methods (SVM, decision trees) are compared to identify relevant emotional states from prosodic, disfluency and lexical cues extracted from the real-life spoken human-human interactions.

Artificial Intelligence↗