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At least 451 records · Page 25Linked to original sources
PubMed2006

Method of predicting splice sites based on signal interactions.

BACKGROUND: Predicting and proper ranking of canonical splice sites (SSs) is a challenging problem in bioinformatics and machine learning communities. Any progress in SSs recognition will lead to better understanding of splicing mechanism. We introduce several new approaches of combining a priori knowledge for improved SS detection. First, we design our new Bayesian SS sensor based on oligonucleotide counting. To further enhance prediction quality, we applied our new de novo motif detection tool MHMMotif to intronic ends and exons. We combine elements found with sensor information using Naive Bayesian Network, as implemented in our new tool SpliceScan. RESULTS: According to our tests, the Bayesian sensor outperforms the contemporary Maximum Entropy sensor for 5' SS detection. We report a number of putative Exonic (ESE) and Intronic (ISE) Splicing Enhancers found by MHMMotif tool. T-test statistics on mouse/rat intronic alignments indicates, that detected elements are on average more conserved as compared to other oligos, which supports our assumption of their functional importance. The tool has been shown to outperform the SpliceView, GeneSplicer, NNSplice, Genio and NetUTR tools for the test set of human genes. SpliceScan outperforms all contemporary ab initio gene structural prediction tools on the set of 5' UTR gene fragments. CONCLUSION: Designed methods have many attractive properties, compared to existing approaches. Bayesian sensor, MHMMotif program and SpliceScan tools are freely available on our web site. REVIEWERS: This article was reviewed by Manyuan Long, Arcady Mushegian and Mikhail Gelfand.

Journal Article↗
PubMed2006

Demonstration of two novel methods for predicting functional siRNA efficiency.

BACKGROUND: siRNAs are small RNAs that serve as sequence determinants during the gene silencing process called RNA interference (RNAi). It is well know that siRNA efficiency is crucial in the RNAi pathway, and the siRNA efficiency for targeting different sites of a specific gene varies greatly. Therefore, there is high demand for reliable siRNAs prediction tools and for the design methods able to pick up high silencing potential siRNAs. RESULTS: In this paper, two systems have been established for the prediction of functional siRNAs: (1) a statistical model based on sequence information and (2) a machine learning model based on three features of siRNA sequences, namely binary description, thermodynamic profile and nucleotide composition. Both of the two methods show high performance on the two datasets we have constructed for training the model. CONCLUSION: Both of the two methods studied in this paper emphasize the importance of sequence information for the prediction of functional siRNAs. The way of denoting a bio-sequence by binary system in mathematical language might be helpful in other analysis work associated with fixed-length bio-sequence.

Algorithms↗
PubMed2001

Feature subset selection by genetic algorithms and estimation of distribution algorithms. A case study in the survival of cirrhotic patients treated with TIPS.

The transjugular intrahepatic portosystemic shunt (TIPS) is an interventional treatment for cirrhotic patients with portal hypertension. In the light of our medical staff's experience, the consequences of TIPS are not homogeneous for all the patients and a subgroup dies in the first 6 months after TIPS placement. Actually, there is no risk indicator to identify this subgroup of patients before treatment. An investigation for predicting the survival of cirrhotic patients treated with TIPS is carried out using a clinical database with 107 cases and 77 attributes. Four supervised machine learning classifiers are applied to discriminate between both subgroups of patients. The application of several feature subset selection (FSS) techniques has significantly improved the predictive accuracy of these classifiers and considerably reduced the amount of attributes in the classification models. Among FSS techniques, FSS-TREE, a new randomized algorithm inspired on the new EDA (estimation of distribution algorithm) paradigm has obtained the best average accuracy results for each classifier.

Algorithms↗
PubMed2005

Sample handling for mass spectrometric proteomic investigations of human sera.

Proteomic investigations of sera are potentially of value for diagnosis, prognosis, choice of therapy, and disease activity assessment by virtue of discovering new biomarkers and biomarker patterns. Much debate focuses on the biological relevance and the need for identification of such biomarkers while less effort has been invested in devising standard procedures for sample preparation and storage in relation to model building based on complex sets of mass spectrometric (MS) data. Thus, development of standardized methods for collection and storage of patient samples together with standards for transportation and handling of samples are needed. This requires knowledge about how sample processing affects MS-based proteome analyses and thereby how nonbiological biased classification errors are avoided. In this study, we characterize the effects of sample handling, including clotting conditions, storage temperature, storage time, and freeze/thaw cycles, on MS-based proteomics of human serum by using principal components analysis, support vector machine learning, and clustering methods based on genetic algorithms as class modeling and prediction methods. Using spiking to artificially create differentiable sample groups, this integrated approach yields data that--even when working with sample groups that differ more than may be expected in biological studies--clearly demonstrate the need for comparable sampling conditions for samples used for modeling and for the samples that are going into the test set group. Also, the study emphasizes the difference between class prediction and class comparison studies as well as the advantages and disadvantages of different modeling methods.

Humans↗
PubMed2006

SNOSID, a proteomic method for identification of cysteine S-nitrosylation sites in complex protein mixtures.

Reversible addition of NO to Cys-sulfur in proteins, a modification termed S-nitrosylation, has emerged as a ubiquitous signaling mechanism for regulating diverse cellular processes. A key first-step toward elucidating the mechanism by which S-nitrosylation modulates a protein's function is specification of the targeted Cys (SNO-Cys) residue. To date, S-nitrosylation site specification has been laboriously tackled on a protein-by-protein basis. Here we describe a high-throughput proteomic approach that enables simultaneous identification of SNO-Cys sites and their cognate proteins in complex biological mixtures. The approach, termed SNOSID (SNO Site Identification), is a modification of the biotin-swap technique [Jaffrey, S. R., Erdjument-Bromage, H., Ferris, C. D., Tempst, P. & Snyder, S. H. (2001) Nat. Cell. Biol. 3, 193-197], comprising biotinylation of protein SNO-Cys residues, trypsinolysis, affinity purification of biotinylated-peptides, and amino acid sequencing by liquid chromatography tandem MS. With this approach, 68 SNO-Cys sites were specified on 56 distinct proteins in S-nitrosoglutathione-treated (2-10 microM) rat cerebellum lysates. In addition to enumerating these S-nitrosylation sites, the method revealed endogenous SNO-Cys modification sites on cerebellum proteins, including alpha-tubulin, beta-tubulin, GAPDH, and dihydropyrimidinase-related protein-2. Whereas these endogenous SNO proteins were previously recognized, we extend prior knowledge by specifying the SNO-Cys modification sites. Considering all 68 SNO-Cys sites identified, a machine learning approach failed to reveal a linear Cys-flanking motif that predicts stable transnitrosation by S-nitrosoglutathione under test conditions, suggesting that undefined 3D structural features determine S-nitrosylation specificity. SNOSID provides the first effective tool for unbiased elucidation of the SNO proteome, identifying Cys residues that undergo reversible S-nitrosylation.

Animals↗
PubMedDate not supplied

A diagnostic marker for speech delay associated with otitis media with effusion: the intelligibility-speech gap.

The goal of this study was to determine if notably reduced intelligibility is a potential diagnostic marker for children with speech delay and histories of early recurrent otitis media with effusion (SD-OME). Intelligibility was assessed in one 5-10 minute conversational speech sample from each of 281 speakers. The OME histories of 148 of these children with normal speech acquisition were described in two prior reports. OME histories of 85 additional children with speech delay were obtained from case history reports. For both groups, the children with positive OME (OME+) histories had significantly lower intelligibility scores but significantly higher speech production scores than children with negative OME (OME-) histories. Findings for a diagnostic marker to discriminate speech delayed children with OME+ versus OME- histories were promising, considering that the data were obtained retrospectively and did not include audiological information characterizing children's concurrent fluctuant hearing loss. The formula for the diagnostic marker, termed the Intelligibility-Speech Gap, was identified by a machine learning routine. Diagnostic accuracy findings for the marker were as follows: positive predictive value = 74%, negative predictive value = 86%, sensitivity = 79%, specificity = 83%, positive likelihood ratio = 4.6 and negative likelihood ratio = 0.25. Discussion considers speech processing perspectives on the source of the intelligibility-speech gap in children with suspected SD-OME, and methodological perspectives on its development as a diagnostic marker of one etiological subtype of speech delay.

Child↗
PubMed2006

Automated discovery of 3D motifs for protein function annotation.

MOTIVATION: Function inference from structure is facilitated by the use of patterns of residues (3D motifs), normally identified by expert knowledge, that correlate with function. As an alternative to often limited expert knowledge, we use machine-learning techniques to identify patterns of 3-10 residues that maximize function prediction. This approach allows us to test the assumption that residues that provide function are the most informative for predicting function. RESULTS: We apply our method, GASPS, to the haloacid dehalogenase, enolase, amidohydrolase and crotonase superfamilies and to the serine proteases. The motifs found by GASPS are as good at function prediction as 3D motifs based on expert knowledge. The GASPS motifs with the greatest ability to predict protein function consist mainly of known functional residues. However, several residues with no known functional role are equally predictive. For four groups, we show that the predictive power of our 3D motifs is comparable with or better than approaches that use the entire fold (Combinatorial-Extension) or sequence profiles (PSI-BLAST). AVAILABILITY: Source code is freely available for academic use by contacting the authors. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Algorithms↗
PubMed2006

Experiments with AdaBoost.RT, an improved boosting scheme for regression.

The application of boosting technique to regression problems has received relatively little attention in contrast to research aimed at classification problems. This letter describes a new boosting algorithm, AdaBoost.RT, for regression problems. Its idea is in filtering out the examples with the relative estimation error that is higher than the preset threshold value, and then following the AdaBoost procedure. Thus, it requires selecting the suboptimal value of the error threshold to demarcate examples as poorly or well predicted. Some experimental results using the M5 model tree as a weak learning machine for several benchmark data sets are reported. The results are compared to other boosting methods, bagging, artificial neural networks, and a single M5 model tree. The preliminary empirical comparisons show higher performance of AdaBoost.RT for most of the considered data sets.

Algorithms↗
PubMed2026

A three-metabolite microbiota-associated signature for early risk stratification of gestational diabetes mellitus.

BACKGROUND: Gestational diabetes mellitus (GDM) is associated with adverse pregnancy outcomes and long-term metabolic and cardiovascular risk. However, oral glucose tolerance testing at 24-28 gestational weeks limits early risk stratification. Gut microbiota-associated metabolites may reflect early metabolic abnormalities, including those relevant to cardiometabolic health, but robust early-pregnancy biomarkers remain limited. METHODS: We conducted a multicenter nested case-control and prospective study involving 2,693 pregnant women. Untargeted metabolomics and metagenomics were integrated to identify GDM-associated metabolites and gut microbial alterations. Three consistently dysregulated metabolites, 3-hydroxydecanoic acid, γ-Glu-Leu, and propionic acid, were quantified by targeted LC-MS/MS. Candidate algorithms were compared using repeated 10-fold cross-validation, and a final generalized linear model was externally and prospectively validated. RESULTS: Women who later developed GDM showed an adverse early-pregnancy metabolic profile, including higher BMI, triglycerides, and platelet count. Untargeted metabolomics identified 14 persistently altered metabolites enriched in energy, oxidative stress, and amino acid metabolism pathways. Metagenomics revealed taxonomic restructuring and coordinated microbiota-metabolite associations. The three-metabolite model achieved AUCs of 0.838 (95% CI, 0.791-0.885) in training, 0.840 (95% CI, 0.769-0.911) in internal validation, 0.955 (95% CI, 0.925-0.985) and 0.917 (95% CI, 0.875-0.958) in two external cohorts, and 0.969 (95% CI, 0.937-1.000) in the prospective cohort. CONCLUSION: Early microbiota-associated metabolic dysregulation is detectable before routine GDM diagnosis. This compact three-metabolite panel may support early GDM risk stratification and provides metabolic evidence relevant to broader cardiometabolic risk assessment in pregnancy.

Humans↗
PubMed2025

Landscape of essential growth and fluconazole-resistance genes in the human fungal pathogen Cryptococcus neoformans.

Fungi can cause devastating invasive infections, typically in immunocompromised patients. Treatment is complicated both by the evolutionary similarity between humans and fungi and by the frequent emergence of drug resistance. Studies in fungal pathogens have long been slowed by a lack of high-throughput tools and community resources that are common in model organisms. Here we demonstrate a high-throughput transposon mutagenesis and sequencing (TN-seq) system in Cryptococcus neoformans that enables genome-wide determination of gene essentiality. We employed a random forest machine learning approach to classify the C. neoformans genome as essential or nonessential, predicting 1,465 essential genes, including 302 that lack human orthologs. These genes are ideal targets for new antifungal drug development. TN-seq also enables genome-wide measurement of the fitness contribution of genes to phenotypes of interest. As proof of principle, we demonstrate the genome-wide contribution of genes to growth in fluconazole, a clinically used antifungal. We show a novel role for the well-studied RIM101 pathway in fluconazole susceptibility. We also show that insertions of transposons into the 5' upstream region can drive sensitization of essential genes, enabling screenlike assays of both essential and nonessential components of the genome. Using this approach, we demonstrate a role for mitochondrial function in fluconazole sensitivity, such that tuning down many essential mitochondrial genes via 5' insertions can drive resistance to fluconazole. Our assay system will be valuable in future studies of C. neoformans, particularly in examining the consequences of genotypic diversity.

Cryptococcus neoformans↗
PubMed2026

Advancing precision tacrolimus therapy: a systems genetics dissection in BXD platform.

BACKGROUND: Tacrolimus is a core immunosuppressant in organ transplantation, but its narrow therapeutic window and significant pharmacokinetic variability hinder precision dosing. Although CYP3A5-guided strategies have established clinical relevance for tacrolimus initial dose adjustment, they do not fully account for the marked interindividual variability in tacrolimus exposure, highlighting the need for complementary models to decode more complex genetic regulation. This study aimed to identify candidate genetic modulators of tacrolimus metabolism and develop an integrated predictive framework for individualized therapy. METHODS: Using 46 BXD recombinant inbred mouse strains, we characterized transcriptomics and machine learning, and validated key genes. We then constructed a clinical model using data from 168 renal transplant recipients. RESULTS: We identified 19 genomic loci associated with tacrolimus pharmacokinetic traits and supported DBP/CYP2A6 as candidate modulators associated with tacrolimus disposition. The clinical prediction model, incorporating these genes and clinical variables, achieved robust AUROC. CONCLUSIONS: These findings support a polygenic contribution to tacrolimus metabolism and provide an experimental and computational framework for identifying candidate modulators relevant to individualized dosing. The BXD mouse platform offers a systems-genetics approach for mechanistic discovery that may inform future translational studies on tacrolimus precision dosing.

Animals↗
PubMed2026

ORBIT: Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space for cancer driver gene identification.

Accurate identification of cancer driver genes is crucial for precision oncology but remains challenging due to the complexity of integrating heterogeneous data and modeling dynamic biological systems. To address these limitations, we propose ORBIT (Oncogenic Representation Learning via Bi-Prototype Contrastive Learning in Hyperbolic Space). Our framework synergistically fuses multi-omics profiles with functional network data using a context-adaptive graph reweighting mechanism to capture cancer-specific dynamics. The model employs a bi-prototype contrastive learning strategy within hyperbolic space, which aligns gene representations around distinct driver and non-driver semantic anchors while preserving the intrinsic hierarchy of biological networks. Comprehensive evaluations demonstrate that ORBIT achieves highly competitive stability in pan-cancer analysis while consistently outperforming state-of-the-art methods in cancer-specific predictions. Furthermore, functional enrichment analysis confirms that the model effectively segregates core cancer pathways, and drug sensitivity profiling validates the clinical relevance of the identified drivers. By integrating hyperbolic geometry with context-adaptive learning, ORBIT offers a robust and interpretable paradigm for precision medicine. The source codes and datasets are publicly accessible at https://github.com/spcho-dev/ORBIT.

Humans↗
PubMed2004

Conserved network motifs allow protein-protein interaction prediction.

MOTIVATION: High-throughput protein interaction detection methods are strongly affected by false positive and false negative results. Focused experiments are needed to complement the large-scale methods by validating previously detected interactions but it is often difficult to decide which proteins to probe as interaction partners. Developing reliable computational methods assisting this decision process is a pressing need in bioinformatics. RESULTS: We show that we can use the conserved properties of the protein network to identify and validate interaction candidates. We apply a number of machine learning algorithms to the protein connectivity information and achieve a surprisingly good overall performance in predicting interacting proteins. Using a 'leave-one-out' approach we find average success rates between 20 and 40% for predicting the correct interaction partner of a protein. We demonstrate that the success of these methods is based on the presence of conserved interaction motifs within the network. AVAILABILITY: A reference implementation and a table with candidate interacting partners for each yeast protein are available at http://www.protsuggest.org.

Algorithms↗
PubMed2006

A compression-based approach for coding sequences identification. I. Application to prokaryotic genomes.

Most of the gene prediction algorithms for prokaryotes are based on Hidden Markov Models or similar machine-learning approaches, which imply the optimization of a high number of parameters. The present paper presents a novel method for the classification of coding and non-coding regions in prokaryotic genomes, based on a suitably defined compression index of a DNA sequence. The main features of this new method are the non-parametric logic and the costruction of a dictionary of words extracted from the sequences. These dictionaries can be very useful to perform further analyses on the genomic sequences themselves. The proposed approach has been applied on some prokaryotic complete genomes, obtaining optimal scores of correctly recognized coding and non-coding regions. Several false-positive and false-negative cases have been investigated in detail, which have revealed that this approach can fail in the presence of highly structured coding regions (e.g., genes coding for modular proteins) or quasi-random non-coding regions (e.g., regions hosting non-functional fragments of copies of functional genes; regions hosting promoters or other protein-binding sequences). We perform an overall comparison with other gene-finder software, since at this step we are not interested in building another gene-finder system, but only in exploring the possibility of the suggested approach.

Algorithms↗
PubMed2004

A novel method for protein secondary structure prediction using dual-layer SVM and profiles.

A high-performance method was developed for protein secondary structure prediction based on the dual-layer support vector machine (SVM) and position-specific scoring matrices (PSSMs). SVM is a new machine learning technology that has been successfully applied in solving problems in the field of bioinformatics. The SVM's performance is usually better than that of traditional machine learning approaches. The performance was further improved by combining PSSM profiles with the SVM analysis. The PSSMs were generated from PSI-BLAST profiles, which contain important evolution information. The final prediction results were generated from the second SVM layer output. On the CB513 data set, the three-state overall per-residue accuracy, Q3, reached 75.2%, while segment overlap (SOV) accuracy increased to 80.0%. On the CB396 data set, the Q3 of our method reached 74.0% and the SOV reached 78.1%. A web server utilizing the method has been constructed and is available at http://www.bioinfo.tsinghua.edu.cn/pmsvm.

Computational Biology↗
PubMed1992

Applying machine learning methods for finding significant amino acid properties in proteins.

There are several possibilities for definition and derivation of sequence patterns associated with structural motifs, in particular on the secondary structure level which may be used to predict these structure elements. Sequence patterns consist of a number of consecutive positions along the polypeptide chain from which a certain quantity is specified. One of the important factors in deriving sequence patterns in terms of amino acid properties is how to find the most characteristic properties to specify a certain position and thus to avoid redundant physical information. We have applied machine learning methods to select the most significant amino acid properties describing a structurally determined sequence position. Results are given for the beginning of alpha-helices. These methods may link the gap between amino acid patterns and property patterns and thus are valuable to improve protein structure prediction.

Amino Acids↗
PubMed2025

Comprehensive analysis of diagnostic biomarkers related to histone acetylation in acute myocardial infarction.

BACKGROUND: Acute myocardial infarction (AMI) has become a serious disease that endangers human health, with high morbidity and mortality. Numerous studies have reported histone acetylation can result in the occurrence of cardiovascular diseases. This article aims to explore the potential biomarkers of histone acetylation regulatory genes (ARGs) in AMI patients. METHODS: Five AMI datasets were downloaded from the Gene Expression Omnibus (GEO) database. Next, ARG-related genes were gathered by gene set variation analysis (GSVA) and Spearman's correlation analysis. Subsequently, weighted gene co-expression network analysis (WGCNA) was performed to identify the module genes related to histone acetylation regulation. In the GSE60993 and GSE48060 datasets, the common differentially expressed genes (DEGs) between AMI and control samples were screened. Importantly, the intersecting genes were obtained by overlapping ARGs-related genes, common DEGs, and module genes. Then, the biomarkers in AMI were determined by machine learning, receiver operating characteristic (ROC) curves, and quantitative PCR (qPCR). In addition, immune analysis, drug prediction, molecular docking, and the lncRNA-miRNA-mRNA regulatory network targeting the biomarkers were analyzed, respectively. RESULTS: Here, a total of 18 intersecting genes were identified by overlapping 7,349 ARGs-related genes, 5,565 module genes, and 25 common DEGs. Further, five biomarkers (AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12) were obtained, and a nomogram was constructed and verified based on these biomarkers. Notably, the biomarkers were significantly associated with CD8 T cells and neutrophils. In addition, the drugs related to biomarkers were predicted, and ATOGEPANT with the molecular target (S100A12) had a high binding affinity (docking score = -10 kcal/mol). CONCLUSION: AQP9, HLA-DQA1, MCEMP1, NKG7, and S100A12 were identified as biomarkers related to ARGs in AMI, which provides a new perspective to study the relationship between ARGs and AMI.

Humans↗