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PubMed2003

Prediction rules for computed tomography in the dementia assessment: do they predict clinical utility of CT?

Neuroimaging is widely employed in the dementia assessment in refining clinical decision-making. However, with rising interest in cost-effective medical practice, efforts have been made in the literature to define clinical prediction rules that select for a subgroup of patients who would most likely benefit from neuroimaging. This short study examined the ability of a group of published clinical predictors to identify patients whose diagnoses or management would be influenced by CT scan results. The study finds that none of the published predictors bears a significant relationship to actual influence of CT scans in a group of memory clinic patients, highlighting the need for the development of clinical predictors for neuroimaging that will impact patient care.

Aged↗
PubMed2004

Detection of 91 potential conserved plant microRNAs in Arabidopsis thaliana and Oryza sativa identifies important target genes.

MicroRNAs (miRNAs) are an extensive class of tiny RNA molecules that regulate the expression of target genes by means of complementary base pair interactions. Although the first miRNAs were discovered in Caenorhabditis elegans, >300 miRNAs were recently documented in animals and plants, both by cloning methods and computational predictions. We present a genome-wide computational approach to detect miRNA genes in the Arabidopsis thaliana genome. Our method is based on the conservation of short sequences between the genomes of Arabidopsis and rice (Oryza sativa) and on properties of the secondary structure of the miRNA precursor. The method was fine-tuned to take into account plant-specific properties, such as the variable length of the miRNA precursor sequences. In total, 91 potential miRNA genes were identified, of which 58 had at least one nearly perfect match with an Arabidopsis mRNA, constituting the potential targets of those miRNAs. In addition to already known transcription factors involved in plant development, the targets also comprised genes involved in several other cellular processes, such as sulfur assimilation and ubiquitin-dependent protein degradation. These findings considerably broaden the scope of miRNA functions in plants.

Arabidopsis↗
PubMed2003

Description and interpretation of adaptive evolution of Escherichia coli K-12 MG1655 by using a genome-scale in silico metabolic model.

Genome-scale in silico metabolic networks of Escherichia coli have been reconstructed. By using a constraint-based in silico model of a reconstructed network, the range of phenotypes exhibited by E. coli under different growth conditions can be computed, and optimal growth phenotypes can be predicted. We hypothesized that the end point of adaptive evolution of E. coli could be accurately described a priori by our in silico model since adaptive evolution should lead to an optimal phenotype. Adaptive evolution of E. coli during prolonged exponential growth was performed with M9 minimal medium supplemented with 2 g of alpha-ketoglutarate per liter, 2 g of lactate per liter, or 2 g of pyruvate per liter at both 30 and 37 degrees C, which produced seven distinct strains. The growth rates, substrate uptake rates, oxygen uptake rates, by-product secretion patterns, and growth rates on alternative substrates were measured for each strain as a function of evolutionary time. Three major conclusions were drawn from the experimental results. First, adaptive evolution leads to a phenotype characterized by maximized growth rates that may not correspond to the highest biomass yield. Second, metabolic phenotypes resulting from adaptive evolution can be described and predicted computationally. Third, adaptive evolution on a single substrate leads to changes in growth characteristics on other substrates that could signify parallel or opposing growth objectives. Together, the results show that genome-scale in silico metabolic models can describe the end point of adaptive evolution a priori and can be used to gain insight into the adaptive evolutionary process for E. coli.

Adaptation, Physiological↗
PubMed1988

Genetic evaluation of dairy goats for milk and fat yield with an animal model.

An animal model was applied to evaluate 120,073 Alpine, LaMancha, Nubian, Saanen, and Toggenburg bucks and does. Parities higher than six were excluded. The model included fixed herd-year-season and random herd-sire interaction, permanent environmental breeding value, and residual effects. Breeding value included additive genetic value and fixed accumulated group effect. A doe's records in different herds were accommodated by predicting a separate permanent environmental effect for each herd. Lactations were weighted according to lactation length. Does of all breeds were used as contemporaries. Breed differences were accounted for with a grouping strategy that grouped unknown parents by breed, sex of parent, sex of progeny, and birth year of progeny. Evaluations for milk and fat were computed as separate traits during the same processing. To achieve three-digit accuracy, 100 iterations were completed, each requiring slightly over 4 s of central processing unit time on a Cray X-MP/48 computer. Predicted producing ability was computed as sum of predicted breeding value, herd-sire interaction, and permanent environmental effects for use in ranking does on expected yield in next lactation. Evaluations of goats served as a test for the animal model evaluation system under development for dairy cattle.

Animals↗
PubMed1996

High-throughput cDNA screening utilizing a low order neural network filter.

A low order neural network-based filter was designed as a rapid screening agent for single-spanning transmembrane regions in an integrated informatics system. A rapid screening algorithm was seen as a compromise between costly structure-specific techniques and simple rules that gave a high false-positive rate for cDNA. The filter was applied to a library of 2123 anonymous cDNA sequences, which resulted in 61 detections. Evaluation of the detections with two other dissimilar computer prediction algorithms yielded strong transmembrane predictions for 15 of the detections, while 8 of the detections resulted in a definitive negative result. Homology searches performed on the sequences with detection reports yielded 13 homologs in the predicted reading frame, four of which are membrane associated.

Animals↗
PubMed2002

Computational analysis of core promoters in the Drosophila genome.

BACKGROUND: The core promoter, a region of about 100 base-pairs flanking the transcription start site (TSS), serves as the recognition site for the basal transcription apparatus. Drosophila TSSs have generally been mapped by individual experiments; the low number of accurately mapped TSSs has limited analysis of promoter sequence motifs and the training of computational prediction tools. RESULTS: We identified TSS candidates for about 2,000 Drosophila genes by aligning 5' expressed sequence tags (ESTs) from cap-trapped cDNA libraries to the genome, while applying stringent criteria concerning coverage and 5'-end distribution. Examination of the sequences flanking these TSSs revealed the presence of well-known core promoter motifs such as the TATA box, the initiator and the downstream promoter element (DPE). We also define, and assess the distribution of, several new motifs prevalent in core promoters, including what appears to be a variant DPE motif. Among the prevalent motifs is the DNA-replication-related element DRE, recently shown to be part of the recognition site for the TBP-related factor TRF2. Our TSS set was then used to retrain the computational promoter predictor McPromoter, allowing us to improve the recognition performance to over 50% sensitivity and 40% specificity. We compare these computational results to promoter prediction in vertebrates. CONCLUSIONS: There are relatively few recognizable binding sites for previously known general transcription factors in Drosophila core promoters. However, we identified several new motifs enriched in promoter regions. We were also able to significantly improve the performance of computational TSS prediction in Drosophila.

Animals↗
PubMed2005

Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures.

MOTIVATION: Since the whole genome sequences of many species have been determined, computational prediction of RNA secondary structures and computational identification of those non-coding RNA regions by comparative genomics become important. Therefore, more advanced alignment methods are required. Recently, an approach of structural alignment for RNA sequences has been introduced to solve these problems. Pair hidden Markov models on tree structures (PHMMTSs) proposed by Sakakibara are efficient automata-theoretic models for structural alignment of RNA secondary structures, although PHMMTSs are incapable of handling pseudoknots. On the other hand, tree adjoining grammars (TAGs), a subclass of context-sensitive grammars, are suitable for modeling pseudoknots. Our goal is to extend PHMMTSs by incorporating TAGs to be able to handle pseudoknots. RESULTS: We propose pair stochastic TAGs (PSTAGs) for aligning and predicting RNA secondary structures including a simple type of pseudoknot which can represent most known pseudoknot structures. First, we extend PHMMTSs defined on alignment of 'trees' to PSTAGs defined on alignment of 'TAG trees' which represent derivation processes of TAGs and are functionally equivalent to derived trees of TAGs. Then, we develop an efficient dynamic programming algorithm of PSTAGs for obtaining an optimal structural alignment including pseudoknots. We implement the PSTAG algorithm and demonstrate the properties of the algorithm by using it to align and predict several small pseudoknot structures. We believe that our implemented program based on PSTAGs is the first grammar-based and practically executable software for comparative analyses of RNA pseudoknot structures, and, further, non-coding RNAs.

Algorithms↗
PubMed2006

Significantly improved prediction of subcellular localization by integrating text and protein sequence data.

Computational prediction of protein subcellular localization is a challenging problem. Several approaches have been presented during the past few years; some attempt to cover a wide variety of localizations, while others focus on a small number of localizations and on specific organisms. We present a comprehensive system, integrating protein sequence-derived data and text-based information. Itis tested on three large data sets, previously used by leading prediction methods. The results demonstrate that our system performs significantly better than previously reported results, for a wide range of eukaryotic subcellular localizations.

Artificial Intelligence↗
PubMed2004

Characterization of complicated new polymorphs of chlorothalonil by X-ray diffraction and computer crystal structure prediction.

A simultaneous experimental and computational search for polymorphs of chlorothalonil (2,4,5,6-tetrachloro-1,3-benzenedicarbonitrile) has been conducted, leading to the first characterization of forms 2 and 3. The crystal structure prediction study, using a specifically developed anisotropic atom-atom potential for chlorothalonil, gave as the global minimum in the lattice energy a structure that was readily refined against powder diffraction data to the known form 1 (P2(1)/a). The structure of form 2 was solved and refined from powder diffraction data, giving a disordered structure in the Rm (166) space group (Z = 3). It could also be refined against a P1 ordered model, starting from a low-energy hypothetical sheet structure found in the computational search. This shows that the disorder could be associated with the stacking of ordered sheets. The disordered structure for form 2 was later confirmed by single-crystal X-ray diffraction. The structure of form 3, determined from single-crystal diffraction, contains three independent molecules in the asymmetric unit in P2(1) (4) (Z = 6). Powder diffraction showed that this single-herringbone structure was similar to two low-energy structures found in the search. Further analysis confirmed that form 3 has a similar lattice energy and contains elements from both these predicted structures, which can be considered as good approximations to the form 3 structure.

Computer Simulation↗
PubMed2004

GDAP: a web tool for genome-wide protein disulfide bond prediction.

The Genomic Disulfide Analysis Program (GDAP) provides web access to computationally predicted protein disulfide bonds for over one hundred microbial genomes, including both bacterial and achaeal species. In the GDAP process, sequences of unknown structure are mapped, when possible, to known homologous Protein Data Bank (PDB) structures, after which specific distance criteria are applied to predict disulfide bonds. GDAP also accepts user-supplied protein sequences and subsequently queries the PDB sequence database for the best matches, scans for possible disulfide bonds and returns the results to the client. These predictions are useful for a variety of applications and have previously been used to show a dramatic preference in certain thermophilic archaea and bacteria for disulfide bonds within intracellular proteins. Given the central role these stabilizing, covalent bonds play in such organisms, the predictions available from GDAP provide a rich data source for designing site-directed mutants with more stable thermal profiles. The GDAP web application is a gateway to this information and can be used to understand the role disulfide bonds play in protein stability both in these unusual organisms and in sequences of interest to the individual researcher. The prediction server can be accessed at http://www.doe-mbi.ucla.edu/Services/GDAP.

Archaeal Proteins↗
PubMed2005

Prediction discrepancies for the evaluation of nonlinear mixed-effects models.

Reliable estimation methods for non-linear mixed-effects models are now available and, although these models are increasingly used, only a limited number of statistical developments for their evaluation have been reported. We develop a criterion and a test to evaluate nonlinear mixed-effects models based on the whole predictive distribution. For each observation, we define the prediction discrepancy (pd) as the percentile of the observation in the whole marginal predictive distribution under H(0). We propose to compute prediction discrepancies using Monte Carlo integration which does not require model approximation. If the model is valid, these pd should be uniformly distributed over (0, 1) which can be tested by a Kolmogorov-Smirnov test. In a simulation study based on a standard population pharmacokinetic model, we compare and show the interest of this criterion with respect to the one most frequently used to evaluate nonlinear mixed-effects models: standardized prediction errors (spe) which are evaluated using a first order approximation of the model. Trends in pd can also be evaluated via several plots to check for specific departures from the model.

Algorithms↗
PubMed2001

Attractor reconstruction for non-linear systems: a methodological note.

Attractor reconstruction is an important step in the process of making predictions for non-linear time-series and in the computation of certain invariant quantities used to characterize the dynamics of such series. The utility of computed predictions and invariant quantities is dependent on the accuracy of attractor reconstruction, which in turn is determined by the methods used in the reconstruction process. This paper suggests methods by which the delay and embedding dimension may be selected for a typical delay coordinate reconstruction. A comparison is drawn between the use of the autocorrelation function and mutual information in quantifying the delay. In addition, a false nearest neighbor (FNN) approach is used in minimizing the number of delay vectors needed. Results highlight the need for an accurate reconstruction in the computation of the Lyapunov spectrum and in prediction algorithms.

Algorithms↗
PubMedDate not supplied

Prediction of biological activity spectra via the Internet.

The majority of biologically active compounds have both pharmacotherapeutic and side/toxic actions. To estimate general efficacy and safety of the molecules under study, their biological potential should be thoroughly evaluated. In an early stage of study, only information about structural formulae was available and was used as an input for computational prediction. Based on a structural formulae of compounds presented as SDF or MOL-files, computer program PASS predicts 900 pharmacological effects, mechanism of action, and specific toxicity. An average accuracy of prediction in leave-one-out cross-validation is about 85%. For evaluating new compounds, scientific community may use PASS via the Internet for free at URL: http://www.ibmh.msk.su/PASS. In the first 18 months of PASS Inet's use, approximately 1000 researchers from 60 countries have obtained predicted biological activity spectra for about 23,000 different chemical compounds. More than 64 million PASS predictions for almost 250,000 compounds from Open NCI database are available on the web site http://cactus.nci.nih.gov/ncidb2/. These predictions are used for selecting compounds with desirable and without unwanted types of biological activities among the NCI samples available for screening.

Computer Simulation↗
PubMed2004

A new algorithm for RNA secondary structure design.

The function of many RNAs depends crucially on their structure. Therefore, the design of RNA molecules with specific structural properties has many potential applications, e.g. in the context of investigating the function of biological RNAs, of creating new ribozymes, or of designing artificial RNA nanostructures. Here, we present a new algorithm for solving the following RNA secondary structure design problem: given a secondary structure, find an RNA sequence (if any) that is predicted to fold to that structure. Unlike the (pseudoknot-free) secondary structure prediction problem, this problem appears to be hard computationally. Our new algorithm, "RNA Secondary Structure Designer (RNA-SSD)", is based on stochastic local search, a prominent general approach for solving hard combinatorial problems. A thorough empirical evaluation on computationally predicted structures of biological sequences and artificially generated RNA structures as well as on empirically modelled structures from the biological literature shows that RNA-SSD substantially out-performs the best known algorithm for this problem, RNAinverse from the Vienna RNA Package. In particular, the new algorithm is able to solve structures, consistently, for which RNAinverse is unable to find solutions. The RNA-SSD software is publically available under the name of RNA Designer at the RNASoft website (www.rnasoft.ca).

Algorithms↗
PubMed2004

Predicting peptide binding to MHC pockets via molecular modeling, implicit solvation, and global optimization.

Development of a computational prediction method based on molecular modeling, global optimization, and implicit solvation has produced accurate structure and relative binding affinity predictions for peptide amino acids binding to five pockets of the MHC molecule HLA-DRB1*0101. Because peptide binding to MHC molecules is essential to many immune responses, development of such a method for understanding and predicting the forces that drive binding is crucial for pharmaceutical design and disease treatment. Underlying the development of this prediction method are two hypotheses. The first is that pockets formed by the peptide binding groove of MHC molecules are independent, separating the prediction of peptide amino acids that bind within individual pockets from those that bind between pockets. The second hypothesis is that the native state of a system composed of an amino acid bound to a protein pocket corresponds to the system's lowest free energy. The prediction method developed from these hypotheses uses atomistic-level modeling, deterministic global optimization, and three methods of implicit solvation: solvent-accessible area, solvent-accessible volume, and Poisson-Boltzmann electrostatics. The method predicts relative binding affinities of peptide amino acids for pockets of HLA-DRB1*0101 by determining computationally an amino acid's global minimum energy conformation. Prediction results from the method are in agreement with X-ray crystallography data and experimental binding assays.

Amino Acids↗
PubMed2026

AI-integrated digital breeding for crop improvement.

Crop breeding increasingly depends on the effective integration and interpretation of large, heterogeneous datasets spanning genomic, phenotypic, multi-omics, and environmental layers. Conventional breeding approaches are often insufficient to capture the complex relationships among these data or to support timely selection decisions. Digital breeding can help address this limitation by complementing field experimentation, mixed models, and genomic prediction with the integration of biological data and computational prediction throughout the breeding process. In particular, the rapid advancement of artificial intelligence (AI) has improved the analysis of high-dimensional datasets and broadened its application to trait prediction, selection, and breeding design. Here, we review recent developments in AI-enabled digital breeding, encompassing genomic, phenomic, and multi-omics data generation and analysis, predictive modeling, explainable and generative AI, and data-driven breeding decision support. We further discuss emerging AI applications, their current contributions to crop research and breeding, and the major considerations affecting their reliable and practical implementation. Collectively, this review provides a structured understanding of the roles of AI across the digital breeding process and offers guidance for future methodological development and practical application in crop improvement.

artificial intelligence↗
PubMed2005

Beyond annotation transfer by homology: novel protein-function prediction methods to assist drug discovery.

Every entirely sequenced genome reveals 100 s to 1000 s of protein sequences for which the only annotation available is 'hypothetical protein'. Thus, in the human genome and in the genomes of pathogenic agents there could be 1000 s of potential, unexplored drug targets. Computational prediction of protein function can play a role in studying these targets. We shall review the challenges, research approaches and recently developed tools in the field of computational function-prediction and we will discuss the ways these issues can change the process of drug discovery.

Computational Biology↗