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PubMed2025

HiCForecast: dynamic network optical flow estimation algorithm for spatiotemporal Hi-C data forecasting.

MOTIVATION: The exploration of the 3D organization of DNA within the nucleus in relation to various stages of cellular development has led to experiments generating spatiotemporal Hi-C data. However, there is limited spatiotemporal Hi-C data for many organisms, impeding the study of 3D genome dynamics. To overcome this limitation and advance our understanding of genome organization, it is crucial to develop methods for forecasting Hi-C data at future time points from existing timeseries Hi-C data. RESULT: In this work, we designed a novel framework named HiCForecast, adopting a dynamic voxel flow algorithm to forecast future spatiotemporal Hi-C data. We evaluated how well our method generalizes forecasting data across different species and systems, ensuring performance in homogeneous, heterogeneous, and general contexts. Using both computational and biological evaluation metrics, our results show that HiCForecast outperforms the current state-of-the-art algorithm, emerging as an efficient and powerful tool for forecasting future spatiotemporal Hi-C datasets. AVAILABILITY AND IMPLEMENTATION: HiCForecast is publicly available at https://github.com/OluwadareLab/HiCForecast.

Algorithms↗

PubMed1988

Bayesian forecasting improves the prediction of intraoperative plasma concentrations of alfentanil.

To achieve therapeutic plasma concentrations of the opioid alfentanil, one must administer the drug as a variable rate continuous infusion. For most patients, using population pharmacokinetic parameters of alfentanil for dosing regimen allows accurate prediction of the plasma concentration of the drug over time. However, for some patients, using such parameters results in systematic over- or underprediction of the concentration. Retrospectively studying a data set (dosage history and measured concentrations) for 34 patients, the authors examined how Bayesian forecasting could improve the precision of prediction. For each patient, a Bayesian regression was performed to estimate "individualized" pharmacokinetic parameters, using population pharmacokinetic values for alfentanil and the measurement of alfentanil in one or more plasma samples from each patient. These individualized parameters were then used to predict the subsequent plasma concentrations of alfentanil over time. By comparing the value of each measured point with its corresponding predicted value, the authors calculated the prediction error as a percentage of the measured value. The precision of the prediction was assessed by the percent mean absolute prediction error. After Bayesian forecasting using a single point sampled at 80 min after start of anesthesia, the average precision of the prediction was 13.8 +/- 6.1% (SD). Using no Bayesian forecasting and only population values of the pharmacokinetic parameters for the prediction of the concentration, the precision was 24.3 +/- 16.9%. The improvement in precision brought by Bayesian forecasting was especially noticeable for those patients whose prediction of alfentanil was poor using population pharmacokinetic values (i.e., "outlier" patients).(ABSTRACT TRUNCATED AT 250 WORDS)

Alfentanil↗

PubMed1997

Amikacin Bayesian forecasting in critically ill patients with sepsis and cirrhosis.

This study was designed to determine the population pharmacokinetic parameters of amikacin in two subpopulations of intensive care unit patients with sepsis and cirrhosis and sepsis without cirrhosis. The authors evaluated the usefulness of the obtained parameters to forecast the serum amikacin concentrations in a validation group of patients with sepsis and cirrhosis when used as a priori distribution in a Bayesian forecaster. The population parameters were estimated by a nonparametric expectation maximization algorithm (NPEM), and the accuracy of the predictions were evaluated through a prediction error analysis. Significant differences (p < 0.05) were found in Vd (0.668 versus 0.470 l/kg) and K (0.0701 versus 0.161 h-1) between subpopulations of patients with and without cirrhosis. The model derived for patients with cirrhosis used as a priori distribution, with and without feedback, was superior to the model derived for patients with sepsis in forecasting amikacin serum concentrations. The results show the relevance of using the specific model for the subgroup with cirrhosis as a priori distribution in a Bayesian forecaster to obtain a nonbiased prediction with an acceptable precision.

Amikacin↗

PubMed2004

Investigation of short-range cedar pollen forecasting.

Pollen forecasting is of increasing interest as a way to help the general public avoid contact with allergy-inducing pollen. It was recently reported that the dynamics underlying pollen concentration series is very similar to that of low-dimensional deterministic chaos, thus opening up new avenues of development in local pollen forecasting. Our analysis of hourly cedar pollen series for two seasons showed evidence of a small degree of determinism underlying the pollen time-series dynamics. However, we could not confirm that our pollen series was generated by a low-dimensional chaotic system. The nearest-neighbor method using local constant prediction applied to hourly pollen forecasting with a 1-h lead time was effective for small to medium pollen variations, but failed to reproduce large and intermittent pollen bursts. The performance of the nearest-neighbor model was significantly improved by applying a nonlinear filter to the source dataset. Standard time-series techniques such as neural networks did not improve upon these results. The difficulty in fully characterizing and accurately forecasting the pollen series was thought to originate in the nonstationarity of the series and in the large and intermittent pollen bursts that were found to have no apparent time structure. Thus the dynamics of hourly pollen series is probably not strongly tied to a low-dimensional chaotic system.

Journal Article↗

PubMed2006

Forecasting the unresponsiveness to verbal command on the basis of EEG frequency progression during anesthetic induction with propofol.

The objective of this study is to model the association between the electroencephalogram (EEG) spectral features and the novel r scale representing the sedative effects of the propofol anesthetic drug. On the basis of the r scale, the unresponsiveness to the verbal command (LVC) is forecasted. EEG recordings are taken from a 16-patient study population undergoing propofol anesthetic induction. EEG was filtered into consecutive 4-Hz passbands up to 28 Hz. Of these time-series, the amplitude envelopes were extracted and used as input features to the first and the second-order polynomial multiple linear regression models. The values r epsilon [0.4, 1] were predicted with the R2 value of 0.775 with a cross validation. The LVC times were forecasted with the median error of 5%-7% or equivalently 10-13 s. In contrast, using the median of the measured LVC times of the training population as a forecast, the corresponding error was 12% or 26 s. The results suggest an acceptable correlation between the r scale and the EEG spectrum in the studied range. Moreover, the r values of an individual can be predicted using a population model. The suggested framework enables forecasting the LVC, which may open new possibilities for steering the drug administration.

Adult↗

PubMed2005

Atmospheric science. Weather forecasting with ensemble methods.

Traditional weather forecasting has been built on a foundation of deterministic modeling--start with initial conditions, put them into a supercomputer model, and end up with a prediction about future weather. But as Gneiting and Raftery discuss in their Perspective, a new approach--ensemble forecasting--was introduced in the early 1990s. In this method, up to 100 different computer runs, each with slightly different starting conditions or model assumptions, are combined into a weather forecast. In concert with statistical techniques, ensembles can provide accurate statements about the uncertainty in daily and seasonal forecasting. The challenge now is to improve the modeling, statistical analysis, and visualization technologies for disseminating the ensemble results.

Journal Article↗

PubMed2001

A short-term temperature forecaster based on a novel radial basis functions neural network.

Many applications dealing with electric load forecasting in buildings require temperature prediction. A new method for short-term temperature forecasting based on a Radial Basis Functions Neural Network, initialized by a Regression Tree, is presented. In this method, each terminal node of the tree contributes one hidden unit to the RBF network. The forecaster uses the current coded hour and the temperature as inputs, and predicts the next hour temperature. The results demonstrate this predictor can be used for load forecasting.

Neural Networks, Computer↗

PubMed2005

Coherent mortality forecasts for a group of populations: an extension of the Lee-Carter method.

Mortality patterns and trajectories in closely related populations are likely to be similar in some respects, and differences are unlikely to increase in the long run. It should therefore be possible to improve the mortality forecasts for individual countries by taking into account the patterns in a larger group. Using the Human Mortality Database, we apply the Lee-Carter model to a group of populations, allowing each its own age pattern and level of mortality but imposing shared rates of, change by age. Our forecasts also allow divergent patterns to continue for a while before tapering off. We forecast greater longevity gains for the United States and lesser ones for Japan relative to separate forecasts.

Canada↗

PubMed1988

Clinical assessment of a two-compartment Bayesian forecasting method for lidocaine.

The predictive performance of a two-compartment Bayesian forecasting method for lidocaine (L) was evaluated concurrently with lidocaine therapy in 46 hospitalized patients; 14 of these patients presented with congestive heart failure (CHF). Using an HP-85 microcomputer, demographic and dose-concentration information obtained during continuous lidocaine therapy was used to forecast subsequent lidocaine concentrations. One lidocaine concentration was obtained within each of the three intervals following initiation of lidocaine infusions: I1 (1-6 h), I2 (6-12 h), and I3 (greater than 12 h). Patients were categorized into 4 groups: (a) short-term infusions (less than 24 h) without CHF, (b) short-term infusions with CHF, (c) long-term infusions (greater than 24 h) without CHF, and (d) long-term infusions with CHF. The mean prediction errors (range -0.60-0.27) included zero (95% confidence limits) in all groups and suggested no bias. Forecasts of the I3 lidocaine concentrations were consistently more precise [lower mean absolute errors (MAE) and root mean squared errors] using the lidocaine concentration obtained during the 6-12-h interval (I2) than when the lidocaine concentration obtained at the earlier interval (I1) was used. The MAE was reduced by 20-40% when a single lidocaine concentration obtained during I2 was used as compared to I1. Precision was only slightly improved with the use of two lidocaine concentrations. We conclude that this Bayesian algorithm is unbiased and delivers acceptable precision in forecasting lidocaine concentrations.

Adult↗

PubMed1979

Forecasting: which type is for you?

Planning--one of the most important functions of laboratory management--cannot be done efficiently without forecasts of external and internal conditions that affect the laboratory. The successful Delphi Method for external forecasting of technological events is discussed. The internal forecasting methods covered in this report include judge and jury methods, time series analysis, and regression models. A brief description of each of these methods is presented along with information on accuracy, typical applications, costs, and references that provide the details of how to perform the forecasts.

Humans↗

PubMed1976

Forecasting patient census: commonalities in time series models.

Highly accurate patient census forecasting models are specified for five hospitals by use of a general equation for integrated autoregressive moving average (IARMA) forecasts. A general census forecasting model, based on features common to all five institution-specific models, is described and its forecasts are compared to those from the specific models.

Hospitals↗

PubMedDate not supplied

Forecasting with growth curves: the effect of error structure.

"The main theme of this paper is an investigation into the importance of error structure as a determinant of the forecasting accuracy of the logistic model. The relationship between the variance of the disturbance term and forecasting accuracy is examined empirically. A general local logistic model is developed as a vehicle to be used in this investigation. Some brief comments are made on the assumptions about error structure, implicit or explicit, in the literature." The results suggest that "the variance of the disturbance term, when using the logistic to forecast human populations, is proportional to at least the square of population size."

Forecasting↗

PubMed1985

Nuptiality models and the two-sex problem in national population forecasts.

"This paper describes the two-sex problem in nuptiality models, focussing on applications in national population forecasts. Requirements for a realistic two-sex marriage model are mentioned, together with additional considerations important in the context of official forecasts. Recent nuptiality models violate the requirements to a certain extent and/or do not meet the additional considerations. A new model, used in the 1980-based population forecasts of the Netherlands compiled by the Netherlands' Central Bureau of Statistics, is described." (summary in FRE)

Demography↗

PubMed1997

Problems and solutions in forecasting geographical populations.

"This paper asks the question: How does the multistate population model need to be adjusted to provide forecasts of geographical populations? Following an exposition of the standard model, possible solutions to the problems posed by excessive number of parameters are discussed. Decomposition, aggregation and parameterization are described, drawing on some new results. Issues in the temporal forecasting of model components are outlined and the alternative approach of using a spatial interaction model is considered. The paper concludes by arguing that the design of forecasting models is a powerful learning device for both designers and users."

Forecasting↗

PubMed1992

Labour market forecasting in Australia: the science of the art.

"This article, which is written for the non-economist, overviews recent attempts at labour market forecasting in Australia and summarizes contemporary views on their contribution towards enhancing the efficiency of the Australian labour market. Current methods of forecasting are described and assessed purely from a theoretical perspective. The paper does not attempt to assess the accuracy or otherwise of Australian labour market forecasts...."

Australia↗

PubMed1987

Population forecasts for South Pacific nations using autoregressive models, 1985-2000.

"This paper uses an autoregressive statistical model to forecast population for Fiji, Western Samoa, Tonga, Solomon Islands, and Vanuatu and compares these forecasts with those obtained from other methods. The growth rate of population is predicted to continue to fall in Fiji and Tonga, rise a little for Western Samoa, and rise considerably in Vanuatu and the Solomon Islands. The implications of the forecasts for recent government development plans are also discussed."

Demography↗

PubMed1992

Forecasting U.S. population totals with the Box-Jenkins approach.

"The use of the Box-Jenkins approach for forecasting the population of the United States up to the year 2080 is discussed. It is shown that the Box-Jenkins approach is equivalent to a simple trend model when making long-range predictions for the United States. An investigation of forecasting accuracy indicates that the Box-Jenkins method produces population forecasts that are at least as reliable as those done with more traditional demographic methods."

Americas↗

PubMed1992

Evaluating the forecast accuracy and bias of alternative population projections for states.

"A common perception among producers (and users) of population projections is that complex and/or sophisticated techniques produce more accurate forecasts than simple and/or naive techniques. In this paper we test the validity of that perception by evaluating the forecast accuracy and bias of eight commonly used projection techniques drawn from...four categories [trend extrapolation, ratio extrapolation, cohort-component, and structural]. Using data for [U.S.] state population projections from a number of different time periods, we find no evidence that complex and/or sophisticated techniques produce more accurate or less biased forecasts than simple, naive techniques."

Americas↗