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PubMedDate not supplied

ADME evaluation in drug discovery. 4. Prediction of aqueous solubility based on atom contribution approach.

A novel method for the estimation of aqueous solubility was solely based on simple atom contribution. Each atom in a molecule has its own contribution to aqueous solubility and was developed. Altogether 76 atom types were used to classify atoms with different chemical environments. Moreover, two correction factors, including hydrophobic carbon and square of molecular weight, were used to account for the inter-/intramolecular hydrophobic interactions and bulkiness effect. The contribution coefficients of different atom types and correction factors were generated based on a multiple linear regression using a learning set consisting of 1290 organic compounds. The obtained linear regression model possesses good statistical significance with an overall correlation coefficient (r) of 0.96, a standard deviation (s) of 0.61, and an unsigned mean error (UME) of 0.48. The actual prediction potential of the model was validated through an external test set with 21 pharmaceutically and environmentally interesting compounds. For the test set, a predictive r=0.94, s=0.84, and UME=0.52 were achieved. Comparisons among eight procedures of solubility calculation for those 21 molecules demonstrate that our model bears very good accuracy and is comparable to or even better than most reported techniques based on molecular descriptors. Moreover, we compared the performance of our model to a test set of 120 molecules with a popular group contribution method developed by Klopman et al. For this test set, our model gives a very effective prediction (r=0.96, s=0.79, UME=0.57), which is obviously superior to the predicted results (r=0.96, s=0.84, UME=0.70) given by the Klopman's group contribution approach. Because of the adoption of atoms as the basic units, our addition model does not contain a "missing fragment" problem and thus may be more simple and universal than the group contribution models and can give predictions for any organic molecules. A program, drug-LOGS, had been developed to identify the occurrence of atom types and estimate the aqueous solubility of a molecule.

Drug Design↗

PubMed2026

Predicting enhancer-promoter interactions using a stacking-based ensemble strategy.

MOTIVATION: Enhancer-promoter interactions (EPIs) are essential for gene regulation and disease progression. Recent studies have shown that distal enhancers can regulate target genes through interactions with nearby promoters, providing important insights into transcriptional regulation mechanisms. Although high-throughput experimental techniques have enabled large-scale identification of EPIs, these methods are often costly and time-consuming. In addition, existing computational approaches still face challenges in effectively integrating heterogeneous feature representations from different cell lines. RESULTS: We propose a stacked ensemble framework for EPI prediction that integrates feature representations from diverse cell line datasets using multiple machine learning algorithms. The extracted complementary patterns are further combined by an XGBoost classifier to improve robustness against overfitting. Experiments on six independent datasets show that the proposed method achieves superior accuracy and generalization compared with existing EPI prediction models, with an average AUROC of 0.909 while maintaining computational efficiency. AVAILABILITY: The source code and its archived release are available at GitHub and Zenodo. The Zenodo archive provides a versioned snapshot of the repository: https://zenodo.org/records/19952998.

Promoter Regions, Genetic↗

PubMed2026

Machine Learning-Based Preoperative Predicting TERT Promoter Mutation and EGFR Gene Amplification Phenotype in IDH Wild-Type Glioblastoma Using Advanced MR Habitat Imaging.

BACKGROUND AND PURPOSE: The telomerase reverse transcriptase (TERT) gene promoter mutation is a crucial factor for identifying an isocitrate dehydrogenase (IDH) wild-type glioblastoma with poor prognosis, and the epidermal growth factor receptor (EGFR) amplification may be a potential prognostic factor. The purpose of this study was to investigate the value of the tumor habitats imaging model on advanced MRI in predicting TERT promoter mutation and EGFR gene amplification phenotype of IDH wild-type glioblastoma. MATERIALS AND METHODS: One hundred seventy-nine patients with pretreatment conventional MRI, DWI, and DSC-PWI were included. The data were divided into the training set (n=112), test set (n=29), and time-independent validation set (n=38). Based on the ADC and CBV map, the solid tumor area was split into several habitat subregions using the k-means clustering algorithm (hypovascular hypercellular area, hypervascular area, and hypovascular hypocellular area). In the training set, TERT promoter mutation and EGFR gene amplification phenotype prediction models were constructed using the random forest method. The reliability of prediction models was validated in the test and the time-independent validation sets. Receiver operating characteristic (ROC) curve analysis, calibration curve, and decision curve analysis (DCA) were used. RESULTS: The area under the curve (AUC) of the training, test, and validation sets of the TERT promoter prediction model was 0.877, 0.783, and 0.796, respectively. The accuracy of the TERT promoter prediction model was 82.1%, 75.9%, and 76.3%, respectively. The AUCs of the 3 sets for the EGFR gene amplification status prediction model were 0.877, 0.784, and 0.878, respectively. The accuracy of the EGFR gene amplification status prediction model was 79.5%, 75.9%, and 89.5%, respectively. Moreover, the prediction probability of these models was in good agreement with the actual result. CONCLUSIONS: The tumor habitat imaging model based on advanced MRI was useful for accurately predicting TERT promoter mutation and EGFR amplification status in IDH wild-type glioblastoma.

Humans↗

PubMed2003

Do psychological measures predict the ability of lower limb amputees to learn to use a prosthesis?

OBJECTIVE: Can psychological tests predict whether, on admission to a rehabilitation ward post amputation, a lower limb amputee will learn to use a prosthesis during the ensuing inpatient rehabilitation programme? DESIGN: A one-sample design in which psychological variables, and transfemoral/transtibial amputation site were tested as predictors of outcome. SETTING: An inpatient rehabilitation unit in the UK offering prosthetic provision. SUBJECTS: Forty-three consecutive patients with peripheral vascular disease (mean age 66.35 years, standard deviation 14.99) who had received an amputation on average 19 days previously on a surgical ward and were transferred to the unit for rehabilitation including assessment for prosthetic provision. MAIN OUTCOME MEASURE: Whether the patient learnt to use a prosthesis independently during the stay on the rehabilitation unit. RESULTS: During their stay in the rehabilitation unit (mean length of stay = 42 days), 31 patients learnt to use a prosthesis and 12 did not. A forward stepwise logistic regression revealed that the Kendrick Object Learning Test score on admission correctly predicted outcome in 70% of cases. The predictive power rose to 81% correct when the amputation site (transfemoral or transtibial) was included amongst the predictors. Anxiety, depression and recovery locus of control scores were not significant predictors of functional prosthetic use in this study. CONCLUSION: A simple test of learning ability and the amputation site can help to predict the patient's ability to learn to use a prosthesis following amputation and is recommended as part of the assessment process.

Aged↗

PubMed2006

Generalized modeling of enzyme-ligand interactions using proteochemometrics and local protein substructures.

Modeling and understanding protein-ligand interactions is one of the most important goals in computational drug discovery. To this end, proteochemometrics uses structural and chemical descriptors from several proteins and several ligands to induce interaction-models. Here, we present a new and generalized approach in which proteins varying greatly in terms of sequence and structure are represented by a library of local substructures. Using linear regression and rule-based learning, we combine such local substructures with chemical descriptors from the ligands to model binding affinity for a training set of hydrolase and lyase enzymes. We evaluate the predictive performance of these models using cross validation and sets of unseen ligand with unknown three-dimensional structure. The models are shown to generalize by outperforming models using descriptors from only proteins or only ligands, or models using global structure similarities rather than local similarities. Thus, we demonstrate that this approach is capable of describing dependencies between local structural properties and ligands in otherwise dissimilar protein structures. These dependencies are often, but not always, associated with local substructures that are in contact with the ligands. Finally, we show that strongly bound enzyme-ligand complexes require the presence of particular local substructures, while weakly bound complexes may be described by the absence of certain properties. The results demonstrate that the alignment-independent approach using local substructures is capable of describing protein-ligand interaction for largely different proteins and hence opens up for proteochemometrics-analysis of the interaction-space of entire proteomes. Current approaches are limited to families of closely related proteins. families of closely related proteins.

Algorithms↗

PubMed2006

Radionuclides in fruit systems: model prediction-experimental data intercomparison study.

This paper presents results from an international exercise undertaken to test model predictions against an independent data set for the transfer of radioactivity to fruit. Six models with various structures and complexity participated in this exercise. Predictions from these models were compared against independent experimental measurements on the transfer of 134Cs and 85Sr via leaf-to-fruit and soil-to-fruit in strawberry plants after an acute release. Foliar contamination was carried out through wet deposition on the plant at two different growing stages, anthesis and ripening, while soil contamination was effected at anthesis only. In the case of foliar contamination, predicted values are within the same order of magnitude as the measured values for both radionuclides, while in the case of soil contamination models tend to under-predict by up to three orders of magnitude for 134Cs, while differences for 85Sr are lower. Performance of models against experimental data is discussed together with the lessons learned from this exercise.

Cesium Radioisotopes↗

PubMed1995

Early disseminated Lyme disease: Lyme meningitis.

Lyme meningitis is the direct result of invasion of the nervous system by Borrelia burgdorferi. Occurring within the first few months of infection, it initially presents as a chronic basilar meningitis. Much about the pathogenesis of Lyme meningitis has been learned from animal models, the best being the adult Rhesus macaque. Injection of these animals with a highly infective strain of B. burgdorferi has led to a very predictable course of events: erythema migrans within the first few weeks after injection, development of anti-B. burgdorferi antibody, detection of spirochetemia in weeks 3 and 4, and central nervous system (CNS) invasion within 1 month with cerebrospinal fluid (CSF) pleocytosis. In humans, facial palsy is the earliest clinical indicator. Headache and meningismus are symptoms of inflammation of the subarachnoid space. Severe fatigue and arthralgia are common extra-CNS symptoms. Culture is not generally useful for detecting or confirming Lyme meningitis. False-positive serologic tests may occur in patients with other infections, inflammatory processes, or malignancies. Immunoblotting will differentiate true-from false-positive antibody reactivity. Lack of a consistently positive serum antibody titer should make the diagnosis of Lyme meningitis suspect. Positive CSF antibody is almost universal in patients with Lyme meningitis. Polymerase chain reaction is a direct test that is highly specific and sensitive. The antibiotic treatment of choice is intravenous (i.v.) cephalosporins or penicillin for 2-3 weeks. If the clinical picture is anything less than absolutely classic, a lumbar puncture and Western blot of serum should be obtained in a seropositive patient before initiating intravenous antibiotic therapy. There is no role at this time for long-term (> 1 month) intravenous antibiotics. Nonsteroidal antiinflammatory agents can also be of benefit.

Diagnosis, Differential↗

PubMed2003

Prediction of drug release profiles using an intelligent learning system: an experimental study in transdermal iontophoresis.

This paper investigates the use of a neural-network-based intelligent learning system for the prediction of drug release profiles. An experimental study in transdermal iontophoresis (TI) is employed to evaluate the applicability of a particular neural network (NN) model, i.e. the Gaussian mixture model (GMM), in modeling and predicting drug release profiles. A number of tests are systematically designed using the face-centered central composite design (CCD) approach to examine the effects of various process variables simultaneously during the iontophoresis process. The GMM is then applied to model and predict the drug release profiles based on the data samples collected from the experiments. The GMM results are compared with those from multiple regression models. In addition, the bootstrap method is used to assess the reliability of the network predictions by estimating confidence intervals associated with the results. The results demonstrate that the combination of the face-centered CCD and GMM can be employed as a useful intelligent tool for the prediction of time-series profiles in pharmaceutical and biomedical experiments.

Administration, Cutaneous↗

PubMed1999

On the whats and hows of retrieval in the acquisition of a simple skill.

Two general views on the role of memory in cognitive skills--an instance-based theory and an associative perspective--were compared with respect to their general assumptions about the information involved and the processes that operate on that information. Characteristics of memory information were examined in terms of predictions for transfer to various stimulus forms as a function of 2 types of learning conditions. Characteristics of memory processes were examined using a set of general process models. Results of 4 experiments indicate that (a) neither theoretical perspective was capable of accounting for all the observed transfer effects, indicating needed refinements to informational assumptions, and that (b) 1 class of process assumptions was consistently supported, whereas other classes were consistently contradicted, indicating a general set of process characteristics that can be used in further model development.

Humans↗

PubMed1998

Dopamine neurons report an error in the temporal prediction of reward during learning.

Many behaviors are affected by rewards, undergoing long-term changes when rewards are different than predicted but remaining unchanged when rewards occur exactly as predicted. The discrepancy between reward occurrence and reward prediction is termed an 'error in reward prediction'. Dopamine neurons in the substantia nigra and the ventral tegmental area are believed to be involved in reward-dependent behaviors. Consistent with this role, they are activated by rewards, and because they are activated more strongly by unpredicted than by predicted rewards they may play a role in learning. The present study investigated whether monkey dopamine neurons code an error in reward prediction during the course of learning. Dopamine neuron responses reflected the changes in reward prediction during individual learning episodes; dopamine neurons were activated by rewards during early trials, when errors were frequent and rewards unpredictable, but activation was progressively reduced as performance was consolidated and rewards became more predictable. These neurons were also activated when rewards occurred at unpredicted times and were depressed when rewards were omitted at the predicted times. Thus, dopamine neurons code errors in the prediction of both the occurrence and the time of rewards. In this respect, their responses resemble the teaching signals that have been employed in particularly efficient computational learning models.

Animals↗

PubMed2005

A global bioheat model with self-tuning optimal regulation of body temperature using Hebbian feedback covariance learning.

In the lower brain, body temperature is continually being regulated almost flawlessly despite huge fluctuations in ambient and physiological conditions that constantly threaten the well-being of the body. The underlying control problem defining thermal homeostasis is one of great enormity: Many systems and sub-systems are involved in temperature regulation and physiological processes are intrinsically complex and intertwined. Thus the defining control system has to take into account the complications of nonlinearities, system uncertainties, delayed feedback loops as well as internal and external disturbances. In this paper, we propose a self-tuning adaptive thermal controller based upon Hebbian feedback covariance learning where the system is to be regulated continually to best suit its environment. This hypothesis is supported in part by postulations of the presence of adaptive optimization behavior in biological systems of certain organisms which face limited resources vital for survival. We demonstrate the use of Hebbian feedback covariance learning as a possible self-adaptive controller in body temperature regulation. The model postulates an important role of Hebbian covariance adaptation as a means of reinforcement learning in the thermal controller. The passive system is based on a simplified 2-node core and shell representation of the body, where global responses are captured. Model predictions are consistent with observed thermoregulatory responses to conditions of exercise and rest, and heat and cold stress. An important implication of the model is that optimal physiological behaviors arising from self-tuning adaptive regulation in the thermal controller may be responsible for the departure from homeostasis in abnormal states, e.g., fever. This was previously unexplained using the conventional "set-point" control theory.

Adaptation, Physiological↗

PubMedDate not supplied

Case study: benefits of IT for older people and their carers.

The first article in this series (Vol 11(11): 759-63) described the development of the ACTION project, an information technology-based initiative designed to enhance the quality of life for family carers and older people, and the second article (Vol 11(12): 827-31) considered how the usability of the ACTION system as a whole was evaluated. This, the third article, presents a case study which describes in more detail the impact of the ACTION project on the lives of both a family carer (Rolf) and his wife (Kerstin), and on the technical worker (Thomas) who helped Rolf and Kerstin to learn to use the ACTION system. The impact of the intervention is assessed using two broad sets of criteria: (1) those relating to the PREP (Preparedness, Enrichment, Predictability) model of nursing interventions, and (2) those relating to the wider implications of participation in a research and development project of this nature.

Aged↗

PubMed2005

Unprotected anal intercourse among HIV-positive men who have a steady male sex partner with negative or unknown HIV serostatus.

OBJECTIVES: We sought to determine the prevalence and predictors of unprotected anal intercourse (UAI) among HIV-positive men who have a single steady male partner with negative or unknown HIV serostatus. METHODS: We analyzed behavioral surveillance data from HIV-positive men who have sex with men (MSM) interviewed in 12 states between 1995 and 2000. RESULTS: Of 970 HIV-positive MSM who had a single steady male sex partner with negative or unknown serostatus, 278 (29%) reported UAI during the previous year. In a subset of 674 men who were aware of their infection, 144 (21%) had UAI. Among the men who were aware of their infection, factors found to be predictive of UAI in multivariate modeling were heterosexual self-identification, crack cocaine use, no education beyond high school, and a partner with unknown serostatus. CONCLUSIONS: Even after learning of their infection, one fifth of HIV-positive MSM who had a single steady male partner with negative or unknown serostatus engaged in UAI, underscoring the need to expand HIV prevention interventions among these men.

Adult↗

PubMed2001

An evaluation of a case of agenesis of the corpus callosum with Rourke's nonverbal learning disorder model.

This article evaluates the case of an 11-year-old male with congenital agenesis of the corpus callosum, against Rourke's NLD models. A comprehensive neuropsychological assessment was completed, and impairments with attention, movement, fine motor and sensory perception, visuo-spatial organization, and verbal memory were identified. Additionally, a volumetric analysis of the magnetic resonance image (MRI) was completed. Although no gross structural abnormalities, beyond agenesis, were present, the possibility of unobservable abnormalities was considered. Many predicted NLD deficits were clearly evidenced in this case, including associated behavioral problems. This study concludes that although Rourke's NLD model is descriptive of many aspects of white matter impairments, it may not account for the full range or severity of deficits that may be observed. In callosal agenesis, children may exhibit verbal learning, auditory attentional, and verbal expressive difficulties.

Clinical Conference↗

PubMed2025

Establishment of a prognostic model based on ER stress-related cell death genes and proposing a novel combination therapy in acute myeloid leukemia.

BACKGROUND: Acute myeloid leukemia (AML) is a highly heterogeneous malignancy, presenting significant challenges in accurately predicting patient prognosis. Dysregulation of endoplasmic reticulum (ER) stress and resistance to programmed cell death (PCD) are hallmarks of AML cells. However, the prognostic significance of the interplay between ER stress and cell death pathways in AML remains largely unexplored. METHODS: We analyzed RNA sequencing and clinical data from 887 AML patients across 4 cohorts to develop an ER stress-related cell death index (ERCDI) using 10 machine-learning algorithms with 117 unique combinations. Survival and time-dependent Receiver Operating Characteristic Curve (ROC) analyses were performed to assess the model's efficacy. Clinical characteristics, the tumor immune microenvironment, and drug sensitivity differences between the high- and low-risk groups were also analyzed. The CMap database was used to identify potential therapeutic drugs. In vitro and in vivo experiments, including CCK-8, colony formation, flow cytometry, Transwell assays, and xenograft mouse models, were conducted to evaluate the effects of the target genes and candidate drugs. RESULTS: The ERCDI demonstrated strong prognostic and predictive performance for prognosis in AML patients. Furthermore, the ERCDI effectively predicted immunotherapy and chemotherapy outcomes and was associated with the immune features of the different risk groups. DNA damage-inducible transcript 4 protein (DDIT4), a key gene associated with ERCDI, is related to poor prognosis in AML patients with high expression. Additionally, the knockdown of DDIT4 significantly inhibited AML cell proliferation, induced cell apoptosis, and promoted cell cycle arrest. Chaetocin was subsequently identified as a candidate compound for AML treatment. Subsequent experiments suggested that combining chaetocin and venetoclax is a potentially promising therapeutic strategy for AML. CONCLUSION: The ERCDI provides personalized risk assessment and treatment recommendations for individual AML patients. The combined use of chaetocin and venetoclax can potentially be repurposed for AML therapy.

Humans↗

PubMed2025

Prediction and functional interpretation of inter-chromosomal genome architecture from DNA sequence with TwinC.

Three-dimensional nuclear DNA architecture comprises well-studied intra-chromosomal (cis) folding and less characterized inter-chromosomal (trans) interfaces. Current predictive models of 3D genome folding can effectively infer pairwise cis-chromatin interactions from the primary DNA sequence but generally ignore trans contacts. There is an unmet need for robust models of trans-genome organization that provide insights into their underlying principles and functional relevance. We present TwinC, an interpretable convolutional neural network model that reliably predicts trans contacts measurable through proximity ligation-dependent (in situ and intact Hi-C) and independent (DNA SPRITE) genome-wide chromatin conformation assays. . TwinC uses a paired sequence design from replicate Hi-C experiments to learn single base pair relevance in trans interactions across two stretches of DNA. The method achieves high predictive accuracy (AUROC=0.80) on a cross-chromosomal test set from in situ and intact Hi-C experiments in heart tissue. Furthermore, we train TwinC using in situ Hi-C data from the widely used GM12878 cell line and validate its performance with orthogonal DNA SPRITE assay in the same cell type. Mechanistically, the neural network learns the importance of compartments, chromatin accessibility, clustered transcription factor binding and G-quadruplexes in forming trans contacts. In summary, TwinC models and interprets trans genome architecture, shedding light on this poorly understood aspect of gene regulation.

Journal Article↗

PubMed2005

A computational principle for hippocampal learning and neurogenesis.

In the three decades since Marr put forward his computational theory of hippocampal coding, many computational models have been built on the same key principles proposed by Marr: sparse representations, rapid Hebbian storage, associative recall and consolidation. Most of these models have focused on either the CA3 or CA1 fields, using "off-the-shelf" learning algorithms such as competitive learning or Hebbian pattern association. Here, we propose a novel coding principle that is common to all hippocampal regions, and from this one principal, we derive learning rules for each of the major pathways within the hippocampus. The learning rules turn out to have much in common with several models of CA3 and CA1 in the literature, and provide a unifying framework in which to view these models. Simulations of the complete circuit confirm that both recognition memory and recall are superior relative to a hippocampally lesioned model, consistent with human data. Further, we propose a functional role for neurogenesis in the dentate gyrus (DG), namely, to create distinct memory traces for highly similar items. Our simulation results support our prediction that memory capacity increases with the number of dentate granule cells, while neuronal turnover with a fixed dentate layer size improves recall, by minimizing interference between highly similar items.

Action Potentials↗

PubMed2004

Age-of-acquisition effects in reading aloud: tests of cumulative frequency and frequency trajectory.

Several studies have reported that the age at which a word is learned affects skilled reading. This age-of-acquisition effect is potentially important for theories of reading and learning. The effect has been difficult to pin down, however, because the age at which a word is learned is correlated with many other lexical properties. Zevin and Seidenberg (2002) analyzed these phenomena, using connectionist models that distinguished between cumulative frequency (the total number of times a word is experienced) and frequency trajectory (the distribution of these experiences over time). The models prompted a reevaluation of the empirical literature on this topic. The present research tested and confirmed three behavioral predictions derived from these models. First, cumulative frequency has an impact on skilled word naming, more so than standard measures of frequency derived from such norms as those of Kucera and Francis (1967). Second, frequency trajectory affects age of acquisition: The timing of exposure to words affects how rapidly they are learned. However, frequency trajectory does not affect skilled reading aloud, because the consistencies in mapping between spelling and sound eventually wash out the effects of early differences in frequency of exposure. Thus, in skilled performance, the timing of exposure to words is less important than the amount of exposure. The results clarify the conditions under which age-dependent learning effects occur in reading aloud.

Adolescent↗