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PubMed2025

Gene Specific Pathogenicity Predictor for Chromatin-Remodeling BAF Complex-Associated Neurodevelopmental Disorders.

Advancements in whole genome sequencing have increased the number of variants of uncertain significance (VUS) identified in patient genomes. This has created a diagnostic bottleneck for genetic counselors tasked with sifting through these variants and determining those most likely to be causative for a patient's clinical presentation. Machine learning (ML) tools can aid in identifying pathogenic variants from VUS, but there is a need for gene-specific algorithms that predict pathogenic variants with high accuracy. To address this need, we present a workflow for developing gene-specific, ensemble-learning ML tools, that leverage outputs from other algorithms, locations of variants within the gene, and evolutionary conservation data to make a prediction of pathogenicity. Variants in SMARCA2 and SMARCA4 that are associated with rare neurodevelopmental diseases were used to screen 15 ML algorithms. A random forest learner was tuned to yield a final accuracy of 0.93 on holdout data. Generalizing this predictor to other BAF complex proteins resulted in a sharp decline in performance. We trained a final predictor for all genes in the study to create a predictor that identifies pathogenic variants in these BAF subunits with an accuracy of 0.91 on holdout data. This predictor specific to BAF complex proteins performs with higher accuracy and AUROC than any other predictor. The decline in performance when generalized to other proteins emphasizes the need for the gene-specific calibration of predictors. Our workflow for the development of such models provides a quick, computationally inexpensive route for improving the ML tools available to genetic counselors.

Journal Article↗

PubMed2026

Predictive Models for Hypoglycemia Risk in Haemodialysis Patients With Diabetic Kidney Disease: Systematic Review and Meta-Analysis.

AIM: To provide evidence for selecting and developing reliable clinical assessment tools for hypoglycemia in diabetic kidney disease patients during haemodialysis. DESIGN: Review. METHODS: Systematic searches were performed in 9 Chinese and English databases to collect literature regarding the development of hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease. Two reviewers independently performed literature screening, data extraction, risk-of-bias assessment, and applicability evaluation. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability of the included studies. Meta-analysis was conducted using R software. DATA SOURCES: CNKI, Wanfang, VIP, CBM, PubMed, Cochrane Library, EMbase, Web of Science, and CINAHL. The search period covered from the establishment date of each database to December 2025. RESULTS: Six studies, comprising six prediction models, were included. Two studies performed internal validation, and three conducted external validation. All models reported the area under the curve, ranging from 0.813 to 0.866, and calibration measures. Four studies were rated as having a high risk of bias, while all six demonstrated good overall applicability. The meta-analysis showed that the pooled AUC value of the six studies was 0.846 (95% CI: 0.823-0.867). CONCLUSION: Research on hypoglycemia risk prediction models in haemodialysis patients with diabetic kidney disease remains in the developmental stage. Although the included prediction models exhibited satisfactory apparent discriminatory ability and clinical applicability, most of the original studies suffered from a high risk of bias and lacked adequate validation. The true predictive performance and clinical application value of these models remain to be further verified. Accordingly, routine and unconditional clinical application is not recommended at this stage. Future studies should include more high-quality, multicenter external validation and develop models with high generalizability, favourable clinical applicability, and robust predictive performance to facilitate early identification of hypoglycemia risk in this population. IMPACT: This study systematically evaluated the hypoglycemia risk prediction models for diabetic kidney disease patients during haemodialysis, and the research on hypoglycemia risk prediction models for maintenance haemodialysis patients during dialysis is still in the development stage. This study provides a reference for clinical medical staff to select or develop hypoglycemia risk prediction and assessment tools for diabetic kidney disease patients during haemodialysis. REPORTING METHOD: This study was conducted in accordance with the relevant guidelines of the EQUATOR Network and followed the TRIPOD-SRMA Checklist. PATIENT OR PUBLIC CONTRIBUTION: No patient or public contribution. TRIAL REGISTRATION: PROSPERO: CRD420251243352.

Humans↗

PubMed1993

ECG compression using long-term prediction.

A new algorithm for ECG signal compression is introduced. The compression system is based on the subautoregression (SAR) model, known also as the long-term prediction (LTP) model. The "periodicity" of the ECG signal is employed in order to further reduce redundancy, thus yielding high compression ratios. The suggested algorithm was evaluated using an in-house database. Very low bit rates on the order of 70 b/s are achieved with a relatively low reconstruction error (percent rms difference-PRD) of less than 10%. The algorithm was compared, using the same database, with the conventional linear prediction (short-term prediction--STP) method, and was found superior at any bit rate. The suggested algorithm can be considered a generalization of the recently published average beat subtraction method.

Algorithms↗

PubMed1983

An algorithm for the bonding-probability map of nucleic acid secondary structure.

We report a more efficient and well-defined algorithm for predicting a secondary structure of single-stranded nucleic acid from a primary nucleotide sequence. Using this algorithm, one- and two-dimensional bonding-probability maps of 5S rRNA of thermus thermophilus HB8 were calculated. These maps well express the stability of the secondary structure.

Chemical Phenomena↗

PubMed1987

Prediction of the three-dimensional structure of human growth hormone.

In recent years, the protein-folding problem has attracted the attention of molecular biologists. Efforts have focused on developing heuristic and energy-based algorithms to predict the three-dimensional structure of a protein from its amino acid sequence. We have applied a series of heuristic algorithms to the sequence of human growth hormone. A family of five structures which are generically right-handed fourfold alpha-helical bundles are found from an investigation of approximately 10(8) structures. A plausible receptor binding site is suggested. Independent crystallographic analysis confirms some aspects of these predictions. These methods only deal with the "core" structure, and conformations of many residues are not defined. Further work is required to identify a unique set of coordinates and to clarify the topological alternative available to alpha-helical proteins.

Growth Hormone↗

PubMed1994

The rational design of highly stable, amphiphilic helical peptides.

A computer algorithm was devised for the evaluation of helical stability of potentially amphiphilic peptide sequences of specified length containing a set number of leucines in the hydrophobic region. All possible combinations of Glu, Lys and Gln in the hydrophilic region are rated using a set of empirical rules for salt bridge formation in alpha-helices, and the sequences which rate the highest are displayed. The rules for salt bridge formation were largely derived from published studies on the effects of salt bridges on helical stability. The algorithm was tested by redesigning a known amphiphilic alpha-helical peptide, alpha 1B or 1, which has been shown to aggregate into four-helix bundles. Comparison of the circular dichroism spectra of two peptides, 2 and 3, to 1 demonstrated that the redesigned peptide with the highest priority score from the algorithm, 2, was more helical when aggregated and slightly more helical as a monomer, whereas the peptide with the low priority score, 3, was somewhat less helical when aggregated and much less helical when monomeric. These results support the design of the algorithm, although conclusions based on aggregation data are complicated by the importance of interhelix contacts in the bundle. Further studies are underway to examine the reliability of the algorithm's predictions regarding the design of other helical peptides.

Algorithms↗

PubMed1991

Seeing "ghost" planes in stereo vision.

I have studied particular ambiguous random dot stereograms where multiple matches (that are equally possible) are available at each point. The human visual system resolves these ambiguities in two qualitatively different ways. In some cases a few transparent surfaces are perceived corresponding to all the ambiguous matches. In other cases a single dominant opaque surface is perceived. The conditions under which each behavior occurs are described. Additional experiments, designed to explore whether a number of modified stereo matching algorithms can predict human perception, are described, and their theoretical implications are discussed.

Algorithms↗

PubMed2026

Comprehensive evaluation of AlphaFold/OpenFold prediction of experimentally unresolved proteins through novel metrics.

Predicting accurate protein structures is essential for understanding molecular mechanisms, interpreting the impact of sequence variation, and supporting translational applications ranging from drug discovery to clinical genomics. Recent advances in deep-learning-based predictors such as AlphaFold2, OpenFold, and AlphaFold3 have transformed structural biology, enabling routine in silico modeling even for challenging or previously uncharacterized proteins. However, systematic benchmarking of these tools-especially for novel targets and single amino acid variants-remains limited. Conventional global metrics often fail to capture biologically meaningful discrepancies. By evaluating multiple implementations of AlphaFold2 and OpenFold, together with ColabFold and the AlphaFold3 server, across 10 different proteins and 222 single amino acid protein variants encompassing a wide range of sizes, structures, and functions, we show that although widely used global indicators-like mean pLDDT, pTM-score, and RMSD-frequently suggest comparable performance, substantial local-level differences remain elusive. To address this gap, we introduce a comparative framework leveraging Bland-Altman agreement analysis, to evaluate per-residue Cα-confidence differences and Per-Residue profiles (PRPs), complemented by Uniform Manifold Approximation and Projection (UMAP). This approach reveals marked localized divergences, particularly within flexible or intrinsically disordered regions, where both predictor choice and single-residue substitutions trigger the largest conformational shifts. We further demonstrate that using reduced homology databases has minimal impact on predicted structural quality, offering computationally efficient alternatives. Collectively, our findings underscore the importance of integrating global and residue-specific evaluations to more accurately assess robustness, agreement, and practical usability across contemporary protein structure prediction methods.

Proteins↗

PubMed2026

Systematic review of machine learning approaches for predicting sickle cell crisis and mortality risk at the climate-health nexus.

BACKGROUND: Sickle cell anemia (SCA) is a severe genetic blood disorder characterized by recurrent vaso-occlusive crises and increased mortality, with the greatest burden occurring in low- and middle-income countries. Climatic and environmental conditions, including temperature variability, humidity, rainfall, air pollution, and seasonal changes, have been associated with disease exacerbation. However, the extent to which these factors have been incorporated into predictive models remains unclear. This study systematically reviews the application of machine learning (ML) models for predicting SCA crises and mortality in relation to climate and environmental factors. METHODOLOGY: The PRISMA guidelines were used, and 34 peer-reviewed studies published between 2005 and 2026 were analyzed to identify the climate variables, ML approaches employed, and predictive performance. The reviewed studies applied a range of ML techniques, including artificial neural networks, random forests, support vector machines, decision trees, logistic regression, and deep learning models. Temperature, humidity, rainfall, wind speed, air quality indicators, and seasonal patterns were the most frequently examined environmental variables. RESULTS: The findings indicate that most existing models rely predominantly on clinical and demographic data, with limited integration of climate information and inadequate representation of high-burden regions, especially Sub-Saharan Africa. Studies incorporating environmental variables reported improved predictive performance and highlighted the potential of climate-informed early warning systems for SCA management. CONCLUSION: The review recommends development of interdisciplinary, climate-aware ML frameworks, expansion of longitudinal environmental datasets, and increased research in underrepresented regions to support climate-resilient and patient-centered SCA care.

Humans↗

PubMed2026

The Progress of Gout Prediction Models Based on Multi-source Data.

INTRODUCTION: Gout, a highly serious inflammatory disease that is caused by monosodium urate crystals, is becoming an increasingly significant health concern. Artificial Intelligence and multi-omics-based research have made significant gains for the early detection and prevention of gout based on diverse approaches. This review intends to summarize current advances in forecasting gout susceptibility and gout-related symptoms, evaluate the predictive efficacy of different features, and ascertain which clinical and omics characteristics are most effective in these prediction models. METHODS: We explored the PubMed database after 2010 using keywords such as "gout", "predictive model", "risk prediction", and "machine learning", and confined our search to Englishlanguage articles. The original peer-reviewed research articles that developed gout models were selected. Research that was not original or lacked internal validation was excluded. RESULTS: Clinical features, genomics, microbiomics, radiomics, and metabolomics have been utilized to construct models related to gout and have demonstrated excellent predictive performance. Multisource data prediction models usually exhibit better effectiveness. DISCUSSION: Gout-oriented models performed excellently in predictive performance but present limitations in certain clinical and omics domains. However, if they are to affect actual patient care, they must overcome some external confirmation roadblocks and the fiscal and practical implications they will face ahead of time. CONCLUSION: This review indicates that clinical and multi-omics models of gout are significant instruments for clinical decision-making. The models constructed in these studies may be crucial for the treatment of gout and its practical benefits.

Gout↗

PubMed1994

An alternate-subsite-coupled model for predicting HIV protease cleavage sites in proteins.

A 2-4-6 subsite-coupled model is proposed to predict the cleavability of peptide sequences by HIV protease. For an enzyme with eight extended specificity subsites, such as HIV protease, the coupling effects of the second subsite with the fourth one and the fourth with the sixth subsite are much more important than those of the others. Accordingly, in establishing a model for predicting whether a given peptide can be cleaved by HIV protease, the 2-4-6 subsite-coupled effect must be incorporated. The model leads to an algorithm for predicting protease-susceptible sites from primary structure. The high rate of correct prediction for both HIV-1 and HIV-2 proteases has borne out that this kind of alternation-coupled mechanism does exist along the extended subsites of HIV protease. The principle of the new method can be used for analyzing the specificity of any multisubsite enzyme. In particular, the new method can serve as a supplementary means for finding effective inhibitors of HIV protease, which is one of the targets in designing potential drugs for AIDS therapy.

Algorithms↗

PubMed1992

Improvement of protein secondary structure prediction by combination of statistical algorithms and circular dichroism.

Three different approaches (propensity curve shifting, hydropathy index evaluation, and iterative attribution/cancellation of secondary structure) to the use of secondary structure percentages derived from circular dichroism measurements to improve the success rate of a protein secondary structure prediction method, without using decision constants, are described and compared. Propensity-curve shifting appears to be the best-performing approach, bearing an increase of 5.3% in the success rate of single-residue structural prediction when exact information on the secondary structure, obtained by X-ray crystallography, is employed; with information of an accuracy comparable to that obtainable by circular dichroism, the improvement stays between 3.5 and 4.9%, for a three-state prediction. Although developed with circular dichroism in mind, the method can use percentages of secondary structure obtained by any other experimental methodology from which they can be inferred, for instance Raman spectroscopy and infrared spectroscopy.

Algorithms↗

PubMed2026

miRNA Target Prediction: An Overview of the Past and Current Tools.

MicroRNAs (miRNAs) are among the most studied molecules in recent years, and since their discovery, many miRNAs have been identified across various species. As members of the non-coding RNA family, miRNAs are key players in post-transcriptional gene regulation. These molecules can inhibit translation or promote degradation of messenger RNA (mRNA) by binding to the 3' untranslated region (UTR) of mRNA, thereby influencing almost all biological processes. To identify a miRNA's biological role, it is essential to predict the target sites to which it binds, a goal made possible through bioinformatics tools. This chapter discusses the bioinformatics tools commonly used for this purpose. Also, it analyzes the main factors considered in target prediction, such as seed match, free energy, conservation, site accessibility, multiple binding site contribution, and machine learning and deep learning approaches. Understanding the principles underlying these predictive methodologies is crucial for advancing one's biological research on miRNAs.

MicroRNAs↗

PubMedDate not supplied

Criteria for admitting patients with tricyclic antidepressant overdose.

Several investigators have recently developed guidelines for determining which patients with tricyclic antidepressant overdose should be hospitalized. The width of the QRS complex on the ECG and several clinical parameters have been proposed to identify patients at risk for major complications. To validate these, we developed an algorithm and then applied it to 45 patients who had overdosed on tricyclic antidepressants. This algorithm correctly predicted which patients required admission, whether due to present or impending complications, and which patients could have been discharged without morbidity or mortality. We conclude that use of the modified algorithm can identify patients with tricyclic antidepressant overdose who can be safely discharged from the emergency department.

Algorithms↗

PubMed1994

Predictive respiratory gating: a new method to reduce motion artifacts on CT scans.

PURPOSE: To evaluate a gating system, called predictive respiratory gating (PRG), that reduces motion-induced artifacts on computed tomographic (CT) scans of patients who cannot suspend respiration. MATERIALS AND METHODS: PRG uses a respiration monitor and a new algorithm to predict when a motionless period is about to occur. It automatically starts scanning so the scan is temporally centered around the motionless period at end inspiration or end expiration. To demonstrate PRG, CT was performed on a motion phantom and a quietly breathing volunteer with and without gating. RESULTS: Scans of the phantom obtained with PRG contained less motion-induced streaking and blurring than did scans acquired without PRG. Scans of the volunteer gated at end expiration contained significantly less artifact than nongated scans (P < .03). CONCLUSION: PRG reduced motion artifact on scans of a spontaneously breathing volunteer. PRG may be able to reduce motion artifacts on scans of patients unable to suspend respiration.

Adult↗

PubMed2026

Mul-PheG2P: decoupled learning and prediction-space fusion enables robust and interpretable multi-phenotype genomic prediction.

Genomic prediction of multiple phenotypes is crucial in modern plant breeding; however, existing methods struggle with negative transfer and lack interpretability, particularly across high-dimensional small-sample data and diverse species. To address this, we propose Mul-PheG2P, a novel paradigm based on decoupled learning and predictive space fusion. It employs a two-stage design: first training phenotype-specific encoders using genetic data, then decoupling phenotype-specific learning from cross-phenotype aggregation via an interpretable prediction layer. Mul-PheG2P outperforms existing methods across diverse crop datasets, including maize (Zea mays), wheat (Triticum aestivum), and tomato (Solanum lycopersicum). It provides a multi-scale interpretability chain: at the macro level, it quantifies phenotypic contributions via attention-based weighting; at the micro level, Integrated Gradients reveal the genetic basis of predictions. Notably, the model successfully identified the CCT (CONSTANS, CO-like, and TOC) motif regulating photoperiodism and the SQUAMOSA (SQUAMOSA promoter binding protein) promoter for inflorescence development, confirming its ability to capture functional biological mechanisms. These results highlight the high performance and interpretability of Mul-PheG2P, showcasing its value for low-cost, large-scale screening to advance precision breeding.

Phenotype↗

PubMed1995

An empirical method for the prediction of T-cell epitopes.

Identification of T-cell epitopes from foreign proteins is the current focus of much research. Methods using simple two or three position motifs have proved useful in epitope prediction for major histocompatibility complex (MHC) class I, but to date not for MHC class II molecules. We utilized data from pool sequence analysis of peptides eluted from two HLA-DR13 alleles to construct a computer algorithm for predicting the probability that a given sequence will be naturally processed and presented on these alleles. We assessed the ability of this method to predict known self-peptides from these DR-13 alleles, DRB1(*)1301 and *1302, as well as an immunodominant T-cell epitope. We also compared the predictions of this scoring procedure with the measured binding affinities of a panel of overlapping peptides from hepatitis B virus surface antigen. We concluded that this method may have wide application for the prediction of T-cell epitopes for both MHC class I and class II molecules.

Amino Acid Sequence↗

PubMedDate not supplied

How far can a medial rectus safely be recessed?

Previous studies have suggested that the location of the equator should be important in determining the site of a "safe maximum recession" of a rectus muscle, and that the location of the equator should be a function of axial length. Exactly where in relationship to the equator a muscle can be safely recessed has never been scientifically determined. Over a 4-year period, we measured axial length on all patients we operated on for strabismus. Using a previously derived formula, we were able to calculate the limbus-to-equator distance, given axial length. Based on our analysis of 28 patients in whom we recessed one or both medial recti posterior to the equator, we believe that recessions of the medial recti up to 1.5 mm posterior to the equator should not produce postoperative medial rectus underaction associated with an overcorrection, but recessions that are further than 1.5 mm posterior to the equator may do so. Recessions to a point greater than 11 mm from the limbus do not appear to be associated with late progressive overcorrection provided that the site of recession is not greater than 1.5 mm posterior to the equator. Using our previously determined formula for estimating the location of the equator, given axial length, we have generated easy-to-use reference tables for determining the location of the equator in terms of millimeters posterior to the limbus. Also, based on axial length data from 180 strabismus patients, we have generated an algorithm for predicting axial length, given age, and refractive error, which may be useful to the strabismus surgeon in predicting the location of the equator when A-scan ultrasonography is not available.

Algorithms↗