Prediction Algorithm Research Articles

Enhancing water security through advanced modeling: integrating deep learning and a novel metaheuristic optimization algorithm for accurate pan evaporation prediction

Enhancing water security through advanced modeling: integrating deep learning and a novel metaheuristic optimization algorithm for accurate pan evaporation prediction

A Comparative Analysis of ANN, LSTM and Hybrid PSO-LSTM Algorithms for Groundwater Level Prediction

A Comparative Analysis of ANN, LSTM and Hybrid PSO-LSTM Algorithms for Groundwater Level Prediction

Performance Comparison of Deep Learning Algorithm for Autism Spectrum Disorder Prediction

Autism Spectrum Disorder is a complex neurodevelopmental condition that significantly impacts social interaction and conduct in individuals. Early diagnosis and timely intervention are crucial for effectively managing the condition and enhancing overall outcomes. This study concentrates on creating predictive models through deep learning algorithms to assist in the early identification of ASD. The study employs an extensive dataset that covers various features linked to ASD traits, such as speech and language development, learning disorders, genetic factors, and behavioral characteristics. The investigation encompasses the assessment of three recurrent neural network (RNN) architectures-standard RNN, Long Short Term Memory(LSTM), and Gated Recurrent Unit(GRU) –for their effectiveness in predicting ASD. The dataset is split into 80% for training and 20% for testing. This study compares RNN, LSTM, and GRU algorithms. The classification results show that the LSTM and GRU models exhibited similar accuracy, with values of 71.03% and 70.78% respectively.

Accelerated multi-kernel sparse stochastic optimization classifier algorithm for explainable prediction

Accelerated multi-kernel sparse stochastic optimization classifier algorithm for explainable prediction

Machine learning algorithms for prediction of measles one vaccination dropout among 12–23 months children in Ethiopia

IntroductionDespite the availability of a safe and effective measles vaccine in Ethiopia, the country has experienced recurrent and significant measles outbreaks, with a nearly fivefold increase in confirmed cases from...

PVACview: an interactive visualization tool for efficient neoantigen prioritization and selection.

Neoantigen-targeting therapies including personalized vaccines have shown promise in the treatment of cancers, particularly when used in combination with checkpoint blockade therapy. At least 100 clinical trials involving these therapies have been initiated globally. Accurate identification and prioritization of neoantigens is crucial for designing these trials, predicting treatment response, and understanding mechanisms of resistance. With the advent of massively parallel DNA and RNA sequencing technologies, it is now possible to computationally predict neoantigens based on patient-specific variant information. However, numerous factors must be considered when prioritizing neoantigens for use in personalized therapies. Complexities such as alternative transcript annotations, various binding, presentation and immunogenicity prediction algorithms, and variable peptide lengths/registers all potentially impact the neoantigen selection process. There has been a rapid development of computational tools that attempt to account for these complexities. While these tools generate numerous algorithmic predictions for neoantigen characterization, results from these pipelines are difficult to navigate and require extensive knowledge of the underlying tools for accurate interpretation. This often leads to over-simplification of pipeline outputs to make them tractable, for example, limiting prediction to a single RNA isoform or only summarizing the top ranked of many possible peptide candidates. In addition to variant detection, gene expression, and predicted peptide binding affinities, recent studies have also demonstrated the importance of mutation location, allele-specific anchor locations, and variation of T-cell response to long versus short peptides. Due to the intricate nature and number of salient neoantigen features, presenting all relevant information to facilitate candidate selection for downstream applications is a difficult challenge that current tools fail to address. We have created pVACview, the first interactive tool designed to aid in the prioritization and selection of neoantigen candidates for personalized neoantigen therapies including cancer vaccines. pVACview has a user-friendly and intuitive interface where users can upload, explore, select, and export their neoantigen candidates. The tool allows users to visualize candidates at multiple levels of detail including variant, transcript, peptide, and algorithm prediction information. pVACview will allow researchers to analyze and prioritize neoantigen candidates with greater efficiency and accuracy in basic and translational settings. The application is available as part of the pVACtools software at pvactools.org and as an online server at pvacview.org.

Neoantigen DNA vaccines are safe, feasible, and induce neoantigen-specific immune responses in triple-negative breast cancer patients

Abstract Background Neoantigen vaccines can induce or enhance highly specific antitumor immune responses with minimal risk of autoimmunity. We have developed a neoantigen DNA vaccine platform capable of efficiently presenting both HLA class I and II epitopes and performed a phase 1 clinical trial in triple-negative breast cancer patients with persistent disease on surgical pathology following neoadjuvant chemotherapy, a patient population at high risk of disease recurrence. Methods Expressed somatic mutations were identified by tumor/normal exome sequencing and tumor RNA sequencing. The pVACtools software suite of neoantigen prediction algorithms was used to identify and prioritize cancer neoantigens and facilitate vaccine design for manufacture in an academic GMP facility. Neoantigen DNA vaccines were administered via electroporation in the adjuvant setting (i.e., following surgical removal of the primary tumor and completion of standard of care therapy). Vaccines were monitored for safety and immune responses via ELISpot, intracellular cytokine production via flow cytometry, and TCR sequencing. Results Eighteen subjects received three doses of a neoantigen DNA vaccine encoding on average 11 neoantigens per patient (range 4–20). The vaccinations were well tolerated with relatively few adverse events. Neoantigen-specific T cell responses were induced in 14/18 patients as measured by ELISpot and flow cytometry. At a median follow-up of 36 months, recurrence-free survival was 87.5% (95% CI: 72.7–100%) in the cohort of vaccinated patients. Conclusion Our study demonstrates neoantigen DNA vaccines are safe, feasible, and capable of inducing neoantigen-specific immune responses. Clinical trial registration number NCT02348320.

Myc 9aaTAD activation domain binds to mediator of transcription with superior high affinity.

The overexpression of MYC genes is frequently found in many human cancers, including adult and pediatric malignant brain tumors. Targeting MYC genes continues to be challenging due to their undruggable nature. Using our prediction algorithm, the nine-amino-acid activation domain (9aaTAD) has been identified in all four Yamanaka factors, including c-Myc. The predicted activation function was experimentally demonstrated for all these short peptides in transactivation assay. We generated a set of c-Myc constructs (1-108, 69-108 and 98-108) in the N-terminal regions and tested their ability to initiate transcription in one hybrid assay. The presence and absence of 9aaTAD (region 100-108) in the constructs strongly correlated with their activation functions (5-, 3- and 67-times respectively). Surprisingly, we observed co-activation function of the myc region 69-103, called here acetyl-TAD, previously described by Faiola et al. (Mol Cell Biol 25:10220-10234, 2005) and characterized in this study as a new domain collaborating with the 9aaTAD. We discovered strong interactions on a nanomolar scale between the Myc-9aaTAD activation domains and the KIX domain of CBP coactivator. We showed conservation of the 9aaTADs in the MYC family. In summary for the c-Myc oncogene, the acetyl-TAD and the 9aaTAD domains jointly mediated activation function. The c-Myc protein is largely intrinsically disordered and therefore difficult to target with small-molecule inhibitors. For the c-Myc driven tumors, the strong c-Myc interaction with the KIX domain represents a promising druggable target.

A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland

AbstractThe spatio-temporal prediction of air pollutant concentrations is vital for assessing regulatory compliance and for producing exposure estimates in epidemiological studies. Numerous approaches have been utilised for making such predictions, including land use regression models, additive models, spatio-temporal smoothing models and machine learning prediction algorithms. However, relatively few studies have compared the predictive performance of these models thoroughly, which is one of the novel contributions of this paper. For the specific challenge of predicting monthly average concentrations of NO$$_{2}$$ 2 , PM$$_{10}$$ 10 and $$\hbox {PM}_{2.5}$$ PM 2.5 in Scotland, we find that random forests typically outperform (or are as good as) more traditional statistical prediction approaches. Additionally, we utilise the best performing model to provide a new data resource, namely, predictions of monthly average concentrations (with uncertainty quantification) of the above pollutants on a regular 1 km$$^{2}$$ 2 grid for all of Scotland between 2016 and 2020.

Abstract B012: M-PACT: Methylation-based predictive algorithm for CNS tumor liquid biopsies

Abstract Background: DNA methylation-based classification has been transformative in the molecular diagnostics of pediatric central nervous system (CNS) tumors. Current diagnostic pipelines integrate histopathology and molecular tools in accordance with the WHO classification of CNS tumors which rely on the availability of tissue specimens. However, some tumors are not amenable to neurosurgical resection due to their high-risk locations and tissue collection is not routinely performed at relapse. In addition, molecular biomarkers are largely lacking for longitudinal disease monitoring in pediatric neuro-oncology. Liquid biopsies (LBs) have emerged as a minimally invasive, longitudinal source of tumor-derived cell-free DNA (cfDNA) ideally suited for detecting minimal residual disease (MRD). In addition, LBs provide a sensitive means of studying tumor evolution at high temporal resolution. Success rates of LB analyses in neuro-oncology have varied drastically between studies, warranting the deployment of more robust and reproducible assays. Methods: We applied a novel cfDNA methylation-based workflow to a large cohort of cerebrospinal fluid (CSF) samples collected from pediatric brain tumor patients (n>200 patients). Enzymatic methylation sequencing (EM-seq) was adapted for CSF-derived cfDNA inputs in the picogram range, enabling the acquisition of genome-wide cfDNA methylation profiles across the cohort. Utilizing non-oncological cfDNA profiles and CNS tumor DNA methylation datasets as a reference, we developed an innovative computational pipeline that incorporates DNA methylation imputation and deep learning of a neural network. In addition, deconvolution of cfDNA profiles facilitated classification of low tumor burden samples, including those with balanced genomes that would have been missed using previous generation assays. Results: Our CSF-based classifier, M-PACT (Methylation-based Predictive Algorithm for CNS Tumors), yielded accurate tumor detection and entity prediction (AUC=0.9) in our benchmarking cohort (n>100 CSF samples). Proof-of-concept studies showed the potential of this methodology to track tumor evolution in serial CSF samples at both the genetic and epigenetic level, illuminating potential mechanisms driving treatment resistance and recurrence. Conclusions: Collectively, this study provides the framework for robust methylation- based tumor detection and classification of CSF LBs. M-PACT can be applied to aid in tumor diagnostics and to detect MRD during follow-up, warranting prospective validation of its broad applicability and clinical utility in future trials. Citation Format: Tom T. Fischer, Kyle S. Smith, Katie Han, Anna Kostecka, Hong Lin, Daniel Senfter, Tatjana Wedig, Nathalie Schwarz, Natalia Stepien, Sibylle Madlener, Christine Haberler, Esther Hulleman, Sandeep K. Dhanda, Stefan M. Pfister, Santhosh Upadhyaya, Amar Gajjar, Giles W. Robinson, Joonas Haapasalo, Hannu Haapasalo, Kristiina Nordfors, Johannes Gojo, Kendra K. Maass, Kristian W. Pajtler, Paul A. Northcott. M-PACT: Methylation-based predictive algorithm for CNS tumor liquid biopsies [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr B012.

Incentivized BiLSTM with Triplet Attention for Predecting Congestion in Network Traffic

In the world of online live video streaming, network traffic congestion poses a serious risk to user experience and service quality. Several factors contributing to the congestion include insufficient bandwidth, an increase in user demand, as well as ineffective data routing. The incapacity of the prediction algorithms to Adjust to modifications in network circumstances and challenges in detecting long-range relationships may restrict their ability to effectively estimate congestion in dynamic contexts when it comes to online streaming video. In order to overcome these constraints, this study created an Incentivized-RF-Triplet ASTM (“Incentivized learning-based triplet attention enabled rat fierce hunting optimized Bidirectional Long Short-Term Memory) for network traffic congestion prediction in online streaming video. A reward” scheme built into the Incentivized learning mechanism pushes the algorithm to prioritize online live video streaming congestion prediction. The sequential structure of network traffic data is handled by the BiLSTM architecture, that is well-known for capturing temporal dependencies. By utilizing the triplet Attention method, the model is better able to identify relevant regions within the input data as well as identify congestion patterns more successfully. The RFSO method incorporates social behavior with selection and searching qualities to further improve the classifier's parameters. Therefore, the characteristics of the model can be tuned more effectively and robustly, improving its effectiveness in congestion detection. The outcomes of the experiment show how well the Incentivized-RF-Triplet ASTM technique works to precisely estimate traffic congestion for the Darpa99week1 dataset. 95.97% accuracy, 96.08% specificity, and 0.22 mean square error are reported.

Abstract 4129523: MicroRNA-1282 Rescues Diabetic Limb Ischemia Via a SERF2-Protein Aggregation Pathway

Introduction: Patients with diabetes are at higher risk of chronic limb-threatening ischemia (CLTI), a severe form of peripheral artery disease (PAD) causing restricted blood flow to the lower limbs due, in part, to impaired angiogenesis. However, the role of microRNAs (miRNAs) in diabetic CLTI remains poorly understood. By integrating plasma miRNA sequencing data from PAD patients with diabetes with a diabetic CLTI mouse model, we have recently identified the conserved miRNA miR-1282 that is in cis-antisense orientation to SERF2, a gene associated with amyloid aggregation. Therefore, we hypothesize that miR-1282 orchestrates endothelial angiogenesis and proteostasis during diabetic CLTI. Methods: Using miR-1282 overexpression or SERF2 knockdown studies, we characterized mouse orthologs of human miR-1282 and SERF2 for angiogenesis, apoptosis, protein aggregation, and oxidative stress in diabetic mouse skeletal muscle endothelial cells (ECs). In vivo, miR-1282 mimics were delivered intramuscularly to assess their impact on blood flow recovery, angiogenesis, and protein aggregation. Mechanistic insights in ECs were gained via RNA-seq, predictive algorithms, and proteomic analyses. Results: miR-1282 inhibited the expression of its cis-antisense target SERF2 by 98%. miR-1282 is a hypoxia-induced endothelial-enriched miRNA, and its expression was inversely correlated with SERF2 expression. miR-1282 levels were markedly reduced after femoral artery ligation (FAL) in diabetic db/db mice. Overexpression of miR-1282 or SERF2 knockdown enhanced angiogenesis and reduced protein aggregation, apoptosis, and oxidative stress in both normal and hypoxic conditions in vitro. Delivery of miR-1282 mimics in db/db mice improved blood flow recovery by 108% and angiogenesis by 98%, and reduced protein aggregation by 48% and tissue necrosis. Coupling RNA-seq profiling and prediction algorithms of ECs upon miR-1282 overexpression or SERF2 knockdown revealed EREG, BAG5, CASP3, ARG1, and HSP90AA1 as potential downstream regulators. Pathway enrichment analysis implicated inhibition of endothelial apoptosis, ER stress, and protein stability among the most dysregulated processes. Conclusion: A novel mouse ortholog of human miR-1282 augments endothelial functions and diminishes protein aggregation in diabetic CLTI via suppression of its cis-antisense target SERF2. These findings uncover new potential therapeutic targets in treating diabetic CLTI.

Comparative analysis of deep learning algorithms for dental caries detection and prediction from radiographic images: a comprehensive umbrella review

Background In recent years, artificial intelligence (AI) and deep learning (DL) have made a considerable impact in dentistry, specifically in advancing image processing algorithms for detecting caries from radiographical images. Despite this progress, there is still a lack of data on the effectiveness of these algorithms in accurately identifying caries. This study provides an overview aimed at evaluating and comparing reviews that focus on the detection of dental caries (DC) using DL algorithms from 2D radiographs. Materials and Methods This comprehensive umbrella review adhered to the “Reporting guideline for overviews of reviews of healthcare interventions” (PRIOR). Specific keywords were generated to assess the accuracy of AI and DL algorithms in detecting DC from radiographical images. To ensure the highest quality of research, thorough searches were performed on PubMed/Medline, Web of Science, Scopus, and Embase. Additionally, bias in the selected articles was rigorously assessed using the Joanna Briggs Institute (JBI) tool. Results In this umbrella review, seven systematic reviews (SRs) were assessed from a total of 77 studies included. Various DL algorithms were used across these studies, with conventional neural networks and other techniques being the predominant methods for detecting DC. The SRs included in the study examined 24 original articles that used 2D radiographical images for caries detection. Accuracy rates varied between 0.733 and 0.986 across datasets ranging in size from 15 to 2,500 images. Conclusion The advancement of DL algorithms in detecting and predicting DC through radiographic imaging is a significant breakthrough. These algorithms excel in extracting subtle features from radiographic images and applying machine learning techniques to achieve highly accurate predictions, often outperforming human experts. This advancement holds immense potential to transform diagnostic processes in dentistry, promising to considerably improve patient outcomes.

Implementation of Generalized Model Predictive Control Algorithm for DC-DC Boost Converter on STM32

Implementation of Generalized Model Predictive Control Algorithm for DC-DC Boost Converter on STM32

Predictive Analysis System for National Ranking and Accreditation of HEIs

Predictive analytical models have been applied in context of the Indian higher educational institutions dataset. This study is aimed to compare the predictive accuracy level of various machine learning algorithms (multi-class classification prediction algorithms) to assess the grading system applicable to a college based on various parameters outlined by NAAC (National Assessment and Accreditation Council). NAAC is an apex body to assess and accredit HEIs of India. An ensemble classification method is proposed to best predict the academic grading of university-affiliated colleges of India for the NAAC accreditation system. This maiden attempt for using PA (Predictive Analysis) on such institutional credential data would be beneficial to colleges applying for NAAC accreditation to judge their grade prior to final assessment. Thus they would be able to identify their strengths and weaknesses and eventually work towards further improving upon their grade. Such a system would pave the way for designing such predictive systems to assess the institutional rankings for several other assessment systems.

Graph neural link predictor based on cycle structure

AbstractCurrently, the link prediction algorithms primarily focus on studying the interaction between nodes based on chain structure and star structure, which predominantly rely on low‐order structural information and do not explore the multivariate interactions between nodes from the perspective of higher‐order structural information present in the network. The cycle structure is a higher‐order structure that lies between the star and clique structures, where all nodes within the same cycle can interact with each other, even in the absence of direct edges. If a node is encompassed by multiple cycles, it indicates that the node interacts and associates with a greater number of nodes in the network, and it means the node is more important in the network to some extent. Furthermore, if two nodes are included in multiple cycles, it signifies the two nodes are more likely to be connected. Therefore, firstly, a multi‐information fusion node importance algorithm based on the cycle structure information is proposed, which integrates both high‐order and low‐order structural information. Secondly, the obtained integrated structure information and node feature information is regarded as the input features, a two‐channel graph neural network model is designed to learn the cycle structure information. Then, the cycle structure information is utilised for the task of link prediction, and a graph neural link predictor with multi‐information interactions based on the cycle structure is developed. Finally, extensive experimental validation and analysis show that the node ranking result of the proposed node importance index is more consistent with the actual situation, the proposed graph neural network model can effectively learn the cycle structure information, and using higher‐order structural information—cycle information proves to significantly enhance the overall link prediction performance.

Discovery of PdHx compounds using genetic algorithms and first-principles calculations

Abstract In the present work, the variable-composition crystal structure prediction algorithm USPEX was combined with first-principles calculations to systematically explore stable PdH x compounds in the pressure range from 0 to 100 GPa. The predicted structures are PdH, Pd4H5, Pd2H, and PdH4, of which PdH has previously been synthesized. The electronic structures show that these compounds can be metallic, with Pd states dominating at the Fermi level. However, the hydrogen atoms do not play a main role in the electronic properties; their vibrations do contribute to the electron–phonon coupling. The predicted Pd4H5 has a superconducting transition temperature of ∼13 K, which is higher than that of PdH (∼8 K in the experiment). Additionally, PdH, Pd4H5, Pd2H, and PdH4 can be classified as phonon-mediated superconductors, falling into the low-coupling regime. Our results demonstrate that as the hydrogen concentration is increased in PdH x compounds, the superconducting transition temperature rises due to hydrogen vibrations, which is consistent with the experiment. Hence, the present study paves the way for discovering new PdH x compounds with promising superconducting transition temperatures.

CRISPRoffT: comprehensive database of CRISPR/Cas off-targets.

The CRISPR (clustered regularly interspaced short palindromic repeats)/Cas (CRISPR-associated protein) programmable nuclease system continues to evolve, with in vivo therapeutic gene editing increasingly applied in clinical settings. However, off-target effects remain a significant challenge, hindering its broader clinical application. To enhance the development of gene-editing therapies and the accuracy of prediction algorithms, we developed CRISPRoffT (https://ccsm.uth.edu/CRISPRoffT/). Users can access a comprehensive repository of off-target regions predicted and validated by a diverse range of technologies across various cell lines, Cas enzyme variants, engineered sgRNAs (single guide RNAs) and CRISPR editing systems. CRISPRoffT integrates results of off-target analysis from 74 studies, encompassing 29 experimental prediction techniques, 368 guide sequences, 226164 potential guide and off-target pairs and 8840 validated off-targets. CRISPRoffT features off-target data from different CRISPR approaches (knockout, base editing and prime editing) applied under diverse experimental conditions, including 85 different Cas/guide RNA (gRNA) combinations used across 34 different human and mouse cell lines. CRISPRoffT provides results of comparative analyses for individual guide sequences, genes, cell types, techniques and Cas/gRNA combinations under different conditions. CRISPRoffT is a unique resource providing valuable insights that facilitate the safety-driven design of CRISPR-based therapeutics, inform experimental design, advance the development of computational off-target prediction algorithms and guide RNA design algorithms.

Data-driven pressure prediction for self-pressurization liquid hydrogen tank using transfer learning method

Prediction of pressure evolution in liquid hydrogen (LH2) tanks utilizing data-driven technology has gradually garnered significant attention. However, training an accurate data-driven model for pressure prediction in LH2 tanks is a challenge due to inadequate experimental data. To end this, this paper presents a Backpropagation Neural Network model for pressure prediction in LH2 tanks based on transfer learning on pre-trained models, using experimental data and results from thermodynamics models. Pre-trained models are firstly optimized by Bayesian optimization algorithms with large sample datasets from the thermal multi-zone model program and then performed the fine-tuning method with small sample datasets from experiments. The effectiveness of the proposed approach is validated by experimental data and numerical results. Compared with baseline models trained only with experimental data, the results demonstrate better model performance, with a maximum of 10.51% accuracy improvement. The proposed approach demonstrates considerable potential in facilitating the application of advanced machine learning algorithms for LH2 tank pressure prediction, significantly reducing the dependence on experimental data collection.

IMMU-03. EXPLORING CANCER-SPECIFIC T CELLS AND ANTIGENS IN GLIOBLASTOMA BASED ON SINGLE-CELL SEQUENCING OF CD8+ TILS

Abstract BACKGROUND The effectiveness of cancer immunotherapy against glioblastoma (GBM) remains limited. This study aims to identify cancer-specific antigens to develop antigen-based cancer immunotherapies for GBM. We investigated candidate tumor antigen-specific T cells in GBM by single-cell RNA sequencing (scRNA-seq) and single-cell TCR sequencing (scTCR-seq), and we explored candidate antigens through genetic analysis of tumor tissues. METHODS Flow cytometry analysis was conducted on fresh tumor digest samples to evaluate tumor-infiltrating lymphocytes (TILs) of GBM. Single-cell RNA and TCR sequencing were performed on CD8+ T cells in TILs. Both data generated by Cell Ranger software were loaded into Seurat at R studio. The dimensional reduction for clustering was performed with uniform manifold approximation and projection (UMAP). Additionally, we performed bulk RNA sequencing (RNA-seq) and whole exome sequencing (WES) on tumor tissues. RESULTS Two out of 20 patients (10%) exhibited abundant TILs in GBM. From these two patients, approximately 20,000 CD8+ T cells in total were analyzed in single-cell analysis. Clustering based on UMAP revealed a predominance of clusters expressing exhaustion markers, with high TCR clonality centered on exhausted T cell (TEX) clusters, like our previous date of lung cancer TILs recognizing tumor antigens. Moreover, within the TEX clusters, the expression of CXCL13, often reported as an antigen-specific marker in recent years, was significantly higher in our data compared to two publicly available datasets. Subsequently, based on RNA-seq and WES, 61 candidate neoantigens were estimated using machine-learning prediction algorithms from NEC Corporation. Furthermore, 5 overexpressed cancer/testis antigens, and 3 bacterial species-derived microbial peptides using Kraken2 were selected as antigen candidates. CONCLUSIONS We identified prospective tumor antigen-specific T cell subsets with high CXCL13 expression from two GBM patients. We plan to identify cancer-specific T cells and antigens from these candidates by T cell activation assay.