Gene Expression Programs Research Articles

Abstract A025: KAT6A/B and Menin-MLL complexes coordinately regulate estrogen receptor-driven gene expression programs in breast cancer

Abstract This abstract is being presented as a short talk in the scientific program. A full abstract is printed in the Proffered Abstracts section (PR012) of the Conference Program/Proceedings. Citation Format: Sarah Naomi Olsen, Bryn Anderson, Charles Hatton, Rinath Jeselsohn, Myles Brown, Eneda Toska, Scott Armstrong. KAT6A/B and Menin-MLL complexes coordinately regulate estrogen receptor-driven gene expression programs in breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr A025.

Abstract PR012: KAT6A/B and Menin-MLL complexes coordinately regulate estrogen receptor-driven gene expression programs in breast cancer

Abstract The estrogen receptor (ER) is expressed in ∼70% of breast cancers where it functions as a critical transcription factor that regulates cell growth and tumor progression. ER inhibition is a mainstay treatment for ER-positive (ER+) breast cancer, but despite the clinical benefit of targeting ER signaling, breast cancer remains the second leading cause of cancer-related death in women, with ∼50% of deaths resulting from ER+ tumors. While recurrent and metastatic ER+ tumors are typically resistant to endocrine therapies they continue to rely on ER signaling suggesting that disruption of other chromatin associated complexes that mediate ER-driven gene expression may present an alternative therapeutic strategy. One chromatin modifier important in regulating ER function is the histone acetyltransferase KAT6A. KAT6A is amplified and/or overexpressed in ∼15% of ER+ tumors and a novel KAT6A/B inhibitor has entered clinical trials for advanced breast cancer. However, as with most single agent therapies, compensatory mechanisms may attenuate the effects of KAT6A/B inhibition on ER-driven gene expression. We hypothesized that the dual targeting of distinct chromatin complexes would enhance the effects of KAT6A/B inhibition, by altering chromatin state and transcriptional output. To that end we performed an epigenetic focused CRISPR-Cas9-based functional genetic screen to identify chromatin-associated proteins that enhance KAT6A/B inhibition. CRISPR guides targeting MEN1 (encoding Menin) were among our top hits as sensitizers to treatment with the KAT6A/B inhibitor PF-9363. We confirmed that co-treatment of PF-9363, and the Menin inhibitor, SNDX-5613, had strong synergistic anti-proliferative effects in a panel of ER+ breast cancer cell lines. Mechanistically, our data revealed that KAT6A/B and Menin cooperatively regulate ER-driven gene expression and chromatin accessibility, both through direct effects on ESR1 expression as well as through effects at ER target genes. Specifically, we found that KAT6A and Menin-KMT2A complexes co-localized at promoters of ER target genes and treatment with KAT6A/B or Menin inhibitors displaced KAT6A and Menin/KMT2A from ER target genes, respectively. Yet only the combination effectively displaced both complexes from chromatin, which produced more dramatic changes in ER-directed gene expression than either small molecule alone. Combined displacement of KAT6A and Menin/KMT2A at ER-regulated genes induced loss of an active chromatin landscape characterized by reduction of activating histone modifications and loss of RNA Polymerase II binding to ER target loci. Importantly, the combination of KAT6A/B and Menin inhibition was effective in a panel of ER+ patient-derived organoid models as well as in various models of endocrine resistance, including models with ESR1 mutations, NF1 loss, and FOXA1 mutations. Both KAT6A/B and Menin inhibitors are in Phase I/II clinical trials and have shown manageable toxicity profiles to date. This combination represents a novel potential therapeutic combination for ER+ breast cancer. Citation Format: Sarah Naomi Olsen, Bryn Anderson, Charles Hatton, Rinath Jeselsohn, Myles Brown, Eneda Toska, Scott Armstrong. KAT6A/B and Menin-MLL complexes coordinately regulate estrogen receptor-driven gene expression programs in breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr PR012.

Three-dimensional chromatin mapping of sensory neurons reveals that cohesin-dependent genomic domains are required for axonal regeneration.

The in vivo three-dimensional genomic architecture of adult mature neurons at homeostasis and after medically relevant perturbations such as axonal injury remains elusive. Here we address this knowledge gap by mapping the three-dimensional chromatin architecture and gene expression programme at homeostasis and after sciatic nerve injury in wild-type and cohesin-deficient mouse sensory dorsal root ganglia neurons via combinatorial Hi-C and RNA-seq. We find that cohesin is required for the full induction of the regenerative transcriptional program, by organising 3D genomic domains required for the activation of regenerative genes. Importantly, loss of cohesin results in disruption of chromatin architecture at regenerative genes and severely impaired nerve regeneration. Together, these data provide an original three-dimensional chromatin map of adult sensory neurons in vivo and demonstrate a role for cohesin-dependent chromatin interactions in neuronal regeneration.

M6a methylation orchestrates IMP1 regulation of microtubules during human neuronal differentiation

Neuronal differentiation requires building a complex intracellular architecture, and therefore the coordinated regulation of defined sets of genes. RNA-binding proteins (RBPs) play a key role in this regulation. However, while their action on individual mRNAs has been explored in depth, the mechanisms used to coordinate gene expression programs shaping neuronal morphology are poorly understood. To address this, we studied how the paradigmatic RBP IMP1 (IGF2BP1), an essential developmental factor, selects and regulates its RNA targets during the human neuronal differentiation. We perform a combination of system-wide and molecular analyses, revealing that IMP1 developmentally transitions to and directly regulates the expression of mRNAs encoding essential regulators of the microtubule network, a key component of neuronal morphology. Furthermore, we show that m6A methylation drives the selection of specific IMP1 mRNA targets and their protein expression during the developmental transition from neural precursors to neurons, providing a molecular principle for the onset of target selectivity.

D-SPIN constructs gene regulatory network models from multiplexed scRNA-seq data revealing organizing principles of cellular perturbation response.

Gene regulatory networks within cells modulate the expression of the genome in response to signals and changing environmental conditions. Reconstructions of gene regulatory networks can reveal the information processing and control principles used by cells to maintain homeostasis and execute cell-state transitions. Here, we introduce a computational framework, D-SPIN, that generates quantitative models of gene regulatory networks from single-cell mRNA-seq datasets collected across thousands of distinct perturbation conditions. D-SPIN models the cell as a collection of interacting gene-expression programs, and constructs a probabilistic model to infer regulatory interactions between gene-expression programs and external perturbations. Using large Perturb-seq and drug-response datasets, we demonstrate that D-SPIN models reveal the organization of cellular pathways, sub-functions of macromolecular complexes, and the logic of cellular regulation of transcription, translation, metabolism, and protein degradation in response to gene knockdown perturbations. D-SPIN can also be applied to dissect drug response mechanisms in heterogeneous cell populations, elucidating how combinations of immunomodulatory drugs can induce novel cell states through additive recruitment of gene expression programs. D-SPIN provides a computational framework for constructing interpretable models of gene-regulatory networks to reveal principles of cellular information processing and physiological control.

Multivariate formulation to predict the frictional strength of fiber reinforced soils using gene expression programming

Modeling and prediction approaches have attracted much interest in recent years to evaluate the efficiency of fiber reinforcement in improving the strength characteristics of loose soils. Several analytical and artificial intelligence (AI) based models have been presented to estimate friction angle of fiber reinforced soils (FRS). However, due to limited input characteristics, these analytical models lack accuracy, whereas AI-based models are developed on advanced programming methods and need substantial programming expertise, resulting in a lack of agreement in mechanism and application. Herein, we provided a multivariate formulation that uses gene expression programming (GEP) to predict the frictional strength of FRS using fifteen influencing factors, which, to the best of our knowledge, has never been reported before. In this regard, two mathematical models are presented, one of which incorporates both qualitative and quantitative input factors, while the other just considers quantitative variables. Laboratory experimental data on FRS from various sources was assessed and properly analysed within the proposed framework while taking into account various affecting factors. The results illustrate the efficiency of GEP in estimating FRS frictional strength and emphasis the need of considering several parameters for prediction. Sensitivity and parametric studies were also performed to identify the relevance of each influencing factor and to determine whether the prediction models meet physical processes or are purely empirical relations. Finally, a user-friendly computer program was developed based on the GEP derived mathematical formulations to anticipate the FRS friction angle, which offers practitioners great field applicability.

Supplementary cementitious materials-based concrete porosity estimation using modeling approaches: A comparative study of GEP and MEP

Abstract Using supplementary cementitious materials in concrete production makes it eco-friendly by decreasing cement usage and the corresponding CO2 emissions. One key measure of concrete’s durability performance is its porosity. An empirical prediction of the porosity of high-performance concrete with added cementitious elements is the goal of this work, which employs machine learning approaches. Binder, water/cement ratio, slag, aggregate content, superplasticizer (SP), fly ash, and curing conditions were considered as inputs in the database. The aim of this study is to create ML models that could evaluate concrete porosity. Gene expression programming (GEP) and multi-expression programming (MEP) were used to develop these models. Statistical tests, Taylor’s diagram, R 2 values, and the difference between experimental and predicted readings were the metrics used to evaluate the models. With R 2 = 0.971, mean absolute error (MAE) = 0.348%, root mean square error (RMSE) = 0.460%, and Nash–Sutcliffe efficiency (NSE) = 0.971, the MEP provided a slightly better-fitted model and improved prediction performance when contrasted with the GEP, which had R 2 = 0.925, MAE = 0.591%, RMSE = 0.745%, and NSE = 0.923. Binder, water/binder ratio, curing conditions, and aggregate content had a direct (positive) relationship with the porosity of concrete, while SP, fly ash, and slag had an indirect (negative) association, according to the SHapley Additive exPlanations study.

Spatially and Single‐Cell Resolved Profiling of RNA Life Cycle and Epitranscriptomics

AbstractThe intricate network of cell functions relies on gene expression programs, where the whole RNA life cycle from DNA to protein is subjected to extensive transcriptional and post‐transcriptional gene regulation events. Established bulk RNA sequencing methods provide an averaged, transcriptome‐wide quantification of the RNA life cycle, including transcription, processing, translation, transport, and degradation through RNA‐protein interactions. Furthermore, numerous studies using bulk epitranscriptomic profiling unveiled that dynamic RNA modifications (e. g., N6‐Methyladenosine), add another layer of gene regulations. However, many regulatory events are cell‐type specific, subcellularly localized, and subjected to cell‐cell communications within the native tissue environment. Thanks to the advances in single‐cell sequencing, spatial sequencing, and highly multiplexed imaging methods, we can routinely measure single‐cell and spatial transcriptomics. Yet more comprehensive methods to profile every step of the RNA life cycle with single‐cell and spatial information are still lacking. In this review, we will summarize and compare early explorations in developing state‐of‐the‐art methods for spatially and single‐cell resolved mapping of RNA kinetics, translation, RNA‐protein interactions, and epitranscriptomics. It is promising that these new techniques will greatly facilitate our understanding of the RNA‐centered regulation landscapes in different cell types and how the post‐transcriptional regulations are interconnected within cellular and tissue architecture.

A prostate cancer gastrointestinal transcriptional phenotype may be associated with diminished response to AR-targeted therapy.

Prostate cancer is a heterogenous disease, but once it becomes metastatic it eventually becomes treatment resistant. One mechanism of resistance to AR-targeting therapy is lineage plasticity, where the tumor undergoes a transformation to an AR-indifferent phenotype, most studied in the context of neuroendocrine prostate cancer (NEPC). However, activation of additional de- or trans-differentiation programs, including a gastrointestinal (GI) gene expression program, has been suggested as an alternative method of resistance. In this study, we explored the previously identified GI prostate cancer phenotype (PCa-GI) in a large cohort of metastatic castration-resistant prostate cancer (mCRPC) patient biopsy samples. We analyzed a dataset of 634 mCRPC samples with batch effect corrected gene expression data from the West Coast Dream Team (WCDT), the East Coast Dream Team (ECDT), the Fred Hutchinson Cancer Research Center (FHCRC) and the Weill Cornell Medical center (WCM). Survival data was available from the WCDT and ECDT cohorts. We calculated a gene expression GI score using the sum of z-scores of genes from a published set of PCa-GI-defining genes (N=38). Survival analysis was performed using the Kaplan-Meier method and Cox proportional hazards regression with endpoint overall survival from time of biopsy to death of any cause. We found that the PCa-GI score had a bimodal distribution, identifying a distinct set of tumors with an activated GI expression pattern. Approximately 35% of samples were classified as PCa-GI high, which was concordant with prior reports. Liver metastases had the highest median score but after excluding liver samples, 29% of the remaining samples were still classified as PCa-GI high, suggesting a distinct phenotype not exclusive to liver metastases. No correlation was observed between GI score and proliferation, AR signaling, or NEPC scores. Furthermore, the PCa-GI score was not associated with genomic alterations in AR, FOXA1, RB1, TP53 or PTEN. However, tumors with MYC amplifications showed significantly higher GI scores (p=0.0001). Patients with PCa-GI tumors had a shorter survival (HR=1.5 [1.1-2.1], p=0.02), but this result was not significant after adjusting for the liver as metastatic site (HR=1.2 [0.82-1.7], p=0.35). Patients with PCa-GI low samples had a better outcome after androgen receptor signaling inhibitors (ASI, abiraterone or enzalutamide) than other therapies (HR=0.37 [0.22-0.61], p=0.0001) while the benefit of ASI was smaller and non-significant for PCa-GI high samples (HR=0.55 [0.29-1.1], p=0.07). A differential pathway analysis identified FOXA2 signaling to be upregulated PCa-GI high tumors (FDR = 3.7 × 10-13). The PCa-GI phenotype is prevalent in clinical mCRPC samples and may represent a distinct biological entity. PCa-GI tumors may respond less to ASI and could offer a strategy to study novel therapeutic targets.

Multiomic profiling of transcription factor binding and function in human brain.

Transcription factors (TFs) orchestrate gene expression programs crucial for brain function, but we lack detailed information about TF binding in human brain tissue. We generated a multiomic resource (ChIP-seq, ATAC-seq, RNA-seq, DNA methylation) on bulk tissues and sorted nuclei from several postmortem brain regions, including binding maps for more than 100 TFs. We demonstrate improved measurements of TF activity, including motif recognition and gene expression modeling, upon identification and removal of high TF occupancy regions. Further, predictive TF binding models demonstrate a bias for these high-occupancy sites. Neuronal TFs SATB2 and TBR1 bind unique regions depleted for such sites and promote neuronal gene expression. Binding sites for TFs, including TBR1 and PKNOX1, are enriched for risk variants associated with neuropsychiatric disorders, predominantly in neurons. This work, titled BrainTF, is a powerful resource for future studies seeking to understand the roles of specific TFs in regulating gene expression in the human brain.

ATP-dependent chromatin remodeller brahma related gene 1 promotes keratinocyte migration and modulates cell Signalling during wound healing in human skin.

Skin wound healing is driven by proliferation, migration and differentiation of several cell types that are controlled by the alterations in the gene expression programmes. Brahma Gene 1 (BRG1) (also known as SMARCA4) is a core ATPase in the BRG1 Associated Factors (BAF) ATP-dependent chromatin remodelling complexes that alter DNA-histone interaction in chromatin at the specific gene regulatory elements resulting in increase or decrease of the target gene transcription. Using siRNA mediated suppression of BRG1 during wound healing in a human exvivo and invitro (scratch assay) models, we demonstrated that BRG1 is essential for efficient skin wound healing by promoting epidermal keratinocytes migration, but not their proliferation or survival. BRG1 controls changes in the expression of genes associated with gene transcription, response to wounding, cell migration and cell signalling. Altogether, our data revealed that BRG1 play positive role in skin repair by promoting keratinocyte migration and impacting the genes expression programmes associated with cell migration and cellular signalling.

Forecasting residual mechanical properties of hybrid fibre-reinforced self-compacting concrete (HFR-SCC) exposed to elevated temperatures

The use of hybrid fibre-reinforced Self-compacting concrete (HFR-SCC) has escalated recently due to its significant advantages in contrast to normal concrete such as increased ductility, crack resistance, and eliminating the need for compaction etc. The process of determining residual strength properties of HFR-SCC after a fire event requires rigorous experimental work and extensive resources. Thus, this study presents a novel approach to develop equations for reliable prediction of compressive strength (cs) and flexural strength (fs) of HFR-SCC using gene expression programming (GEP) algorithm. The models were developed using data obtained from internationally published literature having eight inputs including water-cement ratio, temperature, fibre content etc. and two output parameters i.e., cs and fs. Also, different statistical error metrices like mean absolute error (MAE), coefficient of determination (R2) and objective function (OF) etc. were employed to assess the accuracy of developed equations. The error evaluation and external validation both approved the suitability of developed models to predict residual strengths. Also, sensitivity analysis was performed on the equations which revealed that temperature, water-cement ratio, and superplasticizer are some of the main contributors to predict residual compressive and flexural strength.

In-depth simulation of rainfall–runoff relationships using machine learning methods

ABSTRACT Measurement inaccuracies and the absence of precise parameters value in conceptual and analytical models pose challenges in simulating the rainfall–runoff modeling (RRM). Accurate prediction of water resources, especially in water scarcity conditions, plays a distinctive and pivotal role in decision-making within water resource management. The significance of machine learning models (MLMs) has become pronounced in addressing these issues. In this context, the forthcoming research endeavors to model the RRM utilizing four MLMs: Support Vector Machine, Gene Expression Programming (GEP), Multilayer Perceptron, and Multivariate Adaptive Regression Splines (MARS). The simulation was conducted within the Malwathu Oya watershed, employing a dataset comprising 4,765 daily observations spanning from July 18, 2005, to September 30, 2018, gathered from rainfall stations, and Kappachichiya hydrometric station. Of all input combinations, the model incorporating the input parameters Qt−1, Qt−2, and R̄t was identified as the optimal configuration among the considered alternatives. The models' performance was assessed through root mean square error (RMSE), mean average error (MAE), coefficient of determination (R2), and developed discrepancy ratio (DDR). The GEP model emerged as the superior choice, with corresponding index values (RMSE, MAE, R2, DDRmax) of (43.028, 9.991, 0.909, 0.736) during the training process and (40.561, 10.565, 0.832, 1.038) during the testing process.

Targeting the core program of metastasis with a novel drug combination.

We previously reported that metastases are generally characterized by a core program of gene expression that activates tissue remodeling/vascularization, alters ion homeostasis, induces the oxidative metabolism, and silences extracellular matrix interactions. This core program distinguishes metastases from their originating primary tumors as well as from their destination host tissues. Therefore, the gene products involved are potential targets for anti-metastasis drug treatment. Because the silencing of extracellular matrix interactions predisposes to anoiks in the absence of active survival mechanisms, we tested inhibitors against the other three components. Individually, the low-specificity VEGFR blocker pazopanib (in vivo combined with marimastat), the antioxidant dimethyl sulfoxide (or the substitute atovaquone, which is approved for internal administration), and the ionic modulators bumetanide and tetrathiomolybdate inhibited soft agar colony formation by breast and pancreatic cancer cell lines. The individual candidate agents have a record of use in humans (with limited efficacy when administered individually) and are available for repurposing. In combination, the effects of these drugs were additive or synergistic. In two mouse models of cancer (utilizing 4T1 cells or B16-F10 cells), the combination treatment with these medications, applied immediately (to prevent metastasis formation) or after a delay (to suppress established metastases), dramatically reduced the occurrence of disseminated foci. The combination of tissue remodeling inhibitors, suppressors of the oxidative metabolism, and ion homeostasis modulators has very strong promise for the treatment of metastases by multiple cancers.

Macrophages on the wrinkle: Exploring microscale interactions with substrate topography.

Macrophages play pivotal roles in the immune response, participating in both inflammatory and pro-healing processes. Like other cells, macrophages continually survey their microenvironment through mechanosensing, adapting their intracellular organization in response to mechanical signals. In this study, we elucidate how macrophages perceive the topographical cues of wrinkled surfaces through actin-based structures, which align with the main pattern direction, thus modulating cell cytoskeletal dynamics. Given that such alterations may regulate mechanosensitive gene expression programs, exploring cellular responses to biomaterial design becomes crucial for developing biomaterials that mitigate adverse reactions.

UNLOCKING THE THERAPEUTIC POTENTIAL: EXPLORING NF-κB AS A VIABLE TARGET FOR DIVERSE PHARMACOLOGICAL APPROACHES

NF-κB is a vital transcription factor that responds to diverse stimuli like cytokines, infections, and stress. It forms different dimers, binds to specific DNA sequences, and regulates gene expression. It operates through two pathways: canonical (for inflammation and immunity) and non-canonical (for specific processes). These pathways tightly control activity of NF-κB and impacting gene expression. Aberrant NF-κB activation is linked to cancer and other diseases, making it a potential therapeutic target. This review explores the role of NF-κB in disease and its therapeutic potential in various conditions. Intricate signal transduction processes lead to NF-κB activation by phosphorylating IκB proteins, allowing NF-κB dimers to enter the nucleus and influence gene expression. This dynamic regulation involves co-activators and interactions with other transcription factors, shaping complex gene expression programs. Understanding the multifaceted functions off NF-κB is crucial as its deregulation is associated with a range of diseases, including cancer, autoimmune disorders, and inflammatory conditions. Exploring recent studies offers insights into potential therapeutic strategies aimed at modulating NF-κB activity to restore health and combat various pathological conditions. This Comprehensive review is based on the role of NF-κB in disease pathogenesis and therapeutic implications.

The KAT module of the SAGA complex maintains the oncogenic gene expression program in MYCN-amplified neuroblastoma.

Pediatric cancers are frequently driven by genomic alterations that result in aberrant transcription factor activity. Here, we used functional genomic screens to identify multiple genes within the transcriptional coactivator Spt-Ada-Gcn5-acetyltransferase (SAGA) complex as selective dependencies for MYCN-amplified neuroblastoma, a disease of dysregulated development driven by an aberrant oncogenic transcriptional program. We characterized the DNA recruitment sites of the SAGA complex in neuroblastoma and the consequences of loss of SAGA complex lysine acetyltransferase (KAT) activity on histone acetylation and gene expression. We demonstrate that loss of SAGA complex KAT activity is associated with reduced MYCN binding on chromatin, suppression of MYC/MYCN gene expression programs, and impaired cell cycle progression. Further, we showed that the SAGA complex is pharmacologically targetable in vitro and in vivo with a KAT2A/KAT2B proteolysis targeting chimeric. Our findings expand our understanding of the histone-modifying complexes that maintain the oncogenic transcriptional state in this disease and suggest therapeutic potential for inhibitors of SAGA KAT activity in MYCN-amplified neuroblastoma.

ONECUT2 is a druggable driver of luminal to basal breast cancer plasticity.

Tumor heterogeneity complicates patient treatment and can be due to transitioning of cancer cells across phenotypic cell states. This process is associated with the acquisition of independence from an oncogenic driver, such as the estrogen receptor (ER) in breast cancer (BC), resulting in tumor progression, therapeutic failure and metastatic spread. The transcription factor ONECUT2 (OC2) has been shown to be a master regulator protein of metastatic castration-resistant prostate cancer (mCRPC) tumors that promotes lineage plasticity to a drug-resistant neuroendocrine (NEPC) phenotype. Here, we investigate the role of OC2 in the dynamic conversion between different molecular subtypes in BC. We analyze OC2 expression and clinical significance in BC using public databases and immunohistochemical staining. In vitro, we perform RNA-Seq, RT-qPCR and western-blot after OC2 enforced expression. We also assess cellular effects of OC2 silencing and inhibition with a drug-like small molecule in vitro and in vivo. OC2 is highly expressed in a substantial subset of hormone receptor negative human BC tumors and tamoxifen-resistant models, and is associated with poor clinical outcome, lymph node metastasis and heightened clinical stage. OC2 inhibits ER expression and activity, suppresses a gene expression program associated with luminal differentiation and activates a basal-like state at the gene expression level. We also show that OC2 is required for cell growth and survival in metastatic BC models and that it can be targeted with a small molecule inhibitor providing a novel therapeutic strategy for patients with OC2 active tumors. The transcription factor OC2 is a driver of BC heterogeneity and a potential drug target in distinct cell states within the breast tumors.

Developing improved machine learning methods to predict the flow characteristics through vertical and horizontal transitions in open channels

ABSTRACT Transitions in an open channel refer to the change in flow behavior due to changes in the channel geometry. Determining flow characteristics through transitions is an important topic as it is necessary to guarantee the ideal hydraulic performance of water structures with low costs. This research focuses on the flow characteristics through vertical and horizontal transitions through experimental study and then utilizing machine learning to predict the flow characteristics. The proposed framework aims to develop both the cascade-forward artificial neural network (CFANN) model and the regression model to enhance the prediction of flow characteristics. The first model developed modifies the CFANN using dandelion optimizer (DO) to determine the ideal CFANN configuration. The second model used gene expression programming to develop statistical equations. The obtained CFANN–DO model has proven high accuracy in predicting the flow rates at various water loads and speeds achieving a coefficient of determination of approximately 100% for training data and 99.5% for testing data. Finally, predicting the characteristics of vertical and horizontal transitions in open channels is a critical issue. Manipulating these transitions can create a habitat for aquatic organisms, reduce erosion, and improve the overall environment.

A hybrid long-term industrial electrical load forecasting model using optimized ANFIS with gene expression programming

Electric energy demand forecasting is vital in contemporary power systems, especially amidst market deregulation trends and the increasing influence of industrial customers on power dynamics. However, existing forecasting models encounter challenges such as slow convergence and high complexity. Addressing these issues, this study proposes a hybrid forecasting model that combines the Adaptive Neuro-Fuzzy Inference System (ANFIS) with Gene Expression Programming (GEP) to enhance predictions of electrical energy consumption. Validated using real-time monthly electrical load data from an industrial user in Uganda, the hybrid model outperforms individual ANFIS and GEP models, demonstrating reduced errors and minimal computation time. The application of this hybrid model presents promising results, showcasing exceptional predictive capabilities and offering potential improvements in efficiency and precision for electrical energy consumption forecasting amidst market deregulation and evolving industrial dynamics.