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1.
Cell Rep Methods ; 4(7): 100817, 2024 Jul 15.
Artigo em Inglês | MEDLINE | ID: mdl-38981473

RESUMO

Deep-learning tools that extract prognostic factors derived from multi-omics data have recently contributed to individualized predictions of survival outcomes. However, the limited size of integrated omics-imaging-clinical datasets poses challenges. Here, we propose two biologically interpretable and robust deep-learning architectures for survival prediction of non-small cell lung cancer (NSCLC) patients, learning simultaneously from computed tomography (CT) scan images, gene expression data, and clinical information. The proposed models integrate patient-specific clinical, transcriptomic, and imaging data and incorporate Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome pathway information, adding biological knowledge within the learning process to extract prognostic gene biomarkers and molecular pathways. While both models accurately stratify patients in high- and low-risk groups when trained on a dataset of only 130 patients, introducing a cross-attention mechanism in a sparse autoencoder significantly improves the performance, highlighting tumor regions and NSCLC-related genes as potential biomarkers and thus offering a significant methodological advancement when learning from small imaging-omics-clinical samples.


Assuntos
Carcinoma Pulmonar de Células não Pequenas , Aprendizado Profundo , Neoplasias Pulmonares , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/diagnóstico por imagem , Neoplasias Pulmonares/patologia , Carcinoma Pulmonar de Células não Pequenas/genética , Carcinoma Pulmonar de Células não Pequenas/diagnóstico por imagem , Carcinoma Pulmonar de Células não Pequenas/patologia , Tomografia Computadorizada por Raios X/métodos , Biomarcadores Tumorais/genética , Prognóstico , Masculino , Feminino , Regulação Neoplásica da Expressão Gênica , Transcriptoma
2.
Hum Mol Genet ; 2024 Jun 09.
Artigo em Inglês | MEDLINE | ID: mdl-38850567

RESUMO

Alterations in Dp71 expression, the most ubiquitous dystrophin isoform, have been associated with patient survival across tumours. Intriguingly, in certain malignancies, Dp71 acts as a tumour suppressor, while manifesting oncogenic properties in others. This diversity could be explained by the expression of two Dp71 splice variants encoding proteins with distinct C-termini, each with specific properties. Expression of these variants has impeded the exploration of their unique roles. Using CRISPR/Cas9, we ablated the Dp71f variant with the alternative C-terminus in a sarcoma cell line not expressing the canonical C-terminal variant, and conducted molecular (RNAseq) and functional characterisation of the knockout cells. Dp71f ablation induced major transcriptomic alterations, particularly affecting the expression of genes involved in calcium signalling and ECM-receptor interaction pathways. The genome-scale metabolic analysis identified significant downregulation of glucose transport via membrane vesicle reaction (GLCter) and downregulated glycolysis/gluconeogenesis pathway. Functionally, these molecular changes corresponded with, increased calcium responses, cell adhesion, proliferation, survival under serum starvation and chemotherapeutic resistance. Knockout cells showed reduced GLUT1 protein expression, survival without attachment and their migration and invasion in vitro and in vivo were unaltered, despite increased matrix metalloproteinases release. Our findings emphasise the importance of alternative splicing of dystrophin transcripts and underscore the role of the Dp71f variant, which appears to govern distinct cellular processes frequently dysregulated in tumour cells. The loss of this regulatory mechanism promotes sarcoma cell survival and treatment resistance. Thus, Dp71f is a target for future investigations exploring the intricate functions of specific DMD transcripts in physiology and across malignancies.

3.
Trends Endocrinol Metab ; 35(6): 533-548, 2024 Jun.
Artigo em Inglês | MEDLINE | ID: mdl-38575441

RESUMO

Genome-scale metabolic models (GEMs) are consolidating as platforms for studying mixed microbial populations, by combining biological data and knowledge with mathematical rigor. However, deploying these models to answer research questions can be challenging due to the increasing number of available computational tools, the lack of universal standards, and their inherent limitations. Here, we present a comprehensive overview of foundational concepts for building and evaluating genome-scale models of microbial communities. We then compare tools in terms of requirements, capabilities, and applications. Next, we highlight the current pitfalls and open challenges to consider when adopting existing tools and developing new ones. Our compendium can be relevant for the expanding community of modelers, both at the entry and experienced levels.


Assuntos
Modelos Biológicos , Microbiota/fisiologia , Humanos
4.
Cancer Med ; 2024 Jan 08.
Artigo em Inglês | MEDLINE | ID: mdl-38189631

RESUMO

BACKGROUND: Melanoma, the most lethal skin cancer type, occurs more frequently in Parkinson's disease (PD), and PD is more frequent in melanoma patients, suggesting disease mechanisms overlap. α-synuclein, a protein that accumulates in PD brain, and the oncogene DJ-1, which is associated with PD autosomal recessive forms, are both elevated in melanoma cells. Whether this indicates melanoma progression or constitutes a protective response remains unclear. We hereby investigated the molecular mechanisms through which α-synuclein and DJ-1 interact, suggesting novel biomarkers and targets in melanoma. METHODS: The Cancer Genome Atlas (TCGA) expression profiles derived from UCSC Xena were used to obtain α-synuclein and DJ-1 expression and correlated with survival in skin cutaneous melanoma (SKCM). Immunohistochemistry determined the expression in metastatic melanoma lymph nodes. Protein-protein interactions (PPIs) and molecular docking assessed protein binding and affinity with chemotherapeutic drugs. Further validation was performed using in vitro cellular models and ELISA immunoassays. RESULTS: α-synuclein and DJ-1 were upregulated in primary and metastatic SKCM. Aggregated α-synuclein was selectively detected in metastatic melanoma lymph nodes. α-synuclein overexpression in SK-MEL-28 cells induced the expression of DJ-1, supporting PPI and a positive correlation in melanoma patients. Molecular docking revealed a stable protein complex, with differential binding to chemotherapy drugs such as temozolomide, dacarbazine, and doxorubicin. Parallel reduction of both proteins in temozolomide-treated SK-MEL-28 spheroids suggests drug binding may affect protein interaction and/or stability. CONCLUSION: α-synuclein, together with DJ-1, may play a role in melanoma progression and chemosensitivity, constituting novel targets for therapeutic intervention, and possible biomarkers for melanoma.

5.
Trends Cell Biol ; 34(2): 85-89, 2024 02.
Artigo em Inglês | MEDLINE | ID: mdl-38087709

RESUMO

Artificial intelligence (AI) is widely used for exploiting multimodal biomedical data, with increasingly accurate predictions and model-agnostic interpretations, which are however also agnostic to biological mechanisms. Combining metabolic modelling, 'omics, and imaging data via multimodal AI can generate predictions that can be interpreted mechanistically and transparently, therefore with significantly higher therapeutic potential.


Assuntos
Inteligência Artificial , Multiômica , Modelos Biológicos
6.
PLoS Comput Biol ; 19(7): e1011224, 2023 Jul.
Artigo em Inglês | MEDLINE | ID: mdl-37410704

RESUMO

Data are the most important elements of bioinformatics: Computational analysis of bioinformatics data, in fact, can help researchers infer new knowledge about biology, chemistry, biophysics, and sometimes even medicine, influencing treatments and therapies for patients. Bioinformatics and high-throughput biological data coming from different sources can even be more helpful, because each of these different data chunks can provide alternative, complementary information about a specific biological phenomenon, similar to multiple photos of the same subject taken from different angles. In this context, the integration of bioinformatics and high-throughput biological data gets a pivotal role in running a successful bioinformatics study. In the last decades, data originating from proteomics, metabolomics, metagenomics, phenomics, transcriptomics, and epigenomics have been labelled -omics data, as a unique name to refer to them, and the integration of these omics data has gained importance in all biological areas. Even if this omics data integration is useful and relevant, due to its heterogeneity, it is not uncommon to make mistakes during the integration phases. We therefore decided to present these ten quick tips to perform an omics data integration correctly, avoiding common mistakes we experienced or noticed in published studies in the past. Even if we designed our ten guidelines for beginners, by using a simple language that (we hope) can be understood by anyone, we believe our ten recommendations should be taken into account by all the bioinformaticians performing omics data integration, including experts.


Assuntos
Genômica , Multiômica , Humanos , Proteômica , Biologia Computacional , Metabolômica
7.
Basic Res Cardiol ; 118(1): 16, 2023 05 04.
Artigo em Inglês | MEDLINE | ID: mdl-37140699

RESUMO

The number of "omics" approaches is continuously growing. Among others, epigenetics has appeared as an attractive area of investigation by the cardiovascular research community, notably considering its association with disease development. Complex diseases such as cardiovascular diseases have to be tackled using methods integrating different omics levels, so called "multi-omics" approaches. These approaches combine and co-analyze different levels of disease regulation. In this review, we present and discuss the role of epigenetic mechanisms in regulating gene expression and provide an integrated view of how these mechanisms are interlinked and regulate the development of cardiac disease, with a particular attention to heart failure. We focus on DNA, histone, and RNA modifications, and discuss the current methods and tools used for data integration and analysis. Enhancing the knowledge of these regulatory mechanisms may lead to novel therapeutic approaches and biomarkers for precision healthcare and improved clinical outcomes.


Assuntos
Doenças Cardiovasculares , Insuficiência Cardíaca , Humanos , Metilação de DNA , Epigênese Genética , Insuficiência Cardíaca/genética , Doenças Cardiovasculares/genética , Coração
9.
Cell Rep Methods ; 3(1): 100383, 2023 01 23.
Artigo em Inglês | MEDLINE | ID: mdl-36814842

RESUMO

Multi-omics data integration via mechanistic models of metabolism is a scalable and flexible framework for exploring biological hypotheses in microbial systems. However, although most microorganisms are unculturable, such multi-omics modeling is limited to isolate microbes or simple synthetic communities. Here, we developed an approach for modeling microbial activity and interactions that leverages the reconstruction of metagenome-assembled genomes and associated genome-centric metatranscriptomes. At its core, we designed a method for condition-specific metabolic modeling of microbial communities through the integration of metatranscriptomic data. Using this approach, we explored the behavior of anaerobic digestion consortia driven by hydrogen availability and human gut microbiota dysbiosis associated with Crohn's disease, identifying condition-dependent amino acid requirements in archaeal species and a reduced short-chain fatty acid exchange network associated with disease, respectively. Our approach can be applied to complex microbial communities, allowing a mechanistic contextualization of multi-omics data on a metagenome scale.


Assuntos
Microbioma Gastrointestinal , Microbiota , Humanos , Microbiota/genética , Metagenoma/genética , Archaea/genética , Microbioma Gastrointestinal/genética
10.
BMC Cancer ; 23(1): 174, 2023 Feb 21.
Artigo em Inglês | MEDLINE | ID: mdl-36809974

RESUMO

BACKGROUND: Gliomas are the most common brain tumours with the high-grade glioblastoma representing the most aggressive and lethal form. Currently, there is a lack of specific glioma biomarkers that would aid tumour subtyping and minimally invasive early diagnosis. Aberrant glycosylation is an important post-translational modification in cancer and is implicated in glioma progression. Raman spectroscopy (RS), a vibrational spectroscopic label-free technique, has already shown promise in cancer diagnostics. METHODS: RS was combined with machine learning to discriminate glioma grades. Raman spectral signatures of glycosylation patterns were used in serum samples and fixed tissue biopsy samples, as well as in single cells and spheroids. RESULTS: Glioma grades in fixed tissue patient samples and serum were discriminated with high accuracy. Discrimination between higher malignant glioma grades (III and IV) was achieved with high accuracy in tissue, serum, and cellular models using single cells and spheroids. Biomolecular changes were assigned to alterations in glycosylation corroborated by analysing glycan standards and other changes such as carotenoid antioxidant content. CONCLUSION: RS combined with machine learning could pave the way for more objective and less invasive grading of glioma patients, serving as a useful tool to facilitate glioma diagnosis and delineate biomolecular glioma progression changes.


Assuntos
Neoplasias Encefálicas , Glioblastoma , Glioma , Humanos , Análise Espectral Raman/métodos , Glicosilação , Glioma/patologia , Neoplasias Encefálicas/patologia , Glioblastoma/patologia , Gradação de Tumores
11.
Metab Eng ; 76: 120-132, 2023 03.
Artigo em Inglês | MEDLINE | ID: mdl-36720400

RESUMO

Multi-strain probiotics are widely regarded as effective products for improving gut microbiota stability and host health, providing advantages over single-strain probiotics. However, in general, it is unclear to what extent different strains would cooperate or compete for resources, and how the establishment of a common biofilm microenvironment could influence their interactions. In this work, we develop an integrative experimental and computational approach to comprehensively assess the metabolic functionality and interactions of probiotics across growth conditions. Our approach combines co-culture assays with genome-scale modelling of metabolism and multivariate data analysis, thus exploiting complementary data- and knowledge-driven systems biology techniques. To show the advantages of the proposed approach, we apply it to the study of the interactions between two widely used probiotic strains of Lactobacillus reuteri and Saccharomyces boulardii, characterising their production potential for compounds that can be beneficial to human health. Our results show that these strains can establish a mixed cooperative-antagonistic interaction best explained by competition for shared resources, with an increased individual exchange but an often decreased net production of amino acids and short-chain fatty acids. Overall, our work provides a strategy that can be used to explore microbial metabolic fingerprints of biotechnological interest, capable of capturing multifaceted equilibria even in simple microbial consortia.


Assuntos
Microbioma Gastrointestinal , Probióticos , Humanos , Probióticos/metabolismo , Saccharomyces cerevisiae/metabolismo , Biofilmes , Técnicas de Cocultura
12.
Methods Mol Biol ; 2553: 325-393, 2023.
Artigo em Inglês | MEDLINE | ID: mdl-36227551

RESUMO

Breast cancer is one of the most common cancers in women worldwide, which causes an enormous number of deaths annually. However, early diagnosis of breast cancer can improve survival outcomes enabling simpler and more cost-effective treatments. The recent increase in data availability provides unprecedented opportunities to apply data-driven and machine learning methods to identify early-detection prognostic factors capable of predicting the expected survival and potential sensitivity to treatment of patients, with the final aim of enhancing clinical outcomes. This tutorial presents a protocol for applying machine learning models in survival analysis for both clinical and transcriptomic data. We show that integrating clinical and mRNA expression data is essential to explain the multiple biological processes driving cancer progression. Our results reveal that machine-learning-based models such as random survival forests, gradient boosted survival model, and survival support vector machine can outperform the traditional statistical methods, i.e., Cox proportional hazard model. The highest C-index among the machine learning models was recorded when using survival support vector machine, with a value 0.688, whereas the C-index recorded using the Cox model was 0.677. Shapley Additive Explanation (SHAP) values were also applied to identify the feature importance of the models and their impact on the prediction outcomes.


Assuntos
Neoplasias da Mama , Neoplasias da Mama/diagnóstico , Neoplasias da Mama/genética , Feminino , Humanos , Aprendizado de Máquina , RNA Mensageiro , Análise de Sobrevida , Transcriptoma
13.
Comput Biol Med ; 151(Pt A): 106244, 2022 12.
Artigo em Inglês | MEDLINE | ID: mdl-36343407

RESUMO

BACKGROUND: Recently, multi-omic machine learning architectures have been proposed for the early detection of cancer. However, for rare cancers and their associated small datasets, it is still unclear how to use the available multi-omics data to achieve a mechanistic prediction of cancer onset and progression, due to the limited data available. Hepatoblastoma is the most frequent liver cancer in infancy and childhood, and whose incidence has been lately increasing in several developed countries. Even though some studies have been conducted to understand the causes of its onset and discover potential biomarkers, the role of metabolic rewiring has not been investigated in depth so far. METHODS: Here, we propose and implement an interpretable multi-omics pipeline that combines mechanistic knowledge from genome-scale metabolic models with machine learning algorithms, and we use it to characterise the underlying mechanisms controlling hepatoblastoma. RESULTS AND CONCLUSIONS: While the obtained machine learning models generally present a high diagnostic classification accuracy, our results show that the type of omics combinations used as input to the machine learning models strongly affects the detection of important genes, reactions and metabolic pathways linked to hepatoblastoma. Our method also suggests that, in the context of computer-aided diagnosis of cancer, optimal diagnostic accuracy can be achieved by adopting a combination of omics that depends on the patient's clinical characteristics.


Assuntos
Hepatoblastoma , Neoplasias Hepáticas , Humanos , Criança , Aprendizado de Máquina , Algoritmos , Redes e Vias Metabólicas/genética , Neoplasias Hepáticas/diagnóstico , Neoplasias Hepáticas/genética
14.
Sci Rep ; 12(1): 19868, 2022 11 18.
Artigo em Inglês | MEDLINE | ID: mdl-36400876

RESUMO

Glioblastoma is the most aggressive form of brain cancer, presenting poor prognosis despite current advances in treatment. There is therefore an urgent need for novel biomarkers and therapeutic targets. Interactions between mucin 4 (MUC4) and the epidermal growth factor receptor (EGFR) are involved in carcinogenesis, and may lead to matrix metalloproteinase-9 (MMP9) overexpression, exacerbating cancer cell invasiveness. In this study, the role of MUC4, MMP9, and EGFR in the progression and clinical outcome of glioma patients was investigated. Immunohistochemistry (IHC) and immunofluorescence (IF) in fixed tissue samples of glioma patients were used to evaluate the expression and localization of EGFR, MMP9, and MUC4. Kaplan-Meier survival analysis was also performed to test the prognostic utility of the proteins for glioma patients. The protein levels were assessed with enzyme-linked immunosorbent assay (ELISA) in serum of glioma patients, to further investigate their potential as non-invasive serum biomarkers. We demonstrated that MUC4 and MMP9 are both significantly upregulated during glioma progression. Moreover, MUC4 is co-expressed with MMP9 and EGFR in the proliferative microvasculature of glioblastoma, suggesting a potential role for MUC4 in microvascular proliferation and angiogenesis. The combined high expression of MUC4/MMP9, and MUC4/MMP9/EGFR was associated with poor overall survival (OS). Finally, MMP9 mean protein level was significantly higher in the serum of glioblastoma compared with grade III glioma patients, whereas MUC4 mean protein level was minimally elevated in higher glioma grades (III and IV) compared with control. Our results suggest that MUC4, along with MMP9, might account for glioblastoma progression, representing potential therapeutic targets, and suggesting the 'MUC4/MMP9/EGFR axis' may play a vital role in glioblastoma diagnostics.


Assuntos
Glioblastoma , Glioma , Humanos , Mucina-4/metabolismo , Metaloproteinase 9 da Matriz/metabolismo , Glioblastoma/diagnóstico , Glioblastoma/metabolismo , Prognóstico , Glioma/diagnóstico , Receptores ErbB/metabolismo , Biomarcadores
15.
Microbiologyopen ; 11(5): e1328, 2022 10.
Artigo em Inglês | MEDLINE | ID: mdl-36314754

RESUMO

Salt tolerant organisms are increasingly being used for the industrial production of high-value biomolecules due to their better adaptability compared to mesophiles. Chromohalobacter canadensis is one of the early halophiles to show promising biotechnology potential, which has not been explored to date. Advanced high throughput technologies such as whole-genome sequencing allow in-depth insight into the potential of organisms while at the frontiers of systems biology. At the same time, genome-scale metabolic models (GEMs) enable phenotype predictions through a mechanistic representation of metabolism. Here, we sequence and analyze the genome of C. canadensis 85B, and we use it to reconstruct a GEM. We then analyze the GEM using flux balance analysis and validate it against literature data on C. canadensis. We show that C. canadensis 85B is a metabolically versatile organism with many features for stress and osmotic adaptation. Pathways to produce ectoine and polyhydroxybutyrates were also predicted. The GEM reveals the ability to grow on several carbon sources in a minimal medium and reproduce osmoadaptation phenotypes. Overall, this study reveals insights from the genome of C. canadensis 85B, providing genomic data and a draft GEM that will serve as the first steps towards a better understanding of its metabolism, for novel applications in industrial biotechnology.


Assuntos
Chromohalobacter , Tolerância ao Sal , Chromohalobacter/genética , Chromohalobacter/metabolismo , Biotecnologia , Genômica
16.
Elife ; 112022 09 27.
Artigo em Inglês | MEDLINE | ID: mdl-36164827

RESUMO

Duchenne muscular dystrophy (DMD) affects myofibers and muscle stem cells, causing progressive muscle degeneration and repair defects. It was unknown whether dystrophic myoblasts-the effector cells of muscle growth and regeneration-are affected. Using transcriptomic, genome-scale metabolic modelling and functional analyses, we demonstrate, for the first time, convergent abnormalities in primary mouse and human dystrophic myoblasts. In Dmdmdx myoblasts lacking full-length dystrophin, the expression of 170 genes was significantly altered. Myod1 and key genes controlled by MyoD (Myog, Mymk, Mymx, epigenetic regulators, ECM interactors, calcium signalling and fibrosis genes) were significantly downregulated. Gene ontology analysis indicated enrichment in genes involved in muscle development and function. Functionally, we found increased myoblast proliferation, reduced chemotaxis and accelerated differentiation, which are all essential for myoregeneration. The defects were caused by the loss of expression of full-length dystrophin, as similar and not exacerbated alterations were observed in dystrophin-null Dmdmdx-ßgeo myoblasts. Corresponding abnormalities were identified in human DMD primary myoblasts and a dystrophic mouse muscle cell line, confirming the cross-species and cell-autonomous nature of these defects. The genome-scale metabolic analysis in human DMD myoblasts showed alterations in the rate of glycolysis/gluconeogenesis, leukotriene metabolism, and mitochondrial beta-oxidation of various fatty acids. These results reveal the disease continuum: DMD defects in satellite cells, the myoblast dysfunction affecting muscle regeneration, which is insufficient to counteract muscle loss due to myofiber instability. Contrary to the established belief, our data demonstrate that DMD abnormalities occur in myoblasts, making these cells a novel therapeutic target for the treatment of this lethal disease.


Assuntos
Distrofina , Distrofia Muscular de Duchenne , Mioblastos , Animais , Cálcio/metabolismo , Distrofina/genética , Ácidos Graxos/metabolismo , Humanos , Leucotrienos/metabolismo , Camundongos , Camundongos Endogâmicos mdx , Músculo Esquelético/metabolismo , Distrofia Muscular de Duchenne/genética , Distrofia Muscular de Duchenne/patologia , Mioblastos/patologia
17.
Front Aging Neurosci ; 14: 894994, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35860672

RESUMO

The degu (Octodon degus) is a diurnal long-lived rodent that can spontaneously develop molecular and behavioral changes that mirror those seen in human aging. With age some degu, but not all individuals, develop cognitive decline and brain pathology like that observed in Alzheimer's disease including neuroinflammation, hyperphosphorylated tau and amyloid plaques, together with other co-morbidities associated with aging such as macular degeneration, cataracts, alterations in circadian rhythm, diabetes and atherosclerosis. Here we report the whole-genome sequencing and analysis of the degu genome, which revealed unique features and molecular adaptations consistent with aging and Alzheimer's disease. We identified single nucleotide polymorphisms in genes associated with Alzheimer's disease including a novel apolipoprotein E (Apoe) gene variant that correlated with an increase in amyloid plaques in brain and modified the in silico predicted degu APOE protein structure and functionality. The reported genome of an unconventional long-lived animal model of aging and Alzheimer's disease offers the opportunity for understanding molecular pathways involved in aging and should help advance biomedical research into treatments for Alzheimer's disease.

18.
Methods Mol Biol ; 2399: 87-122, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35604554

RESUMO

Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to as multimodal clinical data. In order to accommodate the volume and heterogeneity of such diverse data types and aid in their interpretation when they are combined with a multi-scale predictive model, machine learning is a useful tool that can be wielded to deconstruct biological complexity and extract relevant outputs. Additionally, genome-scale metabolic models (GSMMs) are one of the main frameworks striving to bridge the gap between genotype and phenotype by incorporating prior biological knowledge into mechanistic models. Consequently, the utilization of GSMMs as a foundation for the integration of multi-omic data originating from different domains is a valuable pursuit towards refining predictions. In this chapter, we show how cancer multi-omic data can be analyzed via multimodal machine learning and metabolic modeling. Firstly, we focus on the merits of adopting an integrative systems biology led approach to biomedical data mining. Following this, we propose how constraint-based metabolic models can provide a stable yet adaptable foundation for the integration of multimodal data with machine learning. Finally, we provide a step-by-step tutorial for the combination of machine learning and GSMMs, which includes: (i) tissue-specific constraint-based modeling; (ii) survival analysis using time-to-event prediction for cancer; and (iii) classification and regression approaches for multimodal machine learning. The code associated with the tutorial can be found at https://github.com/Angione-Lab/Tutorials_Combining_ML_and_GSMM .


Assuntos
Aprendizado de Máquina , Neoplasias , Mineração de Dados , Genoma , Humanos , Neoplasias/genética , Biologia de Sistemas
19.
J Cell Biochem ; 123(5): 964-986, 2022 05.
Artigo em Inglês | MEDLINE | ID: mdl-35342986

RESUMO

The continuous spread and evolution of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and the rapid surge in infection cases in the coronavirus disease 2019 (COVID-19) evoke a dire need for effective therapeutics. In this study, we explored the inhibitory potential of a library of 605 phytocompounds, selected from Indian medicinal plants with reported antiviral and anti-inflammatory activities, against the receptor-binding domain of spike proteins of the SARS-CoV-2 wild-type and the variants of concern, including variants B.1.1.7 (Alpha), B.1.351 (Beta), P.1 (Gamma), B.1.617.2 (Delta), and B.1.1.529 (Omicron). Our approach was based on extensive molecular docking, assessment of drug-likeness, and robust molecular dynamics simulations. We also identified promising inhibitory candidates against the host (human) proteins associated with SARS-CoV-2 spike activation and attachment, namely, ACE2 receptor, proteases TMPRSS2 and CTSL, and the endocytic regulator AAK1. In addition, we screened promising inhibitory compounds against the human proinflammatory cytokines- IL-6, IL-1ß, TNF-α, and IFN-γ, that are associated with the adverse cytokine storm in COVID-19 patients. Our analysis returned an encouraging list of promising inhibitory candidates that includes: abietatriene against the spike proteins of the SARS-CoV-2 wild-type and the variants of concern; taraxerol against the human ACE2, CTSL and TNF-α; ß-amyrin against the human TMPRSS2; cynaroside against the human AAK1 and IL-1ß; and friedelin against the human IL-6 and IFN-γ. Our findings provide substantial evidence for the inhibitory potential of these compounds and encourage further in vitro and in vivo studies to validate their use as safe and effective therapeutics against COVID-19.


Assuntos
Tratamento Farmacológico da COVID-19 , SARS-CoV-2 , Enzima de Conversão de Angiotensina 2/genética , Síndrome da Liberação de Citocina , Humanos , Interleucina-6 , Simulação de Acoplamento Molecular , Glicoproteína da Espícula de Coronavírus/genética , Glicoproteína da Espícula de Coronavírus/metabolismo , Fator de Necrose Tumoral alfa
20.
PLoS One ; 17(2): e0263150, 2022.
Artigo em Inglês | MEDLINE | ID: mdl-35143521

RESUMO

In this proof-of-concept work, we evaluate the performance of multiple machine-learning methods as surrogate models for use in the analysis of agent-based models (ABMs). Analysing agent-based modelling outputs can be challenging, as the relationships between input parameters can be non-linear or even chaotic even in relatively simple models, and each model run can require significant CPU time. Surrogate modelling, in which a statistical model of the ABM is constructed to facilitate detailed model analyses, has been proposed as an alternative to computationally costly Monte Carlo methods. Here we compare multiple machine-learning methods for ABM surrogate modelling in order to determine the approaches best suited as a surrogate for modelling the complex behaviour of ABMs. Our results suggest that, in most scenarios, artificial neural networks (ANNs) and gradient-boosted trees outperform Gaussian process surrogates, currently the most commonly used method for the surrogate modelling of complex computational models. ANNs produced the most accurate model replications in scenarios with high numbers of model runs, although training times were longer than the other methods. We propose that agent-based modelling would benefit from using machine-learning methods for surrogate modelling, as this can facilitate more robust sensitivity analyses for the models while also reducing CPU time consumption when calibrating and analysing the simulation.


Assuntos
Redes Neurais de Computação
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