Browsing by Author "Asif, Muhammad"
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- An integrative system biology approach to delineate complex genotype-phenotype associations in Autism Spectrum DisorderPublication . Asif, Muhammad; Couto, Francisco M.; Vicente, Astrid M.Objectives: To identify clinically similar subgroups of individuals with ASD [To reduce ASD heterogeneous phenotype]; To find biological processes disrupted by rare CNVs targeting brain genes in ASD subjects [To maximize the reproducibility with other data sets]; To train a machine learning classifier for the clinical prediction of disease progression from genetic information in very young children [To facilitate clinical doctors in diagnosis]
- An integrative system biology approach to delineate complex genotype-phenotype associations in Autism Spectrum DisorderPublication . Asif, Muhammadutism Spectrum Disorder (ASD) is a neurodevelopmental disorder which affects the brain structure and the proper establishment of the neuronal connectivity.
- Aplicação de métodos de aprendizagem automática em grafos de conhecimento para medicina personalizadaPublication . Vilela, Joana; Asif, Muhammad; Marques, Ana Rita; Santos, João Xavier; Rasga, Célia; Vicente, Astrid; Martiniano, HugoA Medicina Personalizada é um modelo de prática médica que utiliza o perfil fenotípico e genotípico do indivíduo para melhorar a precisão do diagnóstico, a eficácia terapêutica ou a prevenção de doenças. Neste sentido, a enorme quantidade de dados gerados ao longo dos últimos anos na área biomédica tem contribuído para uma melhor compreensão dos determinantes genéticos de várias patologias e, consequentemente, para a implementação de práticas de Medicina Personalizada em várias áreas, por exemplo na Oncologia e no âmbito das doenças raras. No entanto, ainda subsistem desafios significativos, nomeadamente no que diz respeito à integração de dados biomédicos oriundos de fontes heterogéneas e na obtenção de informação clinicamente relevante. Este trabalho descreve uma abordagem que usa métodos de aprendizagem automática aplicados a um Grafo de Conhecimento (GC) biomédico como um meio para integrar informação armazenada em bases de dados diversas. Este GC contém relações entre genes, doenças e outras entidades biológicas, extraídas de três bases de dados: Ensembl, DisGeNET e Gene Ontology. Neste trabalho exploramos o potencial dos métodos de aprendizagem automática em grafos para produzir informação clinicamente relevante e descrevemos a aplicação desta metodologia à previsão de associações gene-doença. Mostramos ainda que as principais associações gene-doença previstas por esta abordagem podem ser confirmadas em bases de dados externas ou já foram previamente identificadas na literatura.
- Associação entre variantes genéticas e perfil clínico multidimensional de doentes com perturbação do espetro do autismo: uma abordagem integrativaPublication . Asif, Muhammad; Couto, Francisco; Vicente, Astrid M.A complexidade genética e clínica que caracterizam a per turbação do espetro do autismo (PEA) têm limitado o desenvolvimento de biomarcadores que permitam um diagnóstico precoce e um prognóstico fiável, assim como uma abordagem personalizada para a inter venção terapêutica. Neste estudo pretendeu-se desenvolver uma abordagem integrativa para predição da apresentação clínica baseada em informação de variantes genéticas (Copy Number Variants, CNVs), com aplicação clínica no diagnóstico e prognóstico na PEA. Para tal, técnicas de aprendizagem automática (machine learning) foram aplicadas a dados clínicos e genéticos de 2446 doentes com PEA, recrutados no âmbito do consórcio Autism Genome Project. Análise de clustering de dados clínicos multidimensionais definiu, nesta população, dois subgrupos de pacientes com per fis clínicos diferindo significativamente em termos de capacidade verbal, nível cognitivo, gravidade da doença e compor tamento adaptativo. A análise dos CNVs que afetam especificamente genes do cérebro, nos mesmos indivíduos, identificou 15 processos biológicos enriquecidos em genes alterados. A aplicação de um algoritmo de machine learning para classificação dos doentes com apresentação clínica mais disfuncional, com base nos processos biológicos alterados, mostrou que correlações entre fenótipo clínico e biologia subjacente são possíveis na PEA e que, para grupos populacionais com dados informativos, existe um poder preditivo razoável. Para implementação deste conceito na prática clínica serão necessários estudos mais alargados com dados clínicos e genómicos mais completos.
- Autism Spectrum Disorder: modulation of genomic variant effects on brain structure and functionPublication . Vilela, Joana; Martiniano, Hugo; Marques, Ana Rita; Xavier Santos, João; Asif, Muhammad; Rasga, Célia; Oliveira, Guiomar; Vicente, Astrid M.The main objective of this work is to identify Single Nucleotide Variants (SNVs) that play a role in ASD etiology in neurotransmission and synaptic genes since there is strong genomic and functional evidence that these biological processes are altered in ASD.
- Biomedical Knowledge Graph Embeddings for Personalized MedicinePublication . Vilela, Joana; Asif, Muhammad; Marques, Ana Rita; Xavier Santos, João; Rasga, Célia; Vicente, Astrid; Martiniano, HugoPersonalized medicine promises to revolutionize healthcare in the coming years. However significant challenges remain, namely in regard to integrating the vast amount of biomedical knowledge generated in the last few years. Here we describe an approach that uses Knowledge Graph Embedding (KGE) methods on a biomedical Knowledge Graph as a path to reasoning over the wealth of information stored in publicly accessible databases. We use curated databases such as Ensembl, DisGeNET and Gene Ontology as data sources to build a Knowledge Graph containing relationships between genes, diseases and other biological entities and explore the potential of KGE methods to derive medically relevant insights from this KG. To showcase the method’s usefulness we describe two use cases: a) prediction of gene-disease associations and b) clustering of disease embeddings. We show that the top gene-disease associations predicted by this approach can be confirmed in external databases or have already been identified in the literature. An analysis of clusters of diseases, with a focus on Autism Spectrum Disorder (ASD), affords novel insights into the biology of this paradigmatic complex disorder and the overlap of its genetic background with other diseases
- Biomedical knowledge graph embeddings for personalized medicine: Predicting disease‐gene associationsPublication . Vilela, Joana; Asif, Muhammad; Marques, Ana Rita; Santos, João Xavier; Rasga, Célia; Vicente, Astrid; Martiniano, HugoPersonalized medicine is a concept that has been subject of increasing interest in medical research and practice in the last few years. However, significant challenges stand in the way of practical implementations, namely in regard to extracting clinically valuable insights from the vast amount of biomedical knowledge generated in the last few years. Here, we describe an approach that uses Knowledge Graph Embedding (KGE) methods on a biomedical Knowledge Graph (KG) as a path to reasoning over the wealth of information stored in publicly accessible databases. We built a Knowledge Graph using data from DisGeNET and GO, containing relationships between genes, diseases and other biological entities. The KG contains 93,657 nodes of 5 types and 1,705,585 relationships of 59 types. We applied KGE methods to this KG, obtaining an excellent performance in predicting gene-disease associations (MR 0.13, MRR 0.96, HITS@1 0.93, HITS@3 0.99, and HITS@10 0.99). The optimal hyperparameter set was used to predict all possible novel gene-disease associations. An in-depth analysis of novel gene-disease predictions for disease terms related to Autism Spectrum Disorder (ASD) shows that this approach produces predictions consistent with known candidate genes and biological pathways and yields relevant insights into the biology of this paradigmatic complex disorder.
- Exploratory analysis of mutations targeting noncoding RNAs in autismPublication . Marques, Ana Rita; Martiniano, Hugo; Asif, Muhammad; Santos, João Pedro; M. Vicente, AstridAutism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by deficits in social communication/interaction and by unusual repetitive and restricted behaviors. Heritability estimates indicate that genetic factors account for ~50% risk of ASD, sugesting a role of epigenetic factors, such as long noncoding RNA (lncRNA) and microRNA (miRNA), as modulators of genetic expression and clinical presentation. Our goal is to identify variants in lncRNA and miRNA loci that disrupt the function of target genes and modulating the high genotypic and phenotypic heterogeneity characteristic of ASD.
- FunVar: A systematic pipeline to unravel the convergence patterns of genetic variants in ASD, a paradigmatic complex diseasePublication . Asif, Muhammad; Vicente, Astrid M.; Couto, Francisco M.In recent years, the technological advances for capturing genetic variation in large populations led to the identification of large numbers of putative or disease-causing variants. However, their mechanistic understanding is lagging far behind and has posed new challenges regarding their relevance for disease phenotypes, particularly for common complex disorders. In this study, we propose a systematic pipeline to infer biological meaning from genetic variants, namely rare Copy Number Variants (CNVs). The pipeline consists of three modules that seek to (1) improve genetic data quality by excluding low confidence CNVs, (2) identify disrupted biological processes, and (3) aggregate similar enriched biological processes terms using semantic similarity. The proposed pipeline was applied to CNVs from individuals diagnosed with Autism Spectrum Disorder (ASD). We found that rare CNVs disrupting brain expressed genes dysregulated a wide range of biological processes, such as nervous system development and protein polyubiquitination. The disrupted biological processes identified in ASD patients were in accordance with previous findings. This coherence with literature indicates the feasibility of the proposed pipeline in interpreting the biological role of genetic variants in complex disease development. The suggested pipeline is easily adjustable at each step and its independence from any specific dataset and software makes it an effective tool in analyzing existing genetic resources. The FunVar pipeline is available at https://github.com/lasigeBioTM/FunVar and includes pre and post processing steps to effectively interpret biological mechanisms of putative disease causing genetic variants.
- Gene-environment interactions in Autism Spectrum Disorder (ASD)Publication . Xavier Santos, João; Rasga, Célia; Marques, Ana Rita; Asif, Muhammad; Café, Cátia; Nunes, Ana; Oliveira, Guiomar; Moura-Vicente, AstridAutism Spectrum Disorder – Background: Phenotypically heterogeneous neurodevelopmental disorder, with a global prevalence rate of 1% , characterized by deficits in social communication and interaction and stereotyped and repetitive behaviours. - Genetic factors, namely rare copy number variants (CNVs), are responsible for a considerable fraction of ASD cases . - The recent heritability estimates of approximately 50% suggest a role of nongenetic factors in ASD etiology. - Pre-, peri- and post-natal exposure to toxic environmental factors has been implicated in the development of the disorder[5][6] . - ASD is most probably explained by a polygenic and multifactorial mechanism that involves genetic, environmental and epigenetic interactions. Objectives: 1. To identify specific exposure patterns to environmental toxicants, potentially involved in ASD etiology, in a dataset of Portuguese children diagnosed with the disorder, aged 7-9 years old; 2. To identify variants of ASD-candidate genes interacting with environmental factors 3. To build a global mathematical model that integrates genetic and environmental biomarkers with clinical data for risk assessment in ASD.
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