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Artificial intelligence for neurodegenerative experimental models

ALZHEIMERS & DEMENTIA (2023)

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Article Medicine, General & Internal

Lecanemab in Early Alzheimer's Disease

Christopher H. van Dyck et al.

Summary: Lecanemab, a humanized monoclonal antibody, reduced amyloid markers and showed improvement in cognition and function in early Alzheimer's disease patients. However, it also resulted in adverse events. Longer trials are needed to determine its efficacy and safety in early Alzheimer's disease.

NEW ENGLAND JOURNAL OF MEDICINE (2023)

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Review Biochemistry & Molecular Biology

Explainability and white box in drug discovery

Kevser Kubra Kirboga et al.

Summary: Recently, the use of AI techniques in drug discovery has increased. However, the traditional AI techniques may lack explanations for decision processes. Explainable AI (XAI) techniques provide a better understanding of the causes and consequences of decision processes, improving drug discovery and decision-making.

CHEMICAL BIOLOGY & DRUG DESIGN (2023)

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Editorial Material Medicine, General & Internal

AI-Generated Medical Advice-GPT and Beyond

Claudia E. Haupt et al.

Summary: This Viewpoint discusses the usage of generative pretrained transformers (GPTs) and AI technologies in medical applications, and considers the need for new regulations to ensure safety and legal compliance for patients and healthcare professionals.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2023)

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Article Biochemistry & Molecular Biology

qPTM: an updated database for PTM dynamics in human, mouse, rat and yeast

Kai Yu et al.

Summary: Post-translational modifications (PTMs) play critical roles in regulating protein functions and understanding biological processes and diseases. The qPTM database serves as a comprehensive one-stop data resource, providing massive quantitative PTM proteome datasets and a scoring system to assess PTM site reliability.

NUCLEIC ACIDS RESEARCH (2023)

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Article Multidisciplinary Sciences

Evolutionary-scale prediction of atomic-level protein structure with a language model

Zeming Lin et al.

Summary: Recent advances in machine learning have allowed for the prediction of protein structure from multiple sequence alignments. By using a large language model, we are able to directly infer full atomic-level protein structure from primary sequence. This has led to a significant acceleration in high-resolution structure prediction, enabling the characterization of a large number of metagenomic proteins. Utilizing this capability, we have constructed the ESM Metagenomic Atlas, which provides insights into the diversity of natural proteins.

SCIENCE (2023)

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Article Multidisciplinary Sciences

The landscape of tolerated genetic variation in humans and primates

Hong Gao et al.

SCIENCE (2023)

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Article Medicine, General & Internal

Donanemab in Early Symptomatic Alzheimer Disease The TRAILBLAZER-ALZ 2 Randomized Clinical Trial

John R. Sims et al.

Summary: This study evaluated the efficacy and adverse events of donanemab, an antibody treatment for Alzheimer's disease. The findings showed that donanemab significantly slowed disease progression in patients with early symptomatic Alzheimer's disease.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2023)

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Article Computer Science, Artificial Intelligence

Biology-inspired graph neural network encodes reactome and reveals biochemical reactions of disease

Joshua G. Burkhart et al.

Summary: Functional heterogeneity of healthy human tissues complicates molecular studies and precision therapeutic targets identification. A graph neural network (GNN) with Reactome-based architecture was trained using 9,115 samples from GTEx, achieving an adjusted Rand index (ARI) of 0.7909 on 370 healthy human tissue samples from TCGA. The GNN successfully separates healthy skin samples from lesional psoriasis samples, revealing a central mechanism in psoriasis. These results, supported by human multi-omics and mouse studies, suggest the potential benefit of GNN analytical approaches in future molecular disease studies.

PATTERNS (2023)

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Review Clinical Neurology

Artificial intelligence for dementia drug discovery and trials optimization

Thomas Doherty et al.

Summary: Drug discovery and clinical trial design for dementia have always been challenging due to patient heterogeneity, disease course length, and the tractability of brain targets. Big data analytics and machine learning tools show potential in accelerating progress by informing clinical trial design and drug discovery. However, several challenges need to be overcome and a multi-disciplinary approach is required for data-driven decision-making to reach its full potential. This review discusses the current state of machine learning applications in clinical trial design and drug discovery, while providing opportunities and recommendations to overcome implementation barriers.

ALZHEIMERS & DEMENTIA (2023)

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Review Clinical Neurology

Artificial intelligence for biomarker discovery in Alzheimer's disease and dementia

Laura M. Winchester et al.

Summary: With the increase in large multimodal cohorts and high-throughput technologies, the potential for discovering novel biomarkers is no longer limited by data set size. Artificial intelligence (AI) and machine learning approaches have been developed to detect novel biomarkers and interactions in complex data sets. Remaining challenges include a lack of diversity in the data sets available, the sheer complexity of investigating interactions, the invasiveness and cost of some biomarkers, and poor reporting in some studies.

ALZHEIMERS & DEMENTIA (2023)

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Review Clinical Neurology

Artificial intelligence for diagnostic and prognostic neuroimaging in dementia: A systematic review

Robin Borchert et al.

Summary: Artificial intelligence (AI) and neuroimaging provide new opportunities for the diagnosis and prognosis of dementia. However, there are methodological limitations in current studies, and there is a need for better model validation and improved descriptions of algorithm development.

ALZHEIMERS & DEMENTIA (2023)

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Review Clinical Neurology

Artificial intelligence for dementia-Applied models and digital health

Donald Lyall et al.

Summary: This review summarizes the understanding of applied models and digital health technologies in terms of dementia risk prediction, diagnostic discrimination, prognosis, and progression. Machine learning approaches show improved predictive power compared to standard clinical risk scores in predicting dementia, and the ability to decompose large numbers of variables into relatively few critical predictors.

ALZHEIMERS & DEMENTIA (2023)

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Review Clinical Neurology

Artificial intelligence for dementia research methods optimization

Magda Bucholc et al.

Summary: Artificial intelligence and machine learning face methodological challenges in dementia research, including analyzing high-dimensional data and translating research findings. To overcome these challenges, researchers should share code and data, develop models using interpretable methods, and prioritize reproducibility and generalizability.

ALZHEIMERS & DEMENTIA (2023)

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Review Clinical Neurology

Artificial intelligence for dementia genetics and omics

Conceicao Bettencourt et al.

Summary: Genetics and omics studies of Alzheimer's disease and other dementia subtypes provide insights into underlying mechanisms, but there are challenges in enhancing genetic studies, identifying reproducible omics signatures, and utilizing high-dimensional omics data for improved biomarkers. Artificial intelligence and machine learning approaches offer potential solutions, but coordinated multidisciplinary research and diverse phenotyped cohorts are also needed.

ALZHEIMERS & DEMENTIA (2023)

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Article Medicine, General & Internal

Donanemab in Early Symptomatic Alzheimer Disease The TRAILBLAZER-ALZ 2 Randomized Clinical Trial

John R. Sims et al.

Summary: This study assessed the efficacy and adverse events of donanemab, an antibody designed to clear brain amyloid plaque. The results showed that donanemab significantly slowed the clinical progression in individuals with early symptomatic Alzheimer disease and low/medium tau pathology, as well as in the combined population with low/medium and high tau pathology.

JAMA-JOURNAL OF THE AMERICAN MEDICAL ASSOCIATION (2023)

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Article Clinical Neurology

Amyloid-Related Imaging Abnormalities in 2 Phase 3 Studies Evaluating Aducanumab in Patients With Early Alzheimer Disease

Stephen Salloway et al.

Summary: The EMERGE and ENGAGE phase 3 trials provided data on ARIA associated with aducanumab treatment in patients with Alzheimer's disease. A high rate of ARIA was observed, with ARIA-edema being the most common adverse event. Some patients in the aducanumab group experienced associated symptoms, with headache being the most common.

JAMA NEUROLOGY (2022)

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Article Computer Science, Artificial Intelligence

Explainable artificial intelligence (XAI) in deep learning-based medical image analysis

Bas H. M. Van der Velden et al.

Summary: This survey examines the applications of explainable artificial intelligence (XAI) in deep learning-based medical image analysis. It introduces a framework for classifying deep learning-based medical image analysis methods based on XAI criteria. The survey also categorizes and investigates XAI techniques in medical image analysis according to the framework and anatomical location. The paper concludes by discussing future opportunities for XAI in medical image analysis.

MEDICAL IMAGE ANALYSIS (2022)

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Article Biochemical Research Methods

BioGPT: generative pre-trained transformer for biomedical text generation and mining

Renqian Luo et al.

Summary: This paper introduces a domain-specific generative Transformer language model BioGPT pre-trained on large-scale biomedical literature, which performs well on biomedical natural language processing tasks, especially achieving high accuracy in relation extraction tasks.

BRIEFINGS IN BIOINFORMATICS (2022)

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Article Engineering, Biomedical

EEG-Based Graph Neural Network Classification of Alzheimer's Disease: An Empirical Evaluation of Functional Connectivity Methods

Dominik Klepl et al.

Summary: Alzheimer's disease (AD) is the most common form of dementia worldwide. Functional connectivity (FC) graph-based biomarkers have shown to be effective for automated diagnosis of AD using electroencephalography (EEG). Graph neural networks (GNN) have been used to classify EEG brain graphs, but it is unclear which method should be used to estimate the brain graph. This study compares the performance of different FC measures and shows that GNN models outperform other baseline models. Furthermore, using FC measures to estimate brain graphs improves the performance of GNN compared to using a fixed graph based on spatial distance between EEG sensors. However, no FC measure consistently performs better than others.

IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING (2022)

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Review Biochemistry & Molecular Biology

On the road to explainable AI in drug-drug interactions prediction: A systematic review

Thanh Hoa Vo et al.

Summary: Unwanted drug-drug interactions in multi-diseases treatment remain a significant issue. Artificial intelligence (AI) prediction models have potential but concerns about their reliability due to their black-box nature. Building AI models with explainable mechanisms can enhance transparency and promote safety and clarity.

COMPUTATIONAL AND STRUCTURAL BIOTECHNOLOGY JOURNAL (2022)

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Article Biochemistry & Molecular Biology

RCSB Protein Data Bank: powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences

Stephen K. Burley et al.

Summary: RCSB PDB, as the US data center for the global PDB archive, provides free access to 3D macromolecular structure data for millions of users worldwide, including educators, students, and the general public, integrating over 40 external biodata resources. The redesigned website now features improved search functionality and easier access to PDB data, showcasing new structures relevant to the understanding and addressing of the COVID-19 pandemic.

NUCLEIC ACIDS RESEARCH (2021)

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Article Biochemistry & Molecular Biology

The Gene Ontology resource: enriching a GOld mine

Seth Carbon et al.

Summary: The Gene Ontology Consortium has made advancements in the last two years, such as improving the GO-CAM annotation framework, increasing the number of annotations and annotated gene products, and reviewing older annotations for consistency.

NUCLEIC ACIDS RESEARCH (2021)

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Article Biochemistry & Molecular Biology

The Human Phenotype Ontology in 2021

Sebastian Koehler et al.

Summary: The Human Phenotype Ontology (HPO) serves as a global standard for phenotype exchange, with recent major extensions in neurology, nephrology, immunology, pulmonology, and other fields. Efforts have been made to improve computational definitions of phenotypic abnormalities across HPO and multiple phenotype ontologies for animal disease models, benefiting software accuracy and cross-species phenotype matching. Recent initiatives include translating HPO into indigenous languages and advancing its use in electronic health record systems.

NUCLEIC ACIDS RESEARCH (2021)

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Article Biochemistry & Molecular Biology

Open Targets Platform: supporting systematic drug-target identification and prioritisation

David Ochoa et al.

Summary: The Open Targets Platform provides a queryable knowledgebase and user interface for systematic target identification and prioritization for drug discovery, continuously improving evidence for target-disease relationships from various data sources. They have developed a new evidence scoring framework, added evaluation of post-marketing adverse drug reactions and target tractability, and optimized user interface and backend technologies to address the challenge of developing effective and safe drugs.

NUCLEIC ACIDS RESEARCH (2021)

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Article Biochemistry & Molecular Biology

FlyBase: updates to the Drosophila melanogaster knowledge base

Aoife Larkin et al.

Summary: FlyBase is an essential online database for researchers using Drosophila melanogaster, offering a wide range of genetic, molecular, genomic resources. New features include Pathway Reports, paralog information, disease models based on orthology, customizable tables, and expression and disease data overview displays. Recent updates include developmental proteome incorporation, GAL4 search tab upgrades, additional Experimental Tool Reports, migration to JBrowse for genome browsing, and improvements to batch queries/downloads and the Fast-Track Your Paper tool.

NUCLEIC ACIDS RESEARCH (2021)

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Article Computer Science, Artificial Intelligence

A Survey on Explainable Artificial Intelligence (XAI): Toward Medical XAI

Erico Tjoa et al.

Summary: Artificial intelligence and machine learning have shown remarkable performances in various fields, but the challenge of interpretability remains. The medical sector requires higher levels of interpretability to ensure the reliability of machine decisions, and a deeper understanding of the mechanisms behind machine algorithms is needed to advance medical practices.

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS (2021)

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Article Genetics & Heredity

A Comparison for Dimensionality Reduction Methods of Single-Cell RNA-seq Data

Ruizhi Xiang et al.

Summary: The study compared the performance of different dimensionality reduction methods in scRNA-seq data analysis. t-SNE showed the best accuracy and computing cost, while UMAP demonstrated high stability and preserved the cohesion and separation of cell populations.

FRONTIERS IN GENETICS (2021)

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Review Cell Biology

A Comparative Biology of Microglia Across Species

Kaushik Sharma et al.

Summary: Microglia, unique brain-resident myeloid cells, have implications in various central nervous system disorders and have distinct characteristics compared to other cells. Studies on microglial ontogeny and identity across species reveal both conserved and divergent properties, which can be better understood through an evolutionary perspective for developing targeted therapies.

FRONTIERS IN CELL AND DEVELOPMENTAL BIOLOGY (2021)

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Review Cell Biology

Omics sciences for systems biology in Alzheimer's disease: State-of-the-art of the evidence

Harald Hampel et al.

Summary: Alzheimer's disease is characterized by complex pathophysiology with high heterogeneity, requiring a holistic approach to fully capture its multifaceted nature. Integrating omics sciences and systems biology network approaches can help detect and describe the biological changes occurring in the progression of AD.

AGEING RESEARCH REVIEWS (2021)

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Article Biochemical Research Methods

DeepGraphGO: graph neural network for large-scale, multispecies protein function prediction

Ronghui You et al.

Summary: DeepGraphGO is a multispecies graph neural network-based method aimed at solving the problem of automated function prediction of proteins. By utilizing protein sequence and high-order protein network information, a single model can be trained for all species, providing more training samples for AFP. Experimental results demonstrate that DeepGraphGO significantly outperforms other state-of-the-art methods, including network-based GeneMANIA, deepNF, and clusDCA.

BIOINFORMATICS (2021)

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Article Genetics & Heredity

Graph Neural Networks and Their Current Applications in Bioinformatics

Xiao-Meng Zhang et al.

Summary: This study presents a systematic survey of the application of graph neural networks (GNNs) in bioinformatics, introducing basic principles and common models of GNNs, and discussing their applications in tasks such as node classification, link prediction, and graph generation. It also categorizes and discusses related studies based on different omics data applications, pointing out the current shortcomings and future prospects of GNN research in biological tasks.

FRONTIERS IN GENETICS (2021)

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Review Biochemistry & Molecular Biology

A Review of the Current Mammalian Models of Alzheimer's Disease and Challenges That Need to Be Overcome

Natasha Elizabeth Mckean et al.

Summary: Alzheimer's disease is a looming health crisis expected to triple in prevalence by 2050. Current medications only provide temporary relief for symptoms, and animal models, particularly mouse models, have limitations in fully capturing AD symptomatology.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2021)

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WormBase: a modern Model Organism Information Resource

Todd W. Harris et al.

NUCLEIC ACIDS RESEARCH (2020)

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Bijay Jassal et al.

NUCLEIC ACIDS RESEARCH (2020)

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Evolutionary shifts dramatically reorganized the human hippocampal complex

Brian M. Schilder et al.

JOURNAL OF COMPARATIVE NEUROLOGY (2020)

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Correction Biochemistry & Molecular Biology

Cross-Species Single-Cell Analysis Reveals Divergence of the Primate Microglia Program (vol 179, pg 1609, 2019)

Laufey Geirsdottir et al.

CELL (2020)

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Modeling and Targeting Alzheimer's Disease With Organoids

Angelos Papaspyropoulos et al.

FRONTIERS IN PHARMACOLOGY (2020)

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Personalized iPSC-Derived Dopamine Progenitor Cells for Parkinson's Disease

Jeffrey S. Schweitzer et al.

NEW ENGLAND JOURNAL OF MEDICINE (2020)

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Liesbeth Minnoye et al.

GENOME RESEARCH (2020)

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Explainable Artificial Intelligence (XAI): Concepts, taxonomies, opportunities and challenges toward responsible AI

Alejandro Barredo Arrieta et al.

INFORMATION FUSION (2020)

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Serum Tau Proteins as Potential Biomarkers for the Assessment of Alzheimer's Disease Progression

Eunjoo Nam et al.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2020)

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David R. Kelley

PLOS COMPUTATIONAL BIOLOGY (2020)

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Revisiting the Amyloid Cascade Hypothesis: From Anti-Aβ Therapeutics to Auspicious New Ways for Alzheimer's Disease

Md Sahab Uddin et al.

INTERNATIONAL JOURNAL OF MOLECULAR SCIENCES (2020)

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STEM CELL RESEARCH (2020)

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Visual attention as a model for interpretable neuroimage classification in dementia

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Alzheimers & Dementia (2020)

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DeepCDR: a hybrid graph convolutional network for predicting cancer drug response

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BIOINFORMATICS (2020)

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Improving preclinical to clinical translation in Alzheimer's disease research

Stacey J. Sukoff Rizzo et al.

ALZHEIMERS & DEMENTIA-TRANSLATIONAL RESEARCH & CLINICAL INTERVENTIONS (2020)

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A Survey of Methods for Explaining Black Box Models

Riccardo Guidotti et al.

ACM COMPUTING SURVEYS (2019)

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Adversarial attacks on medical machine learning

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SCIENCE (2019)

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Current Challenges of iPSC-Based Disease Modeling and Therapeutic Implications

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CELLS (2019)

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Genetic background modifies CNS-mediated sensorimotor decline in the AD-BXD mouse model of genetic diversity in Alzheimer's disease

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GENES BRAIN AND BEHAVIOR (2019)

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Accelerated evolution of oligodendrocytes in the human brain

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA (2019)

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A quantitative framework for characterizing the evolutionary history of mammalian gene expression

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GENOME RESEARCH (2019)

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Harnessing Genetic Complexity to Enhance Translatability of Alzheimer's Disease Mouse Models: A Path toward Precision Medicine

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qPhos: a database of protein phosphorylation dynamics in humans

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Computational translation of genomic responses from experimental model systems to humans

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Graph Convolution Based Attention Model for Personalized Disease Prediction

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MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2019, PT IV (2019)

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Ruth E. Baker et al.

BIOLOGY LETTERS (2018)

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The Effects of Donepezil, an Acetylcholinesterase Inhibitor, on Impaired Learning and Memory in Rodents

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Metric learning with spectral graph convolutions on brain connectivity networks

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NEUROIMAGE (2018)

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Machine Learning of Toxicological Big Data Enables Read-Across Structure Activity Relationships (RASAR) Outperforming Animal Test Reproducibility

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In vivo and in silico dynamics of the development of Metabolic Syndrome

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PLOS COMPUTATIONAL BIOLOGY (2018)

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Editorial Material Mathematical & Computational Biology

Reproducibility vs. Replicability: A Brief History of a Confused Terminology

Hans E. Plesser

FRONTIERS IN NEUROINFORMATICS (2018)

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Insights into the Mutational Burden of Human Induced Pluripotent Stem Cells from an Integrative Multi-Omics Approach

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CELL REPORTS (2018)

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Reproducibility of Molecular Phenotypes after Long-Term Differentiation to Human iPSC-Derived Neurons: A Multi-Site Omics Study

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STEM CELL REPORTS (2018)

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All (animal) models (of neurodegeneration) are wrong. Are they also useful?

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Review Biotechnology & Applied Microbiology

Rise of Deep Learning for Genomic, Proteomic, and Metabolomic Data Integration in Precision Medicine

Dmitry Grapov et al.

OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY (2018)

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Lessons Learned from Alzheimer Disease: Clinical Trials with Negative Outcomes

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CTS-CLINICAL AND TRANSLATIONAL SCIENCE (2018)

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TimeTree: A Resource for Timelines, Timetrees, and Divergence Times

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MOLECULAR BIOLOGY AND EVOLUTION (2017)

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Common genetic variation drives molecular heterogeneity in human iPSCs (vol 546, pg 370, 2017)

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NATURE (2017)

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Machine Learning and Prediction in Medicine - Beyond the Peak of Inflated Expectations

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DBA/2J Genetic Background Exacerbates Spontaneous Lethal Seizures but Lessens Amyloid Deposition in a Mouse Model of Alzheimer's Disease

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