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Integr. Neurosci."],"abstract":"<jats:p>The arrival of genotype-specific therapies in amyotrophic lateral sclerosis (ALS) signals the dawn of precision medicine in motor neuron diseases (MNDs). After decades of academic studies in ALS, we are now witnessing tangible clinical advances. An ever increasing number of well-designed descriptive studies have been published in recent years, characterizing typical disease-burden patterns in vivo and post mortem. Phenotype- and genotype-associated traits and \u201ctypical\u201d propagation patterns have been described based on longitudinal clinical and biomarker data. The practical caveat of these studies is that they report \u201cgroup-level\u201d, stereotyped trajectories representative of ALS as a whole. In the clinical setting, however, \u201cgroup-level\u201d biomarker signatures have limited practical relevance and what matters is the meaningful interpretation of data from a single individual. The increasing availability of large normative data sets, national registries, extant academic data, consortium repositories, and emerging data platforms now permit the meaningful interpretation of individual biomarker profiles and allow the categorization of single patients into relevant diagnostic, phenotypic, and prognostic categories. A variety of machine learning (ML) strategies have been recently explored in MND to demonstrate the feasibility of interpreting data from a single patient. Despite the considerable clinical prospects of classification models, a number of pragmatic challenges need to be overcome to unleash the full potential of ML in ALS. Cohort size limitations, administrative hurdles, data harmonization challenges, regulatory differences, methodological obstacles, and financial implications and are just some of the barriers to readily implement ML in routine clinical practice. Despite these challenges, machine-learning strategies are likely to be firmly integrated in clinical decision-making and pharmacological trials in the near future.<\/jats:p>","DOI":"10.31083\/j.jin2303058","type":"journal-article","created":{"date-parts":[[2024,3,22]],"date-time":"2024-03-22T02:30:59Z","timestamp":1711074659000},"source":"Crossref","is-referenced-by-count":8,"title":["Harnessing Big Data in Amyotrophic Lateral Sclerosis: Machine Learning Applications for Clinical Practice and Pharmaceutical Trials"],"prefix":"10.31083","volume":"23","author":[{"given":"Ee Ling","family":"Tan","sequence":"first","affiliation":[{"name":"Computational Neuroimaging Group, School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jasmin","family":"Lope","sequence":"first","affiliation":[{"name":"Computational Neuroimaging Group, School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter","family":"Bede","sequence":"first","affiliation":[{"name":"Computational Neuroimaging Group, School of Medicine, Trinity College Dublin, D02 PN40 Dublin, Ireland"},{"name":"Department of Neurology, St James\u2019s Hospital, D08 NHY1 Dublin, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"16242","published-online":{"date-parts":[[2024,3,18]]},"reference":[{"key":"ref1","doi-asserted-by":"crossref","unstructured":"McMackin R, Bede P, Ingre C, Malaspina A, Hardiman O. 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