Application of Centrality Measures for Potential Drug Targets: Review

Sekhar, Akula Chandra; Ambedkar, Ch

doi:10.18535/ijecs/v9i04.4465

Cited by 1 publication

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“…The edges can be weighted based on a metric derived from publications about the interaction and reflects the confidence in that interaction. c Network centrality measures such as eigenvector, betweenness, and closeness centrality measures are used to derive a score for each gene in the network (Geraci et al, 2012;Sekhar and Ambedkar, 2020). A linear combination of node metrics was used to determine which nodes were the most important from a drug target perspective.…”

Section: Discussionmentioning

confidence: 99%

Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS

Geraci,

Bhargava,

Qorri

et al. 2024

Front. Comput. Neurosci.

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IntroductionAdvances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are necessary to tackle the plethora of known disease states.MotivationIn addition to providing much needed insights into potential therapeutic protein targets, we also aim to demonstrate that small patient datasets have the potential to provide insights that usually require many samples (>5,000). There are many such datasets available and novel advancements in ML can provide valuable insights from these patient datasets.Problem statementUsing a public dataset made available by patient advocacy group AnswerALS and a multidisciplinary Open Science approach with a systems biology augmented ML technology, we aim to validate previously reported drug targets in ALS and provide novel insights about ALS subpopulations and potential drug targets using a unique combination of ML methods and graph theory.MethodologyWe use NetraAI to generate hypotheses about specific patient subpopulations, which were then refined and validated through a combination of ML techniques, systems biology methods, and expert input.ResultsWe extracted 8 target classes, each comprising of several genes that shed light into ALS pathophysiology and represent new avenues for treatment. These target classes are broadly categorized as inflammation, epigenetic, heat shock, neuromuscular junction, autophagy, apoptosis, axonal transport, and excitotoxicity. These findings are not mutually exclusive, and instead represent a systematic view of ALS pathophysiology. Based on these findings, we suggest that simultaneous targeting of ALS has the potential to mitigate ALS progression, with the plausibility of maintaining and sustaining an improved quality of life (QoL) for ALS patients. Even further, we identified subpopulations based on disease onset.ConclusionIn the spirit of Open Science, this work aims to bridge the knowledge gap in ALS pathophysiology to aid in diagnostic, prognostic, and therapeutic strategies and pave the way for the development of personalized treatments tailored to the individual’s needs.

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Section: Discussionmentioning

confidence: 99%