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Intersection of network medicine and machine learning towards investigating the key biomarkers and pathways underlying amyotrophic lateral sclerosis: a systematic review

Briefings in Bioinformatics

Background Network medicine is an emerging area of research that focuses on delving into the mole... more Background Network medicine is an emerging area of research that focuses on delving into the molecular complexity of the disease, leading to the discovery of network biomarkers and therapeutic target discovery. Amyotrophic lateral sclerosis (ALS) is a complicated rare disease with unknown pathogenesis and no available treatment. In ALS, network properties appear to be potential biomarkers that can be beneficial in disease-related applications when explored independently or in tandem with machine learning (ML) techniques. Objective This systematic literature review explores recent trends in network medicine and implementations of network-based ML algorithms in ALS. We aim to provide an overview of the identified primary studies and gather details on identifying the potential biomarkers and delineated pathways. Methods The current study consists of searching for and investigating primary studies from PubMed and Dimensions.ai, published between 2018 and 2022 that reported network medic...

Delineating the conformational dynamics of intermediate structures on the unfolding pathway of β-lactoglobulin in aqueous urea and dimethyl sulfoxide

Journal of Biomolecular Structure and Dynamics, 2019

The funnel shaped energy landscape model of the protein folding suggests that progression of fold... more The funnel shaped energy landscape model of the protein folding suggests that progression of folding proceeds through multiple pathways, having the multiple intermediates which led to multidimensional free-energy surface. Herein, we applied all-atom MD simulation to conduct a comparative study on the structure of β-lactoglobulin (β-LgA) in aqueous mixture of 8M urea and 8M dimethyl sulfoxide (DMSO), at different temperatures. The cumulative results of multiple simulations suggest a common unfolding pathway of β-LgA, occurred through the stable and meta-stable intermediates (I), in both urea and DMSO. However, the free-energy landscape (FEL) analyses show that the structural transitions of I-states are energetically different. In urea, FEL shows distinct ensemble of intermediates, I1 and I2, separated by the energy barrier of ~3.0 kcal mol-1. Similarly, we find the population of two distinct I1 and I2 states in DMSO, however, the I1 appeared transiently around ~30-35 ns and is short-lived. But, the I2 ensemble is observed structurally compact and long-lived (~50-150 ns) as compared to unfolding in urea. Furthermore, the I1 and I2 are separated through a high energy barrier of ~6.0 kcal mol-1. Thus, our results provide the structural insights of intermediates which essentially bear the signature of a different unfolding pathway of β-LgA in urea and DMSO.

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Artificial Intelligence in Skin Cancer: Diagnosis and Therapy

Skin Cancer: Pathogenesis and Diagnosis

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