Indian Institute of Technology Indore

About: Indian Institute of Technology Indore is a education organization based out in Indore, Madhya Pradesh, India. It is known for research contribution in the topics: Computer science & Chemistry. The organization has 1606 authors who have published 4803 publications receiving 66500 citations.

Predicting brain age using machine learning algorithms: A comprehensive evaluation.

Abstract: Machine learning (ML) algorithms play a vital role in brain age estimation frameworks. The impact of regression algorithms on prediction accuracy in the brain age estimation frameworks have not been comprehensively evaluated. Here, we sought to assess the efficiency of different regression algorithms on brain age estimation. To this end, we built a brain age estimation framework based on a large set of cognitively healthy (CH) individuals (N = 788) as a training set followed by different regression algorithms (18 different algorithms in total). We then quantified each regression-algorithm on independent test sets composed of 88 CH individuals, 70 mild cognitive impairment patients as well as 30 Alzheimers disease patients. The prediction accuracy in the independent test set (i.e., CH set) varied in regression algorithms (mean absolute error (MAE) from 4.63 to 7.14 yrs, R2 from 0.76 to 0.88). The highest and lowest prediction accuracies were achieved by Quadratic Support Vector Regression algorithm (MAE = 4.63 yrs, R2 = 0.88, 95% CI = [-1.26, 1.42]) and Binary Decision Tree algorithm (MAE = 7.14 yrs, R2 = 0.76, 95% CI = [-1.50, 2.62]), respectively. Our experimental results demonstrate that prediction accuracy in brain age frameworks is affected by regression algorithms, indicating that advanced machine learning algorithms can lead to more accurate brain age predictions in clinical settings.

A dual-functional luminescent Tb(III) metal–organic framework for the selective sensing of acetone and TNP in water

Abstract: Herein, a novel water-stable luminescent terbium metal–organic framework, {[Tb(L1)(L2)0.5(NO3)(DMF)]·DMF}n (TPA-MOF), with 1,10-phenanthroline (phen) and 3,3′,5,5′-azobenzene-tetracarboxylic acid (H4abtc) ligands was solvothermally synthesized and structurally characterized. TPA-MOF possesses a two-dimensional (2D) extended framework featuring an 8-connected uninodal SP2-periodic net topology with the Schlafli point symbol of {3^12;4^14;5^2}. The π-electron rich luminescent TPA-MOF exhibits four characteristic emission bands of Tb3+ ion and acts as a selective and sensitive probe for acetone as well as the electron deficient 2,4,6-trinitrophenol (TNP). Moreover, gas sorption studies confirm that TPA-MOF displays ultra-micropores and adsorbs moderate amounts of N2 and CO2.

Exploring the Potency of Currently Used Drugs Against HIV-1 Protease of Subtype D Variant by Using Multiscale Simulations

Abstract: Acquired immune deficiency syndrome (AIDS) is caused by the human immunodeficiency virus (HIV), type 1 and 2. Further, the diversity in HIV-1 has given rise to many serotypes and recombinant strain...

The Human Body as a Super Network: Digital Methods to Analyze the Propagation of Aging.

Abstract: Biological aging is a complex process involving multiple biological processes. These can be understood theoretically though considering them as individual networks-e.g., epigenetic networks, cell-cell networks (such as astroglial networks), and population genetics. Mathematical modeling allows the combination of such networks so that they may be studied in unison, to better understand how the so-called "seven pillars of aging" combine and to generate hypothesis for treating aging as a condition at relatively early biological ages. In this review, we consider how recent progression in mathematical modeling can be utilized to investigate aging, particularly in, but not exclusive to, the context of degenerative neuronal disease. We also consider how the latest techniques for generating biomarker models for disease prediction, such as longitudinal analysis and parenclitic analysis can be applied to as both biomarker platforms for aging, as well as to better understand the inescapable condition. This review is written by a highly diverse and multi-disciplinary team of scientists from across the globe and calls for greater collaboration between diverse fields of research.