Fuzzy TOPSIS Approach in Selection of Optimal Noise Barrier for Traffic Noise Abatement

The paper presents a retrospective study for selection of noise barrier for road traffic noise abatement. The work proposes the application of Fuzzy TOPSIS (Technique for order preference by similarity to an ideal solution) approach is selection of optimal road traffic noise barrier. The present work utilizes the fuzzy TOPSIS model proposed by Mahdavi et al. (2008) in determination of ranking order of various types of noise barriers with respect to the various criteria considered. It is suggested that application of this approach can be very helpful in selection and application of optimal noise barrier for road traffic noise abatement

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine1,2. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes3. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation4,5. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

Swarm Learning for decentralized and confidential clinical machine learning

Fast and reliable detection of patients with severe and heterogeneous illnesses is a major goal of precision medicine. Patients with leukaemia can be identified using machine learning on the basis of their blood transcriptomes. However, there is an increasing divide between what is technically possible and what is allowed, because of privacy legislation. Here, to facilitate the integration of any medical data from any data owner worldwide without violating privacy laws, we introduce Swarm Learning—a decentralized machine-learning approach that unites edge computing, blockchain-based peer-to-peer networking and coordination while maintaining confidentiality without the need for a central coordinator, thereby going beyond federated learning. To illustrate the feasibility of using Swarm Learning to develop disease classifiers using distributed data, we chose four use cases of heterogeneous diseases (COVID-19, tuberculosis, leukaemia and lung pathologies). With more than 16,400 blood transcriptomes derived from 127 clinical studies with non-uniform distributions of cases and controls and substantial study biases, as well as more than 95,000 chest X-ray images, we show that Swarm Learning classifiers outperform those developed at individual sites. In addition, Swarm Learning completely fulfils local confidentiality regulations by design. We believe that this approach will notably accelerate the introduction of precision medicine

OdoriFy: A conglomerate of Artificial Intelligence-driven prediction engines for olfactory decoding

The molecular mechanisms of olfaction, or the sense of smell, are relatively under-explored compared to other sensory systems, primarily due to its underlying molecular complexity and the limited availability of dedicated predictive computational tools. Odorant receptors allow the detection and discrimination of a myriad of odorant molecules and therefore mediate the first step of the olfactory signaling cascade. To date, odorant (or agonist) information for the majority of these receptors is still unknown, limiting our understanding of their functional relevance in odor-induced behavioral responses. In this study, we introduce OdoriFy, a webserver featuring powerful deep neural network-based prediction engines. OdoriFy enables 1) identification of odorant molecules for wild-type or mutant human odorant receptors (Odor Finder); 2) classification of user-provided chemicals as odorants/non-odorants (Odorant Predictor); 3) identification of responsive odorant receptors for a query odorant (OR Finder); and 4) Interaction validation using Odorant-OR Pair Analysis. Additionally, OdoriFy provides the rationale behind every prediction it makes by leveraging Explainable Artificial Intelligence. This module highlights the basis of the prediction of odorants/non-odorants at atomic resolution and for the odorant receptors at amino acid levels. A key distinguishing feature of OdoriFy is that it is built on a comprehensive repertoire of manually curated information of human odorant receptors with their known agonists and non-agonists, making it a highly interactive and resource-enriched webserver. Moreover, comparative analysis of OdoriFy predictions with an alternative structure-based ligand interaction method revealed comparable results. OdoriFy is available freely as a web service at https://odorify.ahujalab.iiitd.edu.in/olfy/.</p