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Deep Neural Networks for ADMET properties' prediction

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Introduction The absorption, distribution, metabolism, excretion and toxicity properties of any compound contributes to its potential as an orally administered drug. A set of criteria defined by Rule of Five or the Pfizer's Rule of Five stats that any compound meeting these criteria are more likely to have ADMET properties. The importance of these properties has increased in modern medicinal chemistry and new machine learning techniques are employed to predict these properties. The predictive ADMET models have helped in discovery of small molecules with improved safety and dose. Deep Neural Networks are showing usefulness in such predictions due to improved computational efficiency, larger datasets and adaption of image processing in the chemistry. Early Models The early DNNs employed showed improved prediction performance. Many researchers raised concerned that this improvement is in fact has no relation with better computational capacity, rather it is result of mere memorizing of
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Marine eDNA Analysis using DL techniques

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Introduction Environmental DNA (eDNA) is the genetic information or material released by an organism into its surroundings through activities like skin shedding, mucus, urine or other secretions. These materials contain DNA fragments that can be found in soil, water or air samples. The concept of eDNA is based on the fact that organisms constantly leave organic traces behind them. By analysing the eDNA, researchers can detect the presence of any species in that area. This method is a valuable technique for biodiversity monitoring and ecological research.    Unlike traditional methods that involve direct observation or capturing of organisms, eDNA analysis allows researchers to study biodiversity without directly interacting with the species. This non-invasive approach is particularly useful for studying elusive or endangered species. eDNA analysis has been applied in various ecosystems, including freshwater, marine, and terrestrial environments. It is used to study aquatic organisms, t
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Assisting Neuroimaging through DL

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Introduction: In recent years, neuroimaging has been a research attraction, especially due to fMRI (Functional Magnetic Resonance Imaging) advancements. The fMRI data comprises of often several individuals. This data is used by researchers to study the association between the cognitive states of an individual and the underlying brain activities. The fMRI data used for neuroimaging is ideally suited for deep learning applications. The large, structured datasets of fMRI can be used for representation-learning methods of the DL. Generally, DL can be defined as a learning method with multiple levels of abstraction, where at each level the input data is transformed by a simple non-linear function which enables the model to recognize complex patterns. With higher-level representation, DL methods can associate a target variable with variable patterns in input data. Also, DL techniques can independently acquire these transformations from the data, eliminating the need for a comprehensive preex
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What is Terraforming?

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  Humans are working more and more on extraterrestrial habitations. A few decades back we were planning to settle on the Moon and now we are planning the same on Mars. Many researchers also proposed many theories on such habitations as O'Neill Cylinder, McKendree Cylinder, and many more. One such famous hypothetical theory is Terraforming. But what does the term terraforming mean? Terraforming is the process of transforming the atmosphere, topography, temperature and other factors of any moon or planet to make it habitable and similar to the Earth. It was Carl Sagan who first proposed the theory of terraforming. He proposed the terraforming of Venus. He thought of seeding Venus with algae. The algae will convert nitrogen and carbon dioxide to organic matter and eventually, the greenhouse effect will reduce to a comfortable, habitual level.  However, later studies revealed that the clouds of Venus are full of sulphuric acid. Also, the atmosphere of Venus is very thick, any carbon co
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