Part 16 The world, from the past to the present, retold from the timelines.

Part 16

Article: Biotechnology My Blog Title: The world, from the past to the present, retold from the timelines.

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2023: Notable innovations: a large language model (ProGen) that could generate functional protein sequences with a predictable function, with the input including tags specifying protein properties. In linguistics, a corpus is a collection of linguistic data used for research, scholarship, and teaching. Indeed, even if different corpora existed to train protein language models, the correct interpretation of the produced sequences remains a challenge. Protein evolution differed from language evolution, containing irregularities due to randomness and environmental pressure, and with a grammar that unavoidably will contain many irregularities. Finally, we had to remark on the size of the language of proteins that needs to cover millions of species on Earth, which necessitated studying the general properties of proteins rather than studying the proteins of a particular species. While the dissimilarities between human and protein languages presented significant challenges for applying Natural language processing (NLP) to protein design, the apparent connections between the two fields offered a new perspective in protein research, opening the way to the adaptation of NLP models to protein modeling and design. Machine Learning (ML) methods had a long-standing history in natural language processing (NLP), and considering the similarities between natural and protein languages (Ofer et al., 2021), Natural language processing (NLP) methods had been transferred and adapted in the context of protein design and modeling. Indeed, as far back as the 1990s, “shallow” ML methods such as hidden Markov models and support vector machines were applied both in NLP and computational biology (Krogh et al., 1994; Zhou and Su, 2002). A hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or "hidden") Markov process (referred to as X. An HMM requires that there be an observable process Y whose outcomes depend on the outcomes of X in a known way. Since X cannot be observed directly, the goal is to learn about the state of X by observing Y. Support Vector Machine (SVM) is a powerful machine learning algorithm used for linear or nonlinear classification, regression between relationships and variables, and even outlier detection tasks. SVMs can be used for a variety of tasks, such as text classification, image classification, spam detection, handwriting identification, gene expression analysis, face detection, and anomaly detection. Then the application of shallow neural networks for word representation learning (Mikolov et al., 2013) and, more importantly, the advent of deep learning methods introduced significant advances in Natural language processing (NLP) and in protein modeling (Collobert and Weston, 2008; Manning, 2015; Hou et al., 2017). In particular recurrent neural networks (RNN) displayed excellent performance because of their ability to learn long-range relationships between words as well as between amino acids, and demonstrated to be essential for both global text comprehension and to detect long-range distal contacts in proteins (Socher et al., 2011; Krause et al., 2017). In medicine, distal refers to a part of the body that is farther away from the center of the body than another part.

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Part 25 The world, from the past to the present, retold from the timelines.

Part 16 The world, from the past to the present, retold from the timelines.

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