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Part 58 Timeline_the untold_the retold

Part 58 Article: Timeline My Blog Title: The untold the retold Click On This Link 🔗 To Download Full Quality Size Of This Photo Size 4096 × 4096 8.25 MB Click on PDF to download from Part 1 COLUMN R 1279–1368 AD Yuan Dynasty & Mongol   元朝 16 Emperors Duration: 89 Years • Delhi Sultanate India 1206–1527 AD • Chagatai Khanate Transoxania  Central Asia 1225–1687 AD • Tlemcen Kingdom Algeria Africa  1235–1556 AD • Mali Empire West Africa  1235–1610 AD From [ https://www.albert.io ]: Geography, the Camel, and the Foundation of Trade : The Sahara Desert spans approximately 9 million square kilometers — larger than Australia — of sand, rock, and hardship. Crossing it on foot is nearly impossible; crossing it with wheeled vehicles is suicide. The domestication of the dromedary camel (single-humped, adapted to extreme arid heat) transformed the Sahara from an impassable barrier into a navigable corridor. From [Wikipedia]: The one-humped dromedary makes up 94% of th...

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.




Click on PDF to download from Part 3

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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Video Sources: Pexels and Pixabay in PowerDirector and other websites:


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Screen recorded on 23 April 2025 from websites: 


103:https://www.english-corpora.org//coca/

104:https://www.thoughtco.com/what-is-corpus-language-1689806#:~:text=In%20linguistics%2C%20a%20corpus%20is,Plural%3A%20corpora.


Consulted References:


Refer to Part 3 for all consolidated references for all parts.

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