AlexNet is a convolutional neural network that won the ImageNet Large Scale Visual Recognition Challenge in 2012. It was designed by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. The network has eight layers, five of which are convolutional layers and three are fully connected layers. It uses ReLU activation functions, dropout for regularization, and data augmentation techniques to improve performance. AlexNet significantly advanced the field of deep learning and computer vision.

ESM (Evolutionary Scale Modeling) is a family of large-scale protein language models developed by Meta AI. They’re trained on massive protein sequence databases, learning contextual representations of amino acids purely from sequence data. These representations—often called embeddings—capture both structural and functional clues.
In practice, you feed a protein sequence into ESM to obtain per-residue embeddings, which you can then use for downstream tasks like structure prediction, function annotation, or variant effect prediction. If you batch multiple sequences together, ESM aligns them by adding special start/end tokens and padding shorter sequences to match the longest one. You then slice out the valid embedding region for each protein, ignoring any padding.

PCA

High Dimension Data

Logistic regression is a supervised machine learning algorithm used for binary classification tasks. Unlike linear regression, which predicts continuous values, logistic regression predicts the probability that a given input belongs to a certain class.

Linear Model Optimization

Regularization is a way to make sure our model doesn't become too complicated. It ensures the model doesn’t overfit the training data while still making good predictions on new data. Think of it as adding a 'rule' or 'constraint' that prevents the model from relying too much on any specific feature or predictor.

pyrosetta

Heatmap with GGplot

GGplot: Prism style

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HDF5 (Hierarchical Data Format version 5) is a file format designed for efficiently storing and organizing large, complex datasets. It uses a hierarchical structure of **groups** (like directories) and **datasets** (like files) to store data, supporting multidimensional arrays, metadata, and a wide variety of data types. Key advantages include **compression**, **cross-platform compatibility**, and the ability to handle large datasets that don’t fit in memory. It’s widely used in fields like scientific computing, machine learning, and bioinformatics due to its efficiency and flexibility.

Render Your Protein in Blender with Molecular Nodes

ASCP (Aspera Secure Copy Protocol) is a fast, reliable protocol for transferring large files, particularly over long distances or in conditions with network latency or packet loss. It uses a technology called fasp (Fast, Adaptive, and Secure Protocol) to maximize available bandwidth, making transfers faster than traditional methods like FTP.
For uploading data to NCBI, ASCP is particularly useful because it efficiently handles large datasets, such as genomic sequences or omics data. Its ability to resume interrupted transfers ensures that if a connection fails during an upload, the transfer continues from where it left off, saving time and bandwidth. ASCP also provides strong encryption, ensuring data security during the upload process.

Protein Dock Tools and algorithm Overview