192 skills found · Page 1 of 7
claws-lab / JodieA PyTorch implementation of ACM SIGKDD 2019 paper "Predicting Dynamic Embedding Trajectory in Temporal Interaction Networks"
materialyzeai / M3gnetMaterials graph network with 3-body interactions featuring a DFT surrogate crystal relaxer and a state-of-the-art property predictor.
duemig / Stanford Project Predicting Stock Prices Using A LSTM NetworkStanford Project: Artificial Intelligence is changing virtually every aspect of our lives. Today’s algorithms accomplish tasks that until recently only expert humans could perform. As it relates to finance, this is an exciting time to adopt a disruptive technology that will transform how everyone invests for generations. Models that explain the returns of individual stocks generally use company and stock characteristics, e.g., the market prices of financial instruments and companies’ accounting data. These characteristics can also be used to predict expected stock returns out-of-sample. Most studies use simple linear models to form these predictions [1] or [2]. An increasing body of academic literature documents that more sophisticated tools from the Machine Learning (ML) and Deep Learning (DL) repertoire, which allow for nonlinear predictor interactions, can improve the stock return forecasts [3], [4] or [5]. The main goal of this project is to investigate whether modern DL techniques can be utilized to more efficiently predict the movements of the stock market. Specifically, we train a LSTM neural network with time series price-volume data and compare its out-of-sample return predictability with the performance of a simple logistic regression (our baseline model).
franciscozorrilla / MetaGEM:gem: An easy-to-use workflow for generating context specific genome-scale metabolic models and predicting metabolic interactions within microbial communities directly from metagenomic data
FreshAirTonight / Af2complexPredicting direct protein-protein interactions with AlphaFold deep learning neural network models.
twopin / CAMPpredicting peptide-protein interactions
prokia / DrugVQAPredicting Drug Protein Interaction using Quasi-Visual Question Answering System
MCZhi / Predictive Decision[ITSC 2023] Predictive Decision-making Framework with Interaction-aware Motion Forecasting Model
OpenDriveLab / MPI[RSS 2024] Learning Manipulation by Predicting Interaction
lishuya17 / MONNMONN: a Multi-Objective Neural Network for Predicting Pairwise Non-Covalent Interactions and Binding Affinities between Compounds and Proteins
sidmulajkar / Sentiment Predictor For Stress DetectionVoice stress analysis (VSA) aims to differentiate between stressed and non-stressed outputs in response to stimuli (e.g., questions posed), with high stress seen as an indication of deception. In this work, we propose a deep learning-based psychological stress detection model using speech signals. With increasing demands for communication between humans and intelligent systems, automatic stress detection is becoming an interesting research topic. Stress can be reliably detected by measuring the level of specific hormones (e.g., cortisol), but this is not a convenient method for the detection of stress in human- machine interactions. The proposed algorithm first extracts Mel- filter bank coefficients using pre-processed speech data and then predicts the status of stress output using a binary decision criterion (i.e., stressed or unstressed) using CNN (Convolutional Neural Network) and dense fully connected layer networks.
VowpalWabbit / Reinforcement LearningInteraction-side integration library for Reinforcement Learning loops: Predict, Log, [Learn,] Update
kuixu / PrismNetPredicting dynamic cellular protein-RNA interactions using deep learning and in vivo RNA structure
kexinhuang12345 / SkipGNNSkipGNN: Predicting Molecular Interactions with Skip-Graph Networks (Scientific Reports)
liudan111 / PLM InteractPLM-interact: extending protein language models to predict protein-protein interactions.
CSUBioGroup / DTIAMA unified framework for predicting drug-target interactions, binding affinities and activation/inhibition mechanisms.
Yuhan-Fei / SMRTnetPredicting small molecule and RNA target interactions using deep neural networks (Nature Biotechnology, 2026)
yazdanimehdi / AttentionSiteDTIThis is The repository for Paper "AttentionSiteDTI: Attention Based Model for Predicting Drug-Target Interaction Using Graph Representation of Ligands and 3D Structure of Protein Binding Sites"
simonfqy / PADMEThis is the repository containing the source code for my Master's thesis research, about predicting drug-target interaction using deep learning.
aim-uofa / BA DDG[ICLR 2025 Spotlight] Boltzmann-Aligned Inverse Folding Model as a Predictor of Mutational Effects on Protein-Protein Interactions
