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Digitaldlsorter: Deep-Learning on scRNA-Seq to Deconvolute Gene Expression Data

Carlos Torroja, Fátima Sánchez‐Cabo

Frontiers in Genetics 2019 cited by 51

Biology & Life Sciences Single-cell and spatial transcriptomics ai assisted importance 5/10 scale stated

Bulk RNA-Seq cannot distinguish whether observed expression changes come from shifting cell-type proportions or aberrant behavior of a specific cell type, while single-cell experiments remain complex and expensive.

Details Source Open-access PDF model: deep learning (specific architecture not s...

AI-accelerated Nazca survey nearly doubles the number of known figurative geoglyphs

Masato Sakai, Akihisa Sakurai, Siyuan Lu, J. Francisco Sibón Olano, Conrad M Albrecht, Hendrik F. Hamann, Marcus Freitag

Proceedings of the National Academy of Sciences 2024 cited by 18

Research & Humanities Archaeology and Cultural Heritage solved importance 4/10 scale stated

Finding figurative Nazca geoglyphs was extremely slow: only 430 were discovered over nearly a century, and small relief-type ones are hard to see.

Details Source Open-access PDF model: not stated in source

Permutation invariant polynomial neural network approach to fitting potential energy surfaces. IV. Coupled diabatic potential energy matrices

Changjian Xie, Xiaolei Zhu, David R. Yarkony, Hua Guo

The Journal of Chemical Physics 2018 cited by 90

Physics & Astronomy Advanced Chemical Physics Studies solved importance 4/10 scale stated

Accurately representing coupled diabatic potential energy matrices of molecules, needed to model non-adiabatic photodissociation dynamics.

Details Source Open-access PDF model: PIP-NN (permutation invariant polynomial-n...

Machine Learning of Designed Translational Control Allows Predictive Pathway Optimization in Escherichia coli

Adrian J. Jervis, Pablo Carbonell, María Vinaixa, Mark S. Dunstan, Katherine A. Hollywood, Christopher Robinson, Nicholas J. W. Rattray, Cunyu Yan et al.

ACS Synthetic Biology 2018 cited by 118

Biology & Life Sciences Microbial Metabolic Engineering and Bioproduction solved importance 4/10 scale stated

Optimizing multigene production pathways in microbes requires screening huge combinatorial ribosome binding site (RBS) libraries, and methods to predict optimal RBS combinations were lacking.

Details Source Open-access PDF model: not stated in source

Identifying Exoplanets with Deep Learning: A Five-planet Resonant Chain around Kepler-80 and an Eighth Planet around Kepler-90

Christopher J. Shallue, Andrew M. Vanderburg

The Astronomical Journal 2018 cited by 368

Physics & Astronomy Stellar, planetary, and galactic studies solved importance 4/10 scale stated

Weak Kepler signals at low signal-to-noise are hard to distinguish from false positives, blocking accurate occurrence rates of Earth-sized planets.

Details Source Open-access PDF model: deep convolutional neural network

Comparison of neural network and support vector machine methods for Kp forecasting

Eun‐Young Ji, Yong‐Jae Moon, Jongyeob Park, Jin‐Yi Lee, Dong‐Hun Lee

Journal of Geophysical Research Space Physics 2013 cited by 45

Physics & Astronomy Ionosphere and magnetosphere dynamics partially solved importance 4/10 scale stated

Near-real-time forecasting of the Kp geomagnetic activity index was addressed by comparing four forecast models based on neural networks and support vector machines using solar wind data and preliminary Kp values.

Details Source Open-access PDF model: neural network and support vector machine

Machine Learning-Based Prediction of EDM Material Removal Rate and Surface Roughness

Isam Qasem, Amjad Alsakarneh

Journal of Manufacturing and Materials Processing 2025 cited by 11

Engineering & Materials Advanced Machining and Optimization Techniques ai assisted importance 4/10 scale stated

Predicting material removal rate and surface roughness in electrical discharge machining (EDM) is difficult because of complex physical events in the electrode gaps.

Details Source Open-access PDF model: GPR (Gaussian Process Regression) among mu...

Evaluating machine learning models for supernova gravitational wave signal classification

Y. Sultan Abylkairov, Matthew C Edwards, Daniil Orel, Ayan Mitra, Bekdaulet Shukirgaliyev, Ernazar B. Abdikamalov

Machine Learning Science and Technology 2024 cited by 7

Physics & Astronomy Pulsars and Gravitational Waves Research ai assisted importance 4/10 scale stated

Determining the equation of state of nuclear matter from gravitational waves of rotating core-collapse supernovae requires classifying signals generated by different equations of state.

Details Source Open-access PDF model: CNN, RNN, random forest, SVM, naïve Bayes,...

Hybrid Approach to Automated Essay Scoring: Integrating Deep Learning Embeddings with Handcrafted Linguistic Features for Improved Accuracy

Muhammad Faseeh, Abdul Jaleel, Naeem Iqbal, Anwar Ghani, Akmalbek Bobomirzaevich Abdusalomov, Asif Mehmood, Young Im Cho

Mathematics 2024 cited by 39

Education & Everyday Educational Technology and Assessment ai assisted importance 4/10 scale stated

Automated essay scoring systems face persistent challenges in delivering accurate and efficient evaluations.

Details Source Open-access PDF model: RoBERTa embeddings + Lightweight XGBoost (...

Can Large Language Models Transform Computational Social Science?

Caleb Ziems, William Held, Omar Shaikh, Jiaao Chen, Zhehao Zhang, Diyi Yang

Computational Linguistics 2023 cited by 559

Research & Humanities Computational and Text Analysis Methods ai assisted importance 4/10 scale stated

Whether large language models can reliably classify and explain social phenomena (e.g., persuasiveness, ideology) for computational social science.

Details Source Open-access PDF model: not stated in source

Learning cis-regulatory principles of ADAR-based RNA editing from CRISPR-mediated mutagenesis

Xin Liu, Tao Sun, Anna Shcherbina, Qin Li, Inga Jarmoskaite, Kalli Kappel, Gokul Ramaswami, Rhiju Das et al.

Nature Communications 2021 cited by 25

Biology & Life Sciences RNA regulation and disease partially solved importance 4/10 scale stated

It was poorly understood how RNA sequence and structure determine the efficiency and specificity of A-to-I RNA editing, hindering interpretation of known editing events and engineering of desired edits.

Details Source Open-access PDF model: not stated in source

Enhancement of protein thermostability by three consecutive mutations using loop-walking method and machine learning

Kazunori Yoshida, Shun Kawai, Masaya Fujitani, Satoshi Koikeda, Ryuji Kato, Tadashi Ema

Scientific Reports 2021 cited by 26

Biology & Life Sciences Enzyme Catalysis and Immobilization solved importance 4/10 scale stated

Improving the heat stability of Burkholderia cepacia lipase without assaying all 8,000 possible triple mutants.

Details Source Open-access PDF model: not stated in source

A First Step Towards Automated Species Recognition from Camera Trap Images of Mammals Using AI in a European Temperate Forest

Mateusz Choiński, Mateusz Rogowski, Piotr Tynecki, Dries P. J. Kuijper, Marcin Churski, Jakub W. Bubnicki

Lecture notes in computer science 2021 cited by 33

Biology & Life Sciences Advanced Neural Network Applications partially solved importance 4/10 scale stated

Manually labeling camera trap images of mammals for wildlife monitoring is slow and hard to scale; the paper automates species identification in Białowieża Forest, Poland.

Details Source Open-access PDF model: YOLOv5

Automation of surgical skill assessment using a three-stage machine learning algorithm

Joël Lukas Lavanchy, Joël Zindel, Kadir Kirtac, Isabell Twick, Enes Hosgor, Daniel Candinas, Guido Beldi

Scientific Reports 2021 cited by 148

Medicine & Health Surgical Simulation and Training partially solved importance 4/10 scale stated

Manual assessment of surgeons' skills is time-consuming, subjective and prone to inconsistent interpretation.

Details Source Open-access PDF model: Convolutional neural network plus linear r...

Machine-Learning-Guided Library Design Cycle for Directed Evolution of Enzymes: The Effects of Training Data Composition on Sequence Space Exploration

Yutaka Saitō, Misaki Oikawa, Takumi Sato, Hikaru Nakazawa, Tomoyuki Ito, Tomoshi Kameda, Koji Tsuda, Mitsuo Umetsu

ACS Catalysis 2021 cited by 70

Biology & Life Sciences Enzyme Catalysis and Immobilization ai assisted importance 4/10 scale stated

Directed evolution of enzymes is slow, and it was unclear how machine-learning training data composition affects guidance of sequence-space exploration.

Details Source Open-access PDF model: not stated in source

Improving dysarthric speech recognition using empirical mode decomposition and convolutional neural network

Mohammed Sidi Yakoub, Sid‐Ahmed Selouani, Brahim-Fares Zaidi, Asma Bouchair

EURASIP Journal on Audio Speech and Music Processing 2020 cited by 65

Medicine & Health Speech Recognition and Synthesis partially solved importance 4/10 scale stated

Automatic speech recognizers perform poorly on dysarthric speech, making voice technology unreliable for affected speakers.

Details Source Open-access PDF model: Convolutional neural network (CNN)

Automated detection of European wild mammal species in camera trap images with an existing and pre-trained computer vision model

Christin Carl, Fiona Schönfeld, Ingolf Profft, Alisa Klamm, Dirk Landgraf

European Journal of Wildlife Research 2020 cited by 55

Biology & Life Sciences Advanced Neural Network Applications partially solved importance 4/10 scale stated

Camera trap studies generate thousands of images that are expensive and time-consuming to analyze manually.

Details Source Open-access PDF model: FasterRCNN+InceptionResNet V2 (TensorFlow...