22 papers · ranked by Valyu relevance
Anastasia Drozdova, Ekaterina Trofimova, Polina Guseva, Anna Scherbakova + 2 more
The use of program code as a data source is increasingly expanding among data scientists. The purpose of the usage varies from the semantic classification of code to the automatic generation of programs. However, the machine learning model application is somewhat limited without annotating the code snippets. To address…
Anastasia Drozdova, Polina Guseva, Е. В. Трофимова, Anna Scherbakova + 1 more
'A. Ustyuzhanin'] Program code as a data source is gaining popularity in the data science community. Possible applications for models trained on such assets range from classification for data dimensionality reduction to automatic code generation. However, without annotation number of methods that could be applied is…
Valeriy Berezovskiy, Anastasia Gorodilova, Ekaterina Trofimova, Andrey Ustyuzhanin + 1 more
'Andrey Ustyuzhanin' 'Syed Hassan Shah'] Program code has recently become a valuable active data source for training various data science models, from code classification to controlled code synthesis. Annotating code snippets play an essential role in such tasks. This article presents a novel approach that leverages…
Bart van Oort, Luís Cruz, Maurício Aniche, Arie van Deursen
—Artificial Intelligence (AI) and Machine Learning (ML) are pervasive in the current computer science landscape. Yet, there still exists a lack of software engineering experience and best practices in this field. One such best practice, static code analysis, can be used to find code smells, i.e., (potential) defects in…
Ekaterina Trofimova, Emil Sataev, Andrey Ustyuzhanin, Xiangjie Kong
In the ever-evolving landscape of machine learning, seamless translation of natural language descriptions into executable code remains a formidable challenge. This article introduces Linguacodus, an innovative framework designed to tackle this challenge by deploying a dynamic pipeline that iteratively transforms…
Polina Guseva, Anastasia Drozdova, Natalia Denisenko, Daria Sapozhnikova + 3 more
'Daria Sapozhnikova' 'Ivan Pyaternev' 'Anna Scherbakova' 'Andrey Ustuzhanin'] A range of applications for automatic machine learning need the generation process to be controllable. In this work, we propose a way to control the output via a sequence of simple actions, that are called semantic code classes. Finally, we…
Rana Sandouka, Hamoud Aljamaan, Stephen Piccolo
Code smells are poor code design or implementation that affect the code maintenance process and reduce the software quality. Therefore, code smell detection is important in software building. Recent studies utilized machine learning algorithms for code smell detection. However, most of these studies focused on code…
Jiho Shin, Moshi Wei, Junjie Wang, Lin Shi + 1 more
Machine learning (ML) has been increasingly used in a variety of domains, while solving ML programming tasks poses unique challenges because of the fundamentally different nature and construction from general programming tasks, especially for developers who do not have ML backgrounds. Automatic code generation that…
Divyang Deep Tiwari, Nils Hoffmann, Kieran Didi, Sumukh Deshpande + 5 more
Machine learning (ML) models are widely used in life sciences and medicine; however, they are scattered across various platforms and there are several challenges that hinder their accessibility, reproducibility and reuse. In this manuscript, we present the formalisation and pilot implementation of community protocol to…
Tobias Rehfeldt, Ralf Gabriels, Robbin Bouwmeester, Siegfried Gessulat + 6 more
Dataset acquisition and curation are often the hardest and most time-consuming parts of a machine learning endeavor. This is especially true for proteomics-based LC-IM-MS datasets, due to the high-throughput data structure with high levels of noise and complexity between raw and machine learning-ready formats. While…
Lucy Moctezuma, Lorena Benitez Rivera, Florentine van Nouhuijs, Faye Orcales + 4 more
This manuscript describes the development of a module that is part of a learning platform named “NIGMS Sandbox for Cloud-based Learning” https://github.com/NIGMS/NIGMS-Sandbox. The overall genesis of the Sandbox is described in the editorial NIGMS Sandbox at the beginning of this Supplement. This module delivers…
Jacqueline A Jansen, Artür Manukyan, Nour Al Khoury, Altuna Akalin
Data analysis is constrained by a shortage of skilled experts, particularly in biology, where detailed data interpretation is vital for understanding complex biological processes and developing new treatments and diagnostics. To address this, we developed mergen, an R package that leverages Large Language Models (LLMs)…
Nouf Alturayeif, Jameleddine Hassine, Bilal Alatas
With the increasing reliance on machine learning (ML) across diverse disciplines, ML code has been subject to a number of issues that impact its quality, such as lack of documentation, algorithmic biases, overfitting, lack of reproducibility, inadequate data preprocessing, and potential for data leakage, all of which…
Esben Bjerrum, Tobias Rastemo, Ross Irwin, Christos Kannas + 1 more
Recent years have seen a large interest in using the Simplified Molecular Input Line Entry System (SMILES) chemical language as input for deep learning architectures solving chemical tasks. Many successful applications have been demonstrated within de novo molecular design, quantitative structure-activity relationship…
Daiyi Peng, Xuanyi Dong, Esteban Real, Yifeng Lu + 1 more
The increasing complexity and scale of machine learning (ML) has led to the need for more efficient collaboration among multiple teams. For example, when a research team invents a new architecture like "ResNet," it is desirable for multiple engineering teams to adopt it. However, the effort required for each team to…
Vlastimil Martinek, Andrea Gariboldi, Dimosthenis Tzimotoudis, Mark Galea + 7 more
Extracting knowledge from biomedical data is crucial for advancing our understanding of biological systems and developing novel therapeutics. The quantity, quality, and resolution of biomedical data constantly evolves, requiring the automation of biomedical machine learning (ML). Existing Automated ML tools lack…
Hung Q. Vo, Huy Q. Vo, Son T. Ly, Zhihao Wan + 5 more
Conventional tissue image analysis software provides foundational capabilities for cellular analysis, including segmentation, basic morphological feature extraction, and spatial organization analysis. However, these tools often require manual intervention and are not well integrated with code-driven automation…
Huifang Ma, Zhicheng Ji
Large language models have shown remarkable capabilities in algorithm design, but their effectiveness in solving data science challenges remains poorly understood. We conducted a classroom experiment in which graduate students used large language models (LLMs) to solve biomedical data science challenges on Kaggle.…
Pieter Floris Jacobs, Robert Pollice
Scientists across domains are often challenged to master domain-specific languages (DSLs) for their research, which are merely a means to an end but are pervasive in fields like computational chemistry. Automated code generation promises to overcome this barrier, allowing researchers to focus on their core expertise.…
Authors not listed
Recent advances in machine learning force fields (MLFF) have significantly extended the reach of atomistic simulations. Continuous progress in this field requires reliable reference datasets, accurate MLFF architectures, and efficient active learning strategies to enable robust modeling of complex molecular and…
Authors not listed
This research delves into olfaction, a sensory modality that remains complex and inadequately understood. We aim to fill in two gaps in recent studies that attempted to use machine learning and deep learning approaches to predict human smell perception. The first one is that molecules are usually represented with…
Muhammad Hanzla, Abdul Rehman Shinwari
Machine Learning (ML) can be defined as a class of Artificial Intelligence for automated data analysis, which is capable of detecting patterns in data. The extracted patterns can be used to predict un-known data or to assist in decision-making processes under uncertainty. Recent advances in experimental and…