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Search · four archives
26 papers · ranked by Valyu relevance
Adi L Tarca, Vincent J Carey, Xue-wen Chen, Roberto Romero + 2 more
Two main paradigms exist in the field of machine learning: supervised and unsupervised learning. Both have potential applications in biology. In supervised learning, objects in a given collection are classified using a set of attributes, or features. The result of the classification process is a set of rules that…
Luis Sa-Couto, Andreas Wichert
Interest in unsupervised learning architectures has been rising. Besides being biologically unnatural, it is costly to depend on large labeled data sets to get a well-performing classification system. Therefore, both the deep learning community and the more biologically-inspired models community have focused on…
Dhananjay Bhaskar, Darrick Lee, Hildur Knútsdóttir, Cindy Tan + 4 more
Cell morphology is an important indicator of cell state, function, stage of development, and fate in both normal and pathological conditions. Cell shape is among key indicators used by pathologists to identify abnormalities or malignancies. With rapid advancements in the speed and amount of biological data acquisition…
Zhaocong liu, Fa Zhang, Lin Cheng, Huanxi Deng + 3 more
'Zhenyu Zhang' 'Chi-Chun Zhou'] High-quality labeled datasets are essential for deep learning. Traditional manual annotation methods are not only costly and inefficient but also pose challenges in specialized domains where expert knowledge is needed. Self-supervised methods, despite leveraging unlabeled data for…
Chip M. Lynch, Victor H. van Berkel, Hermann B. Frieboes, Bin Liu
This study applies unsupervised machine learning techniques for classification and clustering to a collection of descriptive variables from 10,442 lung cancer patient records in the Surveillance, Epidemiology, and End Results (SEER) program database. The goal is to automatically classify lung cancer patients into…
Jeremy G. Todd, Jamey S. Kain, Benjamin L. de Bivort
To fully understand the mechanisms giving rise to behavior, we need to be able to precisely measure it. When coupled with large behavioral data sets, unsupervised clustering methods offer the potential of unbiased mapping of behavioral spaces. However, unsupervised techniques to map behavioral spaces are in their…
Juan Jovel, Russell Greiner
Machine learning (ML) approaches are a collection of algorithms that attempt to extract patterns from data and to associate such patterns with discrete classes of samples in the data-e.g., given a series of features describing persons, a ML model predicts whether a person is diseased or healthy, or given features of…
Niall Rodgers
Palaeontology has seen widespread and growing use of machine learning to classify and analyse large datasets of fossils. However, palaeontology is a challenging field in which to apply machine learning. Datasets may be small or unlabelled, images may be complex and different from standard datasets and palaeontologists…
Mohammad H. Zhoolideh Haghighi
Classification is a popular task in the field of Machine Learning (ML) and Artificial Intelligence (AI), and it happens when outputs are categorical variables. There are a wide variety of models that attempts to draw some conclusions from observed values, so classification algorithms predict categorical class labels…
Dalya Baron
Astronomy is experiencing a rapid growth in data size and complexity. This change fosters the development of data-driven science as a useful companion to the common model-driven data analysis paradigm, where astronomers develop automatic tools to mine datasets and extract novel information from them. In recent years…
Claus Metzner, Achim Schilling, Maximilian Traxdorf, Konstantin Tziridis + 3 more
'Konstantin Tziridis' 'Andreas Maier' 'Holger Schulze' 'Patrick Krauss'] Data classification, the process of analyzing data and organizing it into categories or clusters, is a fundamental computing task of natural and artificial information processing systems. Both supervised classification and unsupervised clustering…
Sarah E. Lindley, Yiyang Lu, Diwakar Shukla
Guide to Machine Learning for Small Molecule Design Authors: ['Sarah\nE. Lindley' 'Yiyang Lu' 'Diwakar Shukla'] Initially part of the field of artificial intelligence, machine learning (ML) has become a booming research area since branching out into its own field in the 1990s. After three decades of refinement, ML…
Constance Creux, Farida Zehraoui, Blaise Hanczar, Fariza Tahi + 1 more
'Kapil Kumar Nagwanshi'] In the sea of data generated daily, unlabeled samples greatly outnumber labeled ones. This is due to the fact that, in many application areas, labels are scarce or hard to obtain. In addition, unlabeled samples might belong to new classes that are not available in the label set associated with…
Talha Iqbal, Adnan Elahi, William Wijns, Atif Shahzad
Over the past decade, there has been a significant development in wearable health technologies for diagnosis and monitoring, including application to stress monitoring. Most of the wearable stress monitoring systems are built on a supervised learning classification algorithm. These systems rely on the collection of…
Hang Hu, Jyothsna Padmakumar Bindu, Julia Laskin
Mass spectrometry imaging (MSI) is widely used for the label-free molecular mapping of biological samples. The identification of co-localized molecules in MSI data is crucial to the understanding of biochemical pathways. However, complex MSI data are too large for manual annotation but too small for training deep…
Kieran Greer
- This paper describes a design that can be used for Explainable AI. The lower level is a nested ensemble of patterns created by self-organisation. The upper level is a hierarchical tree, where nodes are linked through individual concepts, so there is a transition from mixed ensemble masses to specific categories.…
Jea Kwon, Sunpil Kim, Dong-Kyum Kim, Jinhyeong Joo + 3 more
While huge strides have recently been made in language-based machine learning, the ability of artificial systems to comprehend the sequences that comprise animal behavior has been lagging behind. In contrast, humans instinctively recognize behaviors by finding similarities in behavioral sequences. Here, we develop an…
Sarah Rastegar, Mohammadreza Salehi, Yuki M. Asano, Hazel Doughty + 1 more
'Cees G. M. Snoek'] Abstract. In this paper, we address Generalized Category Discovery, aiming to simultaneously uncover novel categories and accurately classify known ones. Traditional methods, which lean heavily on self-supervision and contrastive learning, often fall short when distinguishing between finegrained…
Shahira Shaaban, Mohamed Hesham Farouk, Hesham Ibrahim Ahmed
—supervised classification predicts classes of objects using the knowledge learned during the training phase. This process requires learning from labeled samples. However, the labeled samples usually limited. Annotation process is annoying, tedious, expensive, and requires human experts. Meanwhile, unlabeled data is…
Daniel D. Seith, Cody Combs, Zuzanna S. Siwy
Mechanobiology has shown great success in revealing complex cellular dynamics in various pathologies and physiologies. Most methods for assessing a cell’s mechanical properties, however, generally extract only a few physical constants such as Young’s modulus. This can limit the potential for accurate classification…
Ashit Gupta, Anirudh Deodhar, Tathagata Mukherjee, Venkataramana Runkana
'Venkataramana Runkana'] The performance of supervised classification techniques often deteriorates when the data has noisy labels. Even the semi- supervised classification approaches have largely focused only on the problem of handling missing labels. Most of the approaches addressing the noisylabel data rely on deep…
Authors not listed
The analysis of nonadiabatic molecular dynamics (NAMD) data presents significant challenges due to its high dimensionality and complexity. To address these issues, we introduce ULaMDyn, a Python-based, open-source package designed to automate the unsupervised analysis of large datasets generated by NAMD simulations.…
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…
Hoang, Cuong Manh
Generalized Category Discovery (GCD) utilizes labeled samples of known classes to discover novel classes in unlabeled samples. Existing methods show effective performance on artificial datasets with balanced distributions. However, real-world datasets are always imbalanced, significantly affecting the effectiveness of…
Chi Zhang, Dmytro Antypov, Matthew J Rosseinsky, Matthew Stephen Dyer
Machine learning has found wide application in the materials field, particularly in discovering structure-property relationships. However, its potential in predicting synthetic accessibility of materials remains relatively unexplored due to the lack of negative data. In this study, we employ several one-class…
Saer Samanipour, Jake O'Brien, Malcolm Reid, Kevin Thomas + 1 more
The European and US chemical agencies have listed approximately 800k chemicals where knowledge on potential risks to human health and the environment are lacking. Filling these data gaps experimentally is impossible so in-silico approaches and prediction are essential. Many existing models are however limited by…