24 papers · ranked by Valyu relevance
Alok Sharma, Artem Lysenko, Shangru Jia, Keith A. Boroevich + 1 more
The field of omics, driven by advances in high-throughput sequencing, faces a data explosion. This abundance of data offers unprecedented opportunities for predictive modeling in precision medicine, but also presents formidable challenges in data analysis and interpretation. Traditional machine learning (ML) techniques…
Siyi Li, Chunyu Sun, Jiahao Zhang, Yuchen Kang + 5 more
Evaluating a Physical AI stack spans operators that differ by more than three orders of magnitude -- from a single foundation-model decoding step to thousands of physics ticks of whole-body control -- varying orthogonally in modality, reward semantics, and resource profile. No existing framework spans this range, so…
Alok Sharma, Yosvany López, Shangru Jia, Artem Lysenko + 2 more
'Keith A. Boroevich' 'Tatsuhiko Tsunoda'] Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods can be used for feature engineering and dimensionality reduction, they often struggle to capture the…
Tansel Ersavas, Martin A. Smith, John S. Mattick
Convolutional Neural Networks (CNNs) have been central to the Deep Learning revolution and played a key role in initiating the new age of Artificial Intelligence. However, in recent years newer architectures such as Transformers have dominated both research and practical applications. While CNNs still play critical…
Alok Sharma, Yosvany López, Shangru Jia, Artem Lysenko + 2 more
Tabular data analysis is a critical task in various domains, enabling us to uncover valuable insights from structured datasets. While traditional machine learning methods have been employed for feature engineering and dimensionality reduction, they often struggle to capture the intricate relationships and dependencies…
Alok Sharma, Artem Lysenko, Keith A Boroevich, Edwin Vans + 1 more
Identifying smaller element or gene subsets from biological or other data types is an essential step in discovering underlying mechanisms. Statistical machine learning methods have played a key role in revealing gene subsets. However, growing data complexity is pushing the limits of these techniques. A review of the…
Alok Sharma, Artem Lysenko, Keith A Boroevich, Tatsuhiko Tsunoda
Modern oncology offers a wide range of treatments and therefore choosing the best option for particular patient is very important for optimal outcomes. Multi-omics profiling in combination with AI-based predictive models have great potential for streamlining these treatment decisions. However, these encouraging…
Elif İlgazi Kılıç, Şafak Kılıç
Background Cervical cancer remains one of the leading causes of gynecological mortality worldwide, largely due to the limitations of manual cytological screening, which is time-consuming and susceptible to inter-observer variability. Although deep learning has demonstrated strong potential for automating cervical…
Travers Ching, Daniel S. Himmelstein, Brett K. Beaulieu-Jones, Alexandr A. Kalinin + 23 more
Deep learning, which describes a class of machine learning algorithms, has recently showed impressive results across a variety of domains. Biology and medicine are data rich, but the data are complex and often ill-understood. Problems of this nature may be particularly well-suited to deep learning techniques. We…
Mohammad Abu Alsheikh, Dusit Niyato, Shaowei Lin, Hwee-Pink Tan + 1 more
'Zhu Han'] Abstract—The proliferation of mobile devices, such as smartphones and Internet of Things (IoT) gadgets, results in the recent mobile big data (MBD) era. Collecting MBD is unprofitable unless suitable analytics and learning methods are utilized for extracting meaningful information and hidden patterns from…
Housam Khalifa Bashier Babiker, Randy Goebel
The practical impact of deep learning on complex supervised learning problems has been significant, so much so that almost every Artificial Intelligence problem, or at least a portion thereof, has been somehow recast as a deep learning problem. The applications appeal is significant, but this appeal is increasingly…
Jaegul Choo, Shi‐Xia Liu
the decisions made by deep learning models and absence of control over their internal processes act as major drawbacks in critical decision-making processes, such as precision medicine and law enforcement. In response, efforts are being made to make deep learning interpretable and controllable by humans. In this paper…
Authors not listed
The rapid progress in Artificial Intelligence (AI) has led to extraordinary achievements across various domains, significantly impacting every aspect of daily life. This advancement is also revolutionizing research in numerous scientific areas, particularly within bioinformatics, chemistry, pharmaceuticals, and…
Zhijie Wang, Yuheng Huang, Da Song, Lei Ma + 1 more
To evaluate user performance on model understanding, two authors manually assessed and coded participants' responses and counted the number of correct insights about model behavior shared by participants. Specifically, these two authors had 4 meetings to develop a codebook and resolve labeling inconsistencies.…
Misako Kimura, Yuuki Matsushita, Masayo Inoue, Shigeto Seno + 5 more
Insight is often described as a sudden shift in or formation of a conceptual representation, enabling humans to restructure existing knowledge and solve problems beyond conventional analytical approaches. Although prior computational studies have modeled aspects of insight using deep neural networks (DNNs) or…
HOSSEIN MORADIMOKHLES, GWO-JEN HWANG, HOSSEIN ZANGENEH, MARYAM POURJAMSHIDI + 1 more
The term “deep learning” has incorrect interpretations in education and technology disciplines. It describes a method of learning in which the objective is to achieve an in-depth understanding of topic rather than succumb to surface learning. "Non-surface learning" is undeniably related to deep information…
Erik Meijering
Deep learning of artificial neural networks has become the de facto standard approach to solving data analysis problems in virtually all fields of science and engineering. Also in biology and medicine, deep learning technologies are fundamentally transforming how we acquire, process, analyze, and interpret data, with…
Imran Razzak, Saeeda Naz, Ahmad Zaib
Healthcare sector is totally different from other industry. It is on high priority sector and people expect highest level of care and services regardless of cost. It did not achieve social expectation even though it consume huge percentage of budget. Mostly the interpretations of medical data is being done by medical…
Davide Bacciu, Paulo Lisböa, José D. Martín‐Guerrero, Ruxandra Stoean + 1 more
'Ruxandra Stoean' 'Alfredo Vellido'] Abstract. Many of the current scientific advances in the life sciences have their origin in the intensive use of data for knowledge discovery. In no area this is so clear as in bioinformatics, led by technological breakthroughs in data acquisition technologies. It has been argued…
Anupam Ojha, Saumya Thakur, Surl-Hee Ahn, Rommie Amaro
Recent advances in computational power and algorithms have made molecular dynamics (MD) simulations reach greater timescales. However, for observing conformational transitions associated with biomolecular processes, MD simulations still have limitations. Several enhanced sampling techniques seek to address this…
Authors not listed
Applications of deep learning (DL) to design nanomaterials are hampered by a lack of suitable data representations and training data. We report efforts to overcome these limitations and leverage DL to optimize the nonlinear optical properties of core-shell upconverting nanoparticles (UCNPs). UCNPs, which have…
Benjamin Hoar, Weitong Zhang, Shuangning Xu, Rana Deeba + 3 more
For decades, employing cyclic voltammetry for mechanistic investigation demands manual inspection of voltammograms. Here we report a deep-learning-based algorithm that automatically analyzes cyclic voltammograms and designates a electrochemical probable mechanism among five of the most common ones in homogenous…
Authors not listed
Predicting drug-induced toxicity remains a central challenge in computational toxicology, particularly for organ-specific adverse effects that arise from diverse structural, biochemical, and mechanistic origins. Existing deep learning models excel at pattern recognition but often lack mechanistic interpretability…
Authors not listed
Raman spectroscopy is an increasingly powerful and fast-growing analytical technique across diverse disciplines, from materials science and chemistry to biology and medicine, thanks to advances in Raman instrumentation and greatly supported by the flourishing of chemometrics and artificial intelligence (AI). However…