Robin Abraham
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View article: Interpretable machine learning leverages proteomics to improve cardiovascular disease risk prediction and biomarker identification
Interpretable machine learning leverages proteomics to improve cardiovascular disease risk prediction and biomarker identification Open
In conclusion, we present a more accurate and explanatory framework for proteomics data analysis, supporting future approaches that prioritize individualized disease risk prediction, and identification of target genes for drug development.
View article: Uncovering Emerging Health Awareness Trends in Education: A Bibliometric Study
Uncovering Emerging Health Awareness Trends in Education: A Bibliometric Study Open
Objective: The study aims to examine the development of research trends related to health awareness in schools or education. Method: Data were obtained from the Scopus database and analyzed using PRISMA analysis, VOSviewer assistance, and …
View article: The Role of Artificial Intelligence in Recruitment and Talent Acquisition-An Empirical Study
The Role of Artificial Intelligence in Recruitment and Talent Acquisition-An Empirical Study Open
The employment process is now much more efficient and effective thanks to artificial intelligence (AI), which has completely transformed talent acquisition and recruitment. AI-powered solutions expedite candidate sourcing by searching enor…
View article: Interpretable Machine Learning Leverages Proteomics to Improve Cardiovascular Disease Risk Prediction and Biomarker Identification
Interpretable Machine Learning Leverages Proteomics to Improve Cardiovascular Disease Risk Prediction and Biomarker Identification Open
Cardiovascular diseases (CVD), primarily coronary heart disease and stroke, rank amongst the leading causes of long-term disability and mortality. Providing accurate disease risk predictions and identifying genes associated with CVD are cr…
View article: BatchPrompt: Accomplish more with less
BatchPrompt: Accomplish more with less Open
As the ever-increasing token limits of large language models (LLMs) have enabled long context as input, prompting with single data samples might no longer an efficient way. A straightforward strategy improving efficiency is to batch data w…
View article: Aligning benchmark datasets for table structure recognition
Aligning benchmark datasets for table structure recognition Open
Benchmark datasets for table structure recognition (TSR) must be carefully processed to ensure they are annotated consistently. However, even if a dataset's annotations are self-consistent, there may be significant inconsistency across dat…
View article: uGLAD: Sparse graph recovery by optimizing deep unrolled networks
uGLAD: Sparse graph recovery by optimizing deep unrolled networks Open
Probabilistic Graphical Models (PGMs) are generative models of complex systems. They rely on conditional independence assumptions between variables to learn sparse representations which can be visualized in a form of a graph. Such models a…
View article: GriTS: Grid table similarity metric for table structure recognition
GriTS: Grid table similarity metric for table structure recognition Open
In this paper, we propose a new class of metric for table structure recognition (TSR) evaluation, called grid table similarity (GriTS). Unlike prior metrics, GriTS evaluates the correctness of a predicted table directly in its natural form…
View article: Multilingual Molecular Representation Learning via Contrastive Pre-training
Multilingual Molecular Representation Learning via Contrastive Pre-training Open
Molecular representation learning plays an essential role in cheminformatics. Recently, language model-based approaches have gained popularity as an alternative to traditional expert-designed features to encode molecules. However, these ap…
View article: PubTables-1M: Towards a universal dataset and metrics for training and evaluating table extraction models.
PubTables-1M: Towards a universal dataset and metrics for training and evaluating table extraction models. Open
Recently, interest has grown in applying machine learning to the problem of
table structure inference and extraction from unstructured documents. However,
progress in this area has been challenging both to make and to measure, due to
sever…
View article: PubTables-1M: Towards comprehensive table extraction from unstructured documents
PubTables-1M: Towards comprehensive table extraction from unstructured documents Open
Recently, significant progress has been made applying machine learning to the problem of table structure inference and extraction from unstructured documents. However, one of the greatest challenges remains the creation of datasets with co…
View article: MM-Deacon: Multimodal molecular domain embedding analysis via contrastive learning.
MM-Deacon: Multimodal molecular domain embedding analysis via contrastive learning. Open
Molecular representation learning plays an essential role in cheminformatics. Recently, language model-based approaches have been popular as an alternative to traditional expert-designed features to encode molecules. However, these approac…
View article: Multilingual Molecular Representation Learning via Contrastive Pre-training
Multilingual Molecular Representation Learning via Contrastive Pre-training Open
Molecular representation learning plays an essential role in cheminformatics. Recently, language model-based approaches have gained popularity as an alternative to traditional expert-designed features to encode molecules. However, these ap…