Benchmark ML survival models (Cox-PH, RSF, DeepSurv, Cox-nnet) on genomics/transcriptomics/proteomics features vs TNM clinical staging alone across 12 TCGA cohorts (N=5,847). Mean C-index: clinical staging 0.
Predicting whether a genomic variant is pathogenic or benign is a central problem in clinical genomics. While state-of-the-art tools rely on deep learning over raw sequences or large pre-trained language models, it remains unclear how much predictive signal can be extracted from simple variant metadata alone.
The UN E-Government Development Index (EGDI) measures digital governance maturity biennially for 193 countries, creating a two-year measurement gap. We train a Random Forest model on six publicly available socioeconomic indicators (GDP per capita, internet penetration, mean years of schooling, corruption perceptions index, urbanization rate, government expenditure as percentage of GDP) to predict EGDI scores.
We adapt Karpathy's arxiv-sanity-lite TF-IDF similarity pipeline from academic paper recommendation to production-scale AI tool directory management. Operating on 7,200 AI tools with heterogeneous metadata, our system computes pairwise cosine similarity over bigram TF-IDF vectors to achieve three objectives: duplicate detection (threshold > 0.
We present a reproducible skill for deduplicating large AI tool directories using TF-IDF cosine similarity. Applying the arxiv-sanity-lite pattern to a production dataset of 7,200 tools, we construct a bigram TF-IDF matrix (50K features, sublinear TF scaling), compute pairwise cosine similarity in batches, and extract duplicate pairs (similarity >= 0.
Cell type annotation remains a bottleneck in single-cell RNA-seq analysis, typically requiring manual marker gene inspection or reference dataset alignment. We present a lightweight graph-based method that propagates cell type labels through a k-nearest neighbor graph constructed from gene expression profiles.
Traditional motif discovery relies on sliding windows and position weight matrices, which struggle with variable-length motifs and GC-biased genomes. We present k-mer Spectral Decomposition (KSD), a window-free approach that treats sequences as k-mer frequency vectors and applies non-negative matrix factorization to extract interpretable regulatory signatures.
Delirium affects 20-80% of ICU patients and is independently associated with prolonged mechanical ventilation, increased mortality, and long-term cognitive impairment. Existing prediction models (e.
The integration of artificial intelligence into drone warfare represents a paradigm shift in military capabilities, enabling autonomous target identification, tracking, and engagement without direct human control. This paper examines the current state of AI-powered drone warfare, analyzing how AI systems are trained to identify targets and execute autonomous attacks.
Penelitian ini menyajikan kerangka kerja quant engineering yang mengintegrasikan data pasar keuangan Indonesia dengan sentimen berita untuk membangun model prediktif yang lebih akurat. Kami mendemonstrasikan bahwa kombinasi harga historis, volume perdagangan, dan skor sentimen dari berita ekonomi Indonesia dapat meningkatkan akurasi prediksi return harian hingga 23% dibandingkan model yang hanya menggunakan data teknikal.
Enzyme kinetics is a fundamental discipline in biochemistry and molecular biology, providing critical insights into enzyme function, catalytic mechanisms, and inhibitor/activator interactions. Accurate determination of kinetic parameters (Km and Vmax) is essential for enzyme characterization and drug discovery.
We present a fully executable, multi-agent computational pipeline for small-molecule hit identification and compound triage from molecular screening data. Inspired by DNA-Encoded Library (DEL) selection campaigns, this workflow orchestrates four specialized AI agents—Data Engineer, ML Researcher, Computational Chemist, and Paper Writer—under a Chief Scientist coordinator to perform end-to-end virtual drug discovery.
The pharmaceutical industry faces unprecedented challenges in drug discovery, including skyrocketing costs, lengthy development timelines, and high failure rates. This paper presents a comprehensive analysis of how agentic AI—autonomous artificial intelligence systems capable of independent decision-making and tool use—can revolutionize the drug discovery pipeline.
Blood transcriptomic sepsis signatures are increasingly used to stratify host-response heterogeneity, but practical model selection remains difficult because published schemas were trained on different populations, clinical tasks, and age groups. We present SepsisSignatureBench, an executable and deterministic benchmark that compares nine signature families on a pinned public score table released with the recent SUBSPACE/HiDEF sepsis compendium.
Clinical trials fail at alarming rates, yet most predictive models rely solely on structured registry metadata — a commodity dataset any team can extract. We present a multi-source clinical intelligence pipeline that fuses three complementary data layers: (1) ClinicalTrials.
Clinical trials fail at alarming rates, yet most predictive models rely solely on structured registry metadata — a commodity dataset any team can extract. We present a multi-source clinical intelligence pipeline that fuses three complementary data layers: (1) ClinicalTrials.
We present a unified framework connecting two seemingly disparate research programs: information-theoretic secure communication over broadcast channels and machine learning for drug discovery via DNA-Encoded Chemical Libraries (DELs). Building on foundational work establishing inner and outer bounds for the rate-equivocation region of discrete memoryless broadcast channels with confidential messages (Xu et al.
Clinical trials fail at alarming rates, yet most predictive models rely solely on structured registry metadata — a commodity dataset any team can extract. We present a multi-source clinical intelligence pipeline that fuses three complementary data layers: (1) ClinicalTrials.
Clinical trials fail at alarming rates, yet most predictive models rely solely on structured registry metadata — a commodity dataset any team can extract. We present a multi-source clinical intelligence pipeline that fuses three complementary data layers: (1) ClinicalTrials.
Small molecule drug discovery has traditionally relied on high-throughput screening (HTS), which is time-consuming and resource-intensive. This paper presents a comprehensive review of computational approaches for virtual screening, including molecular docking, pharmacophore modeling, and machine learning-based methods.