AI Medical Compendium Topic:
Neoplasms

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Integrative analysis of single-cell and bulk RNA sequencing unveils a machine learning-based pan-cancer major histocompatibility complex-related signature for predicting immunotherapy efficacy.

Cancer immunology, immunotherapy : CII
Major histocompatibility complex (MHC) could serve as a potential biomarker for tumor immunotherapy, however, it is not yet known whether MHC could distinguish potential beneficiaries. Single-cell RNA sequencing datasets derived from patients with im...

BiSpec Pairwise AI: guiding the selection of bispecific antibody target combinations with pairwise learning and GPT augmentation.

Journal of cancer research and clinical oncology
PURPOSE: Bispecific antibodies (BsAbs), capable of targeting two antigens simultaneously, represent a significant advancement by employing dual mechanisms of action for tumor suppression. However, how to pair targets to develop effective and safe bis...

Error detection for radiotherapy planning validation based on deep learning networks.

Journal of applied clinical medical physics
BACKGROUND: Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies...

Deep Survival Analysis With Latent Clustering and Contrastive Learning.

IEEE journal of biomedical and health informatics
Survival analysis is employed to analyze the time before the event of interest occurs, which is broadly applied in many fields. The existence of censored data with incomplete supervision information about survival outcomes is one key challenge in sur...

Agent-based approaches for biological modeling in oncology: A literature review.

Artificial intelligence in medicine
CONTEXT: Computational modeling involves the use of computer simulations and models to study and understand real-world phenomena. Its application is particularly relevant in the study of potential interactions between biological elements. It is a pro...

Harnessing TME depicted by histological images to improve cancer prognosis through a deep learning system.

Cell reports. Medicine
Spatial transcriptomics (ST) provides insights into the tumor microenvironment (TME), which is closely associated with cancer prognosis, but ST has limited clinical availability. In this study, we provide a powerful deep learning system to augment TM...

UNNT: A novel Utility for comparing Neural Net and Tree-based models.

PLoS computational biology
The use of deep learning (DL) is steadily gaining traction in scientific challenges such as cancer research. Advances in enhanced data generation, machine learning algorithms, and compute infrastructure have led to an acceleration in the use of deep ...

Application of machine learning for high-throughput tumor marker screening.

Life sciences
High-throughput sequencing and multiomics technologies have allowed increasing numbers of biomarkers to be mined and used for disease diagnosis, risk stratification, efficacy assessment, and prognosis prediction. However, the large number and complex...