The present invention relates to a method for preparing a novel antibody library and a library prepared thereby. The antibody library prepared according to the present invention contains antibodies having excellent physical properties against a plurality of antigens, thereby having functional diversity and containing a plurality of unique sequences, and thus can be favorably used as an antibody library.

Methods and compositions related to the selective, specific disruption of multiple ligand-receptor signaling interactions, such as ligand-receptor interactions implicated in disease, are disclosed. These interactions may involve multiple cytokines in a single receptor family or multiple ligand receptor interactions from at least two distinct ligand-receptor families. The compositions may comprise polypeptides having composite sequences that comprise sequence fragments of two or more ligand binding sites. The methods and compositions may involve sequence fragments of two or more ligand binding sites that are arranged to conserve the secondary structure of each of the ligands from which the sequence fragments were taken.

Methods and compositions related to the selective, specific disruption of multiple ligand-receptor signaling interactions, such as ligand-receptor interactions implicated in disease, are disclosed. These interactions may involve multiple cytokines in a single receptor family or multiple ligand receptor interactions from at least two distinct ligand-receptor families. The compositions may comprise polypeptides having composite sequences that comprise sequence fragments of two or more ligand binding sites. The methods and compositions may involve sequence fragments of two or more ligand binding sites that are arranged to conserve the secondary structure of each of the ligands from which the sequence fragments were taken.

Processes and materials to detect cancer from a biopsy are described. In some cases, cell-free nucleic acids can be sequenced, and the sequencing result can be utilized to detect sequences derived from a neoplasm. Detection of somatic variants occurring in phase can indicate the presence of cancer in a diagnostic scan and a clinical intervention can be performed.

Processes and materials to detect cancer from a biopsy are described. In some cases, cell-free nucleic acids can be sequenced, and the sequencing result can be utilized to detect sequences derived from a neoplasm. Detection of somatic variants occurring in phase can indicate the presence of cancer in a diagnostic scan and a clinical intervention can be performed.

The present disclosure provides methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bionformatic tools and techniques.

The present disclosure provides methods and systems for identifying natural product-encoding multi-gene clusters (MGCs). In some embodiments, the present disclosure also teaches methods for producing sequenced and assembled metagenomic libraries that are amenable to MGC search bionformatic tools and techniques.

A consensus MHC-I binding and processing prediction workflow, methods and systems are described, for improving T-cell immunity against threats such as viruses and cancer. The methods and systems can also be used to determine population fitness against a target antigen such as a pathogen or cancer.

A consensus MHC-I binding and processing prediction workflow, methods and systems are described, for improving T-cell immunity against threats such as viruses and cancer. The methods and systems can also be used to determine population fitness against a target antigen such as a pathogen or cancer.

The present invention relates to an artificial-intelligence-based cancer diagnosis and cancer type prediction method, and, more particularly, to an artificial-intelligence-based cancer diagnosis and cancer type prediction method, which extracts nucleic acids from a biological sample to acquire sequence information, and thus generate vectorized data on the basis of aligned nucleic acid fragments, and then inputs same into a trained artificial intelligence model to analyze a calculated value. Compared with a conventional method, which uses a step of determining the number of chromosomes on the basis of a read count and utilizes each related value as a normalized value, the artificial-intelligence-based cancer diagnosis and cancer type prediction method according to the present invention generates vectorized data to perform an analysis using an AI algorithm, and thus is useful in that similar effects can be exhibited even when read coverage is low.