Abstract: The invention relates to antibodies such as anti-C1 s antibodies, pharmaceutical compositions comprising the same, and methods of using the same. The invention provides antibodies that comprise an antigen-binding region and an antibody constant region, have a displacement function such that the antibody binds to C1qrs complex and promotes dissociation of C1q from C1qrs complex and/or a blocking function such that the antibody binds to C1r2s2 and inhibits the binding of C1q to C1r2s2, and bind to C1s in a pH-dependent manner. The invention also provides pharmaceutical compositions comprising any one of the antibodies, and methods of treating an individual having a complement-mediated disease or disorder, or preventing an individual potentially having a complement-mediated disease or disorder, comprising administering any one of the antibodies to the individual.

Abstract: A sequence learner performs machine learning on the basis of sequence information representing sequences including some or all of the sequences of a plurality of antigen-binding molecules and proteins and thereby generates a trained model. A virtual sequence generator generates, on the basis of the trained model, virtual sequence information obtained by mutating at least one of constituent elements constituting a sequence represented by the sequence information inputted into the trained model.

Abstract: A molecular structure prediction method for predicting the most stable molecular structure of a molecule based on results obtained by a plurality of appraisal systems includes steps of: generating a plurality of data sets by re-sampling from a training data set, determining a parameter set for each data set that has been generated to obtain a plurality of parameter sets, using the plurality of parameter sets to calculate energy of a molecule for molecular data for prediction, taking a consensus based on the results of a plurality of energies or three-dimensional structures, and predicting the most stable molecular structure based on the results of consensus.

Abstract: Disclosed is a survival analysis system for determining an estimated time until an event occurs on the basis of a group of cases each including at least one attribute value indicating a feature value of a case and information on the measured actual time until an event occurs. The survival analysis system includes: an estimator creating section for creating an estimator for estimating whether or not an event occurs according to the attributes of the group of cases for each actual time; an estimator selecting section for judging whether or not the estimator meets a predetermined selection condition and to selecting an estimator used for calculating the estimated time; and a time calculating section for calculating the estimated time by using the estimator selected by the estimator selecting section.

Abstract: A method of learning discriminant function for predicting label information by using computer includes: receiving training data including attribute data and label information, to create an initial prediction model based on the attribute data and the label information; calculating, based on the initial prediction model used as a discriminant function, a gradient of a loss function, which is differentiable with respect to the discriminant function and satisfies a monotonous convex function, from the discriminant function and the label information; creating a prediction model from the attribute data and the gradient while assuming that the gradient is label information of each sample of the training data; and updating the discriminant function based on the created prediction model.

Abstract: A protein search method for searching for, as a target protein, a protein having direct or indirect relevance to information based on protein representation profiling data acquired by means of proteome analysis includes: determining, as a target protein, a protein that is relevant to the information based on significance of proteins obtained by using supervised learning from the information and the protein representation in the profiling data; and evaluating the performance of the target protein by means of evaluation data.

Abstract: The present invention provides a system, method, and program for evaluating the performance of an intermolecular interaction predicting apparatus. A performance evaluation system evaluates the performance of an intermolecular interaction predicting apparatus using a correlation between structure factors and physicochemical parameters of classification model construction compounds with high and low scores calculated by the intermolecular interaction predicting apparatus.

Abstract: A molecular structure prediction method for predicting the most stable molecular structure of a molecule based on results obtained by a plurality of appraisal systems includes steps of: generating a plurality of data sets by re-sampling from a training data set, determining a parameter set for each data set that has been generated to obtain a plurality of parameter sets, using the plurality of parameter sets to calculate energy of a molecule for molecular data for prediction, taking a consensus based on the results of a plurality of energies or three-dimensional structures, and predicting the most stable molecular structure based on the results of consensus.